https://www.spreaker.com/podcast/finnish-center-for-artificial-intelligence-fcai--7315548esLeisureCopyright DASERhttps://d3wo5wojvuv7l.cloudfront.net/t_rss_itunes_square_1400/images.spreaker.com/original/f0be59a85a52cca22a0a8ebfa3be4a2b.jpghttps://www.spreaker.com/podcast/finnish-center-for-artificial-intelligence-fcai--7315548Fri, 04 Sep 2026 01:34:39 +0000DASERDASERfeeds@spreaker.comFinnish Center for Artificial Intelligence FCAI (Suomen tekoälykeskus) is a community of experts that brings together top talents in academia, industry and public sector to solve real-life problems using both existing and novel AI. FCAI is one of the...false9c3508df-f8d3-54f3-b65c-7e9471890098episodichttps://www.spreaker.com/episode/ellis-distinguished-lecture-hatice-gunes--74882389
Talk starts at 06:04

https://www.ellisinstitute.fi/ellis-distinguished-lecture-hatice-gunes]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492111Thu, 19 Feb 2026 12:23:51 +0000DASERHatice Gunes (University of Cambridge): Fairness for Affective and Wellbeing Computing Systems and Agents. Presented on February 17, 2026, at the University of Oulu and online. Talk starts at 06:04...
Talk starts at 06:04

https://www.ellisinstitute.fi/ellis-distinguished-lecture-hatice-gunes]]>3467falsefullhttps://www.spreaker.com/episode/ellis-distinguished-lecture-matthew-e-taylor--74882379Matthew E. Taylor (University of Alberta): Human and agent cooperative learning

Read more: https://fcai.fi/calendar/2023/6/5/ellis-distinguished-lecture-matthew-e-taylor-human-and-agent-cooperative-learning]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492133Thu, 19 Feb 2026 12:16:17 +0000DASERELLIS Distinguished Lecture at ELLIS Unit Helsinki Matthew E. Taylor (University of Alberta): Human and agent cooperative learning Read more:...Matthew E. Taylor (University of Alberta): Human and agent cooperative learning

Read more: https://fcai.fi/calendar/2023/6/5/ellis-distinguished-lecture-matthew-e-taylor-human-and-agent-cooperative-learning]]>2739falsefullhttps://www.spreaker.com/episode/daniel-schmidt-fundamental-limitations-of-foundational-time-series-forecasting-models--74882383
Abstract: In this talk, we will discuss some fundamental limitations we perceive in the current operation of foundational models in the context of time series forecasting. We’ll argue that training on ever more data is not always beneficial, and we'll illustrate how common yet often inadequate evaluation practices can obscure these limitations. Ultimately, we will argue that the way to address these challenges is through multimodality.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492144Thu, 05 Jun 2025 21:00:39 +0000DASERSpeaker: Daniel Schmidt is an Associate Professor of Computer Science at the Department of Data Science and AI, Monash University, Australia. He obtained his PhD in the area of information theoretic statistical inference in 2008 from Monash...
Abstract: In this talk, we will discuss some fundamental limitations we perceive in the current operation of foundational models in the context of time series forecasting. We’ll argue that training on ever more data is not always beneficial, and we'll illustrate how common yet often inadequate evaluation practices can obscure these limitations. Ultimately, we will argue that the way to address these challenges is through multimodality.]]>2590falsefullhttps://www.spreaker.com/episode/francois-xavier-briol-robust-and-conjugate-gaussian-processes-regression--74882382
Speaker: François-Xavier Briol is an Associate Professor in the Department of Statistical Science at University College London, where he leads the Fundamentals of Statistical Machine Learning research group and is co-director of the UCL ELLIS unit. His research focuses on developing statistical and machine learning methods for the sciences and engineering. He is particularly interested in designing methods to merge large-scale scientific models with data, which requires the development of novel computational methods and of inference methods that remain robust to model misspecification.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492147Mon, 12 May 2025 21:01:01 +0000DASERAbstract: To enable closed form conditioning, a common assumption in Gaussian process (GP) regression is independent and identically distributed Gaussian observation noise. This strong and simplistic assumption is often violated in practice, which...
Speaker: François-Xavier Briol is an Associate Professor in the Department of Statistical Science at University College London, where he leads the Fundamentals of Statistical Machine Learning research group and is co-director of the UCL ELLIS unit. His research focuses on developing statistical and machine learning methods for the sciences and engineering. He is particularly interested in designing methods to merge large-scale scientific models with data, which requires the development of novel computational methods and of inference methods that remain robust to model misspecification.]]>2650falsefullhttps://www.spreaker.com/episode/ai-and-drones-fcai-08042025-ville-kyrki-aalto-university--74882371https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492145Mon, 14 Apr 2025 06:55:20 +0000DASERDuring the past few years, the use of drones for various purposes has increased significantly. The integration of artificial intelligence into drone technology adds a new dimension, enhancing capabilities and expanding potential applications. To...851falsefullhttps://www.spreaker.com/episode/empirical-design-of-experiments-with-ai-fcai-webinar-28-11-2024--74882392https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492142Wed, 04 Dec 2024 20:18:24 +0000DASERNovel AI solutions for design of experiments are expected to emerge in topics such as industrial R&D laboratory, pharma, chemical industry, forest industry and metallurgical industry. This webinar will spotlight AI-driven design of materials and...5523falsefullhttps://www.spreaker.com/episode/empirical-design-of-experiments-with-ai-fcai-28-11-2024-arto-klami-uh--74882376https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492130Wed, 04 Dec 2024 17:09:49 +0000DASERNovel AI solutions for design of experiments are expected to emerge in topics such as industrial R&D laboratory, pharma, chemical industry, forest industry and metallurgical industry. This webinar will spotlight AI-driven design of materials and...1145falsefullhttps://www.spreaker.com/episode/empirical-design-of-experiments-with-ai-fcai-28-11-2024-soren-furbo-novo-nordisk--74882370https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492105Wed, 04 Dec 2024 17:08:26 +0000DASERNovel AI solutions for design of experiments are expected to emerge in topics such as industrial R&D laboratory, pharma, chemical industry, forest industry and metallurgical industry. This webinar will spotlight AI-driven design of materials and...837falsefullhttps://www.spreaker.com/episode/empirical-design-of-experiments-with-ai-fcai-28-11-2024-morten-bormann-nielsen-dti--74882375https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492121Wed, 04 Dec 2024 17:07:43 +0000DASERNovel AI solutions for design of experiments are expected to emerge in topics such as industrial R&D laboratory, pharma, chemical industry, forest industry and metallurgical industry. This webinar will spotlight AI-driven design of materials and...1490falsefullhttps://www.spreaker.com/episode/empirical-design-of-experiments-with-ai-fcai-28-11-2024-mikko-makela-vtt--74882369https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492117Wed, 04 Dec 2024 17:05:49 +0000DASERIntroduction to FCAI and to the webinar Empirical design of experiments with AI.361falsefullhttps://www.spreaker.com/episode/grigorios-tsoumakas-neural-abstractive-summarization--74882377
Bio: Dr. Grigorios Tsoumakas received a degree in Computer Science from the Aristotle University of Thessaloniki (AUTH), Greece, in 1999, an MSc in Artificial Intelligence from the University of Edinburgh, United Kingdom, in 2000 and a PhD in Computer Science from AUTH in 2005. He is a Professor of Machine Learning and Knowledge Discovery at the School of Informatics of AUTH since 2024, where he has also served as Associate Professor (2020-2024), Assistant Professor (2013 – 2020) and Lecturer (2007 – 2013). Since 2024, he also serves as an Affiliate Researcher at Archimedes/RC Athena, Greece. In addition, he is co-founder and chief scientific officer at Medoid AI, a spin-off company of AUTH established in 2019, developing custom AI solutions based on cutting-edge Machine Learning technology. Dr. Tsoumakas is a senior member of ACM and IEEE. His research expertise focuses on supervised learning (ensemble methods, multi-target prediction, interpretablity) and natural language processing (semantic indexing, keyphrase extraction, summarization). He has published more than 150 research papers and according to Google Scholar he has more than 19.000 citations and an h-index of 52. His honors include receiving the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 10-Year Test of Time Award in 2017 and the Marco Ramoni best paper award at the 19th International Conference on Artificial Intelligence in Medicine (AIME 2021).]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492103Fri, 29 Nov 2024 22:00:29 +0000DASERAbstract: This talk reviews past and recent work of our team on the topic of neural abstractive summarization. We will first present our divide-and-conquer approach for dealing with long documents and its application to summarizing scientific...
Bio: Dr. Grigorios Tsoumakas received a degree in Computer Science from the Aristotle University of Thessaloniki (AUTH), Greece, in 1999, an MSc in Artificial Intelligence from the University of Edinburgh, United Kingdom, in 2000 and a PhD in Computer Science from AUTH in 2005. He is a Professor of Machine Learning and Knowledge Discovery at the School of Informatics of AUTH since 2024, where he has also served as Associate Professor (2020-2024), Assistant Professor (2013 – 2020) and Lecturer (2007 – 2013). Since 2024, he also serves as an Affiliate Researcher at Archimedes/RC Athena, Greece. In addition, he is co-founder and chief scientific officer at Medoid AI, a spin-off company of AUTH established in 2019, developing custom AI solutions based on cutting-edge Machine Learning technology. Dr. Tsoumakas is a senior member of ACM and IEEE. His research expertise focuses on supervised learning (ensemble methods, multi-target prediction, interpretablity) and natural language processing (semantic indexing, keyphrase extraction, summarization). He has published more than 150 research papers and according to Google Scholar he has more than 19.000 citations and an h-index of 52. His honors include receiving the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 10-Year Test of Time Award in 2017 and the Marco Ramoni best paper award at the 19th International Conference on Artificial Intelligence in Medicine (AIME 2021).]]>2774falsefullhttps://www.spreaker.com/episode/toni-karvonen-probabilistic-richardson-extrapolation--74882381
Preprint: C. J. Oates, T. Karvonen, A. L. Teckentrup, M. Strocchi & S. A. Niederer (2024). Probabilistic Richardson extrapolation. arXiv:2401.07562.

Bio: Toni Karvonen has been an Associate Professor in applied mathematics at the Lappeenranta–Lahti University of Technology LUT since September 2024. He obtained an MSc in applied mathematics from the University of Helsinki in 2015 and a doctoral degree in electrical engineering from Aalto University in 2019. In 2020–21 he was a Research Fellow at the Alan Turing Institute, the UK's national institute for data science and AI located in London, and in 2021–24 an Academy Postdoctoral Researcher in the Department of Mathematics and Statistics at the University of Helsinki. His research focuses on uncertainty quantification and model misspecification in numerical analysis, statistical modelling, and machine learning.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492139Tue, 01 Oct 2024 11:11:37 +0000DASERAbstract: Richardson extrapolation is a classical technique to accelerate the rate of convergence of a numerical method. For example, Romberg's method uses Richardson extrapolation to estimate integrals and the Bulirsch–Stoer algorithm to solve...
Preprint: C. J. Oates, T. Karvonen, A. L. Teckentrup, M. Strocchi & S. A. Niederer (2024). Probabilistic Richardson extrapolation. arXiv:2401.07562.

Bio: Toni Karvonen has been an Associate Professor in applied mathematics at the Lappeenranta–Lahti University of Technology LUT since September 2024. He obtained an MSc in applied mathematics from the University of Helsinki in 2015 and a doctoral degree in electrical engineering from Aalto University in 2019. In 2020–21 he was a Research Fellow at the Alan Turing Institute, the UK's national institute for data science and AI located in London, and in 2021–24 an Academy Postdoctoral Researcher in the Department of Mathematics and Statistics at the University of Helsinki. His research focuses on uncertainty quantification and model misspecification in numerical analysis, statistical modelling, and machine learning.]]>2841falsefullhttps://www.spreaker.com/episode/yoshua-bengio-understanding-and-mitigating-loss-of-control--74882387
Introduction: Professor Volker Tresp, LMU Munich and Munich Center for Machine Learning (MCML)

Event Page: https://www.elise-ai.eu/events/register-now-for-the-elise-wrap-up-conference-ellis-community-event-helsinki-june-27-28]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492135Sat, 29 Jun 2024 00:51:07 +0000DASERProfessor Yoshua Bengio (University of Montreal) keynote at the ELLIS Community Event in Helsinki, June 28 2024. Introduction: Professor Volker Tresp, LMU Munich and Munich Center for Machine Learning (MCML) Event Page:...
Introduction: Professor Volker Tresp, LMU Munich and Munich Center for Machine Learning (MCML)

Event Page: https://www.elise-ai.eu/events/register-now-for-the-elise-wrap-up-conference-ellis-community-event-helsinki-june-27-28]]>3846falsefullhttps://www.spreaker.com/episode/neil-lawrence-the-atomic-human-understanding-ourselves-in-the-age-of-ai--74882384
Event page: https://www.elise-ai.eu/events/register-now-for-the-elise-wrap-up-conference-ellis-community-event-helsinki-june-27-28]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492140Fri, 28 Jun 2024 02:37:32 +0000DASERProfessor Neil Lawrence (University of Cambridge) keynote at the ELLIS Community Event in Helsinki, June 27, 2024. Event page: https://www.elise-ai.eu/events/register-now-for-the-elise-wrap-up-conference-ellis-community-event-helsinki-june-27-28
Event page: https://www.elise-ai.eu/events/register-now-for-the-elise-wrap-up-conference-ellis-community-event-helsinki-june-27-28]]>3846falsefullhttps://www.spreaker.com/episode/elise-panel-and-workshop-ai-ellis-and-the-european-public--74882393Neil Lawrence (University of Cambridge)
Bernhard Schölkopf (Max Planck Institute for Intelligent Systems, ELLIS Institute Tübingen)
Petri Myllymäki (University of Helsinki and Finnish Center for Artificial Intelligence FCAI)
Jessica Montgomery (University of Cambridge)
Timo Harakka (Member of the Parliament of Finland)

Event page: https://www.elise-ai.eu/events/register-now-for-the-elise-wrap-up-conference-ellis-community-event-helsinki-june-27-28]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492119Thu, 27 Jun 2024 21:19:18 +0000DASERSpeakers: Neil Lawrence (University of Cambridge) Bernhard Schölkopf (Max Planck Institute for Intelligent Systems, ELLIS Institute Tübingen) Petri Myllymäki (University of Helsinki and Finnish Center for Artificial Intelligence FCAI) Jessica...Neil Lawrence (University of Cambridge)
Bernhard Schölkopf (Max Planck Institute for Intelligent Systems, ELLIS Institute Tübingen)
Petri Myllymäki (University of Helsinki and Finnish Center for Artificial Intelligence FCAI)
Jessica Montgomery (University of Cambridge)
Timo Harakka (Member of the Parliament of Finland)

Event page: https://www.elise-ai.eu/events/register-now-for-the-elise-wrap-up-conference-ellis-community-event-helsinki-june-27-28]]>5771falsefullhttps://www.spreaker.com/episode/bernhard-scholkopf-on-open-problems-of-european-ai--74882385
Event page: https://www.elise-ai.eu/events/register-now-for-the-elise-wrap-up-conference-ellis-community-event-helsinki-june-27-28]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492118Thu, 27 Jun 2024 19:23:21 +0000DASERProfessor Bernhard Schölkopf (Max Planck Institute for Intelligent Systems and ELLIS Institute Tübingen) keynote at the ELLIS Community Event in Helsinki, June 27 2024. Event page:...
Event page: https://www.elise-ai.eu/events/register-now-for-the-elise-wrap-up-conference-ellis-community-event-helsinki-june-27-28]]>5001falsefullhttps://www.spreaker.com/episode/ai-for-mental-health-fcai-23-5-2024-hanna-renvall-hus-biomag-laboratory--74882378https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492114Tue, 28 May 2024 07:29:13 +0000DASERThis presentation is part of the webinar AI for Mental Health that explored innovative ways in which AI is being integrated into mental health research, diagnosis, and treatment. Hosted by FCAI and Helsinki Brain & Mind, this webinar brought...1046falsefullhttps://www.spreaker.com/episode/tekoaly-ja-terveysdata-tiedekulma-29-4-2024--74882403
Puhujat ja panelistit:
Dosentti Karoliina Snell (Helsingin yliopisto)
Professori ja iCAN-lippulaivahankkeen tutkimusjohtaja Tomi Mäkelä (Helsingin yliopisto)
Apulaisprofessori Aleksei Tiulpin (Oulun yliopisto)
Professori Pekka Marttinen (Aalto yliopisto)
Johtava tutkija Jaakko Lähteenmäki (Teknologian tutkimuskeskus VTT)
Juristi Kirsi Talonen (Helsingin yliopisto)
Professori Antti Honkela (Helsingin yliopisto)
Potilasaktiivi Jukka K. Korpela (Suomen Syöpäpotilaat ry)
Lisätietoa: https://fcai.fi/calendar/2024/1/30/tekoly-ja-terveysdata

Tapahtuman järjestivät: Suomen tekoälykeskus FCAI (https://fcai.fi) & Syöpätutkimuksen lippulaivahanke iCAN (https://ican.fi)
Mukana myös Eurooppalainen potilasakatemia EUPATI Suomi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492146Fri, 24 May 2024 09:01:06 +0000DASERMeistä kaikista kerätään paljon terveystietoa rekistereihin ja potilastietoihin ja mittaamme myös itse itseämme. Mihin ja miten näitä tietoja käytetään? Voiko tekoälyllä löytää parannuskeinoja syöpään? Tapahtumassa keskusteltiin siitä, minkälaista...
Puhujat ja panelistit:
Dosentti Karoliina Snell (Helsingin yliopisto)
Professori ja iCAN-lippulaivahankkeen tutkimusjohtaja Tomi Mäkelä (Helsingin yliopisto)
Apulaisprofessori Aleksei Tiulpin (Oulun yliopisto)
Professori Pekka Marttinen (Aalto yliopisto)
Johtava tutkija Jaakko Lähteenmäki (Teknologian tutkimuskeskus VTT)
Juristi Kirsi Talonen (Helsingin yliopisto)
Professori Antti Honkela (Helsingin yliopisto)
Potilasaktiivi Jukka K. Korpela (Suomen Syöpäpotilaat ry)
Lisätietoa: https://fcai.fi/calendar/2024/1/30/tekoly-ja-terveysdata

Tapahtuman järjestivät: Suomen tekoälykeskus FCAI (https://fcai.fi) & Syöpätutkimuksen lippulaivahanke iCAN (https://ican.fi)
Mukana myös Eurooppalainen potilasakatemia EUPATI Suomi]]>7292falsefullhttps://www.spreaker.com/episode/mikko-koivisto-bayesian-inference-of-causal-graphs-where-we-are-and-where-we-should-go--74882380
Speaker: Mikko Koivisto is a Professor of Computer Science at the University of Helsinki, where he also obtained his PhD in 2004. With interests generally in algorithms and artificial intelligence, his research has focused on exact and approximate algorithms for weighted counting, often inspired by applications in Bayesian machine learning.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492107Thu, 23 May 2024 15:03:22 +0000DASERAbstract: Causal discovery aims at inferring cause–effect relationships between variables from observational data. Recently, there has been notable progress in Bayesian inference of causal graphs, which holds the promise of fully quantifying the...
Speaker: Mikko Koivisto is a Professor of Computer Science at the University of Helsinki, where he also obtained his PhD in 2004. With interests generally in algorithms and artificial intelligence, his research has focused on exact and approximate algorithms for weighted counting, often inspired by applications in Bayesian machine learning.]]>2857falsefullhttps://www.spreaker.com/episode/sustainable-ai-solutions-fcai-16-4-2024-fabrice-saffre-vtt--74882374In this webinar speakers from industry and academia share their insights on smart and sustainable AI methods and solutions.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492141Mon, 22 Apr 2024 11:45:01 +0000DASERCurrent AI and machine learning systems are complex and require a lot of data and energy to operate. Energy efficiency and optimization are key factors in reaching higher level of sustainability in AI solutions, but complexity of the systems...In this webinar speakers from industry and academia share their insights on smart and sustainable AI methods and solutions.]]>1065falsefullhttps://www.spreaker.com/episode/sustainable-ai-solutions-fcai-16-4-2024-frederic-pariente-nvidia--74882373In this webinar speakers from industry and academia share their insights on smart and sustainable AI methods and solutions.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492138Mon, 22 Apr 2024 11:44:56 +0000DASERCurrent AI and machine learning systems are complex and require a lot of data and energy to operate. Energy efficiency and optimization are key factors in reaching higher level of sustainability in AI solutions, but complexity of the systems...In this webinar speakers from industry and academia share their insights on smart and sustainable AI methods and solutions.]]>990falsefullhttps://www.spreaker.com/episode/sustainable-ai-solutions-fcai-16-4-2024-mats-sjoberg-csc--74882372In this webinar speakers from industry and academia share their insights on smart and sustainable AI methods and solutions.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492115Mon, 22 Apr 2024 11:44:50 +0000DASERCurrent AI and machine learning systems are complex and require a lot of data and energy to operate. Energy efficiency and optimization are key factors in reaching higher level of sustainability in AI solutions, but complexity of the systems...In this webinar speakers from industry and academia share their insights on smart and sustainable AI methods and solutions.]]>1259falsefullhttps://www.spreaker.com/episode/sustainable-ai-solutions-fcai-16-4-2024-simo-sarkka-aalto-university--74882391In this webinar speakers from industry and academia share their insights on smart and sustainable AI methods and solutions.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492101Mon, 22 Apr 2024 11:44:43 +0000DASERCurrent AI and machine learning systems are complex and require a lot of data and energy to operate. Energy efficiency and optimization are key factors in reaching higher level of sustainability in AI solutions, but complexity of the systems...In this webinar speakers from industry and academia share their insights on smart and sustainable AI methods and solutions.]]>1104falsefullhttps://www.spreaker.com/episode/chen-he-vms-interactive-visualization-to-support-the-sensemaking-and-selection-of-predictive-models--74882386
Reference: Chen He, Vishnu Raj, Hans Moen, Tommi Gröhn, Chen Wang, Laura-Maria Peltonen, Saila Koivusalo, Pekka Marttinen, Giulio Jacucci. VMS: Interactive Visualization to Support the Sensemaking and Selection of Predictive Models. In: ACM IUI 2024.

Speaker: Chen He is a postdoctoral researcher at the Ubiquitous Interaction Group, Department of Computer Science, University of Helsinki. Her research interests include information visualization and human-centred AI. She collaborates with machine learning and bioinformatics experts to explore how visualization could make AI more accessible to domain experts, such as biologists. On the other hand, her current research also investigates how people discover insights during visual data exploration by evaluating interactive visualization prototypes.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492122Mon, 25 Mar 2024 16:43:18 +0000DASERAbstract: To compare and select machine learning models, relying on performance measures alone may not always be sufficient. This is particularly the case where different subsets, features, and predicted results may vary in importance relative to the...
Reference: Chen He, Vishnu Raj, Hans Moen, Tommi Gröhn, Chen Wang, Laura-Maria Peltonen, Saila Koivusalo, Pekka Marttinen, Giulio Jacucci. VMS: Interactive Visualization to Support the Sensemaking and Selection of Predictive Models. In: ACM IUI 2024.

Speaker: Chen He is a postdoctoral researcher at the Ubiquitous Interaction Group, Department of Computer Science, University of Helsinki. Her research interests include information visualization and human-centred AI. She collaborates with machine learning and bioinformatics experts to explore how visualization could make AI more accessible to domain experts, such as biologists. On the other hand, her current research also investigates how people discover insights during visual data exploration by evaluating interactive visualization prototypes.]]>1544falsefullhttps://www.spreaker.com/episode/daniel-schmidt-fast-and-accurate-probabilistic-time-series-classification--74882395
One of the reasons MINIROCKET is so fast is due to its use of a classifier based around standard squared-error ridge regression. While fast, one drawback of this approach is that it is unable to produce probabilistic predictions. I will demonstrate some very recent work on how to use regular ridge-regression to train L2-regularized multinomial logistic regression models for very large numbers of features, including choosing a suitable degree of regularization, with a time complexity that is no greater than single ordinary least-squares fit. This allows for models based on the MINIROCKET technology to provide well calibrated probabilistic predictions with minimal additional computational overhead.

Speaker: Daniel Schmidt is an Associate Professor of Computer Science at the Department of Data Science and AI, Monash University, Australia. He obtained his PhD in the area of information theoretic statistical inference in 2008 from Monash University. From 2009 to 2018 he was employed at the University of Melbourne, working in the field of statistical genomics (GWAS, epigenetics and cancer). Since 2018 he has been employed at Monash University in a teaching and research position. His research interests are primarily time series classification and forecasting, particularly at scale, and Bayesian inference and MCMC, with an emphasis on sparsity and shrinkage, Bayesian optimisation and Bayesian function approximation. He also has a keen interest in the best ways to provide statistical/machine learning education.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492136Mon, 26 Feb 2024 15:24:35 +0000DASERAbstract: Time series classification is the problem of assigning classes/labels to samples that have one or more time series as features, e.g., in order to identify gestures or activities based on motion sensor data, or to classify land use based on...
One of the reasons MINIROCKET is so fast is due to its use of a classifier based around standard squared-error ridge regression. While fast, one drawback of this approach is that it is unable to produce probabilistic predictions. I will demonstrate some very recent work on how to use regular ridge-regression to train L2-regularized multinomial logistic regression models for very large numbers of features, including choosing a suitable degree of regularization, with a time complexity that is no greater than single ordinary least-squares fit. This allows for models based on the MINIROCKET technology to provide well calibrated probabilistic predictions with minimal additional computational overhead.

Speaker: Daniel Schmidt is an Associate Professor of Computer Science at the Department of Data Science and AI, Monash University, Australia. He obtained his PhD in the area of information theoretic statistical inference in 2008 from Monash University. From 2009 to 2018 he was employed at the University of Melbourne, working in the field of statistical genomics (GWAS, epigenetics and cancer). Since 2018 he has been employed at Monash University in a teaching and research position. His research interests are primarily time series classification and forecasting, particularly at scale, and Bayesian inference and MCMC, with an emphasis on sparsity and shrinkage, Bayesian optimisation and Bayesian function approximation. He also has a keen interest in the best ways to provide statistical/machine learning education.]]>2803falsefullhttps://www.spreaker.com/episode/ayush-bharti-robust-and-sample-efficient-simulation-based-inference--74882400
Speaker: Ayush Bharti is a postdoctoral researcher at the Department of Computer Science, Aalto University, working with Prof. Samuel Kaski. He is affiliated with the Probabilistic Machine Learning research group at Aalto and the Finnish Center for Artificial Intelligence. He received the M.Sc. degree in signal processing and computing and the Ph.D. degree in wireless communications from Aalborg University, Denmark, in 2017 and 2021, respectively. Ayush’s research interests include simulation-based inference, Bayesian optimization, and Bayesian experimental design.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492124Mon, 29 Jan 2024 16:28:01 +0000DASERAbstract: Statistical inference of simulator-based models, used in many domains of science and engineering, is challenging due to the unavailability of the likelihood function, which is the probability density function of the data given the...
Speaker: Ayush Bharti is a postdoctoral researcher at the Department of Computer Science, Aalto University, working with Prof. Samuel Kaski. He is affiliated with the Probabilistic Machine Learning research group at Aalto and the Finnish Center for Artificial Intelligence. He received the M.Sc. degree in signal processing and computing and the Ph.D. degree in wireless communications from Aalborg University, Denmark, in 2017 and 2021, respectively. Ayush’s research interests include simulation-based inference, Bayesian optimization, and Bayesian experimental design.]]>2168falsefullhttps://www.spreaker.com/episode/aurora-ai-visio-arjesta-tiedekulma-20-11-2023--74882411
Keskustelua johti Karoliina Snell (Helsingin yliopisto)
Asiantuntijoina keskustelussa: Niko Ruostetsaari (Valtiovarainministeriö), Nina Wessberg (VTT), Tommi Mikkonen (Jyväskylän yliopisto) ja Aaro Tupasela (Helsingin yliopisto)

Lisätietoa: https://fcai.fi/calendar/2023/11/20/aurora-ai]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492143Tue, 05 Dec 2023 10:40:14 +0000DASERAurora AI oli valtion hanke, joka tähtäsi kansalaisten viranomaisasioinnin helpottamiseen tekoälyn avulla (kts. lisätietoa: https://vm.fi/tekoalyohjelma-auroraai). Mitä haasteita hankkeessa oli? Mitä tästä opimme? Suomen tekoälykeskuksen Ethics...
Keskustelua johti Karoliina Snell (Helsingin yliopisto)
Asiantuntijoina keskustelussa: Niko Ruostetsaari (Valtiovarainministeriö), Nina Wessberg (VTT), Tommi Mikkonen (Jyväskylän yliopisto) ja Aaro Tupasela (Helsingin yliopisto)

Lisätietoa: https://fcai.fi/calendar/2023/11/20/aurora-ai]]>6119falsefullhttps://www.spreaker.com/episode/jorg-tiedemann-releasing-the-mammoth-a-framework-for-large-scale-modular-multilingual-nlp-models--74882394
There are several ways of implementing multilingual NLP systems but little consensus as to whether different approaches exhibit similar effects. Are the trends that we observe when adding more languages the same as those we observe when sharing more parameters? MAMMOTH (https://github.com/Helsinki-NLP/Mammoth) is a flexible framework for training various types of modular architectures making it possible to systematically compare different approaches.

Special care is taken to optimize the scalability in multinode training on large HPC clusters such as LUMI. I will report the current stage of our research including initial results, our efforts on hyper-parameter tuning, the optimization of modular architectures, scalability benchmarks and the final goal of training a large-scale multilingual translation model with massively parallel data sets.

Speaker: Jörg Tiedemann works as professor of language technology at the Department of Digital Humanities at the University of Helsinki. His main research interest is in cross-lingual NLP and machine translation.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492120Mon, 27 Nov 2023 14:43:33 +0000DASERAbstract: Neural language models have been grown in size and importance over the past years. We address two challenging aspects in the field of NLP: The support of a wide variety of languages and the runtime efficiency of such models. We focus on...
There are several ways of implementing multilingual NLP systems but little consensus as to whether different approaches exhibit similar effects. Are the trends that we observe when adding more languages the same as those we observe when sharing more parameters? MAMMOTH (https://github.com/Helsinki-NLP/Mammoth) is a flexible framework for training various types of modular architectures making it possible to systematically compare different approaches.

Special care is taken to optimize the scalability in multinode training on large HPC clusters such as LUMI. I will report the current stage of our research including initial results, our efforts on hyper-parameter tuning, the optimization of modular architectures, scalability benchmarks and the final goal of training a large-scale multilingual translation model with massively parallel data sets.

Speaker: Jörg Tiedemann works as professor of language technology at the Department of Digital Humanities at the University of Helsinki. His main research interest is in cross-lingual NLP and machine translation.]]>3181falsefullhttps://www.spreaker.com/episode/blerta-lindqvist-aalto-university-ai-and-cybersecurity--74882390https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492113Wed, 22 Nov 2023 22:00:32 +0000DASERToday’s complex information systems and societies relying on them are prone to cyber threats. Increasing use of artificial intelligence applications adds its own twist to the plot, compounding the need to raise awareness about AI & cybersecurity...1735falsefullhttps://www.spreaker.com/episode/petri-puhakainen-vtt-ai-and-cybersecurity--74882404https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492125Wed, 22 Nov 2023 22:00:28 +0000DASERToday’s complex information systems and societies relying on them are prone to cyber threats. Increasing use of artificial intelligence applications adds its own twist to the plot, compounding the need to raise awareness about AI & cybersecurity...1553falsefullhttps://www.spreaker.com/episode/mikko-kiviharju-aalto-university-ai-and-cybersecurity--74882405https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492109Wed, 22 Nov 2023 22:00:13 +0000DASERToday’s complex information systems and societies relying on them are prone to cyber threats. Increasing use of artificial intelligence applications adds its own twist to the plot, compounding the need to raise awareness about AI & cybersecurity...2079falsefullhttps://www.spreaker.com/episode/markku-korkiakoski-capgemini-ai-and-cybersecurity--74882388https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492129Wed, 22 Nov 2023 22:00:07 +0000DASERToday’s complex information systems and societies relying on them are prone to cyber threats. Increasing use of artificial intelligence applications adds its own twist to the plot, compounding the need to raise awareness about AI & cybersecurity...1399falsefullhttps://www.spreaker.com/episode/aaro-jarvinen-the-role-of-machine-learning-in-facilitating-fusion-energy-research--74882420
Speaker: Dr. Aaro Järvinen is a senior scientist in the Fusion Energy and Decommissioning research group at the VTT Technical Research Centre of Finland. He is also a visiting scientist in the EUROfusion Advanced Computing Hub at the University of Helsinki and Academy Research Fellow of the Research Council of Finland. He has been actively involved in magnetic confinement fusion energy research since 2009 and has a broad experience in both numerical and experimental fusion research, including experience with several tokamak research reactors. Within the past few years, his research focus has centered around applications of machine learning (ML) methods to facilitate the development of fusion energy, primarily focused on both Bayesian methods to facilitate model validation and on representation learning algorithms for fusion reactor scenario predictions. Overall, he is passionate about the opportunities provided by the development of ML algorithms and high-performance computing systems in accelerating the progress towards abundant and clean fusion energy.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492099Mon, 30 Oct 2023 15:11:58 +0000DASERAbstract: Fusion energy holds a promise of a virtually limitless, carbon-free energy source. However, it is a scientific and technological grand challenge to sustainably maintain high fusion performance, requiring fuel temperatures of ~100 million...
Speaker: Dr. Aaro Järvinen is a senior scientist in the Fusion Energy and Decommissioning research group at the VTT Technical Research Centre of Finland. He is also a visiting scientist in the EUROfusion Advanced Computing Hub at the University of Helsinki and Academy Research Fellow of the Research Council of Finland. He has been actively involved in magnetic confinement fusion energy research since 2009 and has a broad experience in both numerical and experimental fusion research, including experience with several tokamak research reactors. Within the past few years, his research focus has centered around applications of machine learning (ML) methods to facilitate the development of fusion energy, primarily focused on both Bayesian methods to facilitate model validation and on representation learning algorithms for fusion reactor scenario predictions. Overall, he is passionate about the opportunities provided by the development of ML algorithms and high-performance computing systems in accelerating the progress towards abundant and clean fusion energy.]]>2572falsefullhttps://www.spreaker.com/episode/bo-zhao-scalable-and-flexible-distributed-reinforcement-learning-systems--74882423
Speaker: Bo Zhao is a tenure-track Assistant Professor in the Department of Computer Science at Aalto University. His research focuses on efficient data-intensive systems at the intersection of scalable machine learning systems and distributed data management systems, as well as compilation-based optimization techniques. Bo’s long-term goal is to explore and understand the fundamental connections between data management and modern machine learning systems (e.g., ML training on quantum computers) to make decision-making more transparent, robust and efficient. Besides publishing at database and system conferences (e.g., SIGMOD and USENIX ATC), Bo’s research output has been applied into real-world settings (e.g.,smart grid management and public transportation) and integrated into industry software (e.g., Amazon Redshift cloud data warehouse and MindSpore ML framework).]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492100Sun, 22 Oct 2023 19:43:55 +0000DASERAbstract: Efficient machine learning (ML) systems translate data into value for decision making. Recent breakthroughs in large language models (GPT 4, Llama 2, ChatGPT) and remarkable outcomes of reinforcement learning (RL) in real-world settings...
Speaker: Bo Zhao is a tenure-track Assistant Professor in the Department of Computer Science at Aalto University. His research focuses on efficient data-intensive systems at the intersection of scalable machine learning systems and distributed data management systems, as well as compilation-based optimization techniques. Bo’s long-term goal is to explore and understand the fundamental connections between data management and modern machine learning systems (e.g., ML training on quantum computers) to make decision-making more transparent, robust and efficient. Besides publishing at database and system conferences (e.g., SIGMOD and USENIX ATC), Bo’s research output has been applied into real-world settings (e.g.,smart grid management and public transportation) and integrated into industry software (e.g., Amazon Redshift cloud data warehouse and MindSpore ML framework).]]>3077falsefullhttps://www.spreaker.com/episode/alex-jung-a-total-variation-minimization-perspective-on-trustworthy-federated-learning--74882410
Bio: Alexander Jung received his Phd (with "sub auspiciis") in 2012 from TU Vienna. After Post-Doc stints at TU Vienna and ETH Zurich, he joined Aalto as an Assistant Professor in 2015. He has been chosen as the Computer Science Teacher of the Year and received an Amazon Web Services ML Award in 2018. He serves as an Associate Editor of the IEEE Signal Processing Letter and Editorial
Board Member of the Machine Learning Journal (Springer). He authored the textbook "Machine Learning: The Basics" which has been published by Springer in 2022.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492123Sun, 22 Oct 2023 19:25:33 +0000DASERA widely used design principle for federated learning (FL) systems is total variation (TV) minimization. TV minimization is an instance of regularized empirical risk minimization, using the variation of local model parameters as regularizer....
Bio: Alexander Jung received his Phd (with "sub auspiciis") in 2012 from TU Vienna. After Post-Doc stints at TU Vienna and ETH Zurich, he joined Aalto as an Assistant Professor in 2015. He has been chosen as the Computer Science Teacher of the Year and received an Amazon Web Services ML Award in 2018. He serves as an Associate Editor of the IEEE Signal Processing Letter and Editorial
Board Member of the Machine Learning Journal (Springer). He authored the textbook "Machine Learning: The Basics" which has been published by Springer in 2022.]]>3085falsefullhttps://www.spreaker.com/episode/miika-aittala-elucidating-the-design-space-of-diffusion-based-generative-models--74882406
The paper was presented at NeurIPS 2022, where it received an Outstanding Paper Award. Joint work with Tero Karras, Timo Aila and Samuli Laine.

Bio: Miika Aittala is a Senior Research Scientist at NVIDIA Research, which he joined in 2019. He received his PhD in 2016 from Aalto University, working on capture and rendering of surface material appearance. Prior to his current position, he worked as a postdoctoral researcher at MIT CSAIL and visited Inria Sophia Antipolis. His research interests include neural generative modeling and image processing, and realistic image synthesis in computer graphics.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492126Sun, 22 Oct 2023 19:10:59 +0000DASERAbstract: We argue that the theory and practice of diffusion-based generative models are currently unnecessarily convoluted and seek to remedy the situation by presenting a design space that clearly separates the concrete design choices. This lets us...
The paper was presented at NeurIPS 2022, where it received an Outstanding Paper Award. Joint work with Tero Karras, Timo Aila and Samuli Laine.

Bio: Miika Aittala is a Senior Research Scientist at NVIDIA Research, which he joined in 2019. He received his PhD in 2016 from Aalto University, working on capture and rendering of surface material appearance. Prior to his current position, he worked as a postdoctoral researcher at MIT CSAIL and visited Inria Sophia Antipolis. His research interests include neural generative modeling and image processing, and realistic image synthesis in computer graphics.]]>3166falsefullhttps://www.spreaker.com/episode/alexander-ilin-hierarchical-imitation-learning-with-vector-quantized-models--74882402
Speaker: Alexander Ilin is a Professor of Practice in the Department of Computer Science of Aalto University. He was a research scientist at Curious AI, Amazon and Analyse2. He obtained his PhD degree from Helsinki University of Technology in 2006 under the supervision of Prof. Erkki Oja and Dr. Harri Valpola. His research interests focus on deep representation learning and model-based reinforcement learning.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492104Sun, 22 Oct 2023 16:34:10 +0000DASERAbstract: The ability to plan actions on multiple levels of abstraction enables intelligent agents to solve complex tasks effectively. However, learning the models for both low and high-level planning from demonstrations has proven challenging,...
Speaker: Alexander Ilin is a Professor of Practice in the Department of Computer Science of Aalto University. He was a research scientist at Curious AI, Amazon and Analyse2. He obtained his PhD degree from Helsinki University of Technology in 2006 under the supervision of Prof. Erkki Oja and Dr. Harri Valpola. His research interests focus on deep representation learning and model-based reinforcement learning.]]>3071falsefullhttps://www.spreaker.com/episode/maurizio-filippone-functional-priors-for-bayesian-deep-learning--74882408
Speaker: Maurizio Filippone is an associate professor and AXA Chair of Computational Statistics at EURECOM, France. He has over 70 publications in Bayesian statistics and inference of Gaussian processes and neural networks.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492106Sun, 08 Oct 2023 20:31:35 +0000DASERAbstract: The Bayesian treatment of neural networks dictates that a prior distribution is specified over their weight and bias parameters. This poses a challenge because modern neural networks are characterized by a large number of parameters and...
Speaker: Maurizio Filippone is an associate professor and AXA Chair of Computational Statistics at EURECOM, France. He has over 70 publications in Bayesian statistics and inference of Gaussian processes and neural networks.]]>3489falsefullhttps://www.spreaker.com/episode/kyunghyun-cho-generative-multitask-learning-mitigates-target-causing-confounding--74882425
Speaker: Kyunghyun Cho is an associate professor of computer science and data science at New York University and CIFAR Fellow of Learning in Machines & Brains. He is also a senior director of frontier research at the Prescient Design team within Genentech Research & Early Development (gRED). He was a research scientist at Facebook AI Research from June 2017 to May 2020 and a postdoctoral fellow at University of Montreal until Summer 2015 under the supervision of Prof. Yoshua Bengio, after receiving MSc and PhD degrees from Aalto University April 2011 and April 2014, respectively, under the supervision of Prof. Juha Karhunen, Dr. Tapani Raiko and Dr. Alexander Ilin. He received the Samsung Ho-Am Prize in Engineering in 2021. He tries his best to find a balance among machine learning, natural language processing, and life, but almost always fails to do so.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492132Sun, 08 Oct 2023 20:07:07 +0000DASERAbstract: We propose a simple and scalable approach to causal representation learning for multitask learning. Our approach requires minimal modification to existing ML systems, and improves robustness to prior probability shift. The improvement comes...
Speaker: Kyunghyun Cho is an associate professor of computer science and data science at New York University and CIFAR Fellow of Learning in Machines & Brains. He is also a senior director of frontier research at the Prescient Design team within Genentech Research & Early Development (gRED). He was a research scientist at Facebook AI Research from June 2017 to May 2020 and a postdoctoral fellow at University of Montreal until Summer 2015 under the supervision of Prof. Yoshua Bengio, after receiving MSc and PhD degrees from Aalto University April 2011 and April 2014, respectively, under the supervision of Prof. Juha Karhunen, Dr. Tapani Raiko and Dr. Alexander Ilin. He received the Samsung Ho-Am Prize in Engineering in 2021. He tries his best to find a balance among machine learning, natural language processing, and life, but almost always fails to do so.]]>4232falsefullhttps://www.spreaker.com/episode/arto-klami-better-priors-for-everyone--74882415
This talk focuses on the question of how to choose the prior distributions in varying scenarios, describing concepts and tools that help choosing better priors with less cognitive and computational effort. We will go through prior elicitation as means of transforming tacit human knowledge into valid prior distributions and discuss the current state of prior elicitation techniques. In addition, we will introduce an approach for choosing the prior distributions for Bayesian machine learning methods without cross-validation setups, resulting in automatic choice of priors that does not require carrying out computationally costly inference.

Speaker: Pr. Arto Klami leads the Multi-source probabilistic inference research group at the Department of Computer Science of University of Helsinki. He is also a member of Helsinki Institute for Information Technology HIIT and Finnish Center for Artificial Intelligence (FCAI). Until the end of 2012 he was a postdoctoral researcher at the Department of Information and Computer Science, Aalto University, and between March 2015 and June 2015 he was a Visiting Research Scientist at Amazon Berlin.
He conducts research on statistical machine learning and artificial Intelligence.

Affiliation: University of Helsinki]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492131Mon, 02 Oct 2023 12:48:43 +0000DASERAbstract: Statistical modelling and Bayesian machine learning build on the core principle of updating prior beliefs based on data, to estimate a distribution over possible values the unknown parameters of the model could take. Much of the...
This talk focuses on the question of how to choose the prior distributions in varying scenarios, describing concepts and tools that help choosing better priors with less cognitive and computational effort. We will go through prior elicitation as means of transforming tacit human knowledge into valid prior distributions and discuss the current state of prior elicitation techniques. In addition, we will introduce an approach for choosing the prior distributions for Bayesian machine learning methods without cross-validation setups, resulting in automatic choice of priors that does not require carrying out computationally costly inference.

Speaker: Pr. Arto Klami leads the Multi-source probabilistic inference research group at the Department of Computer Science of University of Helsinki. He is also a member of Helsinki Institute for Information Technology HIIT and Finnish Center for Artificial Intelligence (FCAI). Until the end of 2012 he was a postdoctoral researcher at the Department of Information and Computer Science, Aalto University, and between March 2015 and June 2015 he was a Visiting Research Scientist at Amazon Berlin.
He conducts research on statistical machine learning and artificial Intelligence.

Affiliation: University of Helsinki]]>2873falsefullhttps://www.spreaker.com/episode/justin-j-j-van-der-hooft-the-use-of-machine-learning-in-computational-metabolomics-workflows--74882399I will finish off with my perspective on integrating genome and metabolome mining workflows to accelerate specialized metabolite discovery and their structural and functional characterization, as well as a call to action: sharing is caring! I expect that the presented methodological developments will advance our understanding of the role of metabolites and their complex molecular interactions that underpin growth, development, and health.

Speaker: Justin J.J. van der Hooft is an Assistant Professor in Computational Metabolomics in the Bioinformatics Group at Wageningen University, NL, and an author of over 80 peer-reviewed articles in the metabolomics field. Justin is very fascinated by the ingenuity of nature in creating marvelous chemical structures and their diverse roles in ecosystems that include inter-kingdom communication and this a main driver of his research. He obtained his MSc (2007) in Molecular Sciences (Wageningen University, NL) and his PhD (2012) at the Biochemistry and Bioscience groups in Wageningen (Wageningen University & Research, NL). After a postdoctoral period in Glasgow, UK, studying both analytical and computational aspects of metabolite structure annotation, he returned to Wageningen in 2017 to work on linking metabolome and genome mining workflows. Since he started his own group in 2020, his team has continued to develop computational metabolomics methodologies to decompose the mass spectral data of complex metabolite mixtures into structure and substructure information. By linking genome and metabolome mining, his team studies plant, food, and microbiome-associated metabolites to find novel bioactive metabolites and infer their source and function. Since 2022, he is also affiliated with the University of Johannesburg, SA, as a visiting professor. Got interested? Find out more and meet the team here: https://vdhooftcompmet.github.io.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492128Fri, 29 Sep 2023 14:45:41 +0000DASERAbstract: Specialized metabolites play key roles in regulating physiological processes in organisms related to growth and development and serve as communicators between organisms. Their encoded messages include cries for help, embrace yourself, as...I will finish off with my perspective on integrating genome and metabolome mining workflows to accelerate specialized metabolite discovery and their structural and functional characterization, as well as a call to action: sharing is caring! I expect that the presented methodological developments will advance our understanding of the role of metabolites and their complex molecular interactions that underpin growth, development, and health.

Speaker: Justin J.J. van der Hooft is an Assistant Professor in Computational Metabolomics in the Bioinformatics Group at Wageningen University, NL, and an author of over 80 peer-reviewed articles in the metabolomics field. Justin is very fascinated by the ingenuity of nature in creating marvelous chemical structures and their diverse roles in ecosystems that include inter-kingdom communication and this a main driver of his research. He obtained his MSc (2007) in Molecular Sciences (Wageningen University, NL) and his PhD (2012) at the Biochemistry and Bioscience groups in Wageningen (Wageningen University & Research, NL). After a postdoctoral period in Glasgow, UK, studying both analytical and computational aspects of metabolite structure annotation, he returned to Wageningen in 2017 to work on linking metabolome and genome mining workflows. Since he started his own group in 2020, his team has continued to develop computational metabolomics methodologies to decompose the mass spectral data of complex metabolite mixtures into structure and substructure information. By linking genome and metabolome mining, his team studies plant, food, and microbiome-associated metabolites to find novel bioactive metabolites and infer their source and function. Since 2022, he is also affiliated with the University of Johannesburg, SA, as a visiting professor. Got interested? Find out more and meet the team here: https://vdhooftcompmet.github.io.]]>2651falsefullhttps://www.spreaker.com/episode/vikas-garg-new-frontiers-for-ai-assisted-design-and-discovery--74882419
I will give an overview of some of our upcoming work [1, 2, 3] that makes significant advances in this quest. In particular, unlike previous approaches, we’re able to generate high quality molecules without resorting to any validity checks or correction.

[1] Yogesh Verma, Samuel Kaski, Markus Heinonen, and Vikas Garg. Modular Flows: Differential Molecular Generation, NeurIPS 2022.
[2] Giangiacomo Mercatali, Andre Freitas, and Vikas Garg. Symmetry-induced Disentanglement on Graphs, NeurIPS 2022.
[3] Amauri Souza, Diego Mesquita, Samuel Kaski, and Vikas Garg. Provably expressive temporal graph networks, NeurIPS 2022.

Speaker: Vikas Garg is an Assistant Professor at Aalto University and FCAI, and Chief Scientist at YaiYai Ltd. He holds a PhD in Computer Science from MIT, and has led various research and engineering efforts during stints at IBM Research, Microsoft Research, and Amazon A9.
His co-authored works have contributed to advancing multiple domains, including the first graph-based deep learning model for generating new proteins, context-aware recommender systems for e-commerce platforms, fast inference on resource-constrained IoT devices such as smartphones, integration of renewable energy into smart grids, and design of next generation wireless systems; as well as exposing the limitations of Generative Adversarial Networks (GANs) and Graph Neural Networks (GNNs).

Vikas has served as an invited Area Chair/Senior Program Committee member/Panelist at premier AI/ML venues. His select honors include a BP Technologies Energy Fellowship and recognition by MIT EECS as one of its strongest incoming students, and highest evaluation scores for a course on Applied Machine Learning that he co-designed and co-instructed at MIT.

Affiliation: Aalto University]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492112Fri, 29 Sep 2023 14:31:33 +0000DASERAbstract: Generating (macro-)molecules that have desirable physicochemical properties holds the key to enabling better drugs, materials, and batteries, etc. However, several challenges must be overcome, e.g., in order to (a) tractably search over an...
I will give an overview of some of our upcoming work [1, 2, 3] that makes significant advances in this quest. In particular, unlike previous approaches, we’re able to generate high quality molecules without resorting to any validity checks or correction.

[1] Yogesh Verma, Samuel Kaski, Markus Heinonen, and Vikas Garg. Modular Flows: Differential Molecular Generation, NeurIPS 2022.
[2] Giangiacomo Mercatali, Andre Freitas, and Vikas Garg. Symmetry-induced Disentanglement on Graphs, NeurIPS 2022.
[3] Amauri Souza, Diego Mesquita, Samuel Kaski, and Vikas Garg. Provably expressive temporal graph networks, NeurIPS 2022.

Speaker: Vikas Garg is an Assistant Professor at Aalto University and FCAI, and Chief Scientist at YaiYai Ltd. He holds a PhD in Computer Science from MIT, and has led various research and engineering efforts during stints at IBM Research, Microsoft Research, and Amazon A9.
His co-authored works have contributed to advancing multiple domains, including the first graph-based deep learning model for generating new proteins, context-aware recommender systems for e-commerce platforms, fast inference on resource-constrained IoT devices such as smartphones, integration of renewable energy into smart grids, and design of next generation wireless systems; as well as exposing the limitations of Generative Adversarial Networks (GANs) and Graph Neural Networks (GNNs).

Vikas has served as an invited Area Chair/Senior Program Committee member/Panelist at premier AI/ML venues. His select honors include a BP Technologies Energy Fellowship and recognition by MIT EECS as one of its strongest incoming students, and highest evaluation scores for a course on Applied Machine Learning that he co-designed and co-instructed at MIT.

Affiliation: Aalto University]]>3195falsefullhttps://www.spreaker.com/episode/chatgpt-in-research-tiedekulma-29-8-2023--74882455
Speakers and panelists: Professor Hannu Toivonen (University of Helsinki), Associate professor Perttu Hämäläinen (Aalto University) and Research Team Leader (VTT)Arash Hajikhani

Read more: https://fcai.fi/calendar/2023/8/29/chatgpt-in-research]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303701823492098Tue, 12 Sep 2023 08:22:12 +0000DASERDo you wonder how to use ChatGPT in research? What are the ethical considerations related to this? FCAI’s Ethics Advisory Board arranged an event about this very pertinent topic at Tiedekulma on 29 August. Speakers and panelists: Professor Hannu...
Speakers and panelists: Professor Hannu Toivonen (University of Helsinki), Associate professor Perttu Hämäläinen (Aalto University) and Research Team Leader (VTT)Arash Hajikhani

Read more: https://fcai.fi/calendar/2023/8/29/chatgpt-in-research]]>7285falsefullhttps://www.spreaker.com/episode/ossi-raisa-noise-aware-statistical-inference-with-differentially-private-synthetic-data--74882396
Speaker: Ossi Räisä is a second-year PhD student at the university of Helsinki, supervised by Antti Honkela. His current research work is in noise-aware analysis of synthetic data, and differentially private meta-learning.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075959Fri, 12 May 2023 16:09:13 +0000DASERAbstract: While generation of synthetic data under differential privacy (DP) has received a lot of attention in the data privacy community, analysis of synthetic data has received much less. Existing work has shown that simply analysing DP synthetic...
Speaker: Ossi Räisä is a second-year PhD student at the university of Helsinki, supervised by Antti Honkela. His current research work is in noise-aware analysis of synthetic data, and differentially private meta-learning.]]>1627falsefullhttps://www.spreaker.com/episode/jukka-nurminen-challenges-in-ml-engineering--74882407
Speaker: Dr Jukka K. Nurminen is a professor of computer science at the University of Helsinki. He has worked extensively on software research in the telecom industry at Nokia Research Center, in academia at Aalto University, and in applied research at VTT. His key research contributions are on energy-efficient software, mobile peer-to-peer networking, and cloud solutions but his experience ranges widely from applied optimization to AI, from network planning tools to mobile apps, and from software project management to tens of patented inventions. He received his MSc degree in 1986 and PhD degree in 2003 from Helsinki University of Technology (now Aalto University) in applied mathematics. Currently, his main interests are in the engineering of machine learning systems, fair and reliable operation of AI, and software development for quantum computers.

Affiliation: University of Helsinki]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075868Sun, 30 Apr 2023 11:00:22 +0000DASERAbstract: As AI matures, the focus shifts from developing smarter models to questions about how to use them in real-world cases. The shift from handcraft to a more systematic and efficient industrial way of developing, testing, monitoring, and...
Speaker: Dr Jukka K. Nurminen is a professor of computer science at the University of Helsinki. He has worked extensively on software research in the telecom industry at Nokia Research Center, in academia at Aalto University, and in applied research at VTT. His key research contributions are on energy-efficient software, mobile peer-to-peer networking, and cloud solutions but his experience ranges widely from applied optimization to AI, from network planning tools to mobile apps, and from software project management to tens of patented inventions. He received his MSc degree in 1986 and PhD degree in 2003 from Helsinki University of Technology (now Aalto University) in applied mathematics. Currently, his main interests are in the engineering of machine learning systems, fair and reliable operation of AI, and software development for quantum computers.

Affiliation: University of Helsinki]]>3148falsefullhttps://www.spreaker.com/episode/challenges-of-future-supply-chain-management-possibilities-of-ai-hinkka-fcai-18-4-2023--74882416The webinar dived into the future demands and challenges regarding logistics and supply chain management. Speakers from academia and industry discussed the role of AI in reducing CO2-emissions, efficiency, optimization, resilience and security of supply.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075932Sun, 23 Apr 2023 21:00:19 +0000DASERFCAI Industry and Society Webinar on AI for sustainable logistics and supply chain management was held on April 18th 2023, 10:00 – 12:00. The webinar dived into the future demands and challenges regarding logistics and supply chain management....The webinar dived into the future demands and challenges regarding logistics and supply chain management. Speakers from academia and industry discussed the role of AI in reducing CO2-emissions, efficiency, optimization, resilience and security of supply.]]>1047falsefullhttps://www.spreaker.com/episode/ai-as-an-enabler-for-sustainability-case-konecranes-ruotsalainen-fcai-18-4-2023--74882427The webinar dived into the future demands and challenges regarding logistics and supply chain management. Speakers from academia and industry discussed the role of AI in reducing CO2-emissions, efficiency, optimization, resilience and security of supply.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075874Sun, 23 Apr 2023 21:00:14 +0000DASERFCAI Industry and Society Webinar on AI for sustainable logistics and supply chain management was held on April 18th 2023, 10:00 – 12:00. The webinar dived into the future demands and challenges regarding logistics and supply chain management....The webinar dived into the future demands and challenges regarding logistics and supply chain management. Speakers from academia and industry discussed the role of AI in reducing CO2-emissions, efficiency, optimization, resilience and security of supply.]]>1175falsefullhttps://www.spreaker.com/episode/fcai-ai-for-sustainable-logistics-and-supply-chain-management-18-4-2023--74882442The webinar dived into the future demands and challenges regarding logistics and supply chain management. Speakers from academia and industry discussed the role of AI in reducing CO2-emissions, efficiency, optimization, resilience and security of supply.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075885Sun, 23 Apr 2023 21:00:11 +0000DASERFCAI Industry and Society Webinar on AI for sustainable logistics and supply chain management was held on April 18th 2023, 10:00 – 12:00. The webinar dived into the future demands and challenges regarding logistics and supply chain management....The webinar dived into the future demands and challenges regarding logistics and supply chain management. Speakers from academia and industry discussed the role of AI in reducing CO2-emissions, efficiency, optimization, resilience and security of supply.]]>7340falsefullhttps://www.spreaker.com/episode/supporting-sustainability-in-smart-ports-yli-paunu-fcai-18-4-2023-default--74882444The webinar dived into the future demands and challenges regarding logistics and supply chain management. Speakers from academia and industry discussed the role of AI in reducing CO2-emissions, efficiency, optimization, resilience and security of supply.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075933Sun, 23 Apr 2023 21:00:05 +0000DASERFCAI Industry and Society Webinar on AI for sustainable logistics and supply chain management was held on April 18th 2023, 10:00 – 12:00. The webinar dived into the future demands and challenges regarding logistics and supply chain management....The webinar dived into the future demands and challenges regarding logistics and supply chain management. Speakers from academia and industry discussed the role of AI in reducing CO2-emissions, efficiency, optimization, resilience and security of supply.]]>1141falsefullhttps://www.spreaker.com/episode/sustainability-in-the-consumer-goods-value-chain-and-the-role-of-ai-elovehmas-fcai-18-4-2023-default--74882413The webinar dived into the future demands and challenges regarding logistics and supply chain management. Speakers from academia and industry discussed the role of AI in reducing CO2-emissions, efficiency, optimization, resilience and security of supply.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075847Sun, 23 Apr 2023 21:00:00 +0000DASERFCAI Industry and Society Webinar on AI for sustainable logistics and supply chain management was held on April 18th 2023, 10:00 – 12:00. The webinar dived into the future demands and challenges regarding logistics and supply chain management....The webinar dived into the future demands and challenges regarding logistics and supply chain management. Speakers from academia and industry discussed the role of AI in reducing CO2-emissions, efficiency, optimization, resilience and security of supply.]]>1485falsefullhttps://www.spreaker.com/episode/indre-zliobaite-cross-validation-revisited--74882418overview techniques for evaluating machine learned models when data is not IID. I will focus on three main settings: (1) data is autocorrelated spatially, (2) concept drift over time, (3) observations are non-independent phylogenetically. I will discuss challenges and perspectives for knowledge discovery and generalisation from machine learned models with practical examples from macroecology and industrial
process control.

Speaker: Indrė Žliobaitė is an associate professor at the Dept. of Computer science and Dept. of Geosciences and Geography, University of Helsinki, where she leads a research group focusing on data science for understanding evolutionary processes in nature and society. She has contributed popular algorithmic techniques and theory for learning from evolving data, pioneering work in fairness-aware machine learning, and new perspectives to macroevolutionary analyses.

Affiliation: University of Helsinki]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075963Wed, 05 Apr 2023 12:01:48 +0000DASERAbstract: Classical machine learning typically assumes that data is independently and identically distributed (IID), while in practice very often that's not the case. In this talk I will discuss challenges and overview techniques for evaluating...overview techniques for evaluating machine learned models when data is not IID. I will focus on three main settings: (1) data is autocorrelated spatially, (2) concept drift over time, (3) observations are non-independent phylogenetically. I will discuss challenges and perspectives for knowledge discovery and generalisation from machine learned models with practical examples from macroecology and industrial
process control.

Speaker: Indrė Žliobaitė is an associate professor at the Dept. of Computer science and Dept. of Geosciences and Geography, University of Helsinki, where she leads a research group focusing on data science for understanding evolutionary processes in nature and society. She has contributed popular algorithmic techniques and theory for learning from evolving data, pioneering work in fairness-aware machine learning, and new perspectives to macroevolutionary analyses.

Affiliation: University of Helsinki]]>2898falsefullhttps://www.spreaker.com/episode/jean-feydy-fast-geometric-libraries-for-vision-and-data-sciences--74882412
In this talk, I will present extensions for PyTorch, NumPy, Matlab and R that speed up fundamental computations on (generalized) point clouds by several orders of magnitude, when compared to PyTorch or JAX GPU baselines. These software tools allow researchers to break through major computational bottlenecks in the field and have been downloaded more than 400k times over the last few years.
The presentation may be of interest to all researchers who deal with point clouds, time series and segmentation maps, with a special focus on:

1. Fast and scalable computations with (generalized) distance matrices.
2. Efficient and robust solvers for the optimal transport (= “Earth Mover’s”) problem.
3. Applications to shape analysis and geometric deep learning, with a case study on the “pixel-perfect” registration of lung vessel trees.

References:

- Geometric data analyis - MVA lectures and videos : https://www.jeanfeydy.com/Teaching/index.html

- “Geometric data analysis, beyond convolutions”: https://www.jeanfeydy.com/geometric_data_analysis.pdf
- “Fast geometric learning with symbolic matrices”: http://jeanfeydy.com/Papers/KeOps_NeurIPS_2020.pdf
- KeOps library (geometric computations): http://kernel-operations.io/keops/index.html
- GeomLoss library (optimal transport): https://www.kernel-operations.io/geomloss/

Speaker: Jean Feydy is a research fellow at Inria Paris, working in the joint maths / statistics / public health team HeKA.

His work focuses on scalable geometric data analysis, from 3D anatomy to survival analysis on nationwide drug consumption data.

Affiliation: Inria/Inserm team HeKa]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075950Mon, 27 Feb 2023 14:14:02 +0000DASERAbstract: From 3D point clouds to high-dimensional samples, sparse representations have a key position in the data science toolbox. They complement 2D images and 3D volumes effectively, enabling fast geometric computations for e.g. Gaussian processes...
In this talk, I will present extensions for PyTorch, NumPy, Matlab and R that speed up fundamental computations on (generalized) point clouds by several orders of magnitude, when compared to PyTorch or JAX GPU baselines. These software tools allow researchers to break through major computational bottlenecks in the field and have been downloaded more than 400k times over the last few years.
The presentation may be of interest to all researchers who deal with point clouds, time series and segmentation maps, with a special focus on:

1. Fast and scalable computations with (generalized) distance matrices.
2. Efficient and robust solvers for the optimal transport (= “Earth Mover’s”) problem.
3. Applications to shape analysis and geometric deep learning, with a case study on the “pixel-perfect” registration of lung vessel trees.

References:

- Geometric data analyis - MVA lectures and videos : https://www.jeanfeydy.com/Teaching/index.html

- “Geometric data analysis, beyond convolutions”: https://www.jeanfeydy.com/geometric_data_analysis.pdf
- “Fast geometric learning with symbolic matrices”: http://jeanfeydy.com/Papers/KeOps_NeurIPS_2020.pdf
- KeOps library (geometric computations): http://kernel-operations.io/keops/index.html
- GeomLoss library (optimal transport): https://www.kernel-operations.io/geomloss/

Speaker: Jean Feydy is a research fellow at Inria Paris, working in the joint maths / statistics / public health team HeKA.

His work focuses on scalable geometric data analysis, from 3D anatomy to survival analysis on nationwide drug consumption data.

Affiliation: Inria/Inserm team HeKa]]>2877falsefullhttps://www.spreaker.com/episode/pierre-wolinski-gaussian-pre-activations-in-neural-networks-myth-or-reality--74882422
In this paper, we discuss the hypothesis of Gaussian pre-activations at the initialization of a neural network, which we call the "Gaussian hypothesis". More specifically, we have obtained the following results:
* we perform an empirical study of the propagation of the distribution of the pre-activations in a neural network;
* with a ReLU activation function, the pre-activations are not Gaussian, which contradicts the Gaussian hypothesis;
* the "Edge of Chaos" framework, which indicates how the pre-activations propagate at initialization, is shown to output inconsistent results when using neural networks with a small number of neurons per layer (since it makes use of the Gaussian hypothesis, which is not always valid);
* in order to solve the preceding problems, we construct a family of activation functions and initialization distributions such that the Gaussian hypothesis holds;
* in that case, the Edge of Chaos framework is exact, and does not output inconsistent results;
* in that case, information (back-)propagates better through deep and narrow neural networks (i.e., with a large number of layers and a small number of neurons per layer) than when using ReLU or tanh activation functions with Kaiming or Xavier initialization.

Speaker: Pierre Wolinski is currently a post-doctoral researcher in the Statify team, at the Inria Grenoble (France), under the supervision of Julyan Arbel. Before that, he spent one year of post-doc at the University of Oxford with Judith Rousseau. He did his PhD with Guillaume Charpiat (computer vision) and Yann Ollivier (theory of ML) at the Tau team (Inria Saclay, France), on neural network pruning and Bayesian neural networks.
Now, he is studying information propagation through neural networks, both at initialization and during training.

Affiliation: Inria team Statify]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075852Mon, 13 Feb 2023 14:06:38 +0000DASERAbstract: The study of feature propagation at initialization in neural networks lies at the root of numerous initialization designs. An assumption very commonly made in the field states that the pre-activations are Gaussian. Although this convenient...
In this paper, we discuss the hypothesis of Gaussian pre-activations at the initialization of a neural network, which we call the "Gaussian hypothesis". More specifically, we have obtained the following results:
* we perform an empirical study of the propagation of the distribution of the pre-activations in a neural network;
* with a ReLU activation function, the pre-activations are not Gaussian, which contradicts the Gaussian hypothesis;
* the "Edge of Chaos" framework, which indicates how the pre-activations propagate at initialization, is shown to output inconsistent results when using neural networks with a small number of neurons per layer (since it makes use of the Gaussian hypothesis, which is not always valid);
* in order to solve the preceding problems, we construct a family of activation functions and initialization distributions such that the Gaussian hypothesis holds;
* in that case, the Edge of Chaos framework is exact, and does not output inconsistent results;
* in that case, information (back-)propagates better through deep and narrow neural networks (i.e., with a large number of layers and a small number of neurons per layer) than when using ReLU or tanh activation functions with Kaiming or Xavier initialization.

Speaker: Pierre Wolinski is currently a post-doctoral researcher in the Statify team, at the Inria Grenoble (France), under the supervision of Julyan Arbel. Before that, he spent one year of post-doc at the University of Oxford with Judith Rousseau. He did his PhD with Guillaume Charpiat (computer vision) and Yann Ollivier (theory of ML) at the Tau team (Inria Saclay, France), on neural network pruning and Bayesian neural networks.
Now, he is studying information propagation through neural networks, both at initialization and during training.

Affiliation: Inria team Statify]]>2728falsefullhttps://www.spreaker.com/episode/stephane-deny-modelling-mental-rotation-in-the-brain-using-deep-learning--74882421
Speakers: Stephane Deny is assistant professor in the Departments of Neuroscience and Biomedical Engineering and of Computer Science at Aalto University. Before that, he did my PhD in Paris at the Vision Institute in computational neuroscience, and two postdocs respectively at Stanford in theoretical neuroscience and at Facebook AI Research Lab in machine learning. He is interested both in making advances in our understanding of the brain and advances in machine learning.

Affiliation: Aalto University]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075903Sun, 12 Feb 2023 15:28:48 +0000DASERAbstract: Mental rotation denotes our ability to manipulate representations of two-dimensional or three-dimensional objects mentally, which allows us for example to recognise an object seen from an unusual point of view. Deep networks lack such...
Speakers: Stephane Deny is assistant professor in the Departments of Neuroscience and Biomedical Engineering and of Computer Science at Aalto University. Before that, he did my PhD in Paris at the Vision Institute in computational neuroscience, and two postdocs respectively at Stanford in theoretical neuroscience and at Facebook AI Research Lab in machine learning. He is interested both in making advances in our understanding of the brain and advances in machine learning.

Affiliation: Aalto University]]>2967falsefullhttps://www.spreaker.com/episode/kai-puolamaki-explainable-and-robust-ai-for-the-vilma-virtual-laboratory--74882417
References :

· Virtual Laboratory for Molecular Level Atmospheric Transformations (VILMA) Centre of Excellence, https://www.helsinki.fi/en/researchgroups/vilma.

· Björklund, A., Mäkelä, J., & Puolamäki, K. (2022). SLISEMAP: Supervised dimensionality reduction through local explanations. Machine Learning. https://doi.org/10.1007/s10994-022-06261-1

Speakers: Kai Puolamäki is an Associate Professor in computer science and atmospheric sciences at the Department of Computer Science and Institute for Atmospheric and Earth System Research at the University of Helsinki. He has a PhD in theoretical physics and holds a Title of Docent in computer science. His research interests include machine learning and exploratory data analysis. Kai Puolamäki has website at https://www.iki.fi/kaip.

Affiliation: University of Helsinki]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075851Thu, 19 Jan 2023 13:46:31 +0000DASERAbstract: I will provide an overview of our new Virtual Laboratory for Molecular Level Atmospheric Transformations (VILMA) Centre of Excellence and discuss why explainable AI (XAI) and quantifying uncertainties is essential for us. As an example...
References :

· Virtual Laboratory for Molecular Level Atmospheric Transformations (VILMA) Centre of Excellence, https://www.helsinki.fi/en/researchgroups/vilma.

· Björklund, A., Mäkelä, J., & Puolamäki, K. (2022). SLISEMAP: Supervised dimensionality reduction through local explanations. Machine Learning. https://doi.org/10.1007/s10994-022-06261-1

Speakers: Kai Puolamäki is an Associate Professor in computer science and atmospheric sciences at the Department of Computer Science and Institute for Atmospheric and Earth System Research at the University of Helsinki. He has a PhD in theoretical physics and holds a Title of Docent in computer science. His research interests include machine learning and exploratory data analysis. Kai Puolamäki has website at https://www.iki.fi/kaip.

Affiliation: University of Helsinki]]>2362falsefullhttps://www.spreaker.com/episode/pekka-marttinen-how-to-better-compare-representations-learned-by-neural-networks--74882424
Speaker: Pekka Marttinen is an associate professor in machine learning in the department of computer science at Aalto university, where he leads the Machine Learning for Health (Aalto-ML4H) research group. His research interests include for example Bayesian machine learning, causality, deep learning, and applications in biology and healthcare, and he has published over 70 articles in this topics.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075954Mon, 09 Jan 2023 23:20:17 +0000DASERAbstract: Comparing representations learned by different neural networks is required to understand for example differences between model architectures, usefulness of transfer learning, or robustness of the models. In this talk, I will discuss how...
Speaker: Pekka Marttinen is an associate professor in machine learning in the department of computer science at Aalto university, where he leads the Machine Learning for Health (Aalto-ML4H) research group. His research interests include for example Bayesian machine learning, causality, deep learning, and applications in biology and healthcare, and he has published over 70 articles in this topics.]]>2505falsefullhttps://www.spreaker.com/episode/vikas-garg-new-frontiers-for-ai-assisted-design-and-discovery--74882431
I will give an overview of some of our upcoming work [1, 2, 3] that makes significant advances in this quest. In particular, unlike previous approaches, we’re able to generate high quality molecules without resorting to any validity checks or correction.

[1] Yogesh Verma, Samuel Kaski, Markus Heinonen, and Vikas Garg. Modular Flows: Differential Molecular Generation, NeurIPS 2022.
[2] Giangiacomo Mercatali, Andre Freitas, and Vikas Garg. Symmetry-induced Disentanglement on Graphs, NeurIPS 2022.
[3] Amauri Souza, Diego Mesquita, Samuel Kaski, and Vikas Garg. Provably expressive temporal graph networks, NeurIPS 2022.

Speaker: Vikas Garg is an Assistant Professor at Aalto University and FCAI, and Chief Scientist at YaiYai Ltd. He holds a Ph.D. in Computer Science from MIT, and has led various research and engineering efforts during stints at IBM Research, Microsoft Research, and Amazon A9.
His co-authored works have contributed to advancing multiple domains, including the first graph-based deep learning model for generating new proteins, context-aware recommender systems for e-commerce platforms, fast inference on resource-constrained IoT devices such as smartphones, integration of renewable energy into smart grids, and design of next generation wireless systems; as well as exposing the limitations of Generative Adversarial Networks (GANs) and Graph Neural Networks (GNNs).

Vikas has served as an invited Area Chair/Senior Program Committee member/Panelist at premier AI/ML venues. His select honors include a BP Technologies Energy Fellowship and recognition by MIT EECS as one of its strongest incoming students, and highest evaluation scores for a course on Applied Machine Learning that he co-designed and co-instructed at MIT.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075972Mon, 09 Jan 2023 23:08:37 +0000DASERAbstract: Generating (macro-)molecules that have desirable physicochemical properties holds the key to enabling better drugs, materials, and batteries, etc. However, several challenges must be overcome, e.g., in order to (a) tractably search over an...
I will give an overview of some of our upcoming work [1, 2, 3] that makes significant advances in this quest. In particular, unlike previous approaches, we’re able to generate high quality molecules without resorting to any validity checks or correction.

[1] Yogesh Verma, Samuel Kaski, Markus Heinonen, and Vikas Garg. Modular Flows: Differential Molecular Generation, NeurIPS 2022.
[2] Giangiacomo Mercatali, Andre Freitas, and Vikas Garg. Symmetry-induced Disentanglement on Graphs, NeurIPS 2022.
[3] Amauri Souza, Diego Mesquita, Samuel Kaski, and Vikas Garg. Provably expressive temporal graph networks, NeurIPS 2022.

Speaker: Vikas Garg is an Assistant Professor at Aalto University and FCAI, and Chief Scientist at YaiYai Ltd. He holds a Ph.D. in Computer Science from MIT, and has led various research and engineering efforts during stints at IBM Research, Microsoft Research, and Amazon A9.
His co-authored works have contributed to advancing multiple domains, including the first graph-based deep learning model for generating new proteins, context-aware recommender systems for e-commerce platforms, fast inference on resource-constrained IoT devices such as smartphones, integration of renewable energy into smart grids, and design of next generation wireless systems; as well as exposing the limitations of Generative Adversarial Networks (GANs) and Graph Neural Networks (GNNs).

Vikas has served as an invited Area Chair/Senior Program Committee member/Panelist at premier AI/ML venues. His select honors include a BP Technologies Energy Fellowship and recognition by MIT EECS as one of its strongest incoming students, and highest evaluation scores for a course on Applied Machine Learning that he co-designed and co-instructed at MIT.]]>3469falsefullhttps://www.spreaker.com/episode/trung-trinh-tackling-covariate-shift-with-node-based-bayesian-neural-network--74882430
Speakers: Trung Trinh is currently a Ph.D. student in the Probabilistic Machine Learning group at Aalto University under the guidance of Samuel Kaski and Markus Heinonen. His research focuses on using Bayesian methods to quantify predictive uncertainty of deep learning models.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075891Mon, 09 Jan 2023 22:54:44 +0000DASERAbstract: Bayesian neural networks (BNNs) promise improved generalization under covariate shift by providing principled probabilistic representations of epistemic uncertainty. However, weight-based BNNs often struggle with high computational...
Speakers: Trung Trinh is currently a Ph.D. student in the Probabilistic Machine Learning group at Aalto University under the guidance of Samuel Kaski and Markus Heinonen. His research focuses on using Bayesian methods to quantify predictive uncertainty of deep learning models.]]>1718falsefullhttps://www.spreaker.com/episode/mikko-kurimo-automatic-speaking-assessment-systems-for-second-language-learner-s-spontaneous-speech--74882435
Abstract: Developing automatic systems for assessing spontaneous spoken utterances is important for second language learning, because it promotes and democratizes self-regulated learning and can serve as an auxiliary tool in language proficiency assessment and teacher training. While such systems are typically developed for languages with a large number of learners such as English, the languages with fewer learners such as Finnish and Swedish remain at a disadvantage due to the lack of training data. Nevertheless, due to recent advancements in self-supervised machine learning methods it is now possible to develop automatic speech recognition systems without a large amount of annotated training data. This means that it could also be now feasible to develop automatic speaking assessment systems also for under-resourced languages. In this talk I present our automatic speaking assessment demo system for spontaneous second language learners' speech in Finnish and Finland Swedish. Furthermore, I briefly describe the main components of the system and their evaluation results and the personalized feedback it provides to the language learners. Finally, the most important input features for the machine learning models are determined in order to increase the transparency and explainability of the system and to be able to return more accurate feedback for the students and their teachers. This automatic assessment system comprises several machine learning models: (1) an Automatic Speech Recogniser that converts the spoken utterances into written transcripts; (2) Lexico-grammatical Accuracy and Range Evaluators to the transcribed responses by leveraging textual features; (3) Pronunciation and Fluency Evaluators to the transcribed responses by leveraging acoustic and prosodic features; (4) a Task Accomplishment Evaluator to evaluate test-taker adherence to the task assignment; and (5) a separate Evaluator for the final CEFR-like score representing the holistic overall speaking proficiency.

Speaker:  Mikko Kurimo received his M.Sc., Lic.Tech and D.Sc.(Tech.) degrees from Helsinki University of Technology in 1992, 1994 and 1997. In his PhD thesis he developed neural networks based machine learning for automatic speech recognition (ASR). Since then he has been working as a research scientist at IDIAP, a Swiss research centre for artificial intelligence and visited as an international research fellow in a number of research groups specialized in machine learning and ASR including University of Colorado in Boulder, University of Edinburgh, SRI in Stanford, ICSI in Berkeley and NITech in Nagoya. At Aalto University Professor Kurimo has been the head of the automatic speech recognition group since his return from Switzerland in 2000. His work is internationally best known for unsupervised subword language modeling for morphologically complex languages such as Finnish, Estonian, Turkish and Arabic. His recent achievements include the winning of the 2017 multi-genre broadcast speech recognition challenge and success in Tekes Challenge Finland competition (348 competing projects) and in EC's H2020-ICT-2017 call (115 competing projects). His research interests include deep learning methods for automatic speech recognition and spoken language modeling.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075938Mon, 09 Jan 2023 22:41:32 +0000DASERTitle: An Automatic Speaking Assessment System for Second Language Learner's Spontaneous Speech in Under-Resourced Languages Abstract: Developing automatic systems for assessing spontaneous spoken utterances is important for second language...
Abstract: Developing automatic systems for assessing spontaneous spoken utterances is important for second language learning, because it promotes and democratizes self-regulated learning and can serve as an auxiliary tool in language proficiency assessment and teacher training. While such systems are typically developed for languages with a large number of learners such as English, the languages with fewer learners such as Finnish and Swedish remain at a disadvantage due to the lack of training data. Nevertheless, due to recent advancements in self-supervised machine learning methods it is now possible to develop automatic speech recognition systems without a large amount of annotated training data. This means that it could also be now feasible to develop automatic speaking assessment systems also for under-resourced languages. In this talk I present our automatic speaking assessment demo system for spontaneous second language learners' speech in Finnish and Finland Swedish. Furthermore, I briefly describe the main components of the system and their evaluation results and the personalized feedback it provides to the language learners. Finally, the most important input features for the machine learning models are determined in order to increase the transparency and explainability of the system and to be able to return more accurate feedback for the students and their teachers. This automatic assessment system comprises several machine learning models: (1) an Automatic Speech Recogniser that converts the spoken utterances into written transcripts; (2) Lexico-grammatical Accuracy and Range Evaluators to the transcribed responses by leveraging textual features; (3) Pronunciation and Fluency Evaluators to the transcribed responses by leveraging acoustic and prosodic features; (4) a Task Accomplishment Evaluator to evaluate test-taker adherence to the task assignment; and (5) a separate Evaluator for the final CEFR-like score representing the holistic overall speaking proficiency.

Speaker:  Mikko Kurimo received his M.Sc., Lic.Tech and D.Sc.(Tech.) degrees from Helsinki University of Technology in 1992, 1994 and 1997. In his PhD thesis he developed neural networks based machine learning for automatic speech recognition (ASR). Since then he has been working as a research scientist at IDIAP, a Swiss research centre for artificial intelligence and visited as an international research fellow in a number of research groups specialized in machine learning and ASR including University of Colorado in Boulder, University of Edinburgh, SRI in Stanford, ICSI in Berkeley and NITech in Nagoya. At Aalto University Professor Kurimo has been the head of the automatic speech recognition group since his return from Switzerland in 2000. His work is internationally best known for unsupervised subword language modeling for morphologically complex languages such as Finnish, Estonian, Turkish and Arabic. His recent achievements include the winning of the 2017 multi-genre broadcast speech recognition challenge and success in Tekes Challenge Finland competition (348 competing projects) and in EC's H2020-ICT-2017 call (115 competing projects). His research interests include deep learning methods for automatic speech recognition and spoken language modeling.]]>3130falsefullhttps://www.spreaker.com/episode/nikolaj-tatti-coresets-remembered-and-items-forgotten-submodular-maximization-with-deletions--74882447
First, we propose a single-pass streaming algorithm that yields a (1-2 epsilon)/4p-approximation for maximizing a non-decreasing submodular function under a general p-matroid constraint and requires a coreset of size k + d/epsilon, where k is the maximum size of a feasible solution. Second, we devise an offline algorithm that guarantees stronger approximation ratios with a coreset of size O(d log k / epsilon).

The talk is based on IEEE ICDM 2022 paper.

Bio: Nikolaj Tatti is an assistant professor at the Department of Computer Science, University of Helsinki. He has published over 70 papers on the topics of various data mining topics such as pattern mining, graph mining, and time series analysis. His research interest include statistical modelling, optimisation algorithms, and algorithms optimising statistical models.

Affiliation: University of Helsinki]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790447464453Mon, 19 Dec 2022 13:02:24 +0000DASERAbstract: In recent years we have witnessed an increase on the development of methods for submodular optimization, which have been motivated by the wide applicability of submodular functions in real-world data-science problems. In this we consider the...
First, we propose a single-pass streaming algorithm that yields a (1-2 epsilon)/4p-approximation for maximizing a non-decreasing submodular function under a general p-matroid constraint and requires a coreset of size k + d/epsilon, where k is the maximum size of a feasible solution. Second, we devise an offline algorithm that guarantees stronger approximation ratios with a coreset of size O(d log k / epsilon).

The talk is based on IEEE ICDM 2022 paper.

Bio: Nikolaj Tatti is an assistant professor at the Department of Computer Science, University of Helsinki. He has published over 70 papers on the topics of various data mining topics such as pattern mining, graph mining, and time series analysis. His research interest include statistical modelling, optimisation algorithms, and algorithms optimising statistical models.

Affiliation: University of Helsinki]]>2366falsefullhttps://www.spreaker.com/episode/ellogon-ai-histogeometry-elise-project-open-call-1--74882432developed AI algorithms analyze and assess biomarkers in digitized pathology images. The presence of Tumor Infiltrating Lymphocytes (TILs), PD-L1 expression, and the Tumor
Mutational Burden (TMB) in digitized images of histological tissue are strong indicators on whether immunotherapy is a good treatment or not to a specific patient. To detect these
biomarkers, image analysis with deep convolutional neural networks is performed. With the funding from ELISE, Ellogon.AI will incorporate geometric deep learning to the cell detection
and segmentation algorithms they developed to account for the geometric nature of the data. Using the cutting-edge innovative Deep Learning methodology, 'sparse-shot learning' Ellogon.AI will be able to scale to much larger data sets and complex biomarkers, coupled with higher level of accuracy and speed in patient selection. By incorporating geometrical deep learning they try to improve generalisation and performance by creating convolutional neural models which are in-/equivariant against certain transformation groups such a translations and rotations.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075973Mon, 05 Dec 2022 11:43:41 +0000DASEREllogon.AI aims at selecting the right patient for cancer immunotherapy as quickly as possible in cancer treatment with AI, focusing on breast cancer, lung cancer, and melanoma. The developed AI algorithms analyze and assess biomarkers in digitized...developed AI algorithms analyze and assess biomarkers in digitized pathology images. The presence of Tumor Infiltrating Lymphocytes (TILs), PD-L1 expression, and the Tumor
Mutational Burden (TMB) in digitized images of histological tissue are strong indicators on whether immunotherapy is a good treatment or not to a specific patient. To detect these
biomarkers, image analysis with deep convolutional neural networks is performed. With the funding from ELISE, Ellogon.AI will incorporate geometric deep learning to the cell detection
and segmentation algorithms they developed to account for the geometric nature of the data. Using the cutting-edge innovative Deep Learning methodology, 'sparse-shot learning' Ellogon.AI will be able to scale to much larger data sets and complex biomarkers, coupled with higher level of accuracy and speed in patient selection. By incorporating geometrical deep learning they try to improve generalisation and performance by creating convolutional neural models which are in-/equivariant against certain transformation groups such a translations and rotations.]]>242falsefullhttps://www.spreaker.com/episode/algomo-elise-project-open-call-1--74882397
We found that most of the value from ELISE was that we got the opportunity to be paired up with one of the top researchers in multilingual AI, who became our academic advisor. We found her comments very helpful, and hope that we can collaborate more closely with her in the future.

You can find more information about what algomo does on our website, algomo.com]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075892Mon, 05 Dec 2022 11:43:37 +0000DASERAlgomo is a deep-tech company that helps companies automate their customer service across 100+ languages. At ELISE, we developed a family of specialised multilingual models, which could not only understand conversational queries (in every language)...
We found that most of the value from ELISE was that we got the opportunity to be paired up with one of the top researchers in multilingual AI, who became our academic advisor. We found her comments very helpful, and hope that we can collaborate more closely with her in the future.

You can find more information about what algomo does on our website, algomo.com]]>294falsefullhttps://www.spreaker.com/episode/artisense-artimonorec-elise-project-open-call-1--74882398
ArtiMonoRec is a state-of-the-art dense reconstruction pipeline to enable accurate perception and 3D mapping only based on a camera. ArtiMonoRec integrates an AI module which is built on top of the existing Artisense product VINS (visual inertial navigation system) and delivers highly accurate dense reconstruction of the environment from a moving camera. Furthermore, the system is able to detect and mask out moving objects.

A significant innovation of ArtiMonoRec compared to other neural network based solutions is the proposed training pipeline. The training is defined in a completely semi-supervised manner which does not require LiDAR sensors for data collection. Therefore, the complete system relies on nothing else except the data provided by VINS during training and operation. This makes the system cheaper and much more
scalable than other solutions.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790447464458Mon, 05 Dec 2022 11:43:32 +0000DASERWebsite: https://www.artisense.ai/artiblog/elise-research-artimonorec ArtiMonoRec is a state-of-the-art dense reconstruction pipeline to enable accurate perception and 3D mapping only based on a camera. ArtiMonoRec integrates an AI module which is...
ArtiMonoRec is a state-of-the-art dense reconstruction pipeline to enable accurate perception and 3D mapping only based on a camera. ArtiMonoRec integrates an AI module which is built on top of the existing Artisense product VINS (visual inertial navigation system) and delivers highly accurate dense reconstruction of the environment from a moving camera. Furthermore, the system is able to detect and mask out moving objects.

A significant innovation of ArtiMonoRec compared to other neural network based solutions is the proposed training pipeline. The training is defined in a completely semi-supervised manner which does not require LiDAR sensors for data collection. Therefore, the complete system relies on nothing else except the data provided by VINS during training and operation. This makes the system cheaper and much more
scalable than other solutions.]]>231falsefullhttps://www.spreaker.com/episode/fuvex-civil-sl-autonomous-edge-elise-project-open-call-1--74882433developed a system to solve this challenge, which consists of 1) Low-resolution camera, 2) Gyrostabilized high-resolution camera, 3) Onboard computer that detects towers using low-resolution imagery and points the gimbal in the correct direction.

We have validated the system in the lab and in real inspection flights achieving a TRL7. “Our goal in FuVeX is to automate the digitization of power lines by enabling the industrial use of long-range drones. One of the main challenges we face is ensuring that the sensors onboard the drone capture the right power line data. We are grateful to ELISE as it has provided us with the opportunity to develop the project AUTONOMOUS EDGE in which we have achieved to develop a prototype of an AI system to detect power lines and automatically point the sensors in the direction of these infrastructures” Carlos Matilla, FuVeX CEO. Web: www.fuvex.com.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075887Mon, 05 Dec 2022 11:43:29 +0000DASERIn AUTONOMOUS EDGE, the challenge we faced was automating powerline inspections with long-range drones, specifically how to ensure that the cameras onboard the drone are pointed to the power line towers. In this project, we have developed a system to...developed a system to solve this challenge, which consists of 1) Low-resolution camera, 2) Gyrostabilized high-resolution camera, 3) Onboard computer that detects towers using low-resolution imagery and points the gimbal in the correct direction.

We have validated the system in the lab and in real inspection flights achieving a TRL7. “Our goal in FuVeX is to automate the digitization of power lines by enabling the industrial use of long-range drones. One of the main challenges we face is ensuring that the sensors onboard the drone capture the right power line data. We are grateful to ELISE as it has provided us with the opportunity to develop the project AUTONOMOUS EDGE in which we have achieved to develop a prototype of an AI system to detect power lines and automatically point the sensors in the direction of these infrastructures” Carlos Matilla, FuVeX CEO. Web: www.fuvex.com.]]>295falsefullhttps://www.spreaker.com/episode/ithermai-smokeai-elise-project-open-call-1--74882401https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075869Mon, 05 Dec 2022 11:43:24 +0000DASERVia the support of the ELISE funding, iThermAI could develop the core of their smart smoke and flame detection. State-of-the-art deep-tech technology is used in this product to efficiently detect flame and smoke using inexpensive on-the-edge...241falsefullhttps://www.spreaker.com/episode/quartz-unbabel-elise-project-open-call-1--74882436Furthermore, we made steps also towards a faster automatic
machine-learning-based MT evaluation metric based on COMET (37% faster than previous models) and a model that performs estimation of critical translation errors better (improvement of more than 70% in precision and recall).]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075864Mon, 05 Dec 2022 11:43:09 +0000DASERWithin the period of the project, we developed a quality-aware machine translation (MT) approach that optimizes the MT system to produce fewer severe translation errors as evaluated by professional translators. Our approach is effectively reducing on...Furthermore, we made steps also towards a faster automatic
machine-learning-based MT evaluation metric based on COMET (37% faster than previous models) and a model that performs estimation of critical translation errors better (improvement of more than 70% in precision and recall).]]>246falsefullhttps://www.spreaker.com/episode/scr-ai-synamic-technologies-elise-project-open-call-1--74882434using the relevant industry standards, such as MITRE ATT&CK and STIX2. These datasets are used to automate threat analysis, so that companies obtain an early-warning-system for cyber risks.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075882Mon, 05 Dec 2022 11:43:03 +0000DASERCyber security automation requires reliable, machine-readable information. SCR.AI turns full text incident and threat reports into machine-readable data sets. The attack description is analyzed using Natural Language Processing and expressed using the...using the relevant industry standards, such as MITRE ATT&CK and STIX2. These datasets are used to automate threat analysis, so that companies obtain an early-warning-system for cyber risks.]]>308falsefullhttps://www.spreaker.com/episode/robust-self-supervised-model-development-stratio-automotive-elise-project-open-call-1--74882426although still with performance below the manual alternative. Further investments will be done to optimize performance. A model developed with this approach was deployed, providing an additional source of information for our analysts.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075952Mon, 05 Dec 2022 11:42:59 +0000DASERStratio develops models to predict vehicle faults before they occur, so that the driver can intervene before a breakdown occurs. The main objective of this project was to understand if it is possible to shorten the development time of these models by...although still with performance below the manual alternative. Further investments will be done to optimize performance. A model developed with this approach was deployed, providing an additional source of information for our analysts.]]>185falsefullhttps://www.spreaker.com/episode/copper-ml-rovjok-oy-elise-project-open-call-1--74882437operational performance.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075948Mon, 05 Dec 2022 11:42:48 +0000DASERThe CopperML system is a platform for the rapid integration, ML modelling, simulation, and optimisation of operational data within raw materials operations, with the goal of increasing milling efficiency and improving sustainable metal production....operational performance.]]>241falsefullhttps://www.spreaker.com/episode/robomecha-oncomeca-elise-project-open-call-1--74882439are combined together to improve accuracy. The first AI analyzes images obtained from skin lesions under polarized light. The second AI analyzes mechanical data obtained from our patented mechanical system. These 2 AI axes improve reliability and robustness, making the results interpretable and understandable. The technical approach uses a specific neural network developed by ONCOMECA to extract a more accurate diagnosis.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075958Mon, 05 Dec 2022 11:42:44 +0000DASERONCOMECA is both a start-up and a medical device for the diagnosis of skin cancers, which uniquely combines an optical sensor, a robotic palpation and an artificial intelligence (AI). The goal is to drastically improve the quality and timeliness of...are combined together to improve accuracy. The first AI analyzes images obtained from skin lesions under polarized light. The second AI analyzes mechanical data obtained from our patented mechanical system. These 2 AI axes improve reliability and robustness, making the results interpretable and understandable. The technical approach uses a specific neural network developed by ONCOMECA to extract a more accurate diagnosis.]]>269falsefullhttps://www.spreaker.com/episode/aigea-medical-deepmammo-elise-project-open-call-1--74882441
“ELISE Open Call was the driver for a strong acceleration of DeepMammo and a unique opportunity to scale to an international vision of the project” says Carlo Aliprandi, CEO and cofounder of AIGEA Medical.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075897Fri, 02 Dec 2022 08:40:06 +0000DASERAIGEA Medical is a MedTech startup: focusing on Artificial Intelligence applied to radiology workflows and digital imaging, we propose DeepMammoTM, the AI empowering radiologists in early detection of breast cancer. Thanks to the support from ELISE in...
“ELISE Open Call was the driver for a strong acceleration of DeepMammo and a unique opportunity to scale to an international vision of the project” says Carlo Aliprandi, CEO and cofounder of AIGEA Medical.]]>301falsefullhttps://www.spreaker.com/episode/ville-hyvonen-multilabel-classification-framework-for-approximate-nearest-neighbor-search--74882440
The presentation is based on a joint work with Elias Jääsaari (Carnegie Mellon University) and professor Teemu Roos (University of Helsinki) that will appear in Proceedings of Neural Information Processing Systems (NeurIPS) 2022.  

Speakers: Ville Hyvönen received his M.Sc. decree in Statistics from University of Helsinki. He is currently a PhD candidate at the CS department of University of Helsinki under supervision of professor Teemu Roos. He is working on the foundations of tree-based supervised learning methods and their applications (the current application being ANN search).

Affiliation: University of Helsinki]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075980Wed, 30 Nov 2022 19:48:11 +0000DASERAbstract: Approximate nearest neighbor (ANN) search is a classic algorithmic problem. Unsupervised space partitioning trees, such as k-d, random projection (RP), and principal component (PCA) trees, have been traditionally used as index...
The presentation is based on a joint work with Elias Jääsaari (Carnegie Mellon University) and professor Teemu Roos (University of Helsinki) that will appear in Proceedings of Neural Information Processing Systems (NeurIPS) 2022.  

Speakers: Ville Hyvönen received his M.Sc. decree in Statistics from University of Helsinki. He is currently a PhD candidate at the CS department of University of Helsinki under supervision of professor Teemu Roos. He is working on the foundations of tree-based supervised learning methods and their applications (the current application being ANN search).

Affiliation: University of Helsinki]]>2431falsefullhttps://www.spreaker.com/episode/ville-mustonen-quantifying-determinants-of-microbial-growth--74882409
Short Bio: Ville Mustonen, Professor of Bioinformatics, University of Helsinki, Finland. I work at the Organismal and Evolutionary Biology Research Programme, Department of Computer Science and Institute of Biotechnology. My group develops evolutionary theory and its applications to solve problems such as drug resistance. In particular, we try to understand how predictable evolution is and how and to what extent evolving populations can be controlled. My group further develops bioinformatic algorithms needed to analyse big biological data sets. Our work is fundamental science that can lead to applications relevant to human health, for example, in the context of infectious disease and evolution of drug resistance. We work across different scientific disciplines and have a record of successful research collaborations working together with clinicians and experimentalists.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075937Mon, 14 Nov 2022 14:20:54 +0000DASERAbstract: Quantitative understanding of microbial growth and how to manipulate it by the means of human induced control is a prerequisite to successfully combat pathogens and develop biotechnology applications. However, the task is complex as growth...
Short Bio: Ville Mustonen, Professor of Bioinformatics, University of Helsinki, Finland. I work at the Organismal and Evolutionary Biology Research Programme, Department of Computer Science and Institute of Biotechnology. My group develops evolutionary theory and its applications to solve problems such as drug resistance. In particular, we try to understand how predictable evolution is and how and to what extent evolving populations can be controlled. My group further develops bioinformatic algorithms needed to analyse big biological data sets. Our work is fundamental science that can lead to applications relevant to human health, for example, in the context of infectious disease and evolution of drug resistance. We work across different scientific disciplines and have a record of successful research collaborations working together with clinicians and experimentalists.]]>3030falsefullhttps://www.spreaker.com/episode/arto-klami-fcai-uh-virtual-laboratories-ai-assistance-in-process-automation-and-research--74882443
Artificial intelligence is an essential tool as Europe pushes towards sustainable process industry.

Data-based AI methods have already proven their usefulness in many industrial tasks, and in this webinar, we explore the possibilities of AI for improving the operational efficiency, yield and quality in the process industry. We will hear exciting speeches from research and companies, focusing on data-based AI methods for petrochemical, plastics and steel industries.

More information: https://fcai.fi/calendar/2022/10/26/ai-for-process-industry]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075848Mon, 31 Oct 2022 13:56:40 +0000DASERFCAI Industry & Society Webinar AI for Process Industry (26.10.2022) Artificial intelligence is an essential tool as Europe pushes towards sustainable process industry. Data-based AI methods have already proven their usefulness in many...
Artificial intelligence is an essential tool as Europe pushes towards sustainable process industry.

Data-based AI methods have already proven their usefulness in many industrial tasks, and in this webinar, we explore the possibilities of AI for improving the operational efficiency, yield and quality in the process industry. We will hear exciting speeches from research and companies, focusing on data-based AI methods for petrochemical, plastics and steel industries.

More information: https://fcai.fi/calendar/2022/10/26/ai-for-process-industry]]>951falsefullhttps://www.spreaker.com/episode/esa-puukko-outokumpu-digital-manufacturing-in-stainless-steel-industry--74882449
Artificial intelligence is an essential tool as Europe pushes towards sustainable process industry.

Data-based AI methods have already proven their usefulness in many industrial tasks, and in this webinar, we explore the possibilities of AI for improving the operational efficiency, yield and quality in the process industry. We will hear exciting speeches from research and companies, focusing on data-based AI methods for petrochemical, plastics and steel industries.

More information: https://fcai.fi/calendar/2022/10/26/ai-for-process-industry]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075855Mon, 31 Oct 2022 13:56:27 +0000DASERFCAI Industry & Society Webinar AI for Process Industry (26.10.2022) Artificial intelligence is an essential tool as Europe pushes towards sustainable process industry. Data-based AI methods have already proven their usefulness in many...
Artificial intelligence is an essential tool as Europe pushes towards sustainable process industry.

Data-based AI methods have already proven their usefulness in many industrial tasks, and in this webinar, we explore the possibilities of AI for improving the operational efficiency, yield and quality in the process industry. We will hear exciting speeches from research and companies, focusing on data-based AI methods for petrochemical, plastics and steel industries.

More information: https://fcai.fi/calendar/2022/10/26/ai-for-process-industry]]>1540falsefullhttps://www.spreaker.com/episode/francesco-corona-aalto-university-actionable-wastewater-treatment-plants--74882445
Artificial intelligence is an essential tool as Europe pushes towards sustainable process industry.

Data-based AI methods have already proven their usefulness in many industrial tasks, and in this webinar, we explore the possibilities of AI for improving the operational efficiency, yield and quality in the process industry. We will hear exciting speeches from research and companies, focusing on data-based AI methods for petrochemical, plastics and steel industries.

More information: https://fcai.fi/calendar/2022/10/26/ai-for-process-industry]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075909Mon, 31 Oct 2022 13:56:21 +0000DASERFCAI Industry & Society Webinar AI for Process Industry (26.10.2022) Artificial intelligence is an essential tool as Europe pushes towards sustainable process industry. Data-based AI methods have already proven their usefulness in many...
Artificial intelligence is an essential tool as Europe pushes towards sustainable process industry.

Data-based AI methods have already proven their usefulness in many industrial tasks, and in this webinar, we explore the possibilities of AI for improving the operational efficiency, yield and quality in the process industry. We will hear exciting speeches from research and companies, focusing on data-based AI methods for petrochemical, plastics and steel industries.

More information: https://fcai.fi/calendar/2022/10/26/ai-for-process-industry]]>1274falsefullhttps://www.spreaker.com/episode/heli-helaakoski-vtt-possibilities-of-ai-for-process-industry-an-overview--74882414
Artificial intelligence is an essential tool as Europe pushes towards sustainable process industry.

Data-based AI methods have already proven their usefulness in many industrial tasks, and in this webinar, we explore the possibilities of AI for improving the operational efficiency, yield and quality in the process industry. We will hear exciting speeches from research and companies, focusing on data-based AI methods for petrochemical, plastics and steel industries.

More information: https://fcai.fi/calendar/2022/10/26/ai-for-process-industry]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075898Mon, 31 Oct 2022 13:56:15 +0000DASERFCAI Industry & Society Webinar AI for Process Industry (26.10.2022) Artificial intelligence is an essential tool as Europe pushes towards sustainable process industry. Data-based AI methods have already proven their usefulness in many...
Artificial intelligence is an essential tool as Europe pushes towards sustainable process industry.

Data-based AI methods have already proven their usefulness in many industrial tasks, and in this webinar, we explore the possibilities of AI for improving the operational efficiency, yield and quality in the process industry. We will hear exciting speeches from research and companies, focusing on data-based AI methods for petrochemical, plastics and steel industries.

More information: https://fcai.fi/calendar/2022/10/26/ai-for-process-industry]]>1078falsefullhttps://www.spreaker.com/episode/rupesh-more-neste-ai-opportunities-in-chemical-research--74882521
Artificial intelligence is an essential tool as Europe pushes towards sustainable process industry.

Data-based AI methods have already proven their usefulness in many industrial tasks, and in this webinar, we explore the possibilities of AI for improving the operational efficiency, yield and quality in the process industry. We will hear exciting speeches from research and companies, focusing on data-based AI methods for petrochemical, plastics and steel industries.

More information: https://fcai.fi/calendar/2022/10/26/ai-for-process-industry]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075941Mon, 31 Oct 2022 13:56:09 +0000DASERFCAI Industry & Society Webinar AI for Process Industry (26.10.2022) Artificial intelligence is an essential tool as Europe pushes towards sustainable process industry. Data-based AI methods have already proven their usefulness in many...
Artificial intelligence is an essential tool as Europe pushes towards sustainable process industry.

Data-based AI methods have already proven their usefulness in many industrial tasks, and in this webinar, we explore the possibilities of AI for improving the operational efficiency, yield and quality in the process industry. We will hear exciting speeches from research and companies, focusing on data-based AI methods for petrochemical, plastics and steel industries.

More information: https://fcai.fi/calendar/2022/10/26/ai-for-process-industry]]>1215falsefullhttps://www.spreaker.com/episode/thimothee-mickus-linear-structures-in-transformer-embedding-spaces--74882460
Speakers: Timothee Mickus is a post doc at University of Helsinki, working with Jörg Tiedemann on the ERC Fotran. Previously, his PhD research topic was on distributional semantics and dictionaries: do dictionary definitions depict meaning in the same way as neural networks-based word vectors? Can we come up with quantitative ways of measuring how similar these two theories are?

Affiliation: University of Helsinki]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075854Sat, 15 Oct 2022 18:54:20 +0000DASERAbstract: The Transformer architecture has taken the NLP community by storm. A large body of work has subsequently focused on understanding how they behave. In this talk, I will focus on the fact that a Transformer embedding can be expressed as a sum...
Speakers: Timothee Mickus is a post doc at University of Helsinki, working with Jörg Tiedemann on the ERC Fotran. Previously, his PhD research topic was on distributional semantics and dictionaries: do dictionary definitions depict meaning in the same way as neural networks-based word vectors? Can we come up with quantitative ways of measuring how similar these two theories are?

Affiliation: University of Helsinki]]>2891falsefullhttps://www.spreaker.com/episode/women-in-ai-ethics-a-conversation-on-diverse-pathways-to-ethical-and-inclusive-tech-futures--74882469
Wed. September 28, 2022 at 13:30 Helsinki time
Live from Tiedekulma, University of Helsinki, Finland

Speakers:
Mia Shah-Dand, CEO, Lighthouse3 & Founder, Women in AI Ethics (WAIE)
Minna Ruckenstein, Professor, Centre for Consumer Society Research, University of Helsinki
Riikka Koulu, Assistant Professor & Director of the University of Helsinki Legal Tech Lab
Marja Niemi, Development Manager, leading Equity, Diversity & Inclusion (EDI), School of Science, Aalto University
Hosts/moderators:
Teemu Roos, Professor of Computer Science, University of Helsinki
Nitin Sawhney, Professor of Practice, Department of Computer Science, Aalto University

Event: https://fcai.fi/calendar/2022/9/28/women-in-ai-ethics-a-conversation-on-diverse-pathways-to-ethical-and-inclusive-tech-futures


Finnish Center for Artificial Intelligence FCAI: https://www.fcai.fi
Subscribe to our newsletter: http://eepurl.com/gVPBpf
Follow on Twitter: https://twitter.com/FCAI_fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075880Thu, 29 Sep 2022 00:20:53 +0000DASERJoin Women in AI Ethics, University of Helsinki, and Aalto University for a timely discussion on the role of women in AI/tech and learn how multi-disciplinary diversity supports our vision for a more inclusive and ethical tech future. Wed. September...
Wed. September 28, 2022 at 13:30 Helsinki time
Live from Tiedekulma, University of Helsinki, Finland

Speakers:
Mia Shah-Dand, CEO, Lighthouse3 & Founder, Women in AI Ethics (WAIE)
Minna Ruckenstein, Professor, Centre for Consumer Society Research, University of Helsinki
Riikka Koulu, Assistant Professor & Director of the University of Helsinki Legal Tech Lab
Marja Niemi, Development Manager, leading Equity, Diversity & Inclusion (EDI), School of Science, Aalto University
Hosts/moderators:
Teemu Roos, Professor of Computer Science, University of Helsinki
Nitin Sawhney, Professor of Practice, Department of Computer Science, Aalto University

Event: https://fcai.fi/calendar/2022/9/28/women-in-ai-ethics-a-conversation-on-diverse-pathways-to-ethical-and-inclusive-tech-futures


Finnish Center for Artificial Intelligence FCAI: https://www.fcai.fi
Subscribe to our newsletter: http://eepurl.com/gVPBpf
Follow on Twitter: https://twitter.com/FCAI_fi]]>5488falsefullhttps://www.spreaker.com/episode/finnish-center-for-artificial-intelligence-and-women-in-ai-ethics-partnership--74882428
Read more: https://fcai.fi/news/2022/6/28/fcai-and-women-in-ai-ethics-team-up-to-make-ai-more-inclusive-and-ethical

Material credits: Samuli Siltanen, vecteezy.com, Aalto University, University of Helsinki]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075900Thu, 09 Jun 2022 12:14:26 +0000DASERWomen in AI Ethics (WAIE) Board Member, University of Helsinki Professor, and creator of Elements of AI – the internationally renowned free online course – Teemu Roos and AI Researcher, Ioanna Bouri will share the vision for WAIE-FCAI collaboration to...
Read more: https://fcai.fi/news/2022/6/28/fcai-and-women-in-ai-ethics-team-up-to-make-ai-more-inclusive-and-ethical

Material credits: Samuli Siltanen, vecteezy.com, Aalto University, University of Helsinki]]>317falsefullhttps://www.spreaker.com/episode/tuomas-paaso-intelligent-building-energy-management-and-flexibility-trading--74882448
Europe is working harder than ever towards sustainable and self-sufficient energy systems.

Data based AI methods have already proven their usefulness in forecasting the energy consumption and optimizing the use of renewable energy sources. In this webinar we explore the possibilities of AI for improving the operational efficiency in the energy sector. We will discuss data based AI methods for both electric power systems and district heating – and it is worth noting the ongoing sector coupling that is bringing these fields closer together.

More information: https://fcai.fi/calendar/2022/5/31/ai-for-sustainable-energy-and-heating]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075981Mon, 06 Jun 2022 11:46:35 +0000DASERFCAI Industry and Society Webinar AI for Sustainable Energy and Heating (31.5.2022) Europe is working harder than ever towards sustainable and self-sufficient energy systems. Data based AI methods have already proven their usefulness in forecasting...
Europe is working harder than ever towards sustainable and self-sufficient energy systems.

Data based AI methods have already proven their usefulness in forecasting the energy consumption and optimizing the use of renewable energy sources. In this webinar we explore the possibilities of AI for improving the operational efficiency in the energy sector. We will discuss data based AI methods for both electric power systems and district heating – and it is worth noting the ongoing sector coupling that is bringing these fields closer together.

More information: https://fcai.fi/calendar/2022/5/31/ai-for-sustainable-energy-and-heating]]>1060falsefullhttps://www.spreaker.com/episode/vikas-garg-renewable-energy-integration-into-intelligent-systems--74882467
Europe is working harder than ever towards sustainable and self-sufficient energy systems.

Data based AI methods have already proven their usefulness in forecasting the energy consumption and optimizing the use of renewable energy sources. In this webinar we explore the possibilities of AI for improving the operational efficiency in the energy sector. We will discuss data based AI methods for both electric power systems and district heating – and it is worth noting the ongoing sector coupling that is bringing these fields closer together.

More information: https://fcai.fi/calendar/2022/5/31/ai-for-sustainable-energy-and-heating]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075960Mon, 06 Jun 2022 11:43:36 +0000DASERFCAI Industry and Society Webinar AI for Sustainable Energy and Heating (31.5.2022) Europe is working harder than ever towards sustainable and self-sufficient energy systems. Data based AI methods have already proven their usefulness in forecasting...
Europe is working harder than ever towards sustainable and self-sufficient energy systems.

Data based AI methods have already proven their usefulness in forecasting the energy consumption and optimizing the use of renewable energy sources. In this webinar we explore the possibilities of AI for improving the operational efficiency in the energy sector. We will discuss data based AI methods for both electric power systems and district heating – and it is worth noting the ongoing sector coupling that is bringing these fields closer together.

More information: https://fcai.fi/calendar/2022/5/31/ai-for-sustainable-energy-and-heating]]>1038falsefullhttps://www.spreaker.com/episode/kari-maki-application-of-analytics-and-ai-within-energy-transition--74882456
Europe is working harder than ever towards sustainable and self-sufficient energy systems.

Data based AI methods have already proven their usefulness in forecasting the energy consumption and optimizing the use of renewable energy sources. In this webinar we explore the possibilities of AI for improving the operational efficiency in the energy sector. We will discuss data based AI methods for both electric power systems and district heating – and it is worth noting the ongoing sector coupling that is bringing these fields closer together.

More information: https://fcai.fi/calendar/2022/5/31/ai-for-sustainable-energy-and-heating]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075859Mon, 06 Jun 2022 11:38:02 +0000DASERFCAI Industry and Society Webinar AI for Sustainable Energy and Heating (31.5.2022) Europe is working harder than ever towards sustainable and self-sufficient energy systems. Data based AI methods have already proven their usefulness in forecasting...
Europe is working harder than ever towards sustainable and self-sufficient energy systems.

Data based AI methods have already proven their usefulness in forecasting the energy consumption and optimizing the use of renewable energy sources. In this webinar we explore the possibilities of AI for improving the operational efficiency in the energy sector. We will discuss data based AI methods for both electric power systems and district heating – and it is worth noting the ongoing sector coupling that is bringing these fields closer together.

More information: https://fcai.fi/calendar/2022/5/31/ai-for-sustainable-energy-and-heating]]>1178falsefullhttps://www.spreaker.com/episode/verneri-kohonen-caruna-analytics-platform-as-an-enabler-for-the-future-electricity-distribution--74882472
Europe is working harder than ever towards sustainable and self-sufficient energy systems.

Data based AI methods have already proven their usefulness in forecasting the energy consumption and optimizing the use of renewable energy sources. In this webinar we explore the possibilities of AI for improving the operational efficiency in the energy sector. We will discuss data based AI methods for both electric power systems and district heating – and it is worth noting the ongoing sector coupling that is bringing these fields closer together.

More information: https://fcai.fi/calendar/2022/5/31/ai-for-sustainable-energy-and-heating]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075921Mon, 06 Jun 2022 11:37:56 +0000DASERFCAI Industry and Society Webinar AI for Sustainable Energy and Heating (31.5.2022) Europe is working harder than ever towards sustainable and self-sufficient energy systems. Data based AI methods have already proven their usefulness in forecasting...
Europe is working harder than ever towards sustainable and self-sufficient energy systems.

Data based AI methods have already proven their usefulness in forecasting the energy consumption and optimizing the use of renewable energy sources. In this webinar we explore the possibilities of AI for improving the operational efficiency in the energy sector. We will discuss data based AI methods for both electric power systems and district heating – and it is worth noting the ongoing sector coupling that is bringing these fields closer together.

More information: https://fcai.fi/calendar/2022/5/31/ai-for-sustainable-energy-and-heating]]>793falsefullhttps://www.spreaker.com/episode/heikki-ailisto-fcai-greetings-and-welcoming-words-to-the-ai-for-sustainable-energy-and-heating--74882446
Europe is working harder than ever towards sustainable and self-sufficient energy systems.

Data based AI methods have already proven their usefulness in forecasting the energy consumption and optimizing the use of renewable energy sources. In this webinar we explore the possibilities of AI for improving the operational efficiency in the energy sector. We will discuss data based AI methods for both electric power systems and district heating – and it is worth noting the ongoing sector coupling that is bringing these fields closer together.

More information: https://fcai.fi/calendar/2022/5/31/ai-for-sustainable-energy-and-heating]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075843Mon, 06 Jun 2022 11:20:56 +0000DASERFCAI Industry and Society Webinar AI for Sustainable Energy and Heating (31.5.2022) Europe is working harder than ever towards sustainable and self-sufficient energy systems. Data based AI methods have already proven their usefulness in forecasting...
Europe is working harder than ever towards sustainable and self-sufficient energy systems.

Data based AI methods have already proven their usefulness in forecasting the energy consumption and optimizing the use of renewable energy sources. In this webinar we explore the possibilities of AI for improving the operational efficiency in the energy sector. We will discuss data based AI methods for both electric power systems and district heating – and it is worth noting the ongoing sector coupling that is bringing these fields closer together.

More information: https://fcai.fi/calendar/2022/5/31/ai-for-sustainable-energy-and-heating]]>445falsefullhttps://www.spreaker.com/episode/francois-xavier-briol-kernel-based-robust-inference-for-intractable-likelihood-models--74882485
Speaker: François-Xavier Briol
Francois-Xavier Briol is a Lecturer (equivalent to Assistant Professor) in the Department of Statistical Science at University College London, as well as a Group Leader at The Alan Turing Institute, the UK’s national institute for Data Science and AI, where he is affiliated to the Data-Centric Engineering programme. His research interests are at the interface of computational statistics, machine learning and applied mathematics, and his work focuses on methodology for statistical computation and inference for large scale and computationally expensive probabilistic models.

Affiliation: University College London & Alan Turing Institute]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075913Mon, 09 May 2022 09:57:08 +0000DASERAbstract: Modern statistics and machine learning tools are being applied to increasingly complex phenomenon, and as a result make use of increasingly complex models. A large class of such models are the so-called intractable likelihood models, where...
Speaker: François-Xavier Briol
Francois-Xavier Briol is a Lecturer (equivalent to Assistant Professor) in the Department of Statistical Science at University College London, as well as a Group Leader at The Alan Turing Institute, the UK’s national institute for Data Science and AI, where he is affiliated to the Data-Centric Engineering programme. His research interests are at the interface of computational statistics, machine learning and applied mathematics, and his work focuses on methodology for statistical computation and inference for large scale and computationally expensive probabilistic models.

Affiliation: University College London & Alan Turing Institute]]>3046falsefullhttps://www.spreaker.com/episode/miika-aittala-alias-free-generative-adversarial-networks--74882459
Joint work with Tero Karras, Samuli Laine, Erik Härkönen, Jaakko Lehtinen, and Timo Aila. Proc. Advances in Neural Information Processing Systems (NeurIPS 2021), selected for an oral presentation

Miika Aittala is affiliated with NVIDIA Research]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075923Thu, 05 May 2022 11:54:34 +0000DASERAbstract: We observe that despite their hierarchical convolutional nature, the synthesis process of typical generative adversarial networks depends on absolute pixel coordinates in an unhealthy manner. This manifests itself as, e.g., detail appearing...
Joint work with Tero Karras, Samuli Laine, Erik Härkönen, Jaakko Lehtinen, and Timo Aila. Proc. Advances in Neural Information Processing Systems (NeurIPS 2021), selected for an oral presentation

Miika Aittala is affiliated with NVIDIA Research]]>2707falsefullhttps://www.spreaker.com/episode/jarno-seppanen-building-a-game-ai-for-clash-royale--74882475
Computational design is already playing a major role in areas of engineering design and architecture, however its reach has been more modest in other areas of design; in particular in interaction design, service design, graphic design, and design thinking. This webinar surveyed emerging breakthroughs in this space that build on advances in deep learning, optimization, reinforcement learning, and simulation intelligence among others.

More information: https://fcai.fi/calendar/2022/4/26/computational-design]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790447464454Tue, 03 May 2022 07:53:35 +0000DASERFCAI Industry & Society Webinar: Computational Design – Emerging AI Methods Computational design is already playing a major role in areas of engineering design and architecture, however its reach has been more modest in other areas of design; in...
Computational design is already playing a major role in areas of engineering design and architecture, however its reach has been more modest in other areas of design; in particular in interaction design, service design, graphic design, and design thinking. This webinar surveyed emerging breakthroughs in this space that build on advances in deep learning, optimization, reinforcement learning, and simulation intelligence among others.

More information: https://fcai.fi/calendar/2022/4/26/computational-design]]>1134falsefullhttps://www.spreaker.com/episode/marja-liisa-siikonen-elevator-design-process-based-on-simulated-people-flow--74882454
Computational design is already playing a major role in areas of engineering design and architecture, however its reach has been more modest in other areas of design; in particular in interaction design, service design, graphic design, and design thinking. This webinar surveyed emerging breakthroughs in this space that build on advances in deep learning, optimization, reinforcement learning, and simulation intelligence among others.

More information: https://fcai.fi/calendar/2022/4/26/computational-design]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075978Tue, 03 May 2022 07:50:19 +0000DASERFCAI Industry & Society Webinar: Computational Design – Emerging AI Methods Computational design is already playing a major role in areas of engineering design and architecture, however its reach has been more modest in other areas of design; in...
Computational design is already playing a major role in areas of engineering design and architecture, however its reach has been more modest in other areas of design; in particular in interaction design, service design, graphic design, and design thinking. This webinar surveyed emerging breakthroughs in this space that build on advances in deep learning, optimization, reinforcement learning, and simulation intelligence among others.

More information: https://fcai.fi/calendar/2022/4/26/computational-design]]>1364falsefullhttps://www.spreaker.com/episode/antti-oulasvirta-reinforcement-learning-and-optimization-based-methods-for-computational-design--74882457
Computational design is already playing a major role in areas of engineering design and architecture, however its reach has been more modest in other areas of design; in particular in interaction design, service design, graphic design, and design thinking. This webinar surveyed emerging breakthroughs in this space that build on advances in deep learning, optimization, reinforcement learning, and simulation intelligence among others.

More information: https://fcai.fi/calendar/2022/4/26/computational-design]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075955Tue, 03 May 2022 07:44:59 +0000DASERFCAI Industry & Society Webinar: Computational Design – Emerging AI Methods Computational design is already playing a major role in areas of engineering design and architecture, however its reach has been more modest in other areas of design; in...
Computational design is already playing a major role in areas of engineering design and architecture, however its reach has been more modest in other areas of design; in particular in interaction design, service design, graphic design, and design thinking. This webinar surveyed emerging breakthroughs in this space that build on advances in deep learning, optimization, reinforcement learning, and simulation intelligence among others.

More information: https://fcai.fi/calendar/2022/4/26/computational-design]]>1173falsefullhttps://www.spreaker.com/episode/lassi-a-liikkanen-data-driven-design-practice-in-the-finnish-it-industry--74882452
Computational design is already playing a major role in areas of engineering design and architecture, however its reach has been more modest in other areas of design; in particular in interaction design, service design, graphic design, and design thinking. This webinar surveyed emerging breakthroughs in this space that build on advances in deep learning, optimization, reinforcement learning, and simulation intelligence among others.

More information: https://fcai.fi/calendar/2022/4/26/computational-design]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075916Tue, 03 May 2022 07:38:30 +0000DASERFCAI Industry & Society Webinar: Computational Design – Emerging AI Methods Computational design is already playing a major role in areas of engineering design and architecture, however its reach has been more modest in other areas of design; in...
Computational design is already playing a major role in areas of engineering design and architecture, however its reach has been more modest in other areas of design; in particular in interaction design, service design, graphic design, and design thinking. This webinar surveyed emerging breakthroughs in this space that build on advances in deep learning, optimization, reinforcement learning, and simulation intelligence among others.

More information: https://fcai.fi/calendar/2022/4/26/computational-design]]>1260falsefullhttps://www.spreaker.com/episode/martin-andraud-accelerating-various-ai-algorithms-on-the-edge-from-software-to-hardware-challenges--74882494
Speaker: Martin Andraud
Martin Andraud is an assistant professor in the Department of Electronics and Nanoengineering in Aalto University, Helsinki, Finland. He received the Ph.D. degree in micro- and nano electronics from TIMA Laboratory, University of Grenoble Alpes, France, in 2016. Between 2016 and 2019, he was a Post-Doctoral Researcher, successively with TU Eindhoven, The Netherlands and KU Leuven, Belgium. His current research interests are the design of low-power integrated circuits and mixed-signal edge AI hardware accelerators for self-adaptive and self-learning applications.

Affiliation: Aalto University]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075862Mon, 11 Apr 2022 10:44:38 +0000DASERAbstract: This talk intends to shed light on some hardware/software integration challenges to accelerate (large) AI models on (custom) edge AI hardware. We will start with Neural networks, by first detailing the necessary computational steps to...
Speaker: Martin Andraud
Martin Andraud is an assistant professor in the Department of Electronics and Nanoengineering in Aalto University, Helsinki, Finland. He received the Ph.D. degree in micro- and nano electronics from TIMA Laboratory, University of Grenoble Alpes, France, in 2016. Between 2016 and 2019, he was a Post-Doctoral Researcher, successively with TU Eindhoven, The Netherlands and KU Leuven, Belgium. His current research interests are the design of low-power integrated circuits and mixed-signal edge AI hardware accelerators for self-adaptive and self-learning applications.

Affiliation: Aalto University]]>2656falsefullhttps://www.spreaker.com/episode/win-win-optimizing-chemical-reactors-with-ai--74882451
Read more: https://fcai.fi/news/2022/3/29/neste-and-fcai-collaboration-optimizing-chemical-reactors-with-ai]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075881Tue, 05 Apr 2022 06:36:51 +0000DASERAcademy-industry collaboration can be a real win-win. In FCAI's new video series, we present successful collaborations, starting with Neste NAPCON. The company harnessed AI know-how from Aalto University and FCAI while giving a student the opportunity...
Read more: https://fcai.fi/news/2022/3/29/neste-and-fcai-collaboration-optimizing-chemical-reactors-with-ai]]>195falsefullhttps://www.spreaker.com/episode/jakob-macke-simulation-based-inference-and-the-places-it-takes-us--74882461Speaker: Jakob has been Professor for “Machine Learning in Science” at the University of Tübingen, Germany, since May 2020. The W3 professorship has been set up as part of the Cluster of Excellence “Machine Learning: New Perspectives for the Sciences”. He is also an Adjunct Research Scientist at the Max Planck Institute for Intelligent Systems, Director of the Bernstein Center for Computational Neuroscience, and an ELLIS Fellow and member of the ELLIS Unit Tübingen.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075949Mon, 28 Mar 2022 11:10:19 +0000DASERAbstract: Many fields of science make extensive use of mechanistic forward models which are implemented through numerical simulators, requiring the use of simulation-based approaches to statistical inference. I will talk about our recent work on...Speaker: Jakob has been Professor for “Machine Learning in Science” at the University of Tübingen, Germany, since May 2020. The W3 professorship has been set up as part of the Cluster of Excellence “Machine Learning: New Perspectives for the Sciences”. He is also an Adjunct Research Scientist at the Max Planck Institute for Intelligent Systems, Director of the Bernstein Center for Computational Neuroscience, and an ELLIS Fellow and member of the ELLIS Unit Tübingen.]]>2943falsefullhttps://www.spreaker.com/episode/petri-ylikoski-what-is-data-literacy--74882462Bio: Petri Ylikoski is Professor of Science and Technology Studies at the Faculty of Social Sciences at the University of Helsinki and Visiting Professor at the Institute for Analytical Sociology at Linköping University. Petri started his career as a philosopher, defending his dissertation in 2001. Petri has always seen himself at crossroads between philosophy and social sciences, so a position in the interdisciplinary field of Science and Technology Studies (since 2012) is a natural fit for him. His current research focuses on the foundations of mechanism-based social science, institutional epistemology, and the social consequences of artificial intelligence. Petri leads the SRC-funded interdisciplinary research project Data Literacy and Responsible Decision-Making, aiming at understandable and trustworthy practices utilizing Finnish health, social, and welfare data.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075888Mon, 14 Mar 2022 09:33:42 +0000DASERAbstract: The digitalization and the development of machine learning have created new opportunities to utilize data, both in the private and public sectors. This has made data governance a key challenge for both individuals and organizations. Good...Bio: Petri Ylikoski is Professor of Science and Technology Studies at the Faculty of Social Sciences at the University of Helsinki and Visiting Professor at the Institute for Analytical Sociology at Linköping University. Petri started his career as a philosopher, defending his dissertation in 2001. Petri has always seen himself at crossroads between philosophy and social sciences, so a position in the interdisciplinary field of Science and Technology Studies (since 2012) is a natural fit for him. His current research focuses on the foundations of mechanism-based social science, institutional epistemology, and the social consequences of artificial intelligence. Petri leads the SRC-funded interdisciplinary research project Data Literacy and Responsible Decision-Making, aiming at understandable and trustworthy practices utilizing Finnish health, social, and welfare data.]]>2452falsefullhttps://www.spreaker.com/episode/kai-nordlund-machine-learning-approaches-to-facilitate-fusion-research--74882464Bio: Kai Nordlund is professor of computational materials physics and dean of the Faculty of Science at the University of Helsinki. He received his PhD in physics in 1995 at the University of Helsinki, and after postdoc positions at the University of Illinois and Academy of Finland was appointed full professor at his alma mater in 2003. Now he is leading as dean a Faculty with more than 1000 employees, and as professor a 15-person research group doing quantum mechanical, classical and mesoscale atomistic simulations of radiation and other non-equilibrium effects in all classes of materials. As of 2022, he has published more than 560 refereed publications, and his h-index exceeds his age.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790447464451Mon, 28 Feb 2022 08:52:45 +0000DASERAbstract: In this talk I will first briefly introduce the concept of fusion energy, and discuss the pathways towards enabling fusion as a viable source of electricity production. After overviewing the progress to date, I will present the key remaining...Bio: Kai Nordlund is professor of computational materials physics and dean of the Faculty of Science at the University of Helsinki. He received his PhD in physics in 1995 at the University of Helsinki, and after postdoc positions at the University of Illinois and Academy of Finland was appointed full professor at his alma mater in 2003. Now he is leading as dean a Faculty with more than 1000 employees, and as professor a 15-person research group doing quantum mechanical, classical and mesoscale atomistic simulations of radiation and other non-equilibrium effects in all classes of materials. As of 2022, he has published more than 560 refereed publications, and his h-index exceeds his age.]]>2840falsefullhttps://www.spreaker.com/episode/laura-ruotsalainen-computer-vision-is-guiding-cranes--74882478In this presentation, we will discuss our research developing self-supervised deep learning based computer vision methods for localizing the crane in challenging industrial environments and our plans to extend the research for semantic 3D detection of the objects in the premises.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075908Mon, 14 Feb 2022 11:03:54 +0000DASERThe society as a whole has to operate following all three pillars of sustainability: environmental protection, social equity, and economic viability, for a better future. For industry, this means use of lower emission technologies, improved safety for...In this presentation, we will discuss our research developing self-supervised deep learning based computer vision methods for localizing the crane in challenging industrial environments and our plans to extend the research for semantic 3D detection of the objects in the premises.]]>2445falsefullhttps://www.spreaker.com/episode/laurence-aitchison-deep-kernel-machines--74882497https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075883Thu, 03 Feb 2022 10:03:35 +0000DASERDeep neural networks (DNNs) with the flexibility to learn good top-layer representations have eclipsed shallow kernel methods without that flexibility. Here, we take inspiration from DNNs to develop the deep kernel machine. Optimizing the deep kernel...3158falsefullhttps://www.spreaker.com/episode/samuel-kaski-collaborative-modelling-with-ai--74882504https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075945Mon, 24 Jan 2022 03:48:50 +0000DASERI will discuss the problem of building machine learning agents that help other agents reach their goals. This is a core problem underlying AI-assisted decision making, design and modelling which is FCAI’s joint methodological goal, and where the other...2958falsefullhttps://www.spreaker.com/episode/tomasz-kucner-anticipating-motion-patterns-for-improved-navigation--74882488
In this talk, I will provide a birds-eye view on methods enabling dynamic awareness for navigation of mobile robots, with an especial focus on maps of dynamics. Maps of dynamics is a large family of methods built upon the assumption that dynamics can be treated as a feature of the environment. Consequently, they allow the robot to anticipate environmental changes and motion of uncontrolled agents outside of the robot's sensor range. Thus allowing the robot to account for dynamics in the early stages of the planning process.

The goal of the talk is to introduce the listeners to the field and present existing and potential applications of maps of dynamics. Furthermore, the talk will also provide insight into open research questions and under-explored research direction.]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075894Mon, 24 Jan 2022 03:30:22 +0000DASERAutonomous mobile robots are being deployed in more diverse environments than ever before. That includes shared spaces, where robots and humans have to coexist and cooperate. To assure that our joint life will be safe and successful, it is necessary...
In this talk, I will provide a birds-eye view on methods enabling dynamic awareness for navigation of mobile robots, with an especial focus on maps of dynamics. Maps of dynamics is a large family of methods built upon the assumption that dynamics can be treated as a feature of the environment. Consequently, they allow the robot to anticipate environmental changes and motion of uncontrolled agents outside of the robot's sensor range. Thus allowing the robot to account for dynamics in the early stages of the planning process.

The goal of the talk is to introduce the listeners to the field and present existing and potential applications of maps of dynamics. Furthermore, the talk will also provide insight into open research questions and under-explored research direction.]]>2317falsefullhttps://www.spreaker.com/episode/antti-honkela-accurate-privacy-accounting-for-differentially-private-machine-learning--74882489https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075975Wed, 19 Jan 2022 10:12:10 +0000DASERDifferential privacy (DP) has recently emerged as the standard foundation for privacy-preserving machine learning and data analytics. One important theoretical property of DP is compositionality: releasing the results of multiple DP mechanisms...2655falsefullhttps://www.spreaker.com/episode/14-ai-and-safety-saeed-bakhshi-germi-tampere-university--74882471
Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing safe automation to those complex and unstructured environments is under active research and development currently. In this workshop we explored how AI today and in the future could support the data efficient development of safe mobile work machines operating in collaboration with humans.

Link to the event website: https://fcai.fi/calendar/2021/11/24/ai-for-mobile-work-machines]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075889Mon, 13 Dec 2021 17:41:52 +0000DASERFIMA-FCAI workshop AI for Mobile Work Machines was held on Nov 24th, 2021 in UKK Institute, Tampere Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing...
Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing safe automation to those complex and unstructured environments is under active research and development currently. In this workshop we explored how AI today and in the future could support the data efficient development of safe mobile work machines operating in collaboration with humans.

Link to the event website: https://fcai.fi/calendar/2021/11/24/ai-for-mobile-work-machines]]>1262falsefullhttps://www.spreaker.com/episode/11-ai-for-control-and-decision-support-tapio-heikkila-vtt--74882463
Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing safe automation to those complex and unstructured environments is under active research and development currently. In this workshop we explored how AI today and in the future could support the data efficient development of safe mobile work machines operating in collaboration with humans.

Link to the event website: https://fcai.fi/calendar/2021/11/24/ai-for-mobile-work-machines]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075905Mon, 13 Dec 2021 17:40:37 +0000DASERFIMA-FCAI workshop AI for Mobile Work Machines was held on Nov 24th, 2021 in UKK Institute, Tampere Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing...
Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing safe automation to those complex and unstructured environments is under active research and development currently. In this workshop we explored how AI today and in the future could support the data efficient development of safe mobile work machines operating in collaboration with humans.

Link to the event website: https://fcai.fi/calendar/2021/11/24/ai-for-mobile-work-machines]]>1228falsefullhttps://www.spreaker.com/episode/13-ai-and-safety-eetu-heikkila-vtt--74882474
Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing safe automation to those complex and unstructured environments is under active research and development currently. In this workshop we explored how AI today and in the future could support the data efficient development of safe mobile work machines operating in collaboration with humans.

Link to the event website: https://fcai.fi/calendar/2021/11/24/ai-for-mobile-work-machines]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075871Mon, 13 Dec 2021 17:36:12 +0000DASERFIMA-FCAI workshop AI for Mobile Work Machines was held on Nov 24th, 2021 in UKK Institute, Tampere Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing...
Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing safe automation to those complex and unstructured environments is under active research and development currently. In this workshop we explored how AI today and in the future could support the data efficient development of safe mobile work machines operating in collaboration with humans.

Link to the event website: https://fcai.fi/calendar/2021/11/24/ai-for-mobile-work-machines]]>888falsefullhttps://www.spreaker.com/episode/10-ai-for-control-and-decision-support-joni-pajarinen-aalto-university--74882465
Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing safe automation to those complex and unstructured environments is under active research and development currently. In this workshop we explored how AI today and in the future could support the data efficient development of safe mobile work machines operating in collaboration with humans.

Link to the event website: https://fcai.fi/calendar/2021/11/24/ai-for-mobile-work-machines]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075946Mon, 13 Dec 2021 17:29:53 +0000DASERFIMA-FCAI workshop AI for Mobile Work Machines was held on Nov 24th, 2021 in UKK Institute, Tampere Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing...
Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing safe automation to those complex and unstructured environments is under active research and development currently. In this workshop we explored how AI today and in the future could support the data efficient development of safe mobile work machines operating in collaboration with humans.

Link to the event website: https://fcai.fi/calendar/2021/11/24/ai-for-mobile-work-machines]]>745falsefullhttps://www.spreaker.com/episode/12-ai-and-safety-jussi-puura-sandvik--74882480
Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing safe automation to those complex and unstructured environments is under active research and development currently. In this workshop we explored how AI today and in the future could support the data efficient development of safe mobile work machines operating in collaboration with humans.

Link to the event website: https://fcai.fi/calendar/2021/11/24/ai-for-mobile-work-machines]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075924Mon, 13 Dec 2021 17:29:16 +0000DASERFIMA-FCAI workshop AI for Mobile Work Machines was held on Nov 24th, 2021 in UKK Institute, Tampere Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing...
Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing safe automation to those complex and unstructured environments is under active research and development currently. In this workshop we explored how AI today and in the future could support the data efficient development of safe mobile work machines operating in collaboration with humans.

Link to the event website: https://fcai.fi/calendar/2021/11/24/ai-for-mobile-work-machines]]>664falsefullhttps://www.spreaker.com/episode/08-ai-and-perception-juho-kannala-aalto-university--74882453
Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing safe automation to those complex and unstructured environments is under active research and development currently. In this workshop we explored how AI today and in the future could support the data efficient development of safe mobile work machines operating in collaboration with humans.

Link to the event website: https://fcai.fi/calendar/2021/11/24/ai-for-mobile-work-machines]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075970Mon, 13 Dec 2021 17:25:14 +0000DASERFIMA-FCAI workshop AI for Mobile Work Machines was held on Nov 24th, 2021 in UKK Institute, Tampere Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing...
Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing safe automation to those complex and unstructured environments is under active research and development currently. In this workshop we explored how AI today and in the future could support the data efficient development of safe mobile work machines operating in collaboration with humans.

Link to the event website: https://fcai.fi/calendar/2021/11/24/ai-for-mobile-work-machines]]>788falsefullhttps://www.spreaker.com/episode/15-ai-and-safety-ville-kyrki-aalto-university--74882500
Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing safe automation to those complex and unstructured environments is under active research and development currently. In this workshop we explored how AI today and in the future could support the data efficient development of safe mobile work machines operating in collaboration with humans.

Link to the event website: https://fcai.fi/calendar/2021/11/24/ai-for-mobile-work-machines]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075872Mon, 13 Dec 2021 17:24:06 +0000DASERFIMA-FCAI workshop AI for Mobile Work Machines was held on Nov 24th, 2021 in UKK Institute, Tampere Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing...
Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing safe automation to those complex and unstructured environments is under active research and development currently. In this workshop we explored how AI today and in the future could support the data efficient development of safe mobile work machines operating in collaboration with humans.

Link to the event website: https://fcai.fi/calendar/2021/11/24/ai-for-mobile-work-machines]]>491falsefullhttps://www.spreaker.com/episode/09-ai-for-control-and-decision-support-kalle-lahtinen-novatron-oy--74882511
Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing safe automation to those complex and unstructured environments is under active research and development currently. In this workshop we explored how AI today and in the future could support the data efficient development of safe mobile work machines operating in collaboration with humans.

Link to the event website: https://fcai.fi/calendar/2021/11/24/ai-for-mobile-work-machines]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075979Mon, 13 Dec 2021 17:23:50 +0000DASERFIMA-FCAI workshop AI for Mobile Work Machines was held on Nov 24th, 2021 in UKK Institute, Tampere Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing...
Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing safe automation to those complex and unstructured environments is under active research and development currently. In this workshop we explored how AI today and in the future could support the data efficient development of safe mobile work machines operating in collaboration with humans.

Link to the event website: https://fcai.fi/calendar/2021/11/24/ai-for-mobile-work-machines]]>628falsefullhttps://www.spreaker.com/episode/06-ai-and-perception-sami-koskinen-vtt--74882525
Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing safe automation to those complex and unstructured environments is under active research and development currently. In this workshop we explored how AI today and in the future could support the data efficient development of safe mobile work machines operating in collaboration with humans.

Link to the event website: https://fcai.fi/calendar/2021/11/24/ai-for-mobile-work-machines]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075947Mon, 13 Dec 2021 17:04:36 +0000DASERFIMA-FCAI workshop AI for Mobile Work Machines was held on Nov 24th, 2021 in UKK Institute, Tampere Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing...
Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing safe automation to those complex and unstructured environments is under active research and development currently. In this workshop we explored how AI today and in the future could support the data efficient development of safe mobile work machines operating in collaboration with humans.

Link to the event website: https://fcai.fi/calendar/2021/11/24/ai-for-mobile-work-machines]]>916falsefullhttps://www.spreaker.com/episode/07-ai-and-perception-laura-ruotsalainen-university-of-helsinki--74882450
Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing safe automation to those complex and unstructured environments is under active research and development currently. In this workshop we explored how AI today and in the future could support the data efficient development of safe mobile work machines operating in collaboration with humans.

Link to the event website: https://fcai.fi/calendar/2021/11/24/ai-for-mobile-work-machines]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075936Mon, 13 Dec 2021 17:01:02 +0000DASERFIMA-FCAI workshop AI for Mobile Work Machines was held on Nov 24th, 2021 in UKK Institute, Tampere Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing...
Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing safe automation to those complex and unstructured environments is under active research and development currently. In this workshop we explored how AI today and in the future could support the data efficient development of safe mobile work machines operating in collaboration with humans.

Link to the event website: https://fcai.fi/calendar/2021/11/24/ai-for-mobile-work-machines]]>807falsefullhttps://www.spreaker.com/episode/03-ai-and-data-efficiency-simo-sarkka-aalto-university--74882476
Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing safe automation to those complex and unstructured environments is under active research and development currently. In this workshop we explored how AI today and in the future could support the data efficient development of safe mobile work machines operating in collaboration with humans.

Link to the event website: https://fcai.fi/calendar/2021/11/24/ai-for-mobile-work-machines]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790447464459Mon, 13 Dec 2021 16:46:55 +0000DASERFIMA-FCAI workshop AI for Mobile Work Machines was held on Nov 24th, 2021 in UKK Institute, Tampere Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing...
Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing safe automation to those complex and unstructured environments is under active research and development currently. In this workshop we explored how AI today and in the future could support the data efficient development of safe mobile work machines operating in collaboration with humans.

Link to the event website: https://fcai.fi/calendar/2021/11/24/ai-for-mobile-work-machines]]>892falsefullhttps://www.spreaker.com/episode/04-ai-and-data-efficiency-arno-solin-aalto-university--74882496
Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing safe automation to those complex and unstructured environments is under active research and development currently. In this workshop we explored how AI today and in the future could support the data efficient development of safe mobile work machines operating in collaboration with humans.

Link to the event website: https://fcai.fi/calendar/2021/11/24/ai-for-mobile-work-machines]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075850Mon, 13 Dec 2021 16:45:12 +0000DASERFIMA-FCAI workshop AI for Mobile Work Machines was held on Nov 24th, 2021 in UKK Institute, Tampere Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing...
Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing safe automation to those complex and unstructured environments is under active research and development currently. In this workshop we explored how AI today and in the future could support the data efficient development of safe mobile work machines operating in collaboration with humans.

Link to the event website: https://fcai.fi/calendar/2021/11/24/ai-for-mobile-work-machines]]>751falsefullhttps://www.spreaker.com/episode/02-ai-and-data-efficiency-ville-kyrki-aalto-university--74882484
Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing safe automation to those complex and unstructured environments is under active research and development currently. In this workshop we explored how AI today and in the future could support the data efficient development of safe mobile work machines operating in collaboration with humans.

Link to the event website: https://fcai.fi/calendar/2021/11/24/ai-for-mobile-work-machines]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075919Mon, 13 Dec 2021 16:36:15 +0000DASERFIMA-FCAI workshop AI for Mobile Work Machines was held on Nov 24th, 2021 in UKK Institute, Tampere Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing...
Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing safe automation to those complex and unstructured environments is under active research and development currently. In this workshop we explored how AI today and in the future could support the data efficient development of safe mobile work machines operating in collaboration with humans.

Link to the event website: https://fcai.fi/calendar/2021/11/24/ai-for-mobile-work-machines]]>771falsefullhttps://www.spreaker.com/episode/01-ai-and-data-efficiency-pekka-yli-paunu-kalmar-global--74882487
Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing safe automation to those complex and unstructured environments is under active research and development currently. In this workshop we explored how AI today and in the future could support the data efficient development of safe mobile work machines operating in collaboration with humans.

Link to the event website: https://fcai.fi/calendar/2021/11/24/ai-for-mobile-work-machines]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075966Mon, 13 Dec 2021 16:12:27 +0000DASERFIMA-FCAI workshop AI for Mobile Work Machines was held on Nov 24th, 2021 in UKK Institute, Tampere Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing...
Artificial intelligence is expected to bring tremendous value especially in the areas where the work conditions for humans are not ideal. Bringing safe automation to those complex and unstructured environments is under active research and development currently. In this workshop we explored how AI today and in the future could support the data efficient development of safe mobile work machines operating in collaboration with humans.

Link to the event website: https://fcai.fi/calendar/2021/11/24/ai-for-mobile-work-machines]]>621falsefullhttps://www.spreaker.com/episode/dennis-muiruri-practices-and-infrastructures-for-ml-systems-an-interview-study--74882502
Machine Learning Coffee Seminar: https://fcai.fi/mlcs​

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075927Mon, 29 Nov 2021 05:33:19 +0000DASERMachine learning Coffee Seminar, 15 November 2021. Machine Learning Coffee Seminar: https://fcai.fi/mlcs​ Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​ Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Machine Learning Coffee Seminar: https://fcai.fi/mlcs​

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2747falsefullhttps://www.spreaker.com/episode/roderick-murray-smith-variational-inference-for-computational-inversion-cvae-based-forward-inverse--74882458Machine Learning Coffee Seminar: https://fcai.fi/mlcs
Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi
Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075842Sun, 28 Nov 2021 15:00:49 +0000DASERMachine learning Coffee Seminar, 22 November 2021. Machine Learning Coffee Seminar: https://fcai.fi/mlcs Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/Machine Learning Coffee Seminar: https://fcai.fi/mlcs
Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi
Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2824falsefullhttps://www.spreaker.com/episode/timo-koski-likelihood-free-inference-using-jensen-shannon-divergence--74882507
Machine Learning Coffee Seminar: https://fcai.fi/mlcs​

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075974Sun, 14 Nov 2021 20:24:46 +0000DASERMachine learning Coffee Seminar, 1 November 2021. Machine Learning Coffee Seminar: https://fcai.fi/mlcs​ Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​ Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Machine Learning Coffee Seminar: https://fcai.fi/mlcs​

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2336falsefullhttps://www.spreaker.com/episode/nvaitc-webinar-series-on-ai-applications-in-computational-sciences-ai-in-astrophysics--74882501
NVIDIA AI Technology Center Finland in collaboration with FCAI and CSC organize a webinar series focusing on AI applications in computational sciences, with a goal of bringing together AI researchers and researchers in other fields. Each webinar will highlight a different scientific field and they are given by domain-specific experts who are using AI as part of their numerical simulation workflows. The webinars run from the beginning of March 2021 approximately every three weeks as part of the AI Across Fields Forum.

NVAITC: https://fcai.fi/nvaitc​​
AIX Forum: https://fcai.fi/aix-forum​​]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075875Fri, 29 Oct 2021 12:42:51 +0000DASERNVIDIA AI Technology Center (NVAITC) is a joint research center of the Finnish Center for Artificial Intelligence FCAI, NVIDIA, and the Finnish IT Centre for Science CSC. NVAITC accelerates research, education and adoption of artificial intelligence...
NVIDIA AI Technology Center Finland in collaboration with FCAI and CSC organize a webinar series focusing on AI applications in computational sciences, with a goal of bringing together AI researchers and researchers in other fields. Each webinar will highlight a different scientific field and they are given by domain-specific experts who are using AI as part of their numerical simulation workflows. The webinars run from the beginning of March 2021 approximately every three weeks as part of the AI Across Fields Forum.

NVAITC: https://fcai.fi/nvaitc​​
AIX Forum: https://fcai.fi/aix-forum​​]]>2241falsefullhttps://www.spreaker.com/episode/zheyang-shen-de-randomizing-mcmc-dynamics-with-the-diffusion-stein-operator--74882468Machine Learning Coffee Seminar: https://fcai.fi/mlcs
Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi
Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075962Wed, 27 Oct 2021 08:17:23 +0000DASERMachine learning Coffee Seminar, 25 October 2021. Machine Learning Coffee Seminar: https://fcai.fi/mlcs Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/Machine Learning Coffee Seminar: https://fcai.fi/mlcs
Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi
Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2784falsefullhttps://www.spreaker.com/episode/severi-rissanen-a-critical-look-at-consistency-of-causal-estimation-with-deep-latent-variable-model--74882509
Machine Learning Coffee Seminar: https://fcai.fi/mlcs​

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075856Mon, 25 Oct 2021 06:02:54 +0000DASERMachine learning Coffee Seminar, 18 October 2021. Machine Learning Coffee Seminar: https://fcai.fi/mlcs​ Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​ Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Machine Learning Coffee Seminar: https://fcai.fi/mlcs​

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2876falsefullhttps://www.spreaker.com/episode/nuutti-sten-python-for-explainable-reproducible-and-applicable-machine-learning--74882479Machine Learning Coffee Seminar: https://fcai.fi/mlcs
Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi
Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075929Sun, 17 Oct 2021 10:59:22 +0000DASERMachine learning Coffee Seminar, 11 October 2021. Machine Learning Coffee Seminar: https://fcai.fi/mlcs Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/Machine Learning Coffee Seminar: https://fcai.fi/mlcs
Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi
Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2464falsefullhttps://www.spreaker.com/episode/nvaitc-webinar-series-on-ai-applications-in-computational-sciences-ai-meets-nuclear-fusion--74882470
NVIDIA AI Technology Center Finland in collaboration with FCAI and CSC organize a webinar series focusing on AI applications in computational sciences, with a goal of bringing together AI researchers and researchers in other fields. Each webinar will highlight a different scientific field and they are given by domain-specific experts who are using AI as part of their numerical simulation workflows. The webinars run from the beginning of March 2021 approximately every three weeks as part of the AI Across Fields Forum.

NVAITC: https://fcai.fi/nvaitc​​
AIX Forum: https://fcai.fi/aix-forum​​]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075878Thu, 14 Oct 2021 09:37:42 +0000DASERNVIDIA AI Technology Center (NVAITC) is a joint research center of the Finnish Center for Artificial Intelligence FCAI, NVIDIA, and the Finnish IT Centre for Science CSC. NVAITC accelerates research, education and adoption of artificial intelligence...
NVIDIA AI Technology Center Finland in collaboration with FCAI and CSC organize a webinar series focusing on AI applications in computational sciences, with a goal of bringing together AI researchers and researchers in other fields. Each webinar will highlight a different scientific field and they are given by domain-specific experts who are using AI as part of their numerical simulation workflows. The webinars run from the beginning of March 2021 approximately every three weeks as part of the AI Across Fields Forum.

NVAITC: https://fcai.fi/nvaitc​​
AIX Forum: https://fcai.fi/aix-forum​​]]>2032falsefullhttps://www.spreaker.com/episode/joni-pajarinen-reinforcement-learning-using-mcts-and-self-paced-curriculum-reinforcement-learning--74882531
Machine Learning Coffee Seminar: https://fcai.fi/mlcs​

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075920Tue, 12 Oct 2021 22:09:07 +0000DASERMachine learning Coffee Seminar, 4 October 2021. Machine Learning Coffee Seminar: https://fcai.fi/mlcs​ Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​ Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Machine Learning Coffee Seminar: https://fcai.fi/mlcs​

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2384falsefullhttps://www.spreaker.com/episode/arto-klami-markov-chain-monte-carlo-on-monge-patches--74882473
Machine Learning Coffee Seminar: https://fcai.fi/mlcs​

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075895Mon, 27 Sep 2021 08:18:29 +0000DASERMachine learning Coffee Seminar, 27 Sep 2021. Machine Learning Coffee Seminar: https://fcai.fi/mlcs​ Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​ Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Machine Learning Coffee Seminar: https://fcai.fi/mlcs​

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>1788falsefullhttps://www.spreaker.com/episode/lalli-myllyaho-validation-methods-for-ai-systems--74882493
Machine Learning Coffee Seminar: https://fcai.fi/mlcs​

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075915Mon, 13 Sep 2021 08:52:41 +0000DASERMachine learning Coffee Seminar, 13 Sep 2021. Machine Learning Coffee Seminar: https://fcai.fi/mlcs​ Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​ Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Machine Learning Coffee Seminar: https://fcai.fi/mlcs​

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2847falsefullhttps://www.spreaker.com/episode/yancho-todorov-vtt-sebastiaan-de-peuter-aalto-university-project-pitch--74882491
Mobility systems are key enablers of human activity and wellbeing – they connect people with places. However, transport is one of the most polluting sectors in the world and a major contributor to CO2 emissions, and in addition, not equally accessible to all. How could we become more sustainable in the way we move?

Full webinar program: https://fcai.fi/calendar/2021/5/20/ai-for-sustainability]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075884Wed, 26 May 2021 13:28:31 +0000DASERFCAI Industry and Society webinar "AI for Sustainability – Smart Mobility" was held on Thursday, May 20, 2021. Mobility systems are key enablers of human activity and wellbeing – they connect people with places. However, transport is one of the most...
Mobility systems are key enablers of human activity and wellbeing – they connect people with places. However, transport is one of the most polluting sectors in the world and a major contributor to CO2 emissions, and in addition, not equally accessible to all. How could we become more sustainable in the way we move?

Full webinar program: https://fcai.fi/calendar/2021/5/20/ai-for-sustainability]]>748falsefullhttps://www.spreaker.com/episode/panel-discussion-ai-for-sustainability-smart-mobility--74882466
Mobility systems are key enablers of human activity and wellbeing – they connect people with places. However, transport is one of the most polluting sectors in the world and a major contributor to CO2 emissions, and in addition, not equally accessible to all. How could we become more sustainable in the way we move?

Full webinar program: https://fcai.fi/calendar/2021/5/20/ai-for-sustainability]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075844Wed, 26 May 2021 13:21:21 +0000DASERFCAI Industry and Society webinar "AI for Sustainability – Smart Mobility" was held on Thursday, May 20, 2021. Mobility systems are key enablers of human activity and wellbeing – they connect people with places. However, transport is one of the most...
Mobility systems are key enablers of human activity and wellbeing – they connect people with places. However, transport is one of the most polluting sectors in the world and a major contributor to CO2 emissions, and in addition, not equally accessible to all. How could we become more sustainable in the way we move?

Full webinar program: https://fcai.fi/calendar/2021/5/20/ai-for-sustainability]]>1714falsefullhttps://www.spreaker.com/episode/heikki-ailisto-vtt-fcai-ai-as-a-key-enabler-for-sustainability--74882495
Mobility systems are key enablers of human activity and wellbeing – they connect people with places. However, transport is one of the most polluting sectors in the world and a major contributor to CO2 emissions, and in addition, not equally accessible to all. How could we become more sustainable in the way we move?

Full webinar program: https://fcai.fi/calendar/2021/5/20/ai-for-sustainability]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075953Wed, 26 May 2021 13:00:33 +0000DASERFCAI Industry and Society webinar "AI for Sustainability – Smart Mobility" was held on Thursday, May 20, 2021. Mobility systems are key enablers of human activity and wellbeing – they connect people with places. However, transport is one of the most...
Mobility systems are key enablers of human activity and wellbeing – they connect people with places. However, transport is one of the most polluting sectors in the world and a major contributor to CO2 emissions, and in addition, not equally accessible to all. How could we become more sustainable in the way we move?

Full webinar program: https://fcai.fi/calendar/2021/5/20/ai-for-sustainability]]>811falsefullhttps://www.spreaker.com/episode/sampo-hietanen-maas-global-oy-mobility-as-a-service-the-end-of-car-ownership--74882503
Mobility systems are key enablers of human activity and wellbeing – they connect people with places. However, transport is one of the most polluting sectors in the world and a major contributor to CO2 emissions, and in addition, not equally accessible to all. How could we become more sustainable in the way we move?

Full webinar program: https://fcai.fi/calendar/2021/5/20/ai-for-sustainability]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075910Wed, 26 May 2021 12:12:00 +0000DASERFCAI Industry and Society webinar "AI for Sustainability – Smart Mobility" was held on Thursday, May 20, 2021. Mobility systems are key enablers of human activity and wellbeing – they connect people with places. However, transport is one of the most...
Mobility systems are key enablers of human activity and wellbeing – they connect people with places. However, transport is one of the most polluting sectors in the world and a major contributor to CO2 emissions, and in addition, not equally accessible to all. How could we become more sustainable in the way we move?

Full webinar program: https://fcai.fi/calendar/2021/5/20/ai-for-sustainability]]>1035falsefullhttps://www.spreaker.com/episode/juha-salmelin-luxturrim5g-nokia-luxturrim5g-smart-city-data-platform--74882506
Mobility systems are key enablers of human activity and wellbeing – they connect people with places. However, transport is one of the most polluting sectors in the world and a major contributor to CO2 emissions, and in addition, not equally accessible to all. How could we become more sustainable in the way we move?

Full webinar program: https://fcai.fi/calendar/2021/5/20/ai-for-sustainability]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075969Wed, 26 May 2021 11:52:48 +0000DASERFCAI Industry and Society webinar "AI for Sustainability – Smart Mobility" was held on Thursday, May 20, 2021. Mobility systems are key enablers of human activity and wellbeing – they connect people with places. However, transport is one of the most...
Mobility systems are key enablers of human activity and wellbeing – they connect people with places. However, transport is one of the most polluting sectors in the world and a major contributor to CO2 emissions, and in addition, not equally accessible to all. How could we become more sustainable in the way we move?

Full webinar program: https://fcai.fi/calendar/2021/5/20/ai-for-sustainability]]>1213falsefullhttps://www.spreaker.com/episode/laura-ruotsalainen-university-of-helsinki-helsus-fcai-welcome-ai-for-sustainable-mobility--74882492
Mobility systems are key enablers of human activity and wellbeing – they connect people with places. However, transport is one of the most polluting sectors in the world and a major contributor to CO2 emissions, and in addition, not equally accessible to all. How could we become more sustainable in the way we move?

Full webinar program: https://fcai.fi/calendar/2021/5/20/ai-for-sustainability]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075866Wed, 26 May 2021 11:42:22 +0000DASERFCAI Industry and Society webinar "AI for Sustainability – Smart Mobility" was held on Thursday, May 20, 2021. Mobility systems are key enablers of human activity and wellbeing – they connect people with places. However, transport is one of the most...
Mobility systems are key enablers of human activity and wellbeing – they connect people with places. However, transport is one of the most polluting sectors in the world and a major contributor to CO2 emissions, and in addition, not equally accessible to all. How could we become more sustainable in the way we move?

Full webinar program: https://fcai.fi/calendar/2021/5/20/ai-for-sustainability]]>854falsefullhttps://www.spreaker.com/episode/nvaitc-webinar-series-on-ai-applications-in-computational-sciences-ai-and-energy-transition--74882516
NVIDIA AI Technology Center Finland in collaboration with FCAI and CSC organize a webinar series focusing on AI applications in computational sciences, with a goal of bringing together AI researchers and researchers in other fields. Each webinar will highlight a different scientific field and they are given by domain-specific experts who are using AI as part of their numerical simulation workflows. The webinars run from the beginning of March 2021 approximately every three weeks as part of the AI Across Fields Forum.

NVAITC: https://fcai.fi/nvaitc​​
AIX Forum: https://fcai.fi/aix-forum​​]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075965Tue, 25 May 2021 10:34:39 +0000DASERNVIDIA AI Technology Center (NVAITC) is a joint research center of the Finnish Center for Artificial Intelligence FCAI, NVIDIA, and the Finnish IT Centre for Science CSC. NVAITC accelerates research, education and adoption of artificial intelligence...
NVIDIA AI Technology Center Finland in collaboration with FCAI and CSC organize a webinar series focusing on AI applications in computational sciences, with a goal of bringing together AI researchers and researchers in other fields. Each webinar will highlight a different scientific field and they are given by domain-specific experts who are using AI as part of their numerical simulation workflows. The webinars run from the beginning of March 2021 approximately every three weeks as part of the AI Across Fields Forum.

NVAITC: https://fcai.fi/nvaitc​​
AIX Forum: https://fcai.fi/aix-forum​​]]>2504falsefullhttps://www.spreaker.com/episode/alex-jung-federated-multitask-learning-in-big-data-over-networks--74882481
Machine Learning Coffee Seminar: https://fcai.fi/mlcs​

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075873Mon, 17 May 2021 07:58:56 +0000DASERMachine learning Coffee Seminar, 17 May 2021. Machine Learning Coffee Seminar: https://fcai.fi/mlcs​ Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​ Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Machine Learning Coffee Seminar: https://fcai.fi/mlcs​

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2773falsefullhttps://www.spreaker.com/episode/jarno-vanhatalo-on-statistical-inference-prediction-and-data-for-risk-assessment-in-arctic-marine--74882526
Machine Learning Coffee Seminar: https://fcai.fi/mlcs​

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075939Sat, 15 May 2021 12:23:18 +0000DASERMachine learning Coffee Seminar, 10 May 2021. Machine Learning Coffee Seminar: https://fcai.fi/mlcs​ Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​ Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Machine Learning Coffee Seminar: https://fcai.fi/mlcs​

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2757falsefullhttps://www.spreaker.com/episode/nvaitc-webinar-series-on-ai-applications-in-computational-sciences-computational-fluid-dynamics--74882498
NVIDIA AI Technology Center Finland in collaboration with FCAI and CSC organize a webinar series focusing on AI applications in computational sciences, with a goal of bringing together AI researchers and researchers in other fields. Each webinar will highlight a different scientific field and they are given by domain-specific experts who are using AI as part of their numerical simulation workflows. The webinars run from the beginning of March 2021 approximately every three weeks as part of the AI Across Fields Forum.

NVAITC: https://fcai.fi/nvaitc​​
AIX Forum: https://fcai.fi/aix-forum​​]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075964Mon, 10 May 2021 07:18:44 +0000DASERNVIDIA AI Technology Center (NVAITC) is a joint research center of the Finnish Center for Artificial Intelligence FCAI, NVIDIA, and the Finnish IT Centre for Science CSC. NVAITC accelerates research, education and adoption of artificial intelligence...
NVIDIA AI Technology Center Finland in collaboration with FCAI and CSC organize a webinar series focusing on AI applications in computational sciences, with a goal of bringing together AI researchers and researchers in other fields. Each webinar will highlight a different scientific field and they are given by domain-specific experts who are using AI as part of their numerical simulation workflows. The webinars run from the beginning of March 2021 approximately every three weeks as part of the AI Across Fields Forum.

NVAITC: https://fcai.fi/nvaitc​​
AIX Forum: https://fcai.fi/aix-forum​​]]>2995falsefullhttps://www.spreaker.com/episode/nvaitc-webinar-series-on-ai-applications-in-computational-sciences-ai-in-materials-science--74882482
NVIDIA AI Technology Center Finland in collaboration with FCAI and CSC organize a webinar series focusing on AI applications in computational sciences, with a goal of bringing together AI researchers and researchers in other fields. Each webinar will highlight a different scientific field and they are given by domain-specific experts who are using AI as part of their numerical simulation workflows. The webinars run from the beginning of March 2021 approximately every three weeks as part of the AI Across Fields Forum.

NVAITC: https://fcai.fi/nvaitc​
AIX Forum: https://fcai.fi/aix-forum​]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075926Tue, 04 May 2021 10:50:47 +0000DASERNVIDIA AI Technology Center (NVAITC) is a joint research center of the Finnish Center for Artificial Intelligence FCAI, NVIDIA, and the Finnish IT Centre for Science CSC. NVAITC accelerates research, education and adoption of artificial intelligence...
NVIDIA AI Technology Center Finland in collaboration with FCAI and CSC organize a webinar series focusing on AI applications in computational sciences, with a goal of bringing together AI researchers and researchers in other fields. Each webinar will highlight a different scientific field and they are given by domain-specific experts who are using AI as part of their numerical simulation workflows. The webinars run from the beginning of March 2021 approximately every three weeks as part of the AI Across Fields Forum.

NVAITC: https://fcai.fi/nvaitc​
AIX Forum: https://fcai.fi/aix-forum​]]>2775falsefullhttps://www.spreaker.com/episode/nvaitc-webinar-series-on-ai-applications-in-computational-sciences-ai-in-atmospheric-science--74882528
NVIDIA AI Technology Center Finland in collaboration with FCAI and CSC organize a webinar series focusing on AI applications in computational sciences, with a goal of bringing together AI researchers and researchers in other fields. Each webinar will highlight a different scientific field and they are given by domain-specific experts who are using AI as part of their numerical simulation workflows. The webinars run from the beginning of March 2021 approximately every three weeks as part of the AI Across Fields Forum.

NVAITC: https://fcai.fi/nvaitc
AIX Forum: https://fcai.fi/aix-forum]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075928Tue, 04 May 2021 10:50:33 +0000DASERNVIDIA AI Technology Center (NVAITC) is a joint research center of the Finnish Center for Artificial Intelligence FCAI, NVIDIA, and the Finnish IT Centre for Science CSC. NVAITC accelerates research, education and adoption of artificial intelligence...
NVIDIA AI Technology Center Finland in collaboration with FCAI and CSC organize a webinar series focusing on AI applications in computational sciences, with a goal of bringing together AI researchers and researchers in other fields. Each webinar will highlight a different scientific field and they are given by domain-specific experts who are using AI as part of their numerical simulation workflows. The webinars run from the beginning of March 2021 approximately every three weeks as part of the AI Across Fields Forum.

NVAITC: https://fcai.fi/nvaitc
AIX Forum: https://fcai.fi/aix-forum]]>1940falsefullhttps://www.spreaker.com/episode/sara-tahtinen-explainable-ai-in-computer-vision--74882508
Machine Learning Coffee Seminar: https://fcai.fi/mlcs​

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075896Wed, 21 Apr 2021 11:13:41 +0000DASERMachine learning Coffee Seminar, 12 April 2021. Machine Learning Coffee Seminar: https://fcai.fi/mlcs​ Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​ Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Machine Learning Coffee Seminar: https://fcai.fi/mlcs​

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2684falsefullhttps://www.spreaker.com/episode/yanhao-wang-big-data-stream-summarization--74882512
Machine Learning Coffee Seminar: https://fcai.fi/mlcs​

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075904Mon, 19 Apr 2021 13:14:28 +0000DASERMachine learning Coffee Seminar, 19 April 2021. Machine Learning Coffee Seminar: https://fcai.fi/mlcs​ Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​ Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Machine Learning Coffee Seminar: https://fcai.fi/mlcs​

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2834falsefullhttps://www.spreaker.com/episode/aki-vehtari-on-bayesian-workflow--74882513
Machine Learning Coffee Seminar: https://fcai.fi/mlcs​

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075944Mon, 22 Mar 2021 09:17:01 +0000DASERMachine learning Coffee Seminar, 22 March 2021. Machine Learning Coffee Seminar: https://fcai.fi/mlcs​ Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​ Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Machine Learning Coffee Seminar: https://fcai.fi/mlcs​

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2675falsefullhttps://www.spreaker.com/episode/mikko-kemppainen-automated-game-experience-personalization--74882576
Machine Learning Coffee Seminar: https://fcai.fi/mlcs​

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075858Sun, 21 Mar 2021 20:09:13 +0000DASERMachine learning Coffee Seminar, 15 March 2021. Machine Learning Coffee Seminar: https://fcai.fi/mlcs​ Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​ Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Machine Learning Coffee Seminar: https://fcai.fi/mlcs​

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2534falsefullhttps://www.spreaker.com/episode/petteri-alinikula-saab-industrial-application-viewpoint--74882518
Deep learning plays a major role in many contemporary artificial intelligence applications. It has shown impressive power in tasks which require human-like perception and understanding of visual or auditory data, or interpretation of vast collections of information. Even if these models show impressive results and are widely used, they are far from perfect and still under active research. This webinar brings up questions and challenges related to deep learning in practice, such as interpretability, trustworthiness, data bias, and quantification of uncertainty.

Full webinar program: https://fcai.fi/calendar/2021/3/11/deploying-deep-learning-based-models-in-projects-and-real-world-cases-challenges]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075956Tue, 16 Mar 2021 12:38:15 +0000DASERFCAI Industry and Society webinar "Deploying deep learning-based models in projects and real-world cases – challenges and solutions" was held on Thursday, March 11, 2021. Deep learning plays a major role in many contemporary artificial intelligence...
Deep learning plays a major role in many contemporary artificial intelligence applications. It has shown impressive power in tasks which require human-like perception and understanding of visual or auditory data, or interpretation of vast collections of information. Even if these models show impressive results and are widely used, they are far from perfect and still under active research. This webinar brings up questions and challenges related to deep learning in practice, such as interpretability, trustworthiness, data bias, and quantification of uncertainty.

Full webinar program: https://fcai.fi/calendar/2021/3/11/deploying-deep-learning-based-models-in-projects-and-real-world-cases-challenges]]>1230falsefullhttps://www.spreaker.com/episode/niko-vuokko-silo-ai-ai-expert-viewpoint--74882477
Deep learning plays a major role in many contemporary artificial intelligence applications. It has shown impressive power in tasks which require human-like perception and understanding of visual or auditory data, or interpretation of vast collections of information. Even if these models show impressive results and are widely used, they are far from perfect and still under active research. This webinar brings up questions and challenges related to deep learning in practice, such as interpretability, trustworthiness, data bias, and quantification of uncertainty.

Full webinar program: https://fcai.fi/calendar/2021/3/11/deploying-deep-learning-based-models-in-projects-and-real-world-cases-challenges]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075877Tue, 16 Mar 2021 12:38:11 +0000DASERFCAI Industry and Society webinar "Deploying deep learning-based models in projects and real-world cases – challenges and solutions" was held on Thursday, March 11, 2021. Deep learning plays a major role in many contemporary artificial intelligence...
Deep learning plays a major role in many contemporary artificial intelligence applications. It has shown impressive power in tasks which require human-like perception and understanding of visual or auditory data, or interpretation of vast collections of information. Even if these models show impressive results and are widely used, they are far from perfect and still under active research. This webinar brings up questions and challenges related to deep learning in practice, such as interpretability, trustworthiness, data bias, and quantification of uncertainty.

Full webinar program: https://fcai.fi/calendar/2021/3/11/deploying-deep-learning-based-models-in-projects-and-real-world-cases-challenges]]>1488falsefullhttps://www.spreaker.com/episode/markus-heinonen-aalto-university-fcai-ai-research-viewpoint--74882510
Deep learning plays a major role in many contemporary artificial intelligence applications. It has shown impressive power in tasks which require human-like perception and understanding of visual or auditory data, or interpretation of vast collections of information. Even if these models show impressive results and are widely used, they are far from perfect and still under active research. This webinar brings up questions and challenges related to deep learning in practice, such as interpretability, trustworthiness, data bias, and quantification of uncertainty.

Full webinar program: https://fcai.fi/calendar/2021/3/11/deploying-deep-learning-based-models-in-projects-and-real-world-cases-challenges]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075935Tue, 16 Mar 2021 12:38:07 +0000DASERFCAI Industry and Society webinar "Deploying deep learning-based models in projects and real-world cases – challenges and solutions" was held on Thursday, March 11, 2021. Deep learning plays a major role in many contemporary artificial intelligence...
Deep learning plays a major role in many contemporary artificial intelligence applications. It has shown impressive power in tasks which require human-like perception and understanding of visual or auditory data, or interpretation of vast collections of information. Even if these models show impressive results and are widely used, they are far from perfect and still under active research. This webinar brings up questions and challenges related to deep learning in practice, such as interpretability, trustworthiness, data bias, and quantification of uncertainty.

Full webinar program: https://fcai.fi/calendar/2021/3/11/deploying-deep-learning-based-models-in-projects-and-real-world-cases-challenges]]>1138falsefullhttps://www.spreaker.com/episode/heikki-ailisto-vtt-fcai-funding-instruments-to-support-collaboration--74882535
Deep learning plays a major role in many contemporary artificial intelligence applications. It has shown impressive power in tasks which require human-like perception and understanding of visual or auditory data, or interpretation of vast collections of information. Even if these models show impressive results and are widely used, they are far from perfect and still under active research. This webinar brings up questions and challenges related to deep learning in practice, such as interpretability, trustworthiness, data bias, and quantification of uncertainty.

Full webinar program: https://fcai.fi/calendar/2021/3/11/deploying-deep-learning-based-models-in-projects-and-real-world-cases-challenges]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075976Tue, 16 Mar 2021 12:38:03 +0000DASERFCAI Industry and Society webinar "Deploying deep learning-based models in projects and real-world cases – challenges and solutions" was held on Thursday, March 11, 2021. Deep learning plays a major role in many contemporary artificial intelligence...
Deep learning plays a major role in many contemporary artificial intelligence applications. It has shown impressive power in tasks which require human-like perception and understanding of visual or auditory data, or interpretation of vast collections of information. Even if these models show impressive results and are widely used, they are far from perfect and still under active research. This webinar brings up questions and challenges related to deep learning in practice, such as interpretability, trustworthiness, data bias, and quantification of uncertainty.

Full webinar program: https://fcai.fi/calendar/2021/3/11/deploying-deep-learning-based-models-in-projects-and-real-world-cases-challenges]]>746falsefullhttps://www.spreaker.com/episode/panel-discussion-challenges-and-potential-solutions--74882533
Deep learning plays a major role in many contemporary artificial intelligence applications. It has shown impressive power in tasks which require human-like perception and understanding of visual or auditory data, or interpretation of vast collections of information. Even if these models show impressive results and are widely used, they are far from perfect and still under active research. This webinar brings up questions and challenges related to deep learning in practice, such as interpretability, trustworthiness, data bias, and quantification of uncertainty.

Full webinar program: https://fcai.fi/calendar/2021/3/11/deploying-deep-learning-based-models-in-projects-and-real-world-cases-challenges]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075865Tue, 16 Mar 2021 12:37:59 +0000DASERFCAI Industry and Society webinar "Deploying deep learning-based models in projects and real-world cases – challenges and solutions" was held on Thursday, March 11, 2021. Deep learning plays a major role in many contemporary artificial intelligence...
Deep learning plays a major role in many contemporary artificial intelligence applications. It has shown impressive power in tasks which require human-like perception and understanding of visual or auditory data, or interpretation of vast collections of information. Even if these models show impressive results and are widely used, they are far from perfect and still under active research. This webinar brings up questions and challenges related to deep learning in practice, such as interpretability, trustworthiness, data bias, and quantification of uncertainty.

Full webinar program: https://fcai.fi/calendar/2021/3/11/deploying-deep-learning-based-models-in-projects-and-real-world-cases-challenges]]>1872falsefullhttps://www.spreaker.com/episode/arno-solin-aalto-university-fcai-introduction-to-fcai-and-its-research-program-on-deep-learning--74882517
Deep learning plays a major role in many contemporary artificial intelligence applications. It has shown impressive power in tasks which require human-like perception and understanding of visual or auditory data, or interpretation of vast collections of information. Even if these models show impressive results and are widely used, they are far from perfect and still under active research. This webinar brings up questions and challenges related to deep learning in practice, such as interpretability, trustworthiness, data bias, and quantification of uncertainty.

Full webinar program: https://fcai.fi/calendar/2021/3/11/deploying-deep-learning-based-models-in-projects-and-real-world-cases-challenges]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075906Tue, 16 Mar 2021 10:21:49 +0000DASERFCAI Industry and Society webinar "Deploying deep learning-based models in projects and real-world cases – challenges and solutions" was held on Thursday, March 11, 2021. Deep learning plays a major role in many contemporary artificial intelligence...
Deep learning plays a major role in many contemporary artificial intelligence applications. It has shown impressive power in tasks which require human-like perception and understanding of visual or auditory data, or interpretation of vast collections of information. Even if these models show impressive results and are widely used, they are far from perfect and still under active research. This webinar brings up questions and challenges related to deep learning in practice, such as interpretability, trustworthiness, data bias, and quantification of uncertainty.

Full webinar program: https://fcai.fi/calendar/2021/3/11/deploying-deep-learning-based-models-in-projects-and-real-world-cases-challenges]]>285falsefullhttps://www.spreaker.com/episode/harri-lahdesmaki-deep-and-non-parametric-methods-for-longitudinal-data--74882541
Machine Learning Coffee Seminar: https://fcai.fi/mlcs​

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075853Mon, 08 Mar 2021 13:41:35 +0000DASERMachine learning Coffee Seminar, 8 March 2021. Machine Learning Coffee Seminar: https://fcai.fi/mlcs​ Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​ Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Machine Learning Coffee Seminar: https://fcai.fi/mlcs​

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2682falsefullhttps://www.spreaker.com/episode/balthazar-donon-deep-statistical-solvers--74882547
Machine Learning Coffee Seminar: https://fcai.fi/mlcs​

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075857Sun, 07 Mar 2021 21:21:27 +0000DASERMachine learning Coffee Seminar, 1st March 2021. Machine Learning Coffee Seminar: https://fcai.fi/mlcs​ Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​ Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Machine Learning Coffee Seminar: https://fcai.fi/mlcs​

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>3046falsefullhttps://www.spreaker.com/episode/juho-rousu-machine-learning-of-drug-combination-responses--74882536
Machine Learning Coffee Seminar: https://fcai.fi/mlcs​

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075845Mon, 22 Feb 2021 09:56:02 +0000DASERMachine learning Coffee Seminar, 22 February 2021. Machine Learning Coffee Seminar: https://fcai.fi/mlcs​ Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​ Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Machine Learning Coffee Seminar: https://fcai.fi/mlcs​

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2331falsefullhttps://www.spreaker.com/episode/simo-sarkka-gpu-computing-for-large-scale-learning-in-state-space-models--74882515
Machine Learning Coffee Seminar: https://fcai.fi/mlcs​

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075846Mon, 22 Feb 2021 06:53:29 +0000DASERMachine learning Coffee Seminar, 15 February 2021. Machine Learning Coffee Seminar: https://fcai.fi/mlcs​ Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​ Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Machine Learning Coffee Seminar: https://fcai.fi/mlcs​

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi​

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2830falsefullhttps://www.spreaker.com/episode/steering-distortions-to-preserve-classes-and-neighbors-in-supervised-dimensionality-reduction--74882539
Machine learning Coffee Seminar, 8 February 2021.

Machine Learning Coffee Seminar: https://fcai.fi/mlcs

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075942Mon, 08 Feb 2021 08:55:33 +0000DASERPresented by Jaakko Peltonen Machine learning Coffee Seminar, 8 February 2021. Machine Learning Coffee Seminar: https://fcai.fi/mlcs Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi Helsinki Institute for Information Technology...
Machine learning Coffee Seminar, 8 February 2021.

Machine Learning Coffee Seminar: https://fcai.fi/mlcs

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2752falsefullhttps://www.spreaker.com/episode/jorg-tiedemann-what-s-in-a-translation-model-analyzing-neural-seq2seq-models--74882544
Machine Learning Coffee Seminar: https://fcai.fi/mlcs

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075886Mon, 08 Feb 2021 06:13:58 +0000DASERMachine learning Coffee Seminar, 1 February 2021. Machine Learning Coffee Seminar: https://fcai.fi/mlcs Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Machine Learning Coffee Seminar: https://fcai.fi/mlcs

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2684falsefullhttps://www.spreaker.com/episode/rinu-boney-learning-to-play-clash-royale--74882545
Machine Learning Coffee Seminar: https://fcai.fi/mlcs

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075918Mon, 25 Jan 2021 09:20:54 +0000DASERMachine learning Coffee Seminar, 25 January 2021. Machine Learning Coffee Seminar: https://fcai.fi/mlcs Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Machine Learning Coffee Seminar: https://fcai.fi/mlcs

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2091falsefullhttps://www.spreaker.com/episode/tero-karras-training-generative-adversarial-networks-with-limited-data--74882520
Machine Learning Coffee Seminar: https://fcai.fi/mlcs

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075943Wed, 20 Jan 2021 09:24:25 +0000DASERMachine learning Coffee Seminar, 18 January 2021. Machine Learning Coffee Seminar: https://fcai.fi/mlcs Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Machine Learning Coffee Seminar: https://fcai.fi/mlcs

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2694falsefullhttps://www.spreaker.com/episode/ai-day-2020-francesca-rossi-s-keynote--74882542
AI Day 2020: https://fcai.fi/ai-day-2020]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075977Tue, 15 Dec 2020 12:02:05 +0000DASERAI Day is the biggest annual event highlighting the frontrunner AI research in Finland. The event brings together researchers, companies, students and the public sector involved in the fast-developing field of AI. AI Day 2020 was co-organized with the...
AI Day 2020: https://fcai.fi/ai-day-2020]]>1114falsefullhttps://www.spreaker.com/episode/ai-day-2020-michela-milano-s-keynote--74882540
AI Day 2020: https://fcai.fi/ai-day-2020]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075957Tue, 15 Dec 2020 11:38:28 +0000DASERAI Day is the biggest annual event highlighting the frontrunner AI research in Finland. The event brings together researchers, companies, students and the public sector involved in the fast-developing field of AI. AI Day 2020 was co-organized with the...
AI Day 2020: https://fcai.fi/ai-day-2020]]>923falsefullhttps://www.spreaker.com/episode/ai-day-2020-pekka-ala-pietila-s-keynote--74882537
AI Day 2020: https://fcai.fi/ai-day-2020]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075922Tue, 15 Dec 2020 11:26:35 +0000DASERAI Day is the biggest annual event highlighting the frontrunner AI research in Finland. The event brings together researchers, companies, students and the public sector involved in the fast-developing field of AI. AI Day 2020 was co-organized with the...
AI Day 2020: https://fcai.fi/ai-day-2020]]>1011falsefullhttps://www.spreaker.com/episode/ai-day-2020-samuel-kaski-s-keynote--74882538
AI Day 2020: https://fcai.fi/ai-day-2020]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790447464452Tue, 15 Dec 2020 10:59:27 +0000DASERAI Day is the biggest annual event highlighting the frontrunner AI research in Finland. The event brings together researchers, companies, students and the public sector involved in the fast-developing field of AI. AI Day 2020 was co-organized with the...
AI Day 2020: https://fcai.fi/ai-day-2020]]>967falsefullhttps://www.spreaker.com/episode/juho-kannala-visual-localization-and-odometry-for-mobile-devices-and-machines--74882549
Machine Learning Coffee Seminar: https://fcai.fi/mlcs

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075940Mon, 30 Nov 2020 08:41:04 +0000DASERMachine learning Coffee Seminar, 30 November 2020. Machine Learning Coffee Seminar: https://fcai.fi/mlcs Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Machine Learning Coffee Seminar: https://fcai.fi/mlcs

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2847falsefullhttps://www.spreaker.com/episode/mark-granroth-wilding-topic-modelling-with-word-embeddings--74882524
Machine Learning Coffee Seminar: https://fcai.fi/mlcs

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075961Mon, 23 Nov 2020 10:25:05 +0000DASERMachine learning Coffee Seminar, 23rd November 2020. Machine Learning Coffee Seminar: https://fcai.fi/mlcs Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Machine Learning Coffee Seminar: https://fcai.fi/mlcs

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>3062falsefullhttps://www.spreaker.com/episode/saed-khawaldeh-machine-learning-to-predict-movement-intention--74882548
Machine learning Coffee Seminar, 16th November 2020.

Machine Learning Coffee Seminar: https://fcai.fi/mlcs

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075863Tue, 17 Nov 2020 14:23:50 +0000DASERSaed khawaldeh : Deep brain structure activity dynamics and limb movement impairment prediction in Parkinson’s disease Machine learning Coffee Seminar, 16th November 2020. Machine Learning Coffee Seminar: https://fcai.fi/mlcs Finnish Center for...
Machine learning Coffee Seminar, 16th November 2020.

Machine Learning Coffee Seminar: https://fcai.fi/mlcs

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi]]>1646falsefullhttps://www.spreaker.com/episode/arno-solin-stationary-activations-for-uncertainty-calibration-in-deep-learning--74882551
Machine Learning Coffee Seminar: https://fcai.fi/mlcs

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075876Mon, 09 Nov 2020 09:31:46 +0000DASERMachine learning Coffee Seminar, 9th November 2020. Machine Learning Coffee Seminar: https://fcai.fi/mlcs Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Machine Learning Coffee Seminar: https://fcai.fi/mlcs

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2980falsefullhttps://www.spreaker.com/episode/vikas-k-garg-generalization-and-representational-limits-of-graph-neural-networks--74882553
Machine Learning Coffee Seminar: https://fcai.fi/mlcs

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075967Mon, 02 Nov 2020 12:26:37 +0000DASERMachine learning Coffee Seminar, 2nd November 2020. Machine Learning Coffee Seminar: https://fcai.fi/mlcs Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Machine Learning Coffee Seminar: https://fcai.fi/mlcs

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>3215falsefullhttps://www.spreaker.com/episode/antti-hyttinen-and-jussi-viinikka-on-the-identifiability-and-estimation-of-causal-effects--74882527
Machine Learning Coffee Seminar: https://fcai.fi/mlcs

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075890Mon, 26 Oct 2020 09:09:10 +0000DASERMachine learning Coffee Seminar, 19th October 2020. Machine Learning Coffee Seminar: https://fcai.fi/mlcs Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Machine Learning Coffee Seminar: https://fcai.fi/mlcs

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2944falsefullhttps://www.spreaker.com/episode/andrew-howes-computational-rationality--74882522
Machine Learning Coffee Seminar: https://fcai.fi/mlcs

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075901Mon, 19 Oct 2020 08:46:01 +0000DASERMachine learning Coffee Seminar, 19th October 2020. Machine Learning Coffee Seminar: https://fcai.fi/mlcs Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Machine Learning Coffee Seminar: https://fcai.fi/mlcs

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2626falsefullhttps://www.spreaker.com/episode/aapo-hyvarinen-nonlinear-independent-component-analysis--74882546
Machine Learning Coffee Seminar: https://fcai.fi/mlcs

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075870Mon, 12 Oct 2020 08:06:40 +0000DASERMachine learning Coffee Seminar, 12th October 2020. Machine Learning Coffee Seminar: https://fcai.fi/mlcs Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Machine Learning Coffee Seminar: https://fcai.fi/mlcs

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2950falsefullhttps://www.spreaker.com/episode/leo-lahti-probabilistic-machine-learning-in-human-microbiome-research--74882499
Machine Learning Coffee Seminar: https://fcai.fi/mlcs

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075930Mon, 05 Oct 2020 08:49:23 +0000DASERMachine learning Coffee Seminar, 5th October 2020. Machine Learning Coffee Seminar: https://fcai.fi/mlcs Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Machine Learning Coffee Seminar: https://fcai.fi/mlcs

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2535falsefullhttps://www.spreaker.com/episode/niki-loppi-and-lukas-prediger-optimising-differentially-private-learning-for-gpus--74882529
Machine Learning Coffee Seminar: https://fcai.fi/mlcs

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075934Mon, 28 Sep 2020 07:50:40 +0000DASERMachine learning Coffee Seminar, 28th September 2020. Machine Learning Coffee Seminar: https://fcai.fi/mlcs Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Machine Learning Coffee Seminar: https://fcai.fi/mlcs

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2217falsefullhttps://www.spreaker.com/episode/brain-seminar-2-examining-the-future-of-work-on-mind-brain-and-ai-from-around-helsinki--74882575AI pioneer Judea Pearl has said that the state of the art is just very sophisticated fitting of curves to data. This is a long way from intelligent machines we would wish to have: Artificial cognitive systems that support human cognition, and extend thinking beyond human limitations -- in short, human-compatible AI. In order to chart a course towards AI with true intelligence, an AI that is relatable and can be asked to understand us, its users, we must study brain and AI together. The talks in this seminar discuss how this can be done and where it will lead, ranging from anticipated empirical breakthroughs in neural bases of cognition, to the limitations and promises of biologically-inspired learning algorithms, and even to futurism.

Speakers:
5:00 - Prof Jörg Tiedemann: From language technology to artificial consciousness - Where is the intelligence, and what’s language got to do with it?
27:50 - Prof Riitta Salmelin: Towards individual fingerprints of brain function
47:38 - Dr Jami Pekkanen: What machine learning has to do with human learning?
1:11:23 - Assoc Prof Riikka Möttönen: Language learning in cognitive systems
1:35:57 - Docent Michael Laakasuo: Human moral attitudes towards Mind Upload

1:58:58 - Panel Discussion: Human language, algorithmic language, and intelligence]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790447464457Mon, 28 Sep 2020 07:40:24 +0000DASERArtificial Intelligence (AI) is becoming influential throughout technological society, from transport to healthcare, civil engineering to personal electronics. But, what kind of intelligence does our AI possess? AI pioneer Judea Pearl has said that...AI pioneer Judea Pearl has said that the state of the art is just very sophisticated fitting of curves to data. This is a long way from intelligent machines we would wish to have: Artificial cognitive systems that support human cognition, and extend thinking beyond human limitations -- in short, human-compatible AI. In order to chart a course towards AI with true intelligence, an AI that is relatable and can be asked to understand us, its users, we must study brain and AI together. The talks in this seminar discuss how this can be done and where it will lead, ranging from anticipated empirical breakthroughs in neural bases of cognition, to the limitations and promises of biologically-inspired learning algorithms, and even to futurism.

Speakers:
5:00 - Prof Jörg Tiedemann: From language technology to artificial consciousness - Where is the intelligence, and what’s language got to do with it?
27:50 - Prof Riitta Salmelin: Towards individual fingerprints of brain function
47:38 - Dr Jami Pekkanen: What machine learning has to do with human learning?
1:11:23 - Assoc Prof Riikka Möttönen: Language learning in cognitive systems
1:35:57 - Docent Michael Laakasuo: Human moral attitudes towards Mind Upload

1:58:58 - Panel Discussion: Human language, algorithmic language, and intelligence]]>8155falsefullhttps://www.spreaker.com/episode/luigi-acerbi-practical-sample-efficient-bayesian-inference-for-models-with-and-without-likelihoods--74882577
Machine Learning Coffee Seminar: https://fcai.fi/mlcs

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075879Mon, 21 Sep 2020 08:18:10 +0000DASERMachine learning Coffee Seminar, 21st September 2020. Machine Learning Coffee Seminar: https://fcai.fi/mlcs Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Machine Learning Coffee Seminar: https://fcai.fi/mlcs

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2856falsefullhttps://www.spreaker.com/episode/antti-oulasvirta-a-new-look-at-adaptive-user-interfaces--74882554
Machine learning Coffee Seminar, 14th September 2020.

Machine Learning Coffee Seminar: https://fcai.fi/machine-learning-coff...

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303790439075914Mon, 14 Sep 2020 17:34:05 +0000DASERAntti Oulasvirta: A New Look at Adaptive User Interfaces Machine learning Coffee Seminar, 14th September 2020. Machine Learning Coffee Seminar: https://fcai.fi/machine-learning-coff... Finnish Center for Artificial Intelligence (FCAI):...
Machine learning Coffee Seminar, 14th September 2020.

Machine Learning Coffee Seminar: https://fcai.fi/machine-learning-coff...

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>3194falsefullhttps://www.spreaker.com/episode/ai-and-data-science-against-covid-19-part-4--74882566
The webinar series sheds light on how artificial intelligence-based systems and data science could be of help while fighting against COVID-19. Come and listen to the top researchers at University of Helsinki and Aalto University and other related talks in the field of artificial intelligence and data science. The sessions are chaired by FCAI and HiDATA leading professors.

Read more: https://fcai.fi/covid-19-webinar

FCAI website: https://fcai.fi
HiDATA website: https://www.helsinki.fi/en/helsinki-centre-for-data-science]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220463Wed, 17 Jun 2020 08:59:59 +0000DASERStarting on 11 May 2020, Finnish Center for Artificial Intelligence FCAI organises a webinar series together with Helsinki Centre for Data Science HiDATA. The webinar series sheds light on how artificial intelligence-based systems and data science...
The webinar series sheds light on how artificial intelligence-based systems and data science could be of help while fighting against COVID-19. Come and listen to the top researchers at University of Helsinki and Aalto University and other related talks in the field of artificial intelligence and data science. The sessions are chaired by FCAI and HiDATA leading professors.

Read more: https://fcai.fi/covid-19-webinar

FCAI website: https://fcai.fi
HiDATA website: https://www.helsinki.fi/en/helsinki-centre-for-data-science]]>3231falsefullhttps://www.spreaker.com/episode/ai-and-data-science-against-covid-19-part-3--74882556
The webinar series sheds light on how artificial intelligence-based systems and data science could be of help while fighting against COVID-19. Come and listen to the top researchers at University of Helsinki and Aalto University and other related talks in the field of artificial intelligence and data science. The sessions are chaired by FCAI and HiDATA leading professors.

Read more: https://fcai.fi/covid-19-webinar

FCAI website: https://fcai.fi
HiDATA website: https://www.helsinki.fi/en/helsinki-centre-for-data-science]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220437Tue, 09 Jun 2020 13:32:49 +0000DASERStarting on 11 May 2020, Finnish Center for Artificial Intelligence FCAI organises a webinar series together with Helsinki Centre for Data Science HiDATA. The webinar series sheds light on how artificial intelligence-based systems and data science...
The webinar series sheds light on how artificial intelligence-based systems and data science could be of help while fighting against COVID-19. Come and listen to the top researchers at University of Helsinki and Aalto University and other related talks in the field of artificial intelligence and data science. The sessions are chaired by FCAI and HiDATA leading professors.

Read more: https://fcai.fi/covid-19-webinar

FCAI website: https://fcai.fi
HiDATA website: https://www.helsinki.fi/en/helsinki-centre-for-data-science]]>3488falsefullhttps://www.spreaker.com/episode/ai-and-data-science-against-covid-19-part-2--74882505
The webinar series sheds light on how artificial intelligence-based systems and data science could be of help while fighting against COVID-19. Come and listen to the top researchers at University of Helsinki and Aalto University and other related talks in the field of artificial intelligence and data science. The sessions are chaired by FCAI and HiDATA leading professors.

Read more: https://fcai.fi/covid-19-webinar

FCAI website: https://fcai.fi
HiDATA website: https://www.helsinki.fi/en/helsinki-centre-for-data-science]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220464Wed, 27 May 2020 09:26:30 +0000DASERStarting on 11 May 2020, Finnish Center for Artificial Intelligence FCAI organises a webinar series together with Helsinki Centre for Data Science HiDATA. The webinar series sheds light on how artificial intelligence-based systems and data science...
The webinar series sheds light on how artificial intelligence-based systems and data science could be of help while fighting against COVID-19. Come and listen to the top researchers at University of Helsinki and Aalto University and other related talks in the field of artificial intelligence and data science. The sessions are chaired by FCAI and HiDATA leading professors.

Read more: https://fcai.fi/covid-19-webinar

FCAI website: https://fcai.fi
HiDATA website: https://www.helsinki.fi/en/helsinki-centre-for-data-science]]>3326falsefullhttps://www.spreaker.com/episode/ai-and-data-science-against-covid-19-part-1--74882530
The webinar series sheds light on how artificial intelligence-based systems and data science could be of help while fighting against COVID-19. Come and listen to the top researchers at University of Helsinki and Aalto University and other related talks in the field of artificial intelligence and data science. The sessions are chaired by FCAI and HiDATA leading professors.

Read more: https://www.helsinki.fi/en/news/data-science-news/new-webinar-ai-and-data-science-against-covid-19

FCAI website: https://fcai.fi
HiDATA website: https://www.helsinki.fi/en/helsinki-centre-for-data-science]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220428Thu, 14 May 2020 09:15:09 +0000DASERStarting on 11 May 2020, The Finnish Center for Artificial Intelligence FCAI organises a webinar series together with Helsinki Centre for Data Science HiDATA. The webinar series sheds light on how artificial intelligence-based systems and data...
The webinar series sheds light on how artificial intelligence-based systems and data science could be of help while fighting against COVID-19. Come and listen to the top researchers at University of Helsinki and Aalto University and other related talks in the field of artificial intelligence and data science. The sessions are chaired by FCAI and HiDATA leading professors.

Read more: https://www.helsinki.fi/en/news/data-science-news/new-webinar-ai-and-data-science-against-covid-19

FCAI website: https://fcai.fi
HiDATA website: https://www.helsinki.fi/en/helsinki-centre-for-data-science]]>3656falsefullhttps://www.spreaker.com/episode/multitasking-and-rational-hierarchical-control--74882534
Machine learning Coffee seminar - https://fcai.fi/machine-learning-coffee-seminar
Finnish Center for AI - https://fcai.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220438Mon, 11 May 2020 07:51:21 +0000DASERJussi Jokinen: Multitasking and Rational Hierarchical Control Machine learning Coffee seminar - https://fcai.fi/machine-learning-coffee-seminar Finnish Center for AI - https://fcai.fi/
Machine learning Coffee seminar - https://fcai.fi/machine-learning-coffee-seminar
Finnish Center for AI - https://fcai.fi/]]>2717falsefullhttps://www.spreaker.com/episode/discovering-interpretable-gan-controls--74882561
Machine learning Coffee Seminar, 4th May 2020.

Machine Learning Coffee Seminar: https://fcai.fi/machine-learning-coffee-seminar

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220427Mon, 04 May 2020 07:35:37 +0000DASERErik Härkönen: Discovering Interpretable GAN Controls Machine learning Coffee Seminar, 4th May 2020. Machine Learning Coffee Seminar: https://fcai.fi/machine-learning-coffee-seminar Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi...
Machine learning Coffee Seminar, 4th May 2020.

Machine Learning Coffee Seminar: https://fcai.fi/machine-learning-coffee-seminar

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2103falsefullhttps://www.spreaker.com/episode/self-supervised-denoising-using-blind-spot-convolutional-networks--74882564
Machine Learning Coffee Seminar, 27th of April 2020.

Machine Learning Coffee Seminar: https://fcai.fi/mlcs

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220484Mon, 27 Apr 2020 07:59:08 +0000DASERJaakko Lehtinen: Self-supervised denoising using blind-spot convolutional networks Machine Learning Coffee Seminar, 27th of April 2020. Machine Learning Coffee Seminar: https://fcai.fi/mlcs Finnish Center for Artificial Intelligence (FCAI):...
Machine Learning Coffee Seminar, 27th of April 2020.

Machine Learning Coffee Seminar: https://fcai.fi/mlcs

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi]]>2583falsefullhttps://www.spreaker.com/episode/icon-intelligent-container-overlays--74882560
Machine learning Coffee Seminar, 20th April 2020.

Machine Learning Coffee Seminar: https://fcai.fi/machine-learning-coffee-seminar

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220434Mon, 20 Apr 2020 07:31:34 +0000DASERJussi Kangasharju: Intelligent Container Overlays Machine learning Coffee Seminar, 20th April 2020. Machine Learning Coffee Seminar: https://fcai.fi/machine-learning-coffee-seminar Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi...
Machine learning Coffee Seminar, 20th April 2020.

Machine Learning Coffee Seminar: https://fcai.fi/machine-learning-coffee-seminar

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2423falsefullhttps://www.spreaker.com/episode/deep-learning-for-electronic-health-records--74882558
Machine learning Coffee Seminar, 6th April 2020.

Machine learning Coffee Seminar: https://www.fcai.fi/mlcs]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220431Mon, 06 Apr 2020 08:44:47 +0000DASERPekka Marttinen: Deep learning for electronic health records Machine learning Coffee Seminar, 6th April 2020. Machine learning Coffee Seminar: https://www.fcai.fi/mlcs
Machine learning Coffee Seminar, 6th April 2020.

Machine learning Coffee Seminar: https://www.fcai.fi/mlcs]]>2834falsefullhttps://www.spreaker.com/episode/robust-methods-for-explaining-classifiers-and-data--74882571
Machine Learning Coffee Seminar, 30th of March 2020.

Machine Learning Coffee Seminar: https://fcai.fi/machine-learning-coffee-seminar

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220444Mon, 30 Mar 2020 07:58:32 +0000DASERKai Puolamäki: Robust Methods for Explaining Classifiers and Data Machine Learning Coffee Seminar, 30th of March 2020. Machine Learning Coffee Seminar: https://fcai.fi/machine-learning-coffee-seminar Finnish Center for Artificial Intelligence...
Machine Learning Coffee Seminar, 30th of March 2020.

Machine Learning Coffee Seminar: https://fcai.fi/machine-learning-coffee-seminar

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2831falsefullhttps://www.spreaker.com/episode/a-link-between-coding-theory-and-cross-validation-with-applications--74882559
Machine Learning Coffee Seminar, 23rd of March 2020.

Machine Learning Coffee Seminar: https://fcai.fi/machine-learning-coffee-seminar

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220453Mon, 23 Mar 2020 19:11:41 +0000DASERTapio Pahikkala: A Link between Coding Theory and Cross-Validation with Applications Machine Learning Coffee Seminar, 23rd of March 2020. Machine Learning Coffee Seminar: https://fcai.fi/machine-learning-coffee-seminar Finnish Center for Artificial...
Machine Learning Coffee Seminar, 23rd of March 2020.

Machine Learning Coffee Seminar: https://fcai.fi/machine-learning-coffee-seminar

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/]]>2352falsefullhttps://www.spreaker.com/episode/interactive-ai-and-machine-teaching-of-active-sequential-learners--74882562
Machine Learning Coffee Seminar, 9th of March 2020.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220429Mon, 09 Mar 2020 09:53:30 +0000DASERDr. Tomi Peltola: Interactive AI and Machine Teaching of Active Sequential Learners Machine Learning Coffee Seminar, 9th of March 2020. Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/ Helsinki...
Machine Learning Coffee Seminar, 9th of March 2020.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/

Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/

Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi]]>2329falsefullhttps://www.spreaker.com/episode/algorithmization-of-counterfactuals-and-a-probabilistic-theory-of-causality--74882565
Machine Learning Coffee Seminar, March 2nd, 2020.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/

Finnish Center for Artificial Intelligence FCAI: https://fcai.fi

Helsinki Institute for Information Technology HIIT: https://www.hiit.fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220448Mon, 02 Mar 2020 11:20:24 +0000DASERTimo Koski: Algorithmization of Counterfactuals and a Probabilistic Theory of Causality Machine Learning Coffee Seminar, March 2nd, 2020. Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/ Finnish...
Machine Learning Coffee Seminar, March 2nd, 2020.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/

Finnish Center for Artificial Intelligence FCAI: https://fcai.fi

Helsinki Institute for Information Technology HIIT: https://www.hiit.fi]]>2372falsefullhttps://www.spreaker.com/episode/meta-reinforcement-learning-for-sim-to-real-adaptation--74882574
Machine Learning Coffee Seminar, 10 th February 2020.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/

Finnish Center for Artificial Intelligence FCAI: https://fcai.fi

Helsinki Institute for Information Technology HIIT: https://www.hiit.fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220471Mon, 24 Feb 2020 09:08:11 +0000DASERVille kyrki: Meta Reinforcement Learning for Sim-to-real adaptation Machine Learning Coffee Seminar, 10 th February 2020. Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/ Finnish Center for...
Machine Learning Coffee Seminar, 10 th February 2020.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/

Finnish Center for Artificial Intelligence FCAI: https://fcai.fi

Helsinki Institute for Information Technology HIIT: https://www.hiit.fi]]>2952falsefullhttps://www.spreaker.com/episode/towards-participatory-digital-society-using-the-digital-twin-paradigm-citizen-data-model--74882555
Machine Learning Coffee Seminar, February 17th, 2020.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/

Finnish Center for Artificial Intelligence FCAI: https://fcai.fi Helsinki

Institute for Information Technology HIIT: https://www.hiit.fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220474Mon, 17 Feb 2020 11:23:17 +0000DASERAleksi Kopponen, Tommi Mikkonen: Towards Participatory Digital Society Using The Digital Twin Paradigm: Citizen Data Model Machine Learning Coffee Seminar, February 17th, 2020. Machine Learning Coffee Seminar:...
Machine Learning Coffee Seminar, February 17th, 2020.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/

Finnish Center for Artificial Intelligence FCAI: https://fcai.fi Helsinki

Institute for Information Technology HIIT: https://www.hiit.fi]]>2753falsefullhttps://www.spreaker.com/episode/m-deisenroth-ucl-data-efficient-reinforcement-learning-with-probabilistic-models-aalto-cs-forum--74882569
13 February 2020 CS Forum lecture:

Prof. Marc Deisenroth (DeepMind Chair in Artificial Intelligence, University College London): Data-Efficient Reinforcement Learning with Probabilistic Models

Abstract:

On our path toward fully autonomous systems, i.e., systems that operate in the real world without significant human intervention, reinforcement learning (RL) is a promising framework for learning to solve problems by trial and error. While RL has had many successes recently, a practical challenge we face is its data inefficiency: In real-world problems (e.g., robotics) it is not always possible to conduct millions of experiments, e.g., due to time or hardware constraints. In this talk, I will outline three approaches that explicitly address the data-efficiency challenge in reinforcement learning using probabilistic models. First, I will give a brief overview of a model-based RL algorithm that can learn from small datasets. Second, I will describe an idea based on model predictive control that allows us to learn even faster while taking care of state or control constraints, which is important for safe exploration. Finally, I will introduce an idea for meta learning (in the context of model-based RL), which is based on latent variables.

Key references
* Marc P. Deisenroth, Dieter Fox, Carl E. Rasmussen, Gaussian Processes for Data-Efficient Learning in Robotics and Control, IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 37, pp. 408–423, 2015
* Sanket Kamthe, Marc P. Deisenroth, Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control, Proceedings of the International the Conference on Artificial Intelligence and Statistics (AISTATS), 2018
* Steindór Sæmundsson, Katja Hofmann, Marc P. Deisenroth, Meta Reinforcement Learning with Latent Variable Gaussian Processes, Proceedings of the International the Conference on Uncertainty in Artificial Intelligence, 2018

Bio:

Professor Marc Deisenroth is the DeepMind Chair in Artificial Intelligence at University College London. He also holds a visiting faculty position at the University of Johannesburg. From 2014 to 2019, Marc was a faculty member in the Department of Computing, Imperial College London. Marc’s research interests center around data-efficient machine learning, probabilistic modeling and autonomous decision making.

Marc was Program Chair of EWRL 2012, Workshops Chair of RSS 2013 and received Best Paper Awards at ICRA 2014 and ICCAS 2016. In 2019, Marc co-organized the Machine Learning Summer School in London. In 2018, Marc has been awarded The President’s Award for Outstanding Early Career Researcher at Imperial College. He is a recipient of a Google Faculty Research Award and a Microsoft PhD Grant. He is co-author of the book Mathematics for Machine Learning, published by Cambridge University Press.

Host:

Professor Arno Solin, Department of Computer Science]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220447Fri, 14 Feb 2020 13:47:45 +0000DASERCS forum is a seminar series arranged at the Aalto University CS department - open to everyone free-of-charge. 13 February 2020 CS Forum lecture: Prof. Marc Deisenroth (DeepMind Chair in Artificial Intelligence, University College London):...
13 February 2020 CS Forum lecture:

Prof. Marc Deisenroth (DeepMind Chair in Artificial Intelligence, University College London): Data-Efficient Reinforcement Learning with Probabilistic Models

Abstract:

On our path toward fully autonomous systems, i.e., systems that operate in the real world without significant human intervention, reinforcement learning (RL) is a promising framework for learning to solve problems by trial and error. While RL has had many successes recently, a practical challenge we face is its data inefficiency: In real-world problems (e.g., robotics) it is not always possible to conduct millions of experiments, e.g., due to time or hardware constraints. In this talk, I will outline three approaches that explicitly address the data-efficiency challenge in reinforcement learning using probabilistic models. First, I will give a brief overview of a model-based RL algorithm that can learn from small datasets. Second, I will describe an idea based on model predictive control that allows us to learn even faster while taking care of state or control constraints, which is important for safe exploration. Finally, I will introduce an idea for meta learning (in the context of model-based RL), which is based on latent variables.

Key references
* Marc P. Deisenroth, Dieter Fox, Carl E. Rasmussen, Gaussian Processes for Data-Efficient Learning in Robotics and Control, IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 37, pp. 408–423, 2015
* Sanket Kamthe, Marc P. Deisenroth, Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control, Proceedings of the International the Conference on Artificial Intelligence and Statistics (AISTATS), 2018
* Steindór Sæmundsson, Katja Hofmann, Marc P. Deisenroth, Meta Reinforcement Learning with Latent Variable Gaussian Processes, Proceedings of the International the Conference on Uncertainty in Artificial Intelligence, 2018

Bio:

Professor Marc Deisenroth is the DeepMind Chair in Artificial Intelligence at University College London. He also holds a visiting faculty position at the University of Johannesburg. From 2014 to 2019, Marc was a faculty member in the Department of Computing, Imperial College London. Marc’s research interests center around data-efficient machine learning, probabilistic modeling and autonomous decision making.

Marc was Program Chair of EWRL 2012, Workshops Chair of RSS 2013 and received Best Paper Awards at ICRA 2014 and ICCAS 2016. In 2019, Marc co-organized the Machine Learning Summer School in London. In 2018, Marc has been awarded The President’s Award for Outstanding Early Career Researcher at Imperial College. He is a recipient of a Google Faculty Research Award and a Microsoft PhD Grant. He is co-author of the book Mathematics for Machine Learning, published by Cambridge University Press.

Host:

Professor Arno Solin, Department of Computer Science]]>3846falsefullhttps://www.spreaker.com/episode/ode2vae-deep-generative-second-order-odes-with-bayesian-neural-networks--74882543
Machine Learning Coffee Seminar, 10 th February 2020.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/

Finnish Center for Artificial Intelligence FCAI: https://fcai.fi Helsinki

Institute for Information Technology HIIT: https://www.hiit.fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220467Tue, 11 Feb 2020 11:24:38 +0000DASERYildiz Cagatay: ODE2VAE: Deep generative second-order ODEs with Bayesian neural networks Machine Learning Coffee Seminar, 10 th February 2020. Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/...
Machine Learning Coffee Seminar, 10 th February 2020.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/

Finnish Center for Artificial Intelligence FCAI: https://fcai.fi Helsinki

Institute for Information Technology HIIT: https://www.hiit.fi]]>2790falsefullhttps://www.spreaker.com/episode/environmental-impacts-of-novel-food-production-technologies--74882563
Machine Learning Coffee Seminar, February 3rd, 2020.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/

Finnish Center for Artificial Intelligence FCAI: https://fcai.fi Helsinki

Institute for Information Technology HIIT: https://www.hiit.fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220465Mon, 03 Feb 2020 09:45:59 +0000DASERHanna L. Tuomisto: Environmental Impacts of Novel Food Production Technologies Machine Learning Coffee Seminar, February 3rd, 2020. Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/ Finnish...
Machine Learning Coffee Seminar, February 3rd, 2020.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/

Finnish Center for Artificial Intelligence FCAI: https://fcai.fi Helsinki

Institute for Information Technology HIIT: https://www.hiit.fi]]>1021falsefullhttps://www.spreaker.com/episode/quality-of-analytics-as-an-approach-for-optimizing-ml-systems-initial-results-and-roadmap--74882573
Machine Learning Coffee Seminar, 27 th January 2020.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/

Finnish Center for Artificial Intelligence FCAI: https://fcai.fi Helsinki

Institute for Information Technology HIIT: https://www.hiit.fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220440Mon, 27 Jan 2020 10:02:11 +0000DASERHong-Linh Truong: Quality of Analytics as an Approach for Optimizing ML Systems: Initial Results and Roadmap Machine Learning Coffee Seminar, 27 th January 2020. Machine Learning Coffee Seminar:...
Machine Learning Coffee Seminar, 27 th January 2020.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/

Finnish Center for Artificial Intelligence FCAI: https://fcai.fi Helsinki

Institute for Information Technology HIIT: https://www.hiit.fi]]>2987falsefullhttps://www.spreaker.com/episode/brain-seminar-panel-discussion-shared-scientific-problems-and-potential-solutions--74882570
AI pioneer Judea Pearl has said that the state of the art is just very sophisticated fitting of curves to data. This is a long way from intelligent machines we would wish to have: Artificial cognitive systems that support human cognition, and extend thinking beyond human limitations -- in short, human-compatible AI. In order to chart a course towards AI with true intelligence, an AI that is relatable and can be asked to understand us, its users, we must study brain and AI together. The talks in this seminar show how this can involve study of the brain using AI methods; study of AI as a cognitive science; and study of the interaction of brain and AI.

The first BrAIn Seminar took place at Tiedekulma in January 15, 2020 as part of the AIX Forum seminar series (https://fcai.fi/aix-forum).]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220435Thu, 23 Jan 2020 13:39:03 +0000DASERArtificial Intelligence (AI) is becoming influential throughout technological society, from transport to healthcare, civil engineering to personal electronics. But, what kind of intelligence does our AI possess? AI pioneer Judea Pearl has said that...
AI pioneer Judea Pearl has said that the state of the art is just very sophisticated fitting of curves to data. This is a long way from intelligent machines we would wish to have: Artificial cognitive systems that support human cognition, and extend thinking beyond human limitations -- in short, human-compatible AI. In order to chart a course towards AI with true intelligence, an AI that is relatable and can be asked to understand us, its users, we must study brain and AI together. The talks in this seminar show how this can involve study of the brain using AI methods; study of AI as a cognitive science; and study of the interaction of brain and AI.

The first BrAIn Seminar took place at Tiedekulma in January 15, 2020 as part of the AIX Forum seminar series (https://fcai.fi/aix-forum).]]>1989falsefullhttps://www.spreaker.com/episode/brain-seminar-antti-oulasvirta-computational-rationality--74882568
Artificial Intelligence (AI) is becoming influential throughout technological society, from transport to healthcare, civil engineering to personal electronics. But, what kind of intelligence does our AI possess?

AI pioneer Judea Pearl has said that the state of the art is just very sophisticated fitting of curves to data. This is a long way from intelligent machines we would wish to have: Artificial cognitive systems that support human cognition, and extend thinking beyond human limitations -- in short, human-compatible AI. In order to chart a course towards AI with true intelligence, an AI that is relatable and can be asked to understand us, its users, we must study brain and AI together. The talks in this seminar show how this can involve study of the brain using AI methods; study of AI as a cognitive science; and study of the interaction of brain and AI.

The first BrAIn Seminar took place at Tiedekulma in January 15, 2020 as part of the AIX Forum seminar series (https://fcai.fi/aix-forum).]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220455Thu, 23 Jan 2020 13:30:40 +0000DASERAntti Oulasvirta: Computational rationality: Adaptation of behavior as utility-maximization under uncertainty. A new look at an old problem in cognitive science Artificial Intelligence (AI) is becoming influential throughout technological society,...
Artificial Intelligence (AI) is becoming influential throughout technological society, from transport to healthcare, civil engineering to personal electronics. But, what kind of intelligence does our AI possess?

AI pioneer Judea Pearl has said that the state of the art is just very sophisticated fitting of curves to data. This is a long way from intelligent machines we would wish to have: Artificial cognitive systems that support human cognition, and extend thinking beyond human limitations -- in short, human-compatible AI. In order to chart a course towards AI with true intelligence, an AI that is relatable and can be asked to understand us, its users, we must study brain and AI together. The talks in this seminar show how this can involve study of the brain using AI methods; study of AI as a cognitive science; and study of the interaction of brain and AI.

The first BrAIn Seminar took place at Tiedekulma in January 15, 2020 as part of the AIX Forum seminar series (https://fcai.fi/aix-forum).]]>1186falsefullhttps://www.spreaker.com/episode/brain-seminar-jussi-jokinen-emotional-appraisal-and-reinforcement-learning--74882519
Artificial Intelligence (AI) is becoming influential throughout technological society, from transport to healthcare, civil engineering to personal electronics. But, what kind of intelligence does our AI possess?

AI pioneer Judea Pearl has said that the state of the art is just very sophisticated fitting of curves to data. This is a long way from intelligent machines we would wish to have: Artificial cognitive systems that support human cognition, and extend thinking beyond human limitations -- in short, human-compatible AI. In order to chart a course towards AI with true intelligence, an AI that is relatable and can be asked to understand us, its users, we must study brain and AI together. The talks in this seminar show how this can involve study of the brain using AI methods; study of AI as a cognitive science; and study of the interaction of brain and AI.

The first BrAIn Seminar took place at Tiedekulma in January 15, 2020 as part of the AIX Forum seminar series (https://fcai.fi/aix-forum).]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220417Thu, 23 Jan 2020 13:24:12 +0000DASERJussi Jokinen: Emotional appraisal and reinforcement learning Artificial Intelligence (AI) is becoming influential throughout technological society, from transport to healthcare, civil engineering to personal electronics. But, what kind of...
Artificial Intelligence (AI) is becoming influential throughout technological society, from transport to healthcare, civil engineering to personal electronics. But, what kind of intelligence does our AI possess?

AI pioneer Judea Pearl has said that the state of the art is just very sophisticated fitting of curves to data. This is a long way from intelligent machines we would wish to have: Artificial cognitive systems that support human cognition, and extend thinking beyond human limitations -- in short, human-compatible AI. In order to chart a course towards AI with true intelligence, an AI that is relatable and can be asked to understand us, its users, we must study brain and AI together. The talks in this seminar show how this can involve study of the brain using AI methods; study of AI as a cognitive science; and study of the interaction of brain and AI.

The first BrAIn Seminar took place at Tiedekulma in January 15, 2020 as part of the AIX Forum seminar series (https://fcai.fi/aix-forum).]]>1000falsefullhttps://www.spreaker.com/episode/brain-seminar-tuukka-ruotsalo-brain-computer-interfaces--74882532
Artificial Intelligence (AI) is becoming influential throughout technological society, from transport to healthcare, civil engineering to personal electronics. But, what kind of intelligence does our AI possess?

AI pioneer Judea Pearl has said that the state of the art is just very sophisticated fitting of curves to data. This is a long way from intelligent machines we would wish to have: Artificial cognitive systems that support human cognition, and extend thinking beyond human limitations -- in short, human-compatible AI. In order to chart a course towards AI with true intelligence, an AI that is relatable and can be asked to understand us, its users, we must study brain and AI together. The talks in this seminar show how this can involve study of the brain using AI methods; study of AI as a cognitive science; and study of the interaction of brain and AI.

The first BrAIn Seminar took place at Tiedekulma in January 15, 2020 as part of the AIX Forum seminar series (https://fcai.fi/aix-forum).]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220446Thu, 23 Jan 2020 13:17:11 +0000DASERTuukka Ruotsalo: Brain Computer Interfaces Artificial Intelligence (AI) is becoming influential throughout technological society, from transport to healthcare, civil engineering to personal electronics. But, what kind of intelligence does our AI...
Artificial Intelligence (AI) is becoming influential throughout technological society, from transport to healthcare, civil engineering to personal electronics. But, what kind of intelligence does our AI possess?

AI pioneer Judea Pearl has said that the state of the art is just very sophisticated fitting of curves to data. This is a long way from intelligent machines we would wish to have: Artificial cognitive systems that support human cognition, and extend thinking beyond human limitations -- in short, human-compatible AI. In order to chart a course towards AI with true intelligence, an AI that is relatable and can be asked to understand us, its users, we must study brain and AI together. The talks in this seminar show how this can involve study of the brain using AI methods; study of AI as a cognitive science; and study of the interaction of brain and AI.

The first BrAIn Seminar took place at Tiedekulma in January 15, 2020 as part of the AIX Forum seminar series (https://fcai.fi/aix-forum).]]>964falsefullhttps://www.spreaker.com/episode/brain-seminar-l-parkkonen-decoding-attention-states-from-meg-with-convolutional-neural-networks--74882552
Artificial Intelligence (AI) is becoming influential throughout technological society, from transport to healthcare, civil engineering to personal electronics. But, what kind of intelligence does our AI possess?

AI pioneer Judea Pearl has said that the state of the art is just very sophisticated fitting of curves to data. This is a long way from intelligent machines we would wish to have: Artificial cognitive systems that support human cognition, and extend thinking beyond human limitations -- in short, human-compatible AI. In order to chart a course towards AI with true intelligence, an AI that is relatable and can be asked to understand us, its users, we must study brain and AI together. The talks in this seminar show how this can involve study of the brain using AI methods; study of AI as a cognitive science; and study of the interaction of brain and AI.

The first BrAIn Seminar took place at Tiedekulma in January 15, 2020 as part of the AIX Forum seminar series (https://fcai.fi/aix-forum).]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220460Thu, 23 Jan 2020 13:08:52 +0000DASERLauri Parkkonen: Decoding attention states from MEG with convolutional neural networks Artificial Intelligence (AI) is becoming influential throughout technological society, from transport to healthcare, civil engineering to personal electronics....
Artificial Intelligence (AI) is becoming influential throughout technological society, from transport to healthcare, civil engineering to personal electronics. But, what kind of intelligence does our AI possess?

AI pioneer Judea Pearl has said that the state of the art is just very sophisticated fitting of curves to data. This is a long way from intelligent machines we would wish to have: Artificial cognitive systems that support human cognition, and extend thinking beyond human limitations -- in short, human-compatible AI. In order to chart a course towards AI with true intelligence, an AI that is relatable and can be asked to understand us, its users, we must study brain and AI together. The talks in this seminar show how this can involve study of the brain using AI methods; study of AI as a cognitive science; and study of the interaction of brain and AI.

The first BrAIn Seminar took place at Tiedekulma in January 15, 2020 as part of the AIX Forum seminar series (https://fcai.fi/aix-forum).]]>1041falsefullhttps://www.spreaker.com/episode/brain-seminar-otto-lappi-predictive-processing-and-naturalistic-task-performance--74882550
Artificial Intelligence (AI) is becoming influential throughout technological society, from transport to healthcare, civil engineering to personal electronics. But, what kind of intelligence does our AI possess?

AI pioneer Judea Pearl has said that the state of the art is just very sophisticated fitting of curves to data. This is a long way from intelligent machines we would wish to have: Artificial cognitive systems that support human cognition, and extend thinking beyond human limitations -- in short, human-compatible AI. In order to chart a course towards AI with true intelligence, an AI that is relatable and can be asked to understand us, its users, we must study brain and AI together. The talks in this seminar show how this can involve study of the brain using AI methods; study of AI as a cognitive science; and study of the interaction of brain and AI.

The first BrAIn Seminar took place at Tiedekulma in January 15, 2020 as part of the AIX Forum seminar series (https://fcai.fi/aix-forum).]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220442Thu, 23 Jan 2020 12:55:09 +0000DASEROtto Lappi: Predictive Processing and Naturalistic Task Performance Artificial Intelligence (AI) is becoming influential throughout technological society, from transport to healthcare, civil engineering to personal electronics. But, what kind of...
Artificial Intelligence (AI) is becoming influential throughout technological society, from transport to healthcare, civil engineering to personal electronics. But, what kind of intelligence does our AI possess?

AI pioneer Judea Pearl has said that the state of the art is just very sophisticated fitting of curves to data. This is a long way from intelligent machines we would wish to have: Artificial cognitive systems that support human cognition, and extend thinking beyond human limitations -- in short, human-compatible AI. In order to chart a course towards AI with true intelligence, an AI that is relatable and can be asked to understand us, its users, we must study brain and AI together. The talks in this seminar show how this can involve study of the brain using AI methods; study of AI as a cognitive science; and study of the interaction of brain and AI.

The first BrAIn Seminar took place at Tiedekulma in January 15, 2020 as part of the AIX Forum seminar series (https://fcai.fi/aix-forum).]]>1257falsefullhttps://www.spreaker.com/episode/brain-seminar-marijn-van-vliet-boosting-ml-for-eeg-by-incorporating-prior-information--74882557
Artificial Intelligence (AI) is becoming influential throughout technological society, from transport to healthcare, civil engineering to personal electronics. But, what kind of intelligence does our AI possess?

AI pioneer Judea Pearl has said that the state of the art is just very sophisticated fitting of curves to data. This is a long way from intelligent machines we would wish to have: Artificial cognitive systems that support human cognition, and extend thinking beyond human limitations -- in short, human-compatible AI. In order to chart a course towards AI with true intelligence, an AI that is relatable and can be asked to understand us, its users, we must study brain and AI together. The talks in this seminar show how this can involve study of the brain using AI methods; study of AI as a cognitive science; and study of the interaction of brain and AI.

The first BrAIn Seminar took place at Tiedekulma in January 15, 2020 as part of the AIX Forum seminar series (https://fcai.fi/aix-forum).]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220445Thu, 23 Jan 2020 12:27:34 +0000DASERMarijn van Vliet: Boosting machine learning for EEG by incorporating prior information Artificial Intelligence (AI) is becoming influential throughout technological society, from transport to healthcare, civil engineering to personal electronics....
Artificial Intelligence (AI) is becoming influential throughout technological society, from transport to healthcare, civil engineering to personal electronics. But, what kind of intelligence does our AI possess?

AI pioneer Judea Pearl has said that the state of the art is just very sophisticated fitting of curves to data. This is a long way from intelligent machines we would wish to have: Artificial cognitive systems that support human cognition, and extend thinking beyond human limitations -- in short, human-compatible AI. In order to chart a course towards AI with true intelligence, an AI that is relatable and can be asked to understand us, its users, we must study brain and AI together. The talks in this seminar show how this can involve study of the brain using AI methods; study of AI as a cognitive science; and study of the interaction of brain and AI.

The first BrAIn Seminar took place at Tiedekulma in January 15, 2020 as part of the AIX Forum seminar series (https://fcai.fi/aix-forum).]]>910falsefullhttps://www.spreaker.com/episode/a-maximum-likelihood-approach-for-modelling-viral-evolution-using-genome-sequence-data--74884870
Machine Learning Coffee Seminar on 20 January 2020]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220481Tue, 21 Jan 2020 09:07:53 +0000DASERChris Illingworth: A maximum likelihood approach for modelling viral evolution using genome sequence data Machine Learning Coffee Seminar on 20 January 2020
Machine Learning Coffee Seminar on 20 January 2020]]>2223falsefullhttps://www.spreaker.com/episode/a-new-look-into-teaching-ai-and-machine-learning-lessons-from-the-elements-of-ai--74884874
Machine learning Coffee Seminar on January 13, 2020]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220461Mon, 13 Jan 2020 09:48:33 +0000DASERTeemu Roos: A new look into teaching AI and Machine Learning: Lessons from the Elements of AI Machine learning Coffee Seminar on January 13, 2020
Machine learning Coffee Seminar on January 13, 2020]]>2823falsefullhttps://www.spreaker.com/episode/singpro-project-combining-data-analytics-with-process-optimization--74884898
Machine Learning Coffee Seminar on December 02, 2019]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220472Mon, 02 Dec 2019 11:04:44 +0000DASERKeijo Heljanko: SINGPRO project – Combining Data Analytics with Process Optimization Machine Learning Coffee Seminar on December 02, 2019
Machine Learning Coffee Seminar on December 02, 2019]]>2706falsefullhttps://www.spreaker.com/episode/machine-learning-in-drone-remote-sensing--74884882

Machine Learning Coffee Seminar, 18th of November 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/m...
Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220473Wed, 20 Nov 2019 14:46:41 +0000DASEREija Honkavaara: Machine learning in drone remote sensing Machine Learning Coffee Seminar, 18th of November 2019. Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/m... Helsinki Institute for Information Technology (HIIT):...

Machine Learning Coffee Seminar, 18th of November 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/m...
Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi]]>2828falsefullhttps://www.spreaker.com/episode/r5-interactive-ai--74884880
Machine Learning Coffee Seminar, 11th of November 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/m...
Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220421Tue, 12 Nov 2019 08:01:30 +0000DASERAntti Oulasvirta: R5 - Interactive AI Machine Learning Coffee Seminar, 11th of November 2019. Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/m... Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/...
Machine Learning Coffee Seminar, 11th of November 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/m...
Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi]]>2551falsefullhttps://www.spreaker.com/episode/fcai-simulator-based-inference-ai-assisted-vaccine-development--74884888
Machine Learning Coffee Seminar, 7 th October 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/

Finnish Center for Artificial Intelligence FCAI: https://fcai.fi Helsinki

Institute for Information Technology HIIT: https://www.hiit.fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220443Mon, 04 Nov 2019 13:39:26 +0000DASERJukka Corander: FCAI Simulator-based inference: AI-assisted vaccine development Machine Learning Coffee Seminar, 7 th October 2019. Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/ Finnish...
Machine Learning Coffee Seminar, 7 th October 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/

Finnish Center for Artificial Intelligence FCAI: https://fcai.fi Helsinki

Institute for Information Technology HIIT: https://www.hiit.fi]]>2596falsefullhttps://www.spreaker.com/episode/easy-and-privacy-preserving-modeling-tools-or-i-want-you-to-build-your-own-ai--74884899
Machine Learning Coffee Seminar, 28th October 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/m...
Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi
Category
Science & Technology]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220477Mon, 28 Oct 2019 12:13:10 +0000DASERArto Klami: Easy and Privacy-preserving Modeling Tools (or ¨I want you to build your own AI!¨) Machine Learning Coffee Seminar, 28th October 2019. Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/m... Helsinki Institute for...
Machine Learning Coffee Seminar, 28th October 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/m...
Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi
Category
Science & Technology]]>2041falsefullhttps://www.spreaker.com/episode/fcai-privacy-preserving-and-secure-ai--74884886
Machine Learning Coffee Seminar, 14th of October 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/
Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220450Mon, 14 Oct 2019 13:15:30 +0000DASERAntti Honkela: FCAI Privacy-preserving and secure AI Machine Learning Coffee Seminar, 14th of October 2019. Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/ Helsinki Institute for Information...
Machine Learning Coffee Seminar, 14th of October 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/
Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi]]>2139falsefullhttps://www.spreaker.com/episode/fcai-agile-probabilistic-ai--74884952
Machine Learning Coffee Seminar, 7 th October 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/

Finnish Center for Artificial Intelligence FCAI: https://fcai.fi Helsinki

Institute for Information Technology HIIT: https://www.hiit.fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220458Mon, 07 Oct 2019 18:35:54 +0000DASERAki Vehtari: FCAI Agile Probabilistic AI Machine Learning Coffee Seminar, 7 th October 2019. Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/ Finnish Center for Artificial Intelligence FCAI:...
Machine Learning Coffee Seminar, 7 th October 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/

Finnish Center for Artificial Intelligence FCAI: https://fcai.fi Helsinki

Institute for Information Technology HIIT: https://www.hiit.fi]]>2900falsefullhttps://www.spreaker.com/episode/engineering-and-testing-of-ai-systems--74884955
Machine Learning Coffee Seminar, 30th of September 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/
Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220424Mon, 30 Sep 2019 08:37:28 +0000DASERJukka K. Nurminen: Engineering and testing of AI systems Machine Learning Coffee Seminar, 30th of September 2019. Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/ Helsinki Institute for...
Machine Learning Coffee Seminar, 30th of September 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/
Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi]]>2056falsefullhttps://www.spreaker.com/episode/real-world-model-based-reinforcement-learning-with-deep-neural-networks--74884913
Machine Learning Coffee Seminar, 16th September 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/
Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220425Wed, 25 Sep 2019 15:51:06 +0000DASERHarri Valpola: Real-world model-based reinforcement learning with deep neural networks Machine Learning Coffee Seminar, 16th September 2019. Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/...
Machine Learning Coffee Seminar, 16th September 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/
Helsinki Institute for Information Technology (HIIT): https://www.hiit.fi/
Finnish Center for Artificial Intelligence (FCAI): https://fcai.fi]]>2619falsefullhttps://www.spreaker.com/episode/can-ai-be-responsible-introducing-ai-in-society-program--74884926
Petri Ylikoski (Professor of Sociology, University of Helsinki): Introducing AI in Society Program

Machine Learning Coffee Seminar, 23 rd September 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/

Finnish Center for Artificial Intelligence FCAI: https://fcai.fi Helsinki

Institute for Information Technology HIIT: https://www.hiit.fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220466Mon, 23 Sep 2019 13:59:25 +0000DASERRaul Hakli: Can AI be responsible? Petri Ylikoski (Professor of Sociology, University of Helsinki): Introducing AI in Society Program Machine Learning Coffee Seminar, 23 rd September 2019. Machine Learning Coffee Seminar:...
Petri Ylikoski (Professor of Sociology, University of Helsinki): Introducing AI in Society Program

Machine Learning Coffee Seminar, 23 rd September 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/

Finnish Center for Artificial Intelligence FCAI: https://fcai.fi Helsinki

Institute for Information Technology HIIT: https://www.hiit.fi]]>3249falsefullhttps://www.spreaker.com/episode/intelligent-mobile-robots--74884892
Machine Learning Coffee Seminar, 9 th September 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/

Finnish Center for Artificial Intelligence FCAI: https://fcai.fi Helsinki

Institute for Information Technology HIIT: https://www.hiit.fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220479Tue, 10 Sep 2019 08:16:57 +0000DASERJari Saarinen: Intelligent Mobile Robots Machine Learning Coffee Seminar, 9 th September 2019. Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/ Finnish Center for Artificial Intelligence FCAI:...
Machine Learning Coffee Seminar, 9 th September 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/

Finnish Center for Artificial Intelligence FCAI: https://fcai.fi Helsinki

Institute for Information Technology HIIT: https://www.hiit.fi]]>2672falsefullhttps://www.spreaker.com/episode/ai-in-for-design--74884922
Machine Learning Coffee Seminar, 3rd June 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/
Helsinki Institute for Information Technology HIIT: https://www.hiit.fi/
Finnish Center for Artificial Intelligence FCAI: https://fcai.fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220418Mon, 03 Jun 2019 10:15:46 +0000DASERPerttu Hämäläinen: AI in/for design Machine Learning Coffee Seminar, 3rd June 2019. Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/ Helsinki Institute for Information Technology HIIT:...
Machine Learning Coffee Seminar, 3rd June 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/
Helsinki Institute for Information Technology HIIT: https://www.hiit.fi/
Finnish Center for Artificial Intelligence FCAI: https://fcai.fi]]>2283falsefullhttps://www.spreaker.com/episode/efficient-estimation-of-auc-in-a-sliding-window--74884903
Machine Learning Coffee Seminar, 20th May 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/
Helsinki Institute for Information Technology HIIT: https://www.hiit.fi/
Finnish Center for Artificial Intelligence FCAI: https://fcai.fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220456Mon, 20 May 2019 09:46:18 +0000DASERNikolaj Tatti: Efficient estimation of AUC in a sliding window Machine Learning Coffee Seminar, 20th May 2019. Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/ Helsinki Institute for Information...
Machine Learning Coffee Seminar, 20th May 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/
Helsinki Institute for Information Technology HIIT: https://www.hiit.fi/
Finnish Center for Artificial Intelligence FCAI: https://fcai.fi]]>1747falsefullhttps://www.spreaker.com/episode/ai-approaches-to-support-language-learning--74884941https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220476Thu, 16 May 2019 07:08:04 +0000DASER3330falsefullhttps://www.spreaker.com/episode/simulation-based-learning-for-robots--74884906
Machine Learning Coffee Seminar, 1st April 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars
Finnish Center for Artificial Intelligence FCAI: https://fcai.fi
Helsinki Institute for Information Technology HIIT: https://www.hiit.fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220439Wed, 17 Apr 2019 12:43:40 +0000DASERVille Kyrki (Professor of Electrical Engineering and Automation, Aalto University): Simulation-Based Learning for Robots Machine Learning Coffee Seminar, 1st April 2019. Machine Learning Coffee Seminar:...
Machine Learning Coffee Seminar, 1st April 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars
Finnish Center for Artificial Intelligence FCAI: https://fcai.fi
Helsinki Institute for Information Technology HIIT: https://www.hiit.fi]]>3112falsefullhttps://www.spreaker.com/episode/deploying-artificial-intelligence--74884895
Machine Learning Coffee Seminar, 8th April 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/
Helsinki Institute for Information Technology HIIT: https://www.hiit.fi/
Finnish Center for Artificial Intelligence FCAI: https://fcai.fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220449Mon, 08 Apr 2019 10:05:27 +0000DASERJussi Rintanen: Deploying Artificial Intelligence Machine Learning Coffee Seminar, 8th April 2019. Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/ Helsinki Institute for Information Technology...
Machine Learning Coffee Seminar, 8th April 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/
Helsinki Institute for Information Technology HIIT: https://www.hiit.fi/
Finnish Center for Artificial Intelligence FCAI: https://fcai.fi]]>2572falsefullhttps://www.spreaker.com/episode/fcai-kick-off-meeting-20-march-2019--74884947https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220468Thu, 21 Mar 2019 14:17:58 +0000DASER3954falsefullhttps://www.spreaker.com/episode/recent-advances-in-aalto-asr-group--74884910
Machine Learning Coffee Seminar, 11th March 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/
Helsinki Institute for Information Technology HIIT: https://www.hiit.fi/
Finnish Center for Artificial Intelligence FCAI: https://fcai.fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220451Mon, 11 Mar 2019 11:20:28 +0000DASERMikko Kurimo: Recent Advances in Aalto ASR Group Machine Learning Coffee Seminar, 11th March 2019. Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/ Helsinki Institute for Information Technology...
Machine Learning Coffee Seminar, 11th March 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/
Helsinki Institute for Information Technology HIIT: https://www.hiit.fi/
Finnish Center for Artificial Intelligence FCAI: https://fcai.fi]]>2114falsefullhttps://www.spreaker.com/episode/machine-learning-for-clinical-decision-support-how-to-make-it-work--74884956https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220480Thu, 07 Mar 2019 11:48:08 +0000DASER3027falsefullhttps://www.spreaker.com/episode/learning-from-electronic-health-records-from-temporal-abstractions-to-time-series-interpretability--74884932Learning from Electronic Health Records: from temporal abstractions to time series interpretability

Machine Learning Coffee Seminar, 25th February 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/
Helsinki Institute for Information Technology HIIT: https://www.hiit.fi/
Finnish Center for Artificial Intelligence FCAI: https://fcai.fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220419Mon, 25 Feb 2019 09:54:54 +0000DASERPanagiotis Papapetrou: Learning from Electronic Health Records: from temporal abstractions to time series interpretability Machine Learning Coffee Seminar, 25th February 2019. Machine Learning Coffee Seminar:...Learning from Electronic Health Records: from temporal abstractions to time series interpretability

Machine Learning Coffee Seminar, 25th February 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/
Helsinki Institute for Information Technology HIIT: https://www.hiit.fi/
Finnish Center for Artificial Intelligence FCAI: https://fcai.fi]]>2344falsefullhttps://www.spreaker.com/episode/can-physiological-signals-be-used-for-implicit-interaction--74884928https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220432Fri, 22 Feb 2019 08:13:57 +0000DASER2071falsefullhttps://www.spreaker.com/episode/machine-learning-for-tomography--74884942
Machine Learning Coffee Seminar, 11th February 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/
Helsinki Institute for Information Technology HIIT: https://www.hiit.fi/
Finnish Center for Artificial Intelligence FCAI: https://fcai.fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220426Mon, 11 Feb 2019 12:25:06 +0000DASERSamuli Siltanen: Machine learning for tomography Machine Learning Coffee Seminar, 11th February 2019. Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/ Helsinki Institute for Information...
Machine Learning Coffee Seminar, 11th February 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/
Helsinki Institute for Information Technology HIIT: https://www.hiit.fi/
Finnish Center for Artificial Intelligence FCAI: https://fcai.fi]]>1841falsefullhttps://www.spreaker.com/episode/machine-learning-in-cancer-research-and-oncology--74884939
Machine Learning Coffee Seminar, 14th January 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/
Helsinki Institute for Information Technology HIIT: https://www.hiit.fi/
Finnish Center for Artificial Intelligence FCAI: https://fcai.fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220475Mon, 14 Jan 2019 11:58:37 +0000DASERSampsa Hautaniemi: Machine Learning in Cancer Research and Oncology Machine Learning Coffee Seminar, 14th January 2019. Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/ Helsinki Institute for...
Machine Learning Coffee Seminar, 14th January 2019.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/
Helsinki Institute for Information Technology HIIT: https://www.hiit.fi/
Finnish Center for Artificial Intelligence FCAI: https://fcai.fi]]>1974falsefullhttps://www.spreaker.com/episode/something-old-something-new-something-borrowed-something-blue--74884920https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220469Sun, 23 Dec 2018 11:43:42 +0000DASER2781falsefullhttps://www.spreaker.com/episode/exploring-large-and-hierarchical-online-discussion-venues-with-probabilistic-models--74884940https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220462Sat, 15 Dec 2018 22:39:29 +0000DASER2418falsefullhttps://www.spreaker.com/episode/towards-emotion-ai--74884953
Machine Learning Coffee Seminar, 10th December 2018.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/
Helsinki Institute for Information Technology HIIT: https://www.hiit.fi/
Finnish Center for Artificial Intelligence FCAI: https://fcai.fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220454Mon, 10 Dec 2018 10:00:21 +0000DASERGuoying Zhao: Towards Emotion AI Machine Learning Coffee Seminar, 10th December 2018. Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/ Helsinki Institute for Information Technology HIIT:...
Machine Learning Coffee Seminar, 10th December 2018.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/
Helsinki Institute for Information Technology HIIT: https://www.hiit.fi/
Finnish Center for Artificial Intelligence FCAI: https://fcai.fi]]>2895falsefullhttps://www.spreaker.com/episode/high-dimensional-covariance-matrix-estimation-with-applications-in-finance-and-genomic-studies--74884960https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220423Thu, 22 Nov 2018 08:22:09 +0000DASER2334falsefullhttps://www.spreaker.com/episode/natural-language-inference-with-multilingual-supervision--74884963https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220483Tue, 20 Nov 2018 08:18:46 +0000DASER2814falsefullhttps://www.spreaker.com/episode/noise2noise-learning-image-restoration-without-clean-data--74884949https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220482Tue, 20 Nov 2018 07:27:11 +0000DASER2752falsefullhttps://www.spreaker.com/episode/satellite-remote-sensing-and-the-potential-of-machine-learning--74884961
Machine Learning Coffee Seminar, 5th November 2018.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/
Helsinki Institute for Information Technology HIIT: https://www.hiit.fi/
Finnish Center for Artificial Intelligence FCAI: https://fcai.fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220478Mon, 05 Nov 2018 10:03:35 +0000DASERJohanna Tamminen: Satellite remote sensing and the potential of machine learning Machine Learning Coffee Seminar, 5th November 2018. Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/ Helsinki...
Machine Learning Coffee Seminar, 5th November 2018.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/
Helsinki Institute for Information Technology HIIT: https://www.hiit.fi/
Finnish Center for Artificial Intelligence FCAI: https://fcai.fi]]>2476falsefullhttps://www.spreaker.com/episode/human-guided-data-exploration--74884945
Machine Learning Coffee Seminar, 22nd October 2018.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/
Helsinki Institute for Information Technology HIIT: https://www.hiit.fi/
Finnish Center for Artificial Intelligence FCAI: https://fcai.fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220430Thu, 01 Nov 2018 11:04:07 +0000DASERKai Puolamäki: Human-Guided Data Exploration Machine Learning Coffee Seminar, 22nd October 2018. Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/ Helsinki Institute for Information Technology...
Machine Learning Coffee Seminar, 22nd October 2018.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/
Helsinki Institute for Information Technology HIIT: https://www.hiit.fi/
Finnish Center for Artificial Intelligence FCAI: https://fcai.fi]]>2724falsefullhttps://www.spreaker.com/episode/machine-learning-for-data-management-vice-versa--74884948
Machine Learning Coffee Seminar, 24th September 2018.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/
Helsinki Institute for Information Technology HIIT: https://www.hiit.fi/
Finnish Center for Artificial Intelligence FCAI: https://fcai.fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220420Thu, 01 Nov 2018 09:59:09 +0000DASERMichael Mathioudakis: Machine Learning for Data Management & vice-versa Machine Learning Coffee Seminar, 24th September 2018. Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/ Helsinki...
Machine Learning Coffee Seminar, 24th September 2018.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/
Helsinki Institute for Information Technology HIIT: https://www.hiit.fi/
Finnish Center for Artificial Intelligence FCAI: https://fcai.fi]]>2847falsefullhttps://www.spreaker.com/episode/infinitely-deep-models-with-continuous-time-flows--74884959
Machine Learning Coffee Seminar, 10th September 2018.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/
Helsinki Institute for Information Technology HIIT: https://www.hiit.fi/
Finnish Center for Artificial Intelligence FCAI: https://fcai.fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220452Wed, 31 Oct 2018 11:06:30 +0000DASERMarkus Heinonen: Infinitely deep models with continuous-time flows Machine Learning Coffee Seminar, 10th September 2018. Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/ Helsinki Institute for...
Machine Learning Coffee Seminar, 10th September 2018.

Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/
Helsinki Institute for Information Technology HIIT: https://www.hiit.fi/
Finnish Center for Artificial Intelligence FCAI: https://fcai.fi]]>2622falsefullhttps://www.spreaker.com/episode/the-power-of-gaussian-processes-magnetic-localisation-and-mapping--74884936https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220470Wed, 31 Oct 2018 06:16:27 +0000DASERThis talk goes through how to encode knowledge from high-school physics into a GP model for the ambient magnetic field (observed by a smartphone compass).2200falsefullhttps://www.spreaker.com/episode/progressive-growing-of-gans-for-improved-quality-stability-and-variation--74884917
Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/
Helsinki Institute for Information Technology HIIT: https://www.hiit.fi/
Finnish Center for Artificial Intelligence FCAI: https://fcai.fi]]>https://app.pigeonpod.cloud/feed/2095303696467365890/episode/2095303796076220457Fri, 26 Oct 2018 07:23:19 +0000DASERTero Karras presenting a new training methodology for generative adversarial networks where the key idea is to grow both the generator and discriminator progressively. Machine Learning Coffee Seminar:...
Machine Learning Coffee Seminar: https://www.hiit.fi/news-and-events/machine-learning-coffee-seminars/
Helsinki Institute for Information Technology HIIT: https://www.hiit.fi/
Finnish Center for Artificial Intelligence FCAI: https://fcai.fi]]>2191falsefull