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83. Studying Ants Emergent Behavior to Advance AI, Robotics and Even Neuroscience | Deborah Gordon

83. Studying Ants Emergent Behavior to Advance AI, Robotics and Even Neuroscience | Deborah Gordon
Feb 11, 2019 · 48m 47s

Deborah Gordon is a Professor of Biology at Stanford University studying the evolution of collective behavior using ant colonies as a model. Her research focuses on how ant colonies regulate...

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Deborah Gordon is a Professor of Biology at Stanford University studying the evolution of collective behavior using ant colonies as a model. Her research focuses on how ant colonies regulate their behavior in response to environmental changes, and how collective behavior is related to ecology. This work is highly relevant given the nature of swarms/emergent behavior and possibly holds valuable insights into other species or humanity relating to current changes in climate.

She is also a partner in the “Ants in Space” project designed to learn more about how ants search and coordinate, information that may be useful in designing robot search algorithms and possibly colonizing extraterrestrial bodies like Mars, the Moon or other planetary objects.

Deborah's the author of Ants at Work and has given two well-received talks at TED in 2003 and 2014 on the ingenious intelligence of ants and swarms which each received 1M+ views to date





You can listen right here on iTunes

In our wide-ranging conversation, we cover many things, including:

* The emergent intelligence of ants and what humanity can learn from it
* Why intelligence isn't always clean and is usually simple
* How ant colonies help AI researchers program robots
* What we can learn from ants about expansion into space
* Why ants' utilitarian society is so effective yet also inefficient
* The system of task allocation that allows all ants to contribute
* How to solve complex problems with simple incentives
* Deborah's thoughts on blockchain and decentralization
* How researchers use ants to think about neuroscience and the brain
* The we're headed towards a utilitarian system and why it may not work
* Why we're probably going about building AGI all wrong





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