Research talk: Maia Chess: A human-like neural network chess engine

Channel:
Microsoft Research
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345,000
Published on ● Video Link: https://www.youtube.com/watch?v=NkwN6bK_9p0


Duration: 10:43
1,896 views
62

Speaker: Reid McIlroy-Young, PhD Student, University of Toronto

Even when machine learning surpasses human ability in a domain, there are many reasons why AI systems that capture human-like behavior would be desirable. For example, humans may want to learn and collaborate, or humans may need to interact with independent AI agents. A first step in aligning AI agents’ behavior to that of humans is creating agents that better understand human behavior. University of Toronto PhD student Reid McIlroy-Young will present his work, done in collaboration with Microsoft Research, building neural chess engines that can predict human behavior at different skill levels. Furthermore, these engines can be calibrated to target the decisions of specific players via fine-tuning. He will first discuss the value of studying the intersection of human and AI, and the results. He will also discuss where reinforcement learning was outperformed by a classification approach and conclude with a look at the benefits of having research that can be understood by a wide audience.

Learn more about the 2021 Microsoft Research Summit: https://Aka.ms/researchsummit



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Tags:
reward-based learning
reinforcement learning
innovation in artificial environments
accelerate AI
microsoft research summit