Fireside chat: Opportunities and challenges in human-oriented AI
- Ashley Llorens, Katja Hofmann, Siddhartha Sen | Microsoft Research Redmond, Microsoft Research Cambridge, Microsoft Research NYC
- Microsoft Research Summit 2021 | Reinforcement Learning
A key challenge in developing novel AI technology is to ensure that resulting approaches and their applications fit well within the human environments they will be applied in. Recent research at Microsoft develops new approaches and insights into how AI techniques like machine learning and reinforcement learning can model more human-like AI behavior and provide new insights, experiences, and learning opportunities in game settings. In this fireside chat, Principal Researcher Siddhartha Sen (Microsoft Research NYC) and Senior Principal Researcher Katja Hofmann (Microsoft Research Cambridge) will discuss how their teams’ respective work on Maia Chess and Project Paidia pave the way for more human-aware AI—and where they see exciting opportunities and key challenges on the road to human-compatible AI.
Learn more about the 2021 Microsoft Research Summit: https://Aka.ms/researchsummit (opens in new tab)
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Ashley Llorens
Corporate Vice President and Managing Director, Microsoft Research
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Katja Hofmann
Senior Principal Research Manager and lead of the Microsoft Research Game Intelligence team
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Siddhartha Sen
Principal Researcher
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Reinforcement Learning
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Opening remarks: Reinforcement Learning
- Katja Hofmann
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Research talk: Evaluating human-like navigation in 3D video games
- Raluca Georgescu,
- Ida Momennejad
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Research talk: Maia Chess: A human-like neural network chess engine
- Reid McIlroy-Young
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Fireside chat: Opportunities and challenges in human-oriented AI
- Ashley Llorens,
- Katja Hofmann,
- Siddhartha Sen
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Research talk: Making deep reinforcement learning industrially applicable
- Jiang Bian,
- Tie-Yan Liu
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Panel: Generalization in reinforcement learning
- Mingfei Sun,
- Roberta Raileanu,
- Wendelin Böhmer
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Research talk: Project Dexter: Machine learning and automatic decision-making for robotic manipulation
- Andrey Kolobov,
- Ching-An Cheng
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Research talk: Breaking the deadly triad with a target network
- Shangtong Zhang
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Panel: The future of reinforcement learning
- Geoff Gordon,
- Emma Brunskill,
- Craig Boutilier
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Closing remarks: Reinforcement Learning
- John Langford
