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Studying for Collaboration, Not Competitors
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Jakob Foerster an accredited Machine Studying Analysis Scientist who has been on the forefront of analysis on Multi-Agent Studying speaks with interviewer Kegan Strawn.
Dr. Foerster explains why incorporating uncertainty into multi-agent interactions is crucial to creating strong algorithms that may function not solely in video games however in real-world functions.
Jakob Foerster
Jakob Foerster is an Affiliate Professor on the College of Oxford. His papers have gained prestigious awards at prime machine studying conferences (ICML, AAAI) and have helped push deep multi-agent reinforcement studying to the forefront of AI analysis.
Jakob beforehand labored at Fb AI Analysis and acquired his Ph.D. from the College of Oxford underneath the supervision of Shimon Whiteson. Throughout his Ph.D., Jakob additionally interned at Google Mind, OpenAI, and DeepMind.
Jakob’s analysis pursuits span Deep Multi-Agent Reinforcement Studying, Human-AI Coordination, Emergent Communication, Search, Planning, and Sport Principle.
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tags: Algorithm AI-Cognition, Synthetic Intelligence, c-Analysis-Innovation, cx-Politics-Legislation-Society, cx-Analysis-Innovation, human-robot interplay, podcast, reinforcement studying, Analysis
Kegan Strawn
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