Ian Osband
TalkRL: The Reinforcement Learning Podcast
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Exploring Uncertainty Frameworks in LLMs
The chapter delves into the application of a framework for uncertainty in Large Language Models (LLMs) and the benefits of efficient exploration in LLMs, particularly in scenarios involving reinforcement learning. The speakers discuss the development of a framework for addressing epistemic uncertainty in machine learning and propose innovative network architectures like EpiNet for improved performance. They also share future plans centered around artificial intelligence, uncertainty, and learning prioritization in AI systems, aiming to push the boundaries of AI and address AI safety concerns.
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