Teasing Out Symbolic Knowledge
Subbarao discusses the utilization of large language models (LLMs) to extract symbolic knowledge for guiding deep reinforcement learning systems. By incorporating human input to refine the extracted knowledge, a more efficient and effective reasoning process emerges, bridging the gap between symbolic and sub-symbolic reasoning in AI.In this clip
From this podcast
The Gradient
Subbarao Kambhampati: Planning, Reasoning, and Interpretability in the Age of LLMs
Related Questions
What is symbolic reasoning in the context of the episode "Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15" and the clip "Knowledge Representation Challenges"?
What is symbolic reasoning in the context of the episode Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 and the clip Knowledge Representation Challenges?
What is symbolic reasoning as discussed in the episode Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 and the clip Knowledge Representation Challenges?