Today we’re joined by Kamyar Azizzadenesheli, a staff researcher at Nvidia, to continue our AI Trends 2024 series. In our conversation, Kamyar updates us on the latest developments in reinforcement learning (RL), and how the RL community is taking advantage of the abstract reasoning abilities of large language models (LLMs). Kamyar shares his insights on how LLMs are pushing RL performance forward in a variety of applications, such as ALOHA, a robot that can learn to fold clothes, and Voyager, an RL agent that uses GPT-4 to outperform prior systems at playing Minecraft. We also explore the progress being made in assessing and addressing the risks of RL-based decision-making in domains such as finance, healthcare, and agriculture. Finally, we discuss the future of deep reinforcement learning, Kamyar’s top predictions for the field, and how greater compute capabilities will be critical in achieving general intelligence.

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📖 CHAPTERS
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00:00 - Introduction
02:24 - How LLMs have changed RL
18:36 - Voyager paper & Minecraft
22:08 - World models
25:27 - LLMs in robotics
28:16 - RL vs explicit control algorithms
35:19 - ALOHA and RLHF robots
41:51 - Assessing the risks in RL agents
51:22 - The future of RL & AI
01:04:39 - Solving generality & narrow AI
01:19:16 - Is hardware ready for AGI?
01:23:36 - Conclusion


🔗 LINKS & RESOURCES
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Neural Lander: Stable Drone Landing Control Using Learned Dynamics - https://arxiv.org/pdf/1811.08027
Mobile ALOHA: Your Housekeeping Robot -    • Mobile ALOHA: Your Housekeeping Robot  
Voyager: An Open-Ended Embodied Agent with Large Language Models -
https://arxiv.org/abs/2305.16291
Mastering Diverse Domains through World Models - https://arxiv.org/abs/2301.04104
AI Rewind 2021: Trends in Reinforcement Learning with Kamyar Azizzadenesheli - 560 - https://twimlai.com/podcast/twimlai/a...


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