Exploring the Capabilities and Limitations of Language Models in Complex Tasks

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This chapter delves into the capabilities and limitations of language models, focusing on how they can generate detailed plans for high-level tasks like traveling but struggle with low-level physical actions. The conversation highlights the need for joint embedding spaces in robotics for interacting with the physical world and emphasizes the importance of learned plans in human actions.

Yann LeCun is the Chief AI Scientist at Meta, professor at NYU, Turing Award winner, and one of the most influential researchers in the history of AI. Please support this podcast by checking out our sponsors:
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OUTLINE:
Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time.
(00:00) – Introduction
(09:10) – Limits of LLMs
(20:47) – Bilingualism and thinking
(24:39) – Video prediction
(31:59) – JEPA (Joint-Embedding Predictive Architecture)
(35:08) – JEPA vs LLMs
(44:24) – DINO and I-JEPA
(45:44) – V-JEPA
(51:15) – Hierarchical planning
(57:33) – Autoregressive LLMs
(1:12:59) – AI hallucination
(1:18:23) – Reasoning in AI
(1:35:55) – Reinforcement learning
(1:41:02) – Woke AI
(1:50:41) – Open source
(1:54:19) – AI and ideology
(1:56:50) – Marc Andreesen
(2:04:49) – Llama 3
(2:11:13) – AGI
(2:15:41) – AI doomers
(2:31:31) – Joscha Bach
(2:35:44) – Humanoid robots
(2:44:52) – Hope for the future