Hierarchical Planning in AI

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Hierarchical planning in AI involves breaking down tasks into sub-goals, like going down a street involves multiple steps such as getting up from a chair, opening the door, and pushing the elevator button. Planning at the millisecond level is impractical since it requires knowing all conditions down to the smallest details. Effective planning requires hierarchical structuring to adapt and replan as conditions change, something that remains a challenge in AI as training systems for various levels of representation is complex and not yet fully understood.

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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Transcript: https://lexfridman.com/yann-lecun-3-transcript

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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