Learning Through Observation Over Language

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Large language models accumulate vast knowledge through training on a massive amount of text from the internet. However, the data they are trained on is relatively small compared to the information a four-year-old child absorbs through sensory inputs. Researchers estimate that a child receives about 10 to the 15 bytes of information through the visual cortex in four years of life, significantly more than the 10 to the 13 bytes of training data for language models. This demonstrates that observation and interaction with the real world play a more significant role in learning and knowledge acquisition than language. The insight emphasizes that a considerable portion of what we learn, especially in early development, and even in animals, is not language-based, but rather acquired through sensory experiences and real-world interactions.

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