Challenges of Large Language Models and Prompting AI Systems

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Exploring the limitations of large language models in generating accurate responses and the challenges of fine-tuning models to cover diverse prompts. Discusses the impact of slight prompt modifications on AI output and the need for models to evolve beyond language processing to reasoning abilities.

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