Unavoidable Bias in AI Systems

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AI systems reflect biases in society due to training data, debiasing techniques may also offend some; producing a completely unbiased AI system is impossible as bias is subjective. Just like in liberal democracy, diversity is crucial for AI systems to prevent bias and encourage progress. The future involves AI-mediated interactions with the digital world, emphasizing the need for diverse AI sources to avoid monopolization by a few companies.

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