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Yann Lecun: Meta AI, Open Source, Limits of LLMs, AGI & the Future of AI | Lex Fridman Podcast #416

Lex Fridman

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YouTube
  • Recommended by 1 person: 1 on Hacker News.
  • Presented by Yann LeCun, Chief AI Scientist at Meta
  • An opinion. It argues one side of a question engineers disagree on.

Intermediate. Assumes working knowledge.

1.2M views2 years ago1 recommendation

AI and machine learningTransformers and LLMsNeural networks and deep learningAI safety and impact

Yann LeCun discusses the limitations of current large language models, the importance of open-source AI, and alternative paths toward artificial general intelligence.

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: - HiddenLayer: hiddenlayer.com/lex - LMNT: drinkLMNT.com/lex to get free sample pack - Shopify: shopify.com/lex to get $1 per month trial - AG1: drinkag1.com/lex to get 1 month supply of fish oil TRANSCRIPT: lexfridman.com/yann-lecun-3-transcript EPISODE LINKS: Yann's Twitter: twitter.com/ylecun Yann's Facebook: facebook.com/yann.lecun Meta AI: ai.meta.com/ PODCAST INFO: Podcast website: lexfridman.com/podcast Apple Podcasts: apple.co/2lwqZIr Spotify: open.spotify.com/episode/0bXyQJAylGg7E5mZnyHhNb RSS: lexfridman.com/feed/podcast/ Full episodes playlist: youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 Clips playlist: youtube.com/playlist?list=PLrAXtmErZgOeciFP3CBCIEElOJeitOr41 OUTLINE: - Introduction - Limits of LLMs - Bilingualism and thinking - Video prediction - JEPA (Joint-Embedding Predictive Architecture) - JEPA vs LLMs - DINO and I-JEPA - V-JEPA - Hierarchical planning - Autoregressive LLMs - AI hallucination - Reasoning in AI - Reinforcement learning - Woke AI - Open source - AI and ideology - Marc Andreesen - Llama 3 - AGI - AI doomers - Joscha Bach - Humanoid robots - Hope for the future SOCIAL: - Twitter: twitter.com/lexfridman - LinkedIn: linkedin.com/in/lexfridman - Facebook: facebook.com/lexfridman - Instagram: instagram.com/lexfridman - Medium: medium.com/@lexfridman - Reddit: reddit.com/r/lexfridman - Support on Patreon: patreon.com/lexfridman

Why it's here

1 person recommends this

What people say. Recommended for Yann LeCun's skeptical perspective on LLMs and his calculations regarding the data density of human sensory experience versus text.

jtietemaon Hacker News • Recommends

On “Ask HN: Anyone working on something better than LLMs?”

I think you might find Lex Fridmans interview with Yann LeCun interesting1. It discusses exactly this, how LLMs just mimmick intelligent behaviour but have no understanding of the world at all. It also discusses other approaches we should look at instead of current LLMs. 1 link to this video
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hn_ackeron Hacker News • Mentions

On “Physical Intelligence is building a brain for robots”

> A 4 year old child has 16k wake hours x 3600 s/hour x 1^6 optical nerve fibers x 2 eyes x 10 bytes/s = 1^15 bytes (approximation by Yann LeCun). Unfortunate typo. You meant 10^15 bytes at the end. Thanks to your citation I was able to find a podcast transcript 1 with Yann LeCun's explanation: > If you talk to developmental psychologists and they tell you a four-year-old has been awake for 16,000 hours in his or her life, and the amount of information that has reached the visual cortex of that child in four years is about 10 to 15 bytes. The transcript is missing "the" (10 to the 15 bytes). The corresponding timestamp in the podcast on YouTube is 4:48. 1 link 2 link to this video
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jiggawattson Hacker News • Mentions

On ““Meta spent almost as much as the Manhattan Project on GPUs in today's dollars””

These are all facts coming from publicly available information, such as the Llama 3 paper and recent interviews. I'd dig up the specific timestamps, but I'm working at the moment and can't spend the next hour scrolling through long interviews. The interviews are: "Mark Zuckerberg - Llama 3, $10B Models, Caesar Augustus, & 1 GW Datacenters": link "Yann Lecun: Meta AI, Open Source, Limits of LLMs, AGI & the Future of AI | Lex Fridman Podcast #416 ": link to this video The total FLOPS used in training is in bunch of places, I can't find a good link at the moment.
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TaylorAlexanderon Hacker News • Mentions

On “Nvidia is about to pass Apple in market cap”

The Unitree is what made me think we are headed for a bubble. The machines themselves will become commodity hardware. Tesla bot will push the hype side. I just don’t believe current AI systems have what it takes to operate in the real world. It doesn’t have to be AGI at all, but it needs a bunch of capabilities not present in existing models, some of which cannot be created by training on large corpuses from the internet like text and images. 1 Also people can’t afford to spend $20k on a robot just to do their dishes. My good friend tried to make a robot that could pick up dog poop from your back yard, and he went to HAX accelerator and everything, and through extensive research he found t…
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zero-sharpon Hacker News • Mentions

On “Apple Study Reveals Critical Flaws in AI's Logical Reasoning Abilities”

>The findings reveal that even slight changes in the phrasing of questions can cause major discrepancies in model performance that can undermine their reliability in scenarios requiring logical consistency. Relevant conversation with Yann Lecun: link to this video
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