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Frame from Yann Lecun: Meta AI, Open Source, Limits of LLMs, AGI & the Future of AI | Lex Fridman Podcast #416 Plays via YouTube's privacy-enhanced embed

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

48 videos recommended on HN
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1 HN user recommended this podcast episode1 month2024 score 1
Years with an HN recommendation, 2007–2026

Cited for LeCun's views on LLM limits and alternative approaches to intelligence.

Lex Fridman's long podcast interview with Yann LeCun on Meta AI, open source, the limits of LLMs, AGI and the future of AI.

“I think you might find Lex Fridmans interview with Yann LeCun interesting[1].” (HN, 2024)

1 recommendations · 4 citations · Podcasts · intermediate · partly dated

5 Hacker News comments

From the HN archive; up to 20 kept per video, favoring different commenters. Recommendations first.

@jtietema May 2024 Recommends
on HN thread Ask HN: Anyone working on something better than LLMs?
I think you might find Lex Fridmans interview with Yann LeCun interesting[1]. 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] [this video]
LeCun on LLM limits and alternative approaches.

Citations, criticism and jokes

@hn_acker Mar 2024 Cites it
on HN thread Physical Intelligence is building a brain for robots
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> 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] [this video]
LeCun's estimate of visual data a child receives.
@jiggawatts May 2024 Cites it
on HN thread “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 ": [this video] The total FLOPS used in training is in bunch of places, I can't find a good link at the moment.
Source for Meta's training compute facts.
@TaylorAlexander May 2024 Cites it
on HN thread Nvidia is about to pass Apple in market cap
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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 that the addressable market for dog poop robots is just too small to fund a company. Now he makes farming robots. (I do too as it happens, on a separate project.) The point is a humanoid robot worth the price of that robot needs to be very useful, and I don’t believe current approaches can get us there. Maybe some uses will be found, just as current AI systems have their uses, but I say it will be a hype bubble because companies will fund raise on massive promises they will never achieve, and investment will move on to something else after 5 years or so. Could still be robotics, but I actually think modular purpose built machines make way more sense than general purpose humanoids. What if instead of a humanoid to load the dishwasher you had a dishwasher that cleaned dishes one at a time. Yo
LeCun on limits of current AI for real-world operation.

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