111–120 of 680 posts
Fi Fi Lee also recently founded a new AI startup called World Labs, which focus on creating AI world models with spatial intelligence to understand and interact with the 3D world, unlike current LLM AI that primarily processes 2D images and text. Almost exactly the same focus as Yann LeCun's new venture stated in the parent article.
*Fei-Fei Li
Change my mind, Facebook was never invented by Zuck's genius All he's been responsible for is making it worse
Social network wasn't even novel at the inception of FB. MySpace, Friendster, and Hi5 were already popular with millions of users.
Zuck operated it well and was able to grow it from 0 to what it is today. That is what matters.
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That was obviously him getting sidelined. And it's easy to see why. LLMs get results. None of the Yann LeCun's pet projects do. He had ample time to prove that his approach is promising, and he didn't.
LLMs get results is quite the bold statement. If they get results, they should be getting adopted, and they should be making money. This is all built on hazy promises. If you had marketable results, you wouldn't have to hide 20+ billion dollars of debt financing into an obscure SPV. LLMs are the most baffling piece of tech. They are incredible, and yet marred by their non-deterministic hallucinatory nature, and bound…
Frontier models are all profitable. Inference is sold with a damn good margin, and the amounts of inference AI companies sell keeps rising. This necessitates putting more and more money into infrastructure. AI R&D is extremely expensive too, and this necessitates even more spending.
A mistake I see people make over and over again is keeping track of the spending but overlooking the revenue altogether. Which sure is weird: you don't get from $0B in revenue to $12B in revenue in a few years by not having a product anyone wants to buy.
And I find all the talk of "non-deterministic hallucinatory nature" to be overrated. Because humans suffer from all of that too, just less severely. On top of a number of other issues current AIs don't suffer from.
Nonetheless, we use human labor for things. All AI has to do is provide a "good enough" alternative, and it often does.
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He also said other things about LLMs that turned out to be either wrong or easily bypassed with some glue. While I understand where he comes from, and that his stance is pure research-y theory driven, at the end of the day his positions were wrong. Previously, he very publicly and strongly said: a) LLMs can't do math. They trick us in poetry but that's subjective. They can't do objective math. b) they can't plan c) b…
a) Still true: vanilla LLMs can’t do math, they pattern-match unless you bolt on tools. b) Still true: next-token prediction isn’t planning. c) Still true: error accumulation is mitigated, not eliminated. Long-context quality still relies on retrieval, checks, and verifiers. Yann’s claims were about LLMs as LLMs. With tooling, you can work around limits, but the core point stands.
b) reductionism isn't worth our time. Planning works in the real world, today. (try any agentic tool like cc/codex/whatever). And if you're set on the purist view, there's mounting evidence from anthropic that there is planning in the core of an LLM.
c) so ... not true? Long context works today.
This is simply moving goalposts and nothing more. X can't do Y -> well, here they are doing Y -> well, not like that.
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I politely disagree - it is exactly an industry researcher's purpose to do the risky things that may not work, simply because the rest of the corporation cannot take such risks but must walk on more well-trodden paths. Corporate R&D teams are there to absorb risk, innovate, disrupt, create new fields, not for doing small incremental improvements. "If we know it works, it's not research." (Albert Einstein) I also agre…
> it is exactly a researcher's purpose to do the risky things that may not work Maybe at university, but not at a trillion dollar company. That job as chief scientist is leading risky things that will work to please the shareholders.
Also, like… it’s Facebook. It has a history of ploughing billions into complete nonsense (see metaverse). It is clearly not particularly risk averse.
What the hell does Mark see in Wang? Wang was born into a family whose parents got Chinese government scholarships to study abroad but secretly stayed in the US, and then the guy turns super anti-China. From any angle, this dude just doesn't seem reliable at all.
> Wang was born into a family whose parents got Chinese government scholarships to study abroad but secretly stayed in the US, and then the guy turns super anti-China. All I'm hearing is he's a smart guy from a smart family?
They tend to get incredibly offended when they see anyone who doesn't toe the Party's line - let alone believe that the Chinese government is untrustworthy and evil.
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I would pose a question differently, under his leadership did Meta achieve good outcome? If the answer is yes, then better to keep him, because he has already proved himself and you can win in the long-term. With Meta's pockets, you can always create a new department specifically for short-term projects. If the answer is no, then nothing to discuss here.
Meta did exactly that, kept him but reduced his scope. Did the broader research community benefit from his research? Absolutely. But did Meta achieve a good outcome? Probably not. If you follow LeCun on social media, you can see that the way FAIR’s results are assessed is very narrow-minded and still follows the academic mindset. He mentioned that his research is evaluated by: "Research evaluation is a difficult task…
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That guy has survived the AI winter. He can wait 10 years for yet another breakthrough. [but the market can’t] https://en.wikipedia.org/wiki/AI_winter
We're at most in an "AI Autumn" right now. The real Winter is yet to come.
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LeCun always said that LLMs do not lead to AGI.
Can anyone explain to me the non-$$ logic for one working towards AGI, aside from misanthropy? The only other thing I can imagine is not very charitable: intellectual greed. It can't just be that, can it? I genuinely don't understand. I would love to be educated.
https://www.smbc-comics.com/?id=2088
It's true. When it comes to the people doing bleeding edge research and development, the answer often is "BECAUSE IT'S FUCKING AWESOME". Regardless of what they tell the corporate higher-ups or put on the grant application statements.
Sure, a lot of people believe that AGI is going to make the world a better place. But "mad scientist" is a stereotype for a reason. You look into their eyes and you see the flame of madness flickering behind them.