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Meta’s star AI scientist Yann LeCun plans to leave for own startup (arstechnica.com)
submitted 3 months ago by avidamoeba@lemmy.ca to c/technology@lemmy.world
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[–] tal@lemmy.today 0 points 3 months ago* (last edited 3 months ago) (1 child)

Meta’s chief AI scientist and Turing Award winner Yann LeCun plans to leave the company to launch his own startup focused on a different type of AI called “world models,” the Financial Times reported.

World models are hypothetical AI systems that some AI engineers expect to develop an internal “understanding” of the physical world by learning from video and spatial data rather than text alone.

Sounds reasonable.

That being said, I am willing to believe that an LLM could be part of an AGI. It might well be an efficient way to incorporate a lot of knowledge about the world. Wikipedia helps provide me with a lot of knowledge, for example, though I don't have a direct brain link to it. It's just that I don't expect an AGI to be an LLM.

EDIT: Also, IIRC from past reading, Meta has separate groups aimed at near-term commercial products (and I can very much believe that there might be plenty of room for LLMs here) and aimed advanced AI. It's not clear to me from the article whether he just wants more focus on advanced AI or whether he disagrees with an LLM focus in their afvanced AI group.

I do think that if you're a company building a lot of parallel compute capacity now, that to make a return on that, you need to take advantage of existing or quite near-future stuff, even if it's not AGI. Doesn't make sense to build a lot of compute capacity, then spend fifteen years banging on research before you have something to utilize that capacity.

https://datacentremagazine.com/news/why-is-meta-investing-600bn-in-ai-data-centres

Meta reveals US$600bn plan to build AI data centres, expand energy projects and fund local programmes through 2028

So Meta probably cannot only be doing AGI work.

[–] just_another_person@lemmy.world 0 points 3 months ago (1 child)

LLMs are just fast sorting and probability, they have no way to ever develop novel ideas or comprehension.

The system he's talking about is more about using NNL, which builds new relationships to things that persist. It's deferential relationship learning and data path building. Doesn't exist yet, so if he has some ideas, it may be interesting. Also more likely to be the thing that kills all human.

[–] communist@lemmy.frozeninferno.xyz 0 points 3 months ago (1 child)
[–] just_another_person@lemmy.world 0 points 3 months ago* (last edited 3 months ago) (1 child)

Lol 🤣 I'm SO EMBARRASSED. You're totally right and understand these things better than me after reading a GOOGLE BLOG ABOUT THEIR PRODUCT.

I'll never speak to this topic again since I've clearly been bested with your knowledge from a Google Blog.

[–] communist@lemmy.frozeninferno.xyz 0 points 3 months ago* (last edited 3 months ago) (1 child)

yes, google reported about their ai discovering a novel cancer treatment, of course they did?

now tell me about how it isn't true. Do you have anything of substance to discredit this?

this reeks of confirmation bias, did you even try to invalidate your preconcieved notions?

[–] just_another_person@lemmy.world 0 points 3 months ago* (last edited 3 months ago) (2 children)

I sure do. Knowledge, and being in the space for a decade.

Here's a fun one: go ask your LLM why it can't create novel ideas, it'll tell you right away 🤣🤣🤣🤣

LLMs have ZERO intentional logic that allow it to even comprehend an idea, let alone craft a new one and create relationships between others.

I can already tell from your tone you're mostly driven by bullshit PR hype from people like Sam Altman , and are an "AI" fanboy, so I won't waste my time arguing with you. You're in love with human-made logic loops and datasets, bruh. There is not now, nor was there ever, a way for any of it to become some supreme being of ideas and knowledge as you've been pitched. It's super fast sorting from static data. That's it.

You're drunk on Kool-Aid, kiddo.

[–] communist@lemmy.frozeninferno.xyz 0 points 3 months ago* (last edited 3 months ago) (1 child)

You sound drunk on kool-aid, this is a validated scientific report from yale, tell me a problem with the methodology or anything of substance.

so what if that's how it works? It clearly is capable of novel things.

[–] just_another_person@lemmy.world 0 points 3 months ago* (last edited 3 months ago) (2 children)

🤦🤦🤦 No...it really isn't:

Teams at Yale are now exploring the mechanism uncovered here and testing additional AI-generated predictions in other immune contexts.

Not only is there no validation, they have only begun even looking at it.

Again: LLMs can't make novel ideas. This is PR, and because you're unfamiliar with how any of it works, you assume MAGIC.

Like every other bullshit PR release of it's kind, this is simply a model being fed a ton of data and running through thousands of millions of iterative segments testing outcomes of various combinations of things that would take humans years to do. It's not that it is intelligent or making "discoveries", it's just moving really fast.

You feed it 10^2^ combinations of amino acids, and it's eventually going to find new chains needed for protein folding. The thing you're missing there is:

  1. all the logic programmed by humans
  2. The data collected and sanitized by humans
  3. The task groups set by humans
  4. The output validated by humans

It's a tool for moving fast though data, a.k.a. A REALLY FAST SORTING MECHANISM

Nothing at any stage if developed, is novel output, or validated by any models, because...they can't do that.

[–] BrundleFly2077@sh.itjust.works 0 points 3 months ago (1 child)

Wow, if you really do know something about this subject, you’re being a real asshole about it 🙄

[–] communist@lemmy.frozeninferno.xyz 0 points 3 months ago (1 child)

He knows the basics, it's just that they don't lead to any of the conclusions he's claiming they do. He also boldly assumes that everyone who disagrees with him doesn't know anything. He's a beast of confirmation bias.

[–] just_another_person@lemmy.world 0 points 3 months ago (1 child)

Nah, I'm just not going to write a novel on Lemmy, ma dude.

I'm not even spouting anything that's not readily available information anyway. This is all well known, hence everybody calling out the bubble.

[–] communist@lemmy.frozeninferno.xyz 0 points 3 months ago* (last edited 3 months ago) (1 child)

You have not said one thing i did not already know, none of it has to do with anything

an ai did something novel, this is an easily verified fact. The only alternative is that somebody else wrote the hypothesis.

[–] just_another_person@lemmy.world 0 points 3 months ago (1 child)

It most certainly did not...because it can't.

You find me a model that can take multiple disparate pieces of information and combine them into a new idea not fed with a pre-selected pattern, and I'll eat my hat. The very basis of how these models operates is in complete opposition of you thinking it can spontaneously have a new and novel idea. New...that's what novel means.

I can pointlessly link you to papers, blogs from researchers explaining, or just asking one of these things for yourself, but you're not going to listen, which is on you for intentionally deciding to remain ignorant to how they function.

Here's Terrence Kim describing how they set it up using GRPO: https://www.terrencekim.net/2025/10/scaling-llms-for-next-generation-single.html

And then another researcher describing what actually took place: https://joshuaberkowitz.us/blog/news-1/googles-cell2sentence-c2s-scale-27b-ai-is-accelerating-cancer-therapy-discovery-1498

So you can obviously see...not novel ideation. They fed it a bunch of trained data, and it correctly used the different pattern alignment to say "If it works this way otherwise, it should work this way with this example."

Sure, it's not something humans had gotten to get, but that's the entire point of the tool. Good for the progress, certainly, but that's it's job. It didn't come up with some new idea about anything because it works from the data it's given, and the logic boundaries of the tasks it's set to run. It's not doing anything super special here, just very efficiently.

[–] markon@lemmy.world -1 points 3 months ago* (last edited 3 months ago)

Start chewing. You literally admitted it in your own comment: "Sure, it's not something humans had gotten to yet." That is the definition of a novel discovery. You are arguing that because the AI used logic and existing data to reach the conclusion, it doesn't count. By that definition, no human scientist has ever had a novel idea either since we all build on existing data and patterns. The AI looked at the same data humans had, saw a pattern humans missed, and created a solution humans didn't have. That is novelty. But honestly it is hard to take your analysis of these papers seriously when you just argued in the comment above that protein folding involves "10^2 combinations." You realize 10^2 is just 100 right? You think complex biology is a list shorter than a grocery receipt. If your math is off by about 300 zeros I am not sure you are the best judge of what these models are actually capable of.

[–] markon@lemmy.world -1 points 3 months ago

​I was almost with you on the whole expert act until the part where you said we feed the model "10^2 combinations of amino acids." ​You realize 10^2 is literally just 100, right? ​You are writing paragraphs acting like the smartest guy in the room, but you think protein folding gets solved by checking a list shorter than a grocery receipt. That is honestly hilarious. ​It kind of explains your whole point though. No wonder you think it is just a "simple sorting mechanism" if you think the dataset is that small. You might want to check the math before the next lecture because being off by about 300 zeros makes the arrogance look a bit silly.

[–] markon@lemmy.world -1 points 3 months ago

A decade in the space is impressive. It shows dedication and time invested. That alone deserves recognition.

Still, the points you are repeating are familiar. They are recycled claims from years ago. If the goal is to critique novelty, repeating the same arguments does not advance it.

You say LLMs have zero intentional logic. That is true if by intentional logic you mean human consciousness or goals. It is false if you mean emergent behaviors and the ability to combine information in ways no single source explicitly wrote. Eliminating nuance with absolute terms makes it easy to dismiss valid evidence.

Calling someone an AI fanboy signals preference for labels over analysis. That approach does not strengthen an argument. Specific examples do. Concrete failures, reproducible tests, or papers are what advance discussion.

It is also not accurate to suggest that anyone pitches LLMs as supreme beings. Most people treat them as complex tools that produce surprising results. Their speed, scale, and capacity to identify patterns exceed human ability, but they remain tools. Critiquing them as if they were gods is a strawman.

If you want this discussion to matter, show a single reproducible example where an LLM fails in a way your logic cannot explain. Otherwise, repeating slogans and metaphors only illustrates a resistance to evidence.

I am not here to argue for ideology. I am here to examine claims. That is a choice. It is also a choice to resist slogans and demand specificity. Fun, fun. Another fun day.