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Yann LeCun raises $1B to build AI that understands the physical world

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Re: Yann LeCun raises $1B to build AI that understands the physical world

#381

Post by MITSardine »

jandrewrogers wrote: post #303
MITSardine wrote: post #280

Earlier quoted context omitted.

> You could in principle create a simulation with the same mathematical properties as the physical world but no one has ever done that. I'm not sure if we even know how. What do you mean by that? Simulating physics is a rich field, which incidentally was one of the main drivers of parallel/super computing before AI came along.

The mapping of the physical world onto a computer representation introduces idiosyncratic measurement issues for every data point. The idiosyncratic bias, errors, and non-repeatability changes dynamically at every point in space and time, so it can be modeled neither globally nor statically. Some idiosyncratic bias exhibits coupling across space and time. Reconstructing ground truth from these measurements, which is…

I'm probably missing most of your point, but wouldn't the fact that we have inverse problems being applied in real-world situations somewhat contradict your qualms? In those cases too, we have to deal with noisy real-world information.

I'll admit I'm not very familiar with that type of work - I'm in the forward solve business - but if assumptions are made on the sensor noise distribution, couldn't those be inferred by more generic models? I realize I'm talking about adding a loop on top of an inverse problem loop, which is two steps away (just stuffing a forward solve in a loop is already not very common due to cost and engineering difficulty).

Or better yet, one could probably "primal-adjoint" this and just solve at once for physical parameters and noise model, too. They're but two differentiable things in the way of a loss function.

Re: Yann LeCun raises $1B to build AI that understands the physical world

#382

Post by O4epegb »

vit05 wrote: post #107
az226 wrote: post #104

Yann LeCun seeks $5B+ valuation for world model startup AMI (Amilabs). He has hired LeBrun to the helm as CEO. AMI has also hired LeFunde as CFO and LeTune as head of post-training. They’re also considering hiring LeMune as Head of Growth and LePrune to lead inference efficiency. https://techcrunch.com/2025/12/19/yann-lecun-confirms-his-ne...

Why didn't they just call it LeLabs?

LeBron is missing out an opportunity to invest

Re: Yann LeCun raises $1B to build AI that understands the physical world

#383

Post by mirekrusin »

lich_king wrote: post #243
perfmode wrote: post #232

Earlier quoted context omitted.

Humans are notoriously bad at formal logic. The Wason selection task is the classic example: most people fail a simple conditional reasoning problem unless it’s dressed up in familiar social context, like catching cheaters. That looks a lot more like pattern matching than rule application. Kahneman’s whole framework points the same direction. Most of what people call “reasoning” is fast, associative, pattern-based. T…

> The Wason selection task is the classic example: most people fail a simple conditional reasoning problem unless it’s dressed up in familiar social context, like catching cheaters. I've never heard about the Wason selection task, looked it up, and could tell the right answer right away. But I can also tell you why: because I have some familiarity with formal logic and can, in your words, pattern-match the gotcha tha…

As they say, "think about how smart the average person is, then realize half the population is below that". There are far more haikus than opuses walking this planet.

We keep benchmarking models against the best humans and the best human institutions - then when someone points out that swarms, branching, or scale could close the gap, we dismiss it as "cheating". But that framing smuggles in an assumption that intelligence only counts if it works the way ours does. Nobody calls a calculator a cheat for not understanding multiplication - it just multiplies better than you, and that's what matters.

LLMs are a different shape of intelligence. Superhuman on some axes, subpar on others. The interesting question isn't "can they replicate every aspect of human cognition" - it's whether the axes they're strong on are sufficient to produce better than human outcomes in domains that matter. Calculators settled that question for arithmetic. LLMs are settling it for an increasingly wide range of cognitive work. The fact that neither can flip a burger is irrelevant.

Humans don't have a monopoly on intelligence. We just had a monopoly on generality and that moat is shrinking fast.

Re: Yann LeCun raises $1B to build AI that understands the physical world

#384

Post by andy12_ »

anon7000 wrote: post #354
andy12_ wrote: post #30

Earlier quoted context omitted.

I don't understand this view. How I see it the fundamental bottleneck to AGI is continual learning and backpropagation. Models today are static, and human brains don't learn or adapt themselves with anything close to backpropagation. World models don't solve any of these problems; they are fundamentally the same kind of deep learning architectures we are used to work with. Heck, if you think learning from the world i…

I don’t understand your view. Reality is that we need some way to encode the rules of the world in a more definitive way. If we want models to be able to make assertive claims about important information and be correct, it’s very fair to theorize they might need a more deterministic approach than just training them more. But it’s just a theory that this will actually solve the problem. Ultimately, we still have a lot…

> Reality is that we need some way to encode the rules of the world in a more definitive way

I mean, sure. But do world models the way LeCun proposes them solves this? I don't think so. JEPAs are just an unsupervised machine learning model at the end of the day; they might end up being better that just autoregressive pretraining on text+images+video, but they are not magic. For example, if you train a JEPA model on data of orbital mechanics, will it learn actually sensible algorithms to predict the planets' motions or will it just learn a mix of heuristic?

Re: Yann LeCun raises $1B to build AI that understands the physical world

#385

Post by vidarh »

A_D_E_P_T wrote: post #11

Justifiable. There are a lot more degrees of freedom in world models. LLMs are fundamentally capped because they only learn from static text -- human communications about the world -- rather than from the world itself, which is why they can remix existing ideas but find it all but impossible to produce genuinely novel discoveries or inventions. A well-funded and well-run startup building physical world models (ground…

It's just not true LLMs are limited to "static text". Data is data. Sensory input is still just data, and multimodal models has been a thing for a while. Ongoing learning and more extensive short term memory is a challenge, and so I am all for research in alternative architectures, but so much of the discourse about the limitations of LLMs act as if they have limitations they do not have.

Re: Yann LeCun raises $1B to build AI that understands the physical world

#386

Post by stevenhuang »

A_D_E_P_T wrote: post #322
ml-anon wrote: post #168

Earlier quoted context omitted.

Honestly, how do people who know so little have this much confidence to post here?

Care to explain what led to this reaction?

https://news.ycombinator.com/item?id=47325940

Re: Yann LeCun raises $1B to build AI that understands the physical world

#387

Post by ghywertelling »

mirekrusin wrote: post #383
lich_king wrote: post #243

Earlier quoted context omitted.

> The Wason selection task is the classic example: most people fail a simple conditional reasoning problem unless it’s dressed up in familiar social context, like catching cheaters. I've never heard about the Wason selection task, looked it up, and could tell the right answer right away. But I can also tell you why: because I have some familiarity with formal logic and can, in your words, pattern-match the gotcha tha…

As they say, "think about how smart the average person is, then realize half the population is below that". There are far more haikus than opuses walking this planet. We keep benchmarking models against the best humans and the best human institutions - then when someone points out that swarms, branching, or scale could close the gap, we dismiss it as "cheating". But that framing smuggles in an assumption that intelli…

The "God of the gaps" theory is a theological and philosophical viewpoint where gaps in scientific knowledge are cited as evidence for the existence and direct intervention of a divine creator. It asserts that phenomena currently unexplained by science—such as the origin of life or consciousness—are caused by God.

We are doing inversion of God of gaps to "LLM of Gaps" where gaps in LLM capabilities are considered inherently negative and limiting

Re: Yann LeCun raises $1B to build AI that understands the physical world

#388

Post by suddenlybananas »

bethekidyouwant wrote: post #258
ordu wrote: post #231

Earlier quoted context omitted.

> Of course you can still improve the models, but you get much more upside from data, or even better - from interactive environments. I'm on the contrary believe that the hunt for better data is an attempt to climb the local hill and be stuck there without reaching the global maximum. Interactive environments are good, they can help, but it is just one of possible ways to learn about causality. Is it the best way? I…

Are you asking how many books a large language model would need to read to learn a new language if it was only trained on a different language? probably just 1 (the dictionary)

Do you know anything about how languages work? A dictionary doesn't have sufficient information to speak a language.

Re: Yann LeCun raises $1B to build AI that understands the physical world

#389

Post by har2008preet »

az226 wrote: post #104

Yann LeCun seeks $5B+ valuation for world model startup AMI (Amilabs). He has hired LeBrun to the helm as CEO. AMI has also hired LeFunde as CFO and LeTune as head of post-training. They’re also considering hiring LeMune as Head of Growth and LePrune to lead inference efficiency. https://techcrunch.com/2025/12/19/yann-lecun-confirms-his-ne...

These all are claude agents name right?

Re: Yann LeCun raises $1B to build AI that understands the physical world

#390

Post by kergonath »

10xDev wrote: post #178
kergonath wrote: post #73

Earlier quoted context omitted.

> The fact that models aren't continually updating seems more like a feature. I think this is true to some extent: we like our tools to be predictable. But we’ve already made one jump by going from deterministic programs to stochastic models. I am sure the moment a self-evolutive AI shows up that clears the "useful enough" threshold we’ll make that jump as well.

Stochastic and unpredictability aren't exactly the same. I would claim current LLMs are generally predictable even if it is not as predictable as a deterministic program.

No, but my point is that to some extent we value determinism. By making the jump to stochastic models we already move away from the status quo; further jumps are entirely possible. Depending on use case we can accept more uncertainty if it comes with benefits.

I also don’t think there is a reason to believe that self-learning models must be unpredictable.

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