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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

#291

Post by edanm »

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…

Agree with much of your comment.

Though note that as GP said, on the Wason selection task, people famously do much better when it's framed in a social context. That at least partially undermines your theory that its lack of familiarity with the terminology of formal logic.

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

#292

Post by program_whiz »

ben_w wrote: post #50
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…

> Models today are static, and human brains don't learn or adapt themselves with anything close to backpropagation. While I suspect latter is a real problem (because all mammal brains* are much more example-efficient than all ML), the former is more about productisation than a fundamental thing: the models can be continuously updated already, but that makes it hard to deal with regressions. You kinda want an artefact…

they can be continuously updated, assuming you re-run representative samples of the training set through them continuously. Unlike a mammal brain which preserves the function of neurons unless they activate in a situation which causes a training signal, deep nets have catastrophic forgetting because signals get scattered everywhere. If you had a model continuously learning about you in your pocket, without tons of cycles spent "remembering" old examples. In fact, this is a major stumbling block in standard training, sampling is a huge problem. If you just iterate through the training corpus, you'll have forgotten most of the english stuff by the time you finish with chinese or spanish. You have to constantly mix and balance training info due to this limitation.

The fundamental difference is that physical neurons have a discrete on/off activation, while digital "neurons" in a network are merely continuous differentiable operations. They also don't have a notion of "spike timining dependency" to avoid overwriting activations that weren't related to an outcome. There are things like reward-decay over time, but this applies to the signal at a very coarse level, updates are still scattered to almost the entire system with every training example.

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

#293

Post by ljm »

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…

I'm gonna be a cynic and say this is money following money and Yann LeCun is an excellent salesman.

I 100% guarantee that he will not be holding the bag when this fails. Society will be protecting him.

On that proviso I have zero respect for this guy.

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

#294

Post by teleforce »

It's really inevitable isn't it, we are going from RAG to PAG, or physical augmented generation.

We already have PINN or physics-informed neural networks [1]. Soon we are going to have physical field computing by complex-valued network quantization or CVNN that has been recently proposed for more efficient physical AI [2].

[1] Physics-informed neural networks:

https://en.wikipedia.org/wiki/Physics-informed_neural_networ...

[2] Ultra-efficient physical field computing by complex-valued network quantization:

https://www.nature.com/articles/s41467-026-70319-0

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

#295

Post by hodgehog11 »

A_D_E_P_T wrote: post #68
Unearned5161 wrote: post #57

Earlier quoted context omitted.

I have a pet peeve with the concept of "a genuinely novel discovery or invention", what do you imagine this to be? Can you point me towards a discovery or invention that was "genuinely novel", ever? I don't think it makes sense conceptually unless you're literally referring to discovering new physical things like elements or something. Humans are remixers of ideas. That's all we do all the time. Our thoughts and acti…

Suno is transformer-based; in a way it's a heavily modified LLM. You can't get Suno to do anything that's not in its training data. It is physically incapable of inventing a new musical genre. No matter how detailed the instructions you give it, and even if you cheat and provide it with actual MP3 examples of what you want it to create, it is impossible. The same goes for LLMs and invention generally, which is why th…

I don't see how this is an architectural problem though. The problem is that music datasets are highly multimodal, and the training process is relying almost entirely on this dataset instead of incorporating basic musical knowledge to allow it to explore a bit further. That's what happens when computer scientists aim to "upset" a field without consulting with experts in said field.

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

#296

Post by lugu »

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…

Your response contains a performative contradiction: you are asserting that humans are naturally logical while simultaneously committing several logical errors to defend that claim.

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

#297

Post by kadushka »

masteranza wrote: post #272
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…

A few years ago I've made this simple thought experiment to convince myself that LLM's won't achieve superhuman level (in the sense of being better than all human experts): Imagine that we made an LLM out of all dolphin songs ever recorded, would such LLM ever reach human level intelligence? Obviously and intuitively the answer is NO. Your comment actually extended this observation for me sparking hope that systems c…

Imagine that we made an LLM out of all dolphin songs ever recorded, would such LLM ever reach human level intelligence?

It could potentially reach super-dolphin level intelligence

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

#298

Post by dabeeeenster »

Oras wrote: post #58

> But this is not an applied AI company. There is absolutely no doubt about Yann's impact on AI/ML, but he had access to many more resources in Meta, and we didn't see anything. It could be a management issue, though, and I sincerely wish we will see more competition, but from what I quoted above, it does not seem like it. Understanding world through videos (mentioned in the article), is just what video models have a…

> It could be a management issue, though

Or, maybe it's just hard?

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

#299

Post by hodgehog11 »

masteranza wrote: post #272
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…

A few years ago I've made this simple thought experiment to convince myself that LLM's won't achieve superhuman level (in the sense of being better than all human experts): Imagine that we made an LLM out of all dolphin songs ever recorded, would such LLM ever reach human level intelligence? Obviously and intuitively the answer is NO. Your comment actually extended this observation for me sparking hope that systems c…

I mean no offense here, but I really don't like this attitude of "I thought for a bit and came up with something that debunks all of the experts!". It's the same stuff you see with climate denialism, but it seems to be considered okay when it comes to AI. As if the people that spend all day every day for decades have not thought of this.

Dataset limitations have been well understood since the dawn of statistics-based AI, which is why these models are trained on data and RL tasks that are as wide as possible, and are assessed by generalization performance. Most of the experts in ML, even the mathematically trained ones, within the last few years acknowledge that superintelligence (under a more rigorous definition than the one here) is quite possible, even with only the current architectures. This is true even though no senior researcher in the field really wants superintelligence to be possible, hence the dozens of efforts to disprove its potential existence.

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

#300

Post by robwwilliams »

steego wrote: post #164
zelphirkalt wrote: post #37

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. Even with continuous backpropagation and "learning", enriching the training data, so called online-learning, the limitations will not disappear. The LLMs will not be able to conclude t…

I think people MOSTLY foresee and anticipate events in OUR training data, which mostly comprises information collected by our senses. Our training data is a lot more diverse than an LLMs. We also leverage our senses as a carrier for communicating abstract ideas using audio and visual channels that may or may not be grounded in reality. We have TV shows, video games, programming languages and all sorts of rich and int…

> We can steer our own learning corpus

This is critical. We have some degree of attentional autonomy. And we have a complex tapestry of algorithms running in thalamocortical circuits that generate “Nows”. Truncation commands produce sequences of acts (token-like products).

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