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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

#331

Post by edgyquant »

andy12_ wrote: post #30
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 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…

Iirc LeCunn talks about a self organizing hierarchy of real world objects and imo this is exactly how the human brain actually learns

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

#332

Post by edgyquant »

10xDev wrote: post #52
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…

The fact that models aren't continually updating seems more like a feature. I want to know the model is exactly the same as it was the last time I used it. Any new information it needs can be stored in its context window or stored in a file to read the next it needs to access it.

It’s a feature of a good tool, but a sentient intelligence is more than just a tool

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

#334

Post by charcircuit »

andy12_ wrote: post #188
charcircuit wrote: post #185

Earlier quoted context omitted.

Agents have the ability of continual learning.

Putting stuff you have learned into a markdown file is a very "shallow" version of continual learning. It can remember facts, yes, but I doubt a model can master new out-of-distribution tasks this way. If anything, I think that Google's Titans[1] and Hope[2] architectures are more aligned with true continual learning (without being actual continual learning still, which is why they call it "test-time memorization").…

I have had it master tasks by doing this. The first time it tries to solve an issue it may take a long time, but it documents its findings and how it was able to do it and then it applies that knowledge the next time the task comes up.

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

#335

Post by _s_a_m_ »

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…

Really? As if not everyone told him the last 10 years, especially Gary Marcus which he ridiculed on Twitter at every occasion and now silently like a dog returning home switches to Gary's position. As if anyone was waiting for this, even 5 years ago this was old news, Tenenbaum is building world models for a long time. People in pop venture capital culture don't seem to know what is going on in research. Makes them easier to milk.

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

#336

Post by thinkling »

ljm wrote: post #293
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.

Um, why would anyone be "holding the bag" and who needs protecting by society? He's not taking out a loan, he's getting capital investment in a startup. People are gambling that he will do well and make money for them. If they gamble wrong, that's on them. Society won't be doing anything either way because investors in startups that fail don't get anything.

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

#337

Post by jonahx »

perfmode wrote: post #232
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…

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 story that humans have access to some pure deductive engine and LLMs are just faking it with statistics might be flattering to humans more than it’s accurate.

Your point rings true with most human reasoning most of the time. Still, at least some humans do have the capability to run that deductive engine, and it seems to be a key part (though not the only part) of scientific and mathematical reasoning. Even informal experimentation and iteration rest on deductive feedback loops.

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

#338

Post by jimbo808 »

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…

You're right that world models are the bottleneck, but people underestimate the staggering complexity gap between modeling the physical world and modeling a one-dimensional stream of text. Not only is the real world high-dimensional, continuous, noisy, and vastly more information dense, it's also not something for which there is an abundance of training data.

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

#339

Post by stanfordkid »

andy12_ wrote: post #30
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 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…

It's pretty simple... the word circle and what you can correlate to it via english language description has somewhat less to do with reality than a physical 3D model of a circle and what it would do in an environment. You can't just add more linguistic description via training data to change that. It doesn't really matter that you can keep back propagating because what you are back propagating over is fundamentally and qualitatively less rich.

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

#340

Post by mountainriver »

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…

Okay but most modern LLMs are multimodal, and it’s fairly easy to make an LLM multimodal.

Also there is no evidence that novel discoveries are more than remixes. This is heavily debated but from what we’ve seen so far I’m not sure I would bet against remix.

World models are great for specific kinds of RL or MPC. Yann is betting heavily on MPC, I’m not sure I agree with this as it’s currently computationally intractable at scale

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