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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

#371

Post by retsibsi »

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…

Quoting the Wikipedia article's formulation of the task for clarity:

> You are shown a set of four cards placed on a table, each of which has a number on one side and a color on the other. The visible faces of the cards show 3, 8, blue and red. Which card(s) must you turn over in order to test that if a card shows an even number on one face, then its opposite face is blue?

Confusion over the meaning of 'if' can only explain why people select the Blue card; it can't explain why people fail to select the Red card. If 'if' meant 'if and only if', then it would still be necessary to check that the Red card didn't have an even number. But according to Wason[0], "only a minority" of participants select (the study's equivalent of) the Red card.

[0] https://web.mit.edu/curhan/www/docs/Articles/biases/20_Quart...

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

#372

Post by Mistletoe »

booleandilemma wrote: post #368
hdivider wrote: post #347

It's curious to me why we have no theory of intelligence. By which I mean an actual hard and verified theory, as in physics for gravity, electromagnetism, quantum mechanics. Intelligence is simply not well-understood at a mathematical level. Like medieval engineers, we rely so heavily on experimentation in AI. We have no idea how far away from the human level we actually are. Or how far above the human level we can g…

Who is more intelligent: a twenty-something influencer making money from her bedroom, or a grad student barely making ends meet? Who is more intelligent: a politician, or a high school teacher? What is intelligence, anyway?

We have a pretty good answer to your questions, they are called IQ tests. It’s not like measuring intelligence is uncharted territory.

https://www.scientificamerican.com/article/i-gave-chatgpt-an...

https://www.reddit.com/r/singularity/comments/1p5f0b1/gemini...

Gemini 3 Pro has an IQ of 130 now but we keep moving the goalposts and being like “not THAT intelligence, we mean this other intelligence”. I suspect, and history shows us this will be the case, that humans will judge AIs as not human and not intelligent and not needing rights way past the point where they should have rights, even when vastly superior to human intelligence.

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

#373

Post by retsibsi »

lich_king wrote: post #317
lugu wrote: post #296

Earlier quoted context omitted.

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

When someone tells you "you can have this if you pay me", they don't mean "you can also have it if you don't pay". They are implicitly but clearly indicating you gotta pay. It's as simple as that. In common use, "if x then y" frequently implies "if not x then not y". Pretending that it's some sort of a cognitive defect to interpret it this way is silly.

In the original studies, most people made an error that can't be explained by that misunderstanding: they failed to select the card showing 'not y'.

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

#375

Post by YetAnotherNick »

echelon wrote: post #355
chriskanan wrote: post #284

I had lunch with Yann last August, about a week after Alex Wang became his "boss." I asked him how he felt about that, and at the time he told me he would give it a month or two and see how it goes, and then figure out if he should stay or find employment elsewhere. I told him he ought to just create his own company if he decides to leave Meta to chase his own dream, rather than work on the dream's of others. That sa…

You have to understand the strategy of all the other players: Build attention-grabbing, monetizable models that subsidize (at least in part) the run up to AGI. Nobody is trying to one-shot AGI. They're grinding and leveling up while (1) developing core competencies around every aspect of the problem domain and (2) winning users. I don't know if Meta is doing a good job of this, but Google, Anthropic, and OpenAI are.…

> Trying to go straight for the goal is risky.

That's the point of it. You need to take more risk for different approach. Same as what OpenAI did initially.

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

#376

Post by rhubarbtree »

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…

Brilliant insight. The success of LLM reasoning, ie “telling yourself a story”, has greatly increased my belief that humans are actually much less impressive than they seem. I do think it’s mostly pattern matching and a bunch of interacting streams analogous to LLM tokens. Obviously the implementations are different, because nature has to be robust and learn online, but I do not think we are as different from these machines as most people assume. There’s a reason Hofstadter et al. reacted as they did even to the earlier models.

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

#377

Post by 0x3f »

jsnell wrote: post #310
0x3f wrote: post #236

Earlier quoted context omitted.

For example?

For example: The METR time-horizon benchmark shows steady exponential growth. The frontier lab revenue has been growing exponentially from basically the moment they had any revenues. (The latter has confounding factors. For example it doesn't just depend on the quality of the model but on the quality of the apps and products using the model. But the model quality is still the main component, the products seem to pop…

Note we're in a sub-thread about whether 'only data matters, not architecture', so I don't disagree that functionality or revenue are growing _in general_, but that's not we're talking about here.

The point is that core model architectures don't just keep scaling without modification. MoE, inference-time, RAG, etc. are all modifications that aren't 'just use more data to get better results'.

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

#378

Post by jacquesm »

aurareturn wrote: post #357
jacquesm wrote: post #311

Earlier quoted context omitted.

The main difference is humans are learning all the time and models learn batch wise and forget whatever happened in a previous session unless someone makes it part of the training data so there is a massive lag. Whoever cracks the continuous customized (per user, for instance) learning problem without just extending the context window is going to be making a big splash. And I don't mean cheats and shortcuts, I mean a…

Why not just provide more compute for say, 1 billion token context for each user to mimic continuous learning. Then retrain the model in the background to include learnings. The user wouldn’t know if the continuous learning came from the context or the model retrained. It wouldn’t matter. Continuous learning seems to be a compute and engineering problem.

Because that re-training is not strong enough to hold, or so it seems. The same dumb factual errors keep coming up on different generations of the same models. I've yet to see proof that something 'stuck' from model to model. They get better in a general sense but not in the specific sense that what was corrected stays put, not from session to session and not from one generation to the next.

My solution is to have this massive 'boot up' prompt but it becomes extremely tedious to maintain.

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

#379

Post by 8bitsrule »

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…

Gotta say, good luck with that effort. Lenat started Cyc 42 years ago, and after a while it seemed to disappear. 'Understanding' the 'physical world' is something that a few -may- start to approach intuitively after a decade or five of experience. (Einstein, Maxwell, et.al.) But the idea of feeding a machine facts and equations ... and dependence on human observations ... seems unlikely to lead to 'mastering the physical world'. Let alone for $1Billon.

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

#380

Post by mirekrusin »

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…

Thank you for not saying "language", but "text".

It's true, but it's also true that text is very expressive.

Programming languages (huge, formalized expressiveness), math and other formal notation, SQL, HTML, SVG, JSON/YAML, CSV, domain specific encoding ie. for DNA/protein sequences, for music, verilog/VHDL for hardware, DOT/Graphviz/Mermaid, OBJ for 3D, Terraform/Nix, Dockerfiles, git diffs/patches, URLs etc etc.

The scope is very wide and covers enough to be called generic especially if you include multi modalities that are already being blended in (images, videos, sound).

I'm cheering for Yann, hope he's right and I really like his approach to openness (hope he'll carry it over to his new company).

At the same time current architectures do exist now and do work, by far exceeding his or anybody's else expectations and continue doing so. It may also be true they're here to stay for long on text and other supported modalities as cheaper to train.

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