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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

#441

Post by [deleted] »

edanm wrote: post #291
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…

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.

[deleted]

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

#442

Post by busyant »

heisig wrote: post #427
nprateem wrote: post #426

Earlier quoted context omitted.

Lol. A lump of metal can't be sentient.

Says the bag of lipids and proteins :)

Carbon, Hydrogen, Oxygen, Nitrogen, Phosphorus, Sulfur and a dash of other elements.

$99.85 at Sigma-Aldrich

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

#443

Post by coldtea »

retsibsi wrote: post #371
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…

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…

People in everyday life are not evaluating rules. They evaluate cases, for whether a case fits a rule.

So, when being told:

"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?"

they translate it to:

"Check the cards that show an even number on one face to see whether their opposite face is blue and vice versa"

Based on this, many would naturally pick the blue card (to test the direct case), and the 8 card (to test the "vice versa" case).

They wont check the red to see if there's an odd number there that invalidates the formulation as a general rule, because they're not in the mindset of testing a general rule.

Would they do the same if they had more familiarity with rule validation in everyday life or if the had a more verbose and explicit explanation of the goal?

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

#444

Post by HarHarVeryFunny »

sothatsit wrote: post #344
HarHarVeryFunny wrote: post #288

Earlier quoted context omitted.

RL adds a lot of capability in the areas where it can be applied, but I don't think it really changes the fundamental nature of LLMs - they are still predicting training set continuations, but now trying to predict/select continuations that amount to reasoning steps steering the output in a direction that had been rewarded during training. At the end of the day it's still copying, not learning. RL seems to mostly onl…

You can’t really say it is just predicting continuations when it is learning to write proofs for Erdos problems, formalise significant math results, or perform automated AI research. Those are far beyond what you get by just being a copying and re-forming machine, a lot of these problems require sophisticated application of logic. I don’t know if this can reach AGI, or if that term makes any sense to begin with. But…

I think you're conflating mechanism with function/capability.

I'm not sure what I wrote that made you conclude that I thought these models are not learning anything from their RL training?! Let me say it again: they are learning to steer towards reasoning steps that during training led to rewards.

The capabilities of LLMs, both with and without RL, are a bit counter-intuitive, and I think that, at least in part, comes down to the massive size of the training sets and the even more massive number of novel combinations of learnt patterns they can therefore potentially generate...

In a way it's surprising how FEW new mathematical results they've been coaxed into generating, given that they've probably encountered a huge portion of mankind's mathematical knowledge, and can potentially recombine all of these pieces in at least somewhat arbitrary ways. You might have thought that there are results A, B and C hiding away in some obscure mathematical papers that no human has previously considered to put together before (just because of the vast number of such potential combinations), that might lead to some interesting result.

If you are unsure yourself about whether LLMs are sufficient to reach AGI (meaning full human-level intelligence), then why not listen to someone like Demis Hassabis, one of the brightest and best placed people in the field to have considered this, who says the answer is "no", and that a number of major new "transformer-level" discoveries/inventions will be needed to get there.

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

#445

Post by nashashmi »

voxleone wrote: post #422

I rank with those who think human-like intelligence will require embeddings grounded in multiple physical sensory domains (vision, touch, audio, chemical sensing, etc.) fused into a shared world representation. That seems much closer to how biological intelligence works than text-only models. But if this path succeeds and produces systems with something like genuine understanding or sentience, there’s a deeper questi…

Personal Agency is a strong characteristic of a personality. AI would have to acquire a personality first. It could probably do this by copying others statistically. In that case, it is only doing what someone else has done.

There is no such thing as real sentient AI theoretically. Our current models are only emulations of humans. Maybe in the future someone will figure out a way for computers to learn how to learn. Then maybe someone will codify computers to acquire base methodologies vs just implementing any methodology it finds in the world.

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

#446

Post by pegasus »

djeastm wrote: post #425
voxleone wrote: post #422

I rank with those who think human-like intelligence will require embeddings grounded in multiple physical sensory domains (vision, touch, audio, chemical sensing, etc.) fused into a shared world representation. That seems much closer to how biological intelligence works than text-only models. But if this path succeeds and produces systems with something like genuine understanding or sentience, there’s a deeper questi…

It's an interesting question. On one hand we don't worry about this much with animals, the most advanced of which we know have personalities, moods, etc (Pigs, for instance). They really only seem to lack the language and higher-order reasoning skills. But where's the line?

We do worry much more about animal well-being than we worry about our "lumps of metal" (as a cousin comment fittingly put it). As we should, and generally I think we should worry much more about animal welfare. I find concerns for AI system welfare voiced by people like Thomas Metzinger wildly misguided.

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

#447

Post by mikkupikku »

jonahx wrote: post #337
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 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 info…

When people do math or rigorous deductive reasoning, are we sure they aren't just pattern matching with a set of carefully chosen interacting patterns that have been refined by ancient philosophers as being useful patterns that produce consistent results when applied in correctly patterned ways?

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

#448

Post by namero999 »

Mistletoe wrote: post #372
booleandilemma wrote: post #368

Earlier quoted context omitted.

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 hi…

IQ tests only measure the ability to pass IQ tests, they say very little about intelligence. MMA fighters might be among the most intelligent people on the planet, playing 4D bullet chess with each part of their body at light speed, while scoring a flat 100 at IQ tests (the average).

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

#449

Post by HarHarVeryFunny »

sothatsit wrote: post #344
HarHarVeryFunny wrote: post #288

Earlier quoted context omitted.

RL adds a lot of capability in the areas where it can be applied, but I don't think it really changes the fundamental nature of LLMs - they are still predicting training set continuations, but now trying to predict/select continuations that amount to reasoning steps steering the output in a direction that had been rewarded during training. At the end of the day it's still copying, not learning. RL seems to mostly onl…

You can’t really say it is just predicting continuations when it is learning to write proofs for Erdos problems, formalise significant math results, or perform automated AI research. Those are far beyond what you get by just being a copying and re-forming machine, a lot of these problems require sophisticated application of logic. I don’t know if this can reach AGI, or if that term makes any sense to begin with. But…

> What do you think training to predict when to use different continuations is other than learning?

Sure, training = learning, but the problem with LLMs is that is where it stops, other than a limited amount of ephemeral in-context learning/extrapolation.

With an LLM, learning stops post-training when it is "born" and deployed, while with an animal that's when it starts! The intelligence of an animal is a direct result of it's lifelong learning, whether that's imitation learning from parents and peers (and subsequent experimentation to refine the observed skill), or the never ending process of observation/prediction/surprise/exploration/discovery which is what allows humans to be truly creative - not just behaving in ways that are endless mashups of things they have seen and read about other humans doing (cf training set), but generating truly novel behaviors (such as creating scientific theories) based on their own directed exploration of gaps in mankind's knowledge.

Application of AGI to science and new discovery is a large part of why Hassabis defines AGI as human-equivalent intelligence, and understands what is missing, while others like Sam Altman are content to define AGI as "whatever makes us lots of money".

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

#450

Post by butlike »

carra wrote: post #431
voxleone wrote: post #422

I rank with those who think human-like intelligence will require embeddings grounded in multiple physical sensory domains (vision, touch, audio, chemical sensing, etc.) fused into a shared world representation. That seems much closer to how biological intelligence works than text-only models. But if this path succeeds and produces systems with something like genuine understanding or sentience, there’s a deeper questi…

I don't think they will have sentience or agency unless they are designed to: 1) Keep thinking continuously, as opposed to current AIs that stop functioning between prompts. 2) Have permanent memory of their previous experiences. 3) Be able to alter their own weights based on those experiences (a.k.a. learn).

They won't have sentience because it will be antithetical to capitalist business ideology. There's no good business value proposition for having the AI daydream like humans do, or 'sleep' while 'on', or have inspirational thought that might be seen as 'wrong' or useless. If that behavior ever manifests, it will probably be stamped out in a future release.

You can't justify to the board the wasted money to have the android dream.

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