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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

#411

Post by ismailmaj »

sylware wrote: post #391
ismailmaj wrote: post #262

Earlier quoted context omitted.

Don’t think that’s a fair interpretation of what I said. Liquid money rich? No. Can get pulled for big tech packages? Also no, for most of the employees. AFAIK, big tech didn’t aggressively poach OpenAI-like talent, they did spend 10M+ pay packages but it was for a select few research scientists. Some folks left and came but it boiled down to culture mostly.

What??? microsoft openai is Big Tech. Are you ok?

Ah yes, OpenAI the puppet of Microsoft that is currently declaring war against GitHub, sounds logical.

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

#414

Post by booleandilemma »

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 are nonsense. The more IQ tests you take the better at them you get. And who is "we", you pretentious dirtbag.

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

#415

Post by a1371 »

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…

I never understood why we believe humans don't backprop. Isn't it that during the day we fill up our context (short term memory) and sleep is actually where we use that to backprop? Heck, everyone knows what "sleep on it" means.

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

#416

Post by samrus »

_giorgio_ wrote: post #171
samrus wrote: post #94

Earlier quoted context omitted.

Its a matter of needing more time, which is a resource even SV VCs are scared to throw around. Look at the timeline of all these advancements and how long it took Lecun introduced backprop for deep learning back in 1989 Hinton published about contrastive divergance in next token prediction in 2002 Alexnet was 2012 Word2vec was 2013 Seq2seq was 2014 AiAYN was 2017 UnicornAI was 2019 Instructgpt was 2022 This makes alo…

If his ideas had real substance, we would have seen substantial results by now. He introduced I-JEPA in 2023, so almost three years ago at this point. If he still hasn’t produced anything truly meaningful after all these years at Meta, when is that supposed to happen? Yann LeCun has been at Facebook/Meta since December 2013. Your chronological sequence is interesting, but it refers to a time when the number of resear…

> If his ideas had real substance, we would have seen substantial results by now

This is naive. Like saying if backprop had any real substance, it would have had results within 10 years of its publication in 1989

> Your chronological sequence is interesting, but it refers to a time when the number of researchers and the amount of compute available were a tiny fraction of what they are today.

Again. Those resources are important. But one resource being ignored is time. Try baking a turkey at 300 for 4 hours veruss at 900 for 1 hour and see how edible each one is

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

#417

Post by cedilla »

a1371 wrote: post #415
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 never understood why we believe humans don't backprop. Isn't it that during the day we fill up our context (short term memory) and sleep is actually where we use that to backprop? Heck, everyone knows what "sleep on it" means.

Brains are not doing linear algebra, and they don't follow a concise algorithm.

What LLM do is even farther away from what neural nets do, and even there - artificial neurons are inspired by but not reimplementing biological neurons.

You can understand human thought in terms of LLMs, but that is just a simile, like understanding physical reality in terms of computers or clockworks.

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

#418

Post by Yizahi »

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…

I've confidently picked 8+blue and is now trying to understand why I personally did that. I think that maybe the text of the puzzle is not quite unambiguous. The question states "test a card" followed by "which cards", so this is what my brain immediately starts to check - every card one by one. Do I need to test "3"? No, not even. Do I need to test "8"? yes. Do I need to test "blue"? Yes, because I need to test "a card" to fit the criteria. And lastly "red" card also immediately fails verification of a "a card" fitting that criteria.

I think a corrected question should clarify in any obvious way that we are verifying not "a card" but "a rule" applicable to all cards. So a needs to be replaced with all or any, and mention of rule or pattern needs to be added.

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

#419

Post by smokel »

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? Obviously and intuitively the answer is NO.

Not so fast. People have built pretty amazing thought frameworks out of a few axioms, a few bits, or a few operations in a Turing machine. Dolphin songs are probably more than enough to encode the game of life. It's just how you look at it that makes it intelligence.

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