Comment by ACCount37
14 hours ago
That was obviously him getting sidelined. And it's easy to see why.
LLMs get results. None of the Yann LeCun's pet projects do. He had ample time to prove that his approach is promising, and he didn't.
14 hours ago
That was obviously him getting sidelined. And it's easy to see why.
LLMs get results. None of the Yann LeCun's pet projects do. He had ample time to prove that his approach is promising, and he didn't.
I agree. I never understood LeCun's statement that we need to pivot toward the visual aspects of things because the bitrate of text is low while visual input through the eye is high.
Text and languages contain structured information and encode a lot of real-world complexity (or it's "modelling" that).
Not saying we won't pivot to visual data or world simulations, but he was clearly not the type of person to compete with other LLM research labs, nor did he propose any alternative that could be used to create something interesting for end-users.
Text and language contain only approximate information filtered through humans eyes and brains. Also animals don't have language and can show quite advanced capabilities compared to what we can currently do in robotics. And if you do enough mindfulness you can dissociate cognition/consciousness from language. I think we are lured because how important language is for us humans, but intuitively it's obvious to me language (and LLMs) are only a subcomponent, or even irrelevant for say self driving or robotics.
Seems like that "approximation" is perfectly sufficient for just about any task.
That whole take about the language being basically useless without a human mind to back it lost its legs in 2022.
In the meanwhile, what do those "world model" AIs do? Video generation? Meta didn't release anything like that. Robotics, self-driving? Also basically nothing from Meta there.
In the meanwhile, other companies are perfectly content with bolting multimodal transformers together for robotics tasks. Gemini Robotics being a research example - while modern Tesla FSD stack being a production grade one. Gemini even uses a language transformer as a key part of its stack.
Thats where the research is leading.
The issue is context. trying to make an AI assistant with just text only inputs is doeable but limiting. You need to know the _context_ of all the data, and without visual input most of it is useful.
For example "Where is the other half of this" is almost impossible to solve unless you have an idea of what "this" is.
but to do that you need to have cameras, to use cameras you need to have position, object, and people tracking. And that is a hard problem thats not solved.
the hypothesis is that "world models" solve that with an implicit understanding of the worl and the objects in context
If LeCun's research has made Meta a powerhouse of video generation or general purpose robotics - the two promising directions that benefit from working with visual I/O and world modeling as LeCun sees it - it could have been a justified detour.
But that sure didn't happen.
LLMs get results is quite the bold statement. If they get results, they should be getting adopted, and they should be making money. This is all built on hazy promises. If you had marketable results, you wouldn't have to hide 20+ billion dollars of debt financing into an obscure SPV. LLMs are the most baffling piece of tech. They are incredible, and yet marred by their non-deterministic hallucinatory nature, and bound to fail in adoption unless you convince everyone that they don't need precision and accuracy, but they can do their business at 75% quality, just with less human overhead. It's quite the thing to convince people of, and that's why it needs the spend it's needing. A lot of we-need-to-stay-in-the-loop CEOs and bigwigs got infatuated with the idea, and most probably they just had their companies get addicted to the tech equivalent of crack cocaine. A reckoning is coming.
LLMs get results, yes. They are getting adopted, and they are making money.
Frontier models are all profitable. Inference is sold with a damn good margin, and the amounts of inference AI companies sell keeps rising. This necessitates putting more and more money into infrastructure. AI R&D is extremely expensive too, and this necessitates even more spending.
A mistake I see people make over and over again is keeping track of the spending but overlooking the revenue altogether. Which sure is weird: you don't get from $0B in revenue to $12B in revenue in a few years by not having a product anyone wants to buy.
And I find all the talk of "non-deterministic hallucinatory nature" to be overrated. Because humans suffer from all of that too, just less severely. On top of a number of other issues current AIs don't suffer from.
Nonetheless, we use human labor for things. All AI has to do is provide a "good enough" alternative, and it often does.
> Frontier models are all profitable.
This is an extraordinary claim and needs extraordinary proof.
LLMs are raising lots of investor money, but that's a completely different thing from being profitable.
6 replies →
> Frontier models are all profitable.
They generate revenue, but most companies are in the hole for the research capital outlay.
If open source models from China become popular, then the only thing that matters is distribution / moat.
Can these companies build distribution advantage and moats?
In this comment you proceeded to basically reinvent the meaning of "profitable company", but sure. I won't even get into the point of comparing LLM to humans, because I choose not to engage with whoever doesn't have the human decency, humanistic compass, or basic phylosophical understanding of how putting LLMs and human labor on the same level to justify hallucinations and non-determinism is deranged and morally bankrupt.
2 replies →
OpenAI and Anthropic are making north of 4B/year revenue so some companies have figured out the money making part. ChatGPT has some 800M users according to some calculations. Whether it's enough money today, enough money tomorrow, is of course a question but there is a lot of money. Users would not use them in a scale if they do not solve their problems.
OpenAI lost 12bn last quarter
It’s easy to make 1 billion by spending 10 billion. That’s not “making money” though, it is lighting it on fire.
5 replies →
There is someone else at Facebook who's pet projects do not get results...
If you hire a house cleaner to clean your house, and the cleaner didn't do well, would you eject yourself out of the house? You would not. You would change to a new cleaner.
But if we hire someone to deal on R&D to automate fully the house cleaning process, we might not necessarily expect the office to be maintained in clean state by the researchers themselves any time we enter the room.
Sure, but that "someone else" is the man writing the checks. If the roles were reversed, he'd be the one being fired now.
Who are you referring to?
I think he means Zuckerberg himself, the metaverse isn't exactly a major success, but this is a false equivalency the way he organized it only his vote matters he does what he wants