Making LeCun report to Wang was the most boneheaded move imaginable. But… I suppose Zuckerberg knows what he wants, which is AI slopware and not truly groundbreaking foundation models.
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If the answer is yes, then better to keep him, because he has already proved himself and you can win in the long-term. With Meta's pockets, you can always create a new department specifically for short-term projects.
If the answer is no, then nothing to discuss here.
If you follow LeCun on social media, you can see that the way FAIR’s results are assessed is very narrow-minded and still follows the academic mindset. He mentioned that his research is evaluated by: "Research evaluation is a difficult task because the product impact may occur years (sometimes decades) after the work. For that reason, evaluation must often rely on the collective opinion of the research community through proxies such as publications, citations, invited talks, awards, etc."
But as an industry researcher, he should know how his research fits with the company vision and be able to assess that easily. If the company's vision is to be the leader in AI, then as of now, he seems to have failed that objective, even though he has been at Meta for more than 10 years.
I really resonate with his view due to my background in physics and information theory. I for one welcome his new experimentation in other realms while so many still hack away at their LLMs in pursuit of SOTA benchmarks.
Its pretty much dog eat dog at top management positions.
Its not exactly a space for free thinking timelines.
Is the real bubble ignorance? Maybe you'll cool down but the rest of the world? There will just be more DeepSeek and more advances until the US loses its standing.
[1] Doctor of Philosophy:
This is why we're losing innovation.
Look at electric cars, batteries, solar panels, rare earths and many more. Bubble or struggle for survival? Right, because if US has no AI the world will have no AI? That's the real bubble - being stuck in an ancient world view.
Meta's stock has already tanked for "over" investing in AI. Bubble, where?
But the skill sets to avoid and survive personnel issues in academia is different from industry. My 2c.
The US government basically forced AT&T to use revenue from its monopoly to do fundamental research for the public good. Could the government do the same thing to our modern megacorps? Absolutely! Will it? I doubt it.
https://www.nytimes.com/1956/01/25/archives/att-settles-anti...
You assume that's the only use of it.
And are people not using these code generators?
Is this an issue with a lost generation that forgot what Capex is? We've moved from Capex to Opex and now the notion is lost, is it? You can hire an army of software developers but can't build hardware.
Is it better when everyone buys DeepSeek or a non-US version? Well then you don't need to spend Capex but you won't have revenue either.
It seems they've given up on the research and are now doubling down on LLMs.
"Why Bell Labs Worked" [1]
"The Influence of Bell Labs" [2]
"Bringing back the golden days of Bell Labs" [3]
"Remembering Bell Labs as legendary idea factory prepares to leave N.J. home" [4] or
"Innovation and the Bell Labs Miracle" [5]
interesting too.
[1] https://news.ycombinator.com/item?id=43957010 [2] https://news.ycombinator.com/item?id=42275944 [3] https://news.ycombinator.com/item?id=32352584 [4] https://news.ycombinator.com/item?id=39077867 [5] https://news.ycombinator.com/item?id=3635489
If LLMs actually hit a plateau, then investment will flow towards other architectures.
> is massively monopolistic and have unbounded discretionary research budget
that is the case for most megacorps. if you look at all the financial instruments.
modern monopolies are not equal to single corporation domination. modern monopolies are portfolios who do business using the same methods and strategies.
the problem is that private interests strive mostly for control, not money or progress. if they have to spend a lot of money to stay in control of (their (share of the)) segments, they will do that, which is why stuff like the current graph of investments of, by and for AI companies and the industries works.
A modern equivalent and "breadth" of a Bell Labs (et. al) kind of R&D speed could not be controlled and would 100% result in actual Artificial Intelligence vs all those white labelababbebel (sry) AI toys we get now.
Post WW I and II "business psychology" have build a culture that cannot thrive in a free world (free as in undisturbed and left to all devices available) for a variety of reasons, but mostly because of elements with a medieval/dark-age kind of aggressive tendency to come to power and maintain it that way.
In other words: not having a Bell Labs kind of setup anymore ensures that the variety of approaches taken on large scales aka industry-wide or systemic, remains narrow enough.
If Deepseek is free it undermines the value of LLMs, so the value of these US companies is mainly speculation/FOMO over AGI.
And I stopped reading him, since he - in my opinion - trashed on autopilot everything 99% did - and these 99% were already beyond the two standard deviation of greatness.
It is even more highly problematic if you have absolutely no results eg products to back your claims.
I'll happily step out of the way once someone simply tells me what it is you're trying to accomplish. Until you can actually define it, you can't do "it".
yes, a glib response, but think about it: we define an intelligence test for humans, which by definition is an artificial construct. If we then get a computer to do well on the test we haven't proved it's on par with human intelligence, just that both meet some of the markers that the test makers are using as rough proxies for human intelligence. Maybe this helps signal or judge if AI is a useful tool for specific problems, but it doesn't mean AGI
Same goes for academia. People's visions compete for other people's financial budgets, time and other resources. Some dogs get to eat, study, train at the frontier and with top tools in top environments while the others hope to find a good enough shelter.
As for IQ tests and the like, to the extent they are "scientific" they are designed based on empirical observations of humans. It is not designed to measure the intelligence of a statistical system containing a compressed version of the internet.
Who says they don't make money? Same with open source software that offer a hosted version.
> If Deepseek is free it undermines the value of LLMs, so the value of these US companies is mainly speculation/FOMO over AGI
Freemium, open source and other models all exist. Does it undermine the value of e.g. Salesforce?
Like the new spin out Episteme from OpenAI?
And of course it doesn't work. Humans don't have world models. There's no such thing as a world model!
LeCun had chosen to focus on the latter. He can't be blamed for not having taken the second hat.
Why they decided not to do that is kind of a puzzle.
That kind of hallucination is somewhat acceptable for something marketed as a chatbot, less so for an assistant helping you with scientific knowledge and research.
LLMs cannot do any of the major claims made for them, so competing at the current frontier is a massive resource waste.
Right now a locally running 8b model with large context window (10k tokens+) beat google/openAI models easily on any task you like.
why would anyone then pay for something that is possible to run on consumer hardware with higher token/second throughput and better performance? What exactly have the billions invested given google/oai in return? Nothing more than an existensial crisis I'd say.
Companies aren't trying to force AI costs into their subscription models in dishonest ways because they've got a winning product.
And animals' main concern is energy conservation, so they must be doing something else.
But the principle is there. I think that when a company sits on a load of cash, that's what they should do. Either that or become a kind of alternative investments allocator. These are risky bets. But they should be incentivized to take those risks. From a fiscal policy standpoint for instance. Well it probably is the case already via lower taxation of capital gains and so on. But there should probably exist a more streamlined framework to make sure incentives are aligned.
And/or assigned government projects? Besides implementing their Cloud infrastructure that is...
The animal learns as it encounters learning signals - prediction failure - which is the only way to do it. Of course you need to learn/remember something before you can use that in the future, so in that sense it's "ahead of time", but the reason it's done that way because evolution has found that learning patterns will ultimately prove beneficial.