If you're looking to learn about JEPA, LeCun's vision document "A Path Towards Autonomous Machine Intelligence" is long but sketches out a very comprehensive vision of AI research: https://openreview.net/pdf?id=BZ5a1r-kVsf
Training JEPA models within reach, even for startups. For example, we're a 3-person startup who trained a health timeseries JEPA. There are JEPA models for computer vision and (even) for LLMs.
You don't need a $1B seed round to do interesting things here. We need more interesting, orthogonal ideas in AI. So I think it's good we're going to have a heavyweight lab in Europe alongside the US and China.
BTW, I went to your website looking for this, but didn't find your blog. I do now see that it's linked in the footer, but I was looking for it in the hamburger menu.
That said, have you considered that “Measure 100+ biomarkers with a single blood draw” combined with "heart health is a solved problem” reads a lot like Theranos?
The specific biomarkers being predicted are the ones most relevant to heart health, like cholesterol or HbA1c. These tend to be more stable from hour to hour -- they may vary on a timescale of weeks as you modify your diet or take medications.
Of course, each relevant newspaper on those areas highlight that it's coming to their place, but it really seems to be distributed.
Europe in general has been tightening up their rules / taxes / laws around startups / companies especially tech and remote.
It's been less friendly. these days.
As such, They are more likely to talk about singapore news and exaggerate the claims.
Singapore isn't the Key location. From what I am seeing online, France is the major location.
Singapore is just one of the more satellite like offices. They have many offices around the world it seems.
Like? Care to provide any specific examples? "Europe" is a continent composed of various countries, most of which have been doing a lot to make it easier for startups and companies in general.
Might be to be close to some of Yann's collaborators like Xavier Bresson at NUS
Almost certainly the IP will be held in Singapore for tax reasons.
What’s different about investing in this than investing in say a young researcher’s startup, or Ilya’s superintelligence? In both those cases, if a model architecture isn’t working out, I believe they will pivot. In YL’s case, I’m not sure that is true.
In that light, this bet is a bet on YL’s current view of the world. If his view is accurate, this is very good for Europe. If inaccurate, then this is sort of a nothing-burger; company will likely exit for roughly the investment amount - that money would not have gone to smaller European startups anyway - it’s a wash.
FWIW, I don’t think the original complaint about auto-regression “errors exist, errors always multiply under sequential token choice, ergo errors are endemic and this architecture sucks” is intellectually that compelling. Here: “world model errors exist, world model errors will always multiply under sequential token choice, ergo world model errors are endemic and this architecture sucks.” See what I did there?
On the other hand, we have a lot of unused training tokens in videos, I’d like very much to talk to a model with excellent ‘world’ knowledge and frontier textual capabilities, and I hope this goes well. Either way, as you say, Europe needs a frontier model company and this could be it.
If you think that LLMs are sufficient and RSI is imminent (<1 year), this is horrible for Europe. It is a distracting boondoggle exactly at the wrong time.
Sure LLMs are getting better and better, and at least for me more and more useful, and more and more correct. Arguably better than humans at many tasks yet terribly lacking behind in some others.
Coding wise, one of the things it does “best”, it still has many issues: For me still some of the biggest issues are still lack of initiative and lack of reliable memory. When I do use it to write code the first manifests for me by often sticking to a suboptimal yet overly complex approach quite often. And lack of memory in that I have to keep reminding it of edge cases (else it often breaks functionality), or to stop reinventing the wheel instead of using functions/classes already implemented in the project.
All that can be mitigated by careful prompting, but no matter the claim about information recall accuracy I still find that even with that information in the prompt it is quite unreliable.
And more generally the simple fact that when you talk to one the only way to “store” these memories is externally (ie not by updating the weights), is kinda like dealing with someone that can’t retain memories and has to keep writing things down to even get a small chance to cope. I get that updating the weights is possible in theory but just not practical, still.
What's still missing is the general reasoning ability to plan what to build or how to attack novel problems - how to assess the consequences of deciding to build something a given way, and I doubt that auto-regressively trained LLMs is the way to get there, but there is a huge swathe of apps that are so boilerplate in nature that this isn't the limitation.
I think that LeCun is on the right track to AGI with JEPA - hardly a unique insight, but significant to now have a well funded lab pursuing this approach. Whether they are successful, or timely, will depend if this startup executes as a blue skies research lab, or in more of an urgent engineering mode. I think at this point most of the things needed for AGI are more engineering challenges rather than what I'd consider as research problems.
You see this in construction - the capital is used for certain things and is operated by labour.
Eventually (maybe taking a lot longer than a lot of people expect and/or are hoping for) we'll achieve full human-equivalent AI, at which point you won't NEED a centaur approach - the mechanical horse will be capable of doing ALL non-physical work by itself, but that doesn't mean this is how this will actually play out. If we do end up heading for some dystopian "Soylent Green" type future where most humans are unemployed, surviving poorly on government handouts, then I expect there would eventually be riots and uprising that would push back against it. It also just doesn't work - you can't create profits without customers, and customers need money to buy what you're selling.
Part of why we may (and hopefully will) continue to see humans, from CEO on down, still working when they could be replaced with AI, is that even "AGI", which we've yet to achieve, doesn't mean human-like - it's really just focusing on intelligence. Creating an actual remote-worker replacement requires more than just automating the intelligent decision-making part of a human (the "AGI" part) - it also requires the human/social/emotional part, which will take longer, and there may not even be any desire to push for that. I think people maybe discount how much of being a successful member of a team is based around human soft skills, our ability to understand and interact with each other, not just raw intellectual capacity, and certainly at this point in time corporate success is still very much "who you know, not what you know".
Wait, we have another acronym to track. Is this the same/different than AGI and/or ASI?
Of course now we know this was delusional and it seems almost funny in retrospect. I feel the same way when I hear that 'just scale language models' suddenly created something that's true AGI, indistinguishable from human intelligence.
Whenever I see people think the model architecture matters much, I think they have a magical view of AI. Progress comes from high quality data, the models are good as they are now. Of course you can still improve the models, but you get much more upside from data, or even better - from interactive environments. The path to AGI is not based on pure thinking, it's based on scaling interaction.
To remain in the same miasma theory of disease analogy, if you think architecture is the key, then look at how humans dealt with pandemics... Black Death in the 14th century killed half of Europe, and none could think of the germ theory of disease. Think about it - it was as desperate a situation as it gets, and none had the simple spark to keep hygiene.
The fact is we are also not smart from the brain alone, we are smart from our experience. Interaction and environment are the scaffolds of intelligence, not the model. For example 1B users do more for an AI company than a better model, they act like human in the loop curators of LLM work.
I'm not aware that we have notably different data sources before or after transformers, so what confounding event are you suggesting transformers 'lucked' in to being contemporaneous with?
Also, why are we seeing diminishing returns if only the data matters. Are we running out of data?
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 into existence the moment the necessary model capabilities exist.)
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'.
It really depends what you mean by 'we'. Laymen? Maybe. But people said it was wrong at the time with perfectly good reasoning. It might not have been accessible to the average person, but that's hardly to say that only hindsight could reveal the correct answer.
It's only with hindsight that we think contagionism is obviously correct.
We, the mere median citizen on any specific topic which is out of our expertise, certainly not. And this also have an impact as a social pressure in term of which theory is going to be given the more credits.
That's not actually specific to science. Even theological arguments can be dumb as hell or super refined by the smartest people able to thrive in their society of the time.
Correctness of the theories and how great a match they are with collected data is only a part of what make mass adoption of any theory, and not necessarily the most weighted. It's interdependence with feedback loops everywhere, so even the data collected, the tool used to collect and analyze and the metatheorical frameworks to evaluate different models are nothing like absolute objective givens.
I'm on the contrary believe that the hunt for better data is an attempt to climb the local hill and be stuck there without reaching the global maximum. Interactive environments are good, they can help, but it is just one of possible ways to learn about causality. Is it the best way? I don't think so, it is the easier way: just throw money at the problem and eventually you'll get something that you'll claim to be the goal you chased all this time. And yes, it will have something in it you will be able to call "causal inference" in your marketing.
But current models are notoriously difficult to teach. They eat enormous amount of training data, a human needs much less. They eat enormous amount of energy to train, a human needs much less. It means that the very approach is deficient. It should be possible to do the same with the tiny fraction of data and money.
> The fact is we are also not smart from the brain alone, we are smart from our experience. Interaction and environment are the scaffolds of intelligence, not the model.
Well, I learned English almost all the way to B2 by reading books. I was too lazy to use a dictionary most of the time, so it was not interactive: I didn't interact even with dictionary, I was just reading books. How many books I've read to get to B2? ~10 or so. Well, I read a lot of English in Internet too, and watched some movies. But lets multiply 10 books by 10. Strictly speaking it was not B2, I was almost completely unable to produce English and my pronunciation was not just bad, it was worse. Even now I stumble sometimes on words I cannot pronounce. Like I know the words and I mentally constructed a sentence with it, but I cannot say it, because I don't know how. So to pass B2 I spent some time practicing speech, listening and writing. And learning some stupid topic like "travel" to have a vocabulary to talk about them in length.
How many books does LLM need to consume to get to B2 in a language unknown to it? How many audio records it needs to consume? Life wouldn't be enough for me to read and/or listen so much.
If there was a human who needed to consume as much information as LLM to learn, they would be the stupidest person in all the history of the humanity.
I'm not sure, because Gemini knows a lot of languages. The third language is easier to learn than the second one, I suppose 100th language is even easier? But still Gemini do better, than I believed.
Just because RNNs and Transformers both work with enormous datasets doesn't mean that architecture/algorithm is irrelevant, it just suggests that they share underlying primitives. But those primitives may not be the right ones for 'AGI'.
It was empirical and, though ultimately wrong, useful. Apply as you will to theories of learning.
Collectively at global level, it seems we are just unable to avoid escalating conflicts into things as horrible as genocides. Individuals which have remarkable ability to achieve technical feats sometime can in the same time fall behind the most basic expectation in term of empathy which can also be considered a form off intelligence.
I won't comment on Yann LeCun or his current technical strategy, but if you can avoid sunk cost fallacy and pivot nimbly I don't think it is bad for Europe at all. It is "1 billion dollars for an AI research lab", not "1 billion dollars to do X".
And even if you think the chance is zero, unless you also think there is a zero chance they will be capable of pivoting quickly, it might still be beneficial.
I think his views are largely flawed, but chances are there will still be lots of useful science coming out of it as well. Even if current architectures can achieve AGI, it does not mean there can't also be better, cheaper, more effective ways of doing the same things, and so exploring the space more broadly can still be of significant value.
I believe he didn't think that reasoning/CoT would work well or scale like it has
Tech is ultimately a red herring as far as what's needed to keep the EU competitive. The EU has a trillion dollar hole[0] to fill if they want to replace US military presence, and current net import over 50% of their energy. Unfortunately the current situation in Iran is not helping either of these as they constrains energy further and risks requiring military intervention.
0. https://www.wsj.com/world/europe/europes-1-trillion-race-to-...
The need for a military is tightly coupled with the EU's need for energy. You can see this in the immediate impact that the war in Iran has had on Germany's natural gas prices [0]. But already unable to defend itself from Russia, EU countries are in a tough spot since they can't really afford to expend military resources defending their energy needs, and yet also don't have the energy independence to ignore these military engagements without risk. Meanwhile Russia has spend the last 4 years transition to a wartime economy and is getting hungry for expanded resource acquisition.
The world hasn't fundamentally changed since the stone age: humans need resources to survive and if there aren't enough people for those resources then violence will decide who has access the them.
0. https://tradingeconomics.com/commodity/germany-natural-gas-t...
I'm sorry, but this is just crazy talk. Russia cannot enforce its will on Ukraine, one of the poorest and most corrupt countries in Europe, with a (at time of invasion) relatively small and underequipped army. Yes it has grown through conscription, has been equipped by foreign and domestic supplies, has made some brilliant advances in tech and tactics... but when it was attacked, it was weak. And Russia lost its best troops and equipment failing to defat that.
Why would anyone think that the Russia that cannot defeat Ukraine would fare better against Poland? Let alone French warning strike nukes, or French, British, German troops and planes and what not.
As Russia’s economy has continually reshaped over the last 4 years there has been increasingly a domestic demand for war. You point out all the evidence yourself:
> Yes it has grown through conscription, has been equipped by foreign and domestic supplies, has made some brilliant advances in tech and tactics...
Russia (well its oligarchs and rulers) has increasingly benefited from perpetual war. Yes, soon it will need to switch positions to expansion to maintain its economy, but this situation in Iran presents a perfect opportunity if things play it Russia’s interests.
You also will find that if you paid any attention to European politics over the years this is a serious topic to all leaders there.
But I don’t mind if you’re not convinced, I had similar people on hacker news unconvinced Russia could sustain operations in Russia longer than a few months because they were doing so poorly… 4 years ago.
No it has not. It has a ballooning debt crisis (at different levels - regions, military contractors, banks) which will pop at some point; the budget is so unbalanced they're projecting to reduce military spending (unlikely), increase taxes, and still have a pretty heavy deficit. They've been given the gift of the Strait of Hormuz being closed, so oil and gas revenues will grow, which will definitely buy them more time. But they are running against a clock, and they cannot win in Ukraine.
> You also will find that if you paid any attention to European politics over the years this is a serious topic to all leaders there.
Yes, because Russia only responds to strength, so you need to be strong militarily to be able to dissuade them from attacking you. That doesn't mean that realistically they have a chance of winning any conflict.
It’s not wrong, but unusual. Most seed round might be 10-20% of business. Your point on quantum of capital could be the disconnect for me.
My main concern with Lecunn are the amount of times he has repeatedly told people software is open source when it’s license directly violates the open source definition.
If you invested in that you knew what you were getting yourself into!
> He is the Jacob T. Schwartz Professor of Computer Science at the Courant Institute of Mathematical Sciences at New York University. He served as Chief AI Scientist at Meta Platforms before leaving to work on his own startup company.
That entire sentence before the remarks about him service at Meta could have been axed, its weird to me when people compare themselves to someone else who is well known. It's the most Kanye West thing you can do. Mind you the more I read about him, the more I discovered he is in fact egotistical. Good luck having a serious engineering team with someone who is egotistical.
This is just the official name of a chair at NYU. I'm not even sure Jacob T. Schwartz is more well known than Yann LeCun
Either you have not read enough Wikipedia pages, or you have too much to complain about. (Or both.)