[ZeroCool2u]

Regardless of your opinion of Yann or his views on auto regressive models being "sufficient" for what most would describe as AGI or ASI, this is probably a good thing for Europe. We need more well capitalized labs that aren't US or China centric and while I do like Mistral, they just haven't been keeping up on the frontier of model performance and seem like they've sort of pivoted into being integration specialists and consultants for EU corporations. That's fine and they've got to make money, but fully ceding the research front is not a good way to keep the EU competitive.

[brandonb]

LeCun's technical approach with AMI will likely be based on JEPA, which is also a very different approach than most US-based or Chinese AI labs are taking.

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.

[sanderjd]

Have you published anything about your health time series model? Sounds interesting!

[brandonb]

Sure! Here’s a description: https://www.empirical.health/blog/wearable-foundation-model-...

[sanderjd]

Thanks! This is very neat.

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.

[brandonb]

Thanks! We need to re-do the top navigation / hamburger menu -- we've added a bunch of new things in the past few months, and it badly needs to be re-organized.

[smugma]

Very interesting. I am keenly interested in this space and coincidentally had my blood drawn this morning.

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?

[brandonb]

FWIW, the single blood draw is 6-8 vials -- so we're not claiming to get 100 biomarkers from a single drop. The point of that is mostly that it just takes one appointment / is convenient.

[mkeoliya]

This is very cool work! I have a quick follow-up: in the biomarker prediction task, what horizon (ie. how far into the future) did you set for the predictions? Prediction is hard beyond an hour, so it'd be impressive if your model handles that.

[brandonb]

The prediction task is set up as predicting the next measured biomarkers based on a week of wearable data. So it's not necessarily predicting into the future, but predicting dataset Y given dataset X.

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.

[volkk]

oh nice, i actually used you guys for some labs a few months ago. Glad you're competing with function & superpower

[mandeepj]

Appreciate your work! Healthcare is a regulated industry. Everything (Research, proposals, FDA submissions, Compliance docs, Accreditation Standards, etc.) is documented and follows a process, which means there's a lot of thesis. You can't sneak in anything unverified or unreliable. Why does healthcare need a JEPA\World model?

[brandonb]

Regulation is quickly catching up to modern AI techniques; for the most part, the approach is to verify outputs rather than process. For example, Utah's pilot to let AI prescribe medications has doctors check the first N prescriptions of each medication. Medicare is starting to pay for AI-enabled care, but tying payment to objective biomarkers like cholesterol or blood pressure actually got better.

[tomrod]

I've been working to understand the potential uses for JEPA. Outside of video, has anyone made a list of any type (geared towards dummies like me)?

[Brajeshwar]

There seem to be other news articles mentioning that they are setting up in Singapore as their base. https://www.straitstimes.com/business/ai-godfather-raises-1-...

[Signez]

Hm, Singapour looks more like "one of their base"; they will have offices in Paris, Montréal, Singapour and New York (according to both this article and the interview Yann Le Cun did this morning on France Inter, the most listened radio in France).

Of course, each relevant newspaper on those areas highlight that it's coming to their place, but it really seems to be distributed.

[rubzah]

All your base are belong to Yann LeCun.

[fnands]

Probably just a satellite office.

Might be to be close to some of Yann's collaborators like Xavier Bresson at NUS

[stingraycharles]

That's a Singaporian newspaper, though; not sure if it's objectively their main base, or just one of them

[throwpoaster]

"Show me the incentive and I will show you the outcome."

Almost certainly the IP will be held in Singapore for tax reasons.

[RamblingCTO]

Which would be a good idea, as a European. I'd hate to see the investment go to waste on taxes that are spent on stupid shit anyway. Should go into R&D not fighting bureaucracy.

[re-thc]

> they are setting up in Singapore as their base

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.

[Signez]

Yann Le Cun litteraly said this morning on the radio in France that it is headquarted in Paris and will pay taxes in France. Go figure…

[ttoinou]

No he said something like “well yes, only for the parts of profits made in France”

[lotsofpulp]

Why would it be any other way?

[ttoinou]

French people have this pipe dream all others french people to pay 75% of what they produce worldwide to pay for their retreats, hospital, useless schools system and all theirs “comité Théodule”

[roromainmain]

For such companies, France also offers generous R&D tax credits (Crédit Impôt Recherche): companies can recover roughly 30% of eligible R&D expenses incurred in France as a tax credit, which can eventually be refunded (in cash) if the company has no taxable profit.

[storus]

Is that alongside 100% of R&D expenses amortized in taxes when a company has taxable profit covering them?

[roromainmain]

Yes indeed, if the company is profitable.

[mi_lk]

Doesn’t he live in New York himself? Although not sure if that matters depending on his role

[kvgr]

There will be no corporate taxes for a long time, so alls good.

[Imustaskforhelp]

This is a singaporean news article from a singporean company[0] (Had to look it up)

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.

[0]: https://www.sgpbusiness.com/company/Sph-Media-Limited

[sofixa]

> Europe in general has been tightening up their rules / taxes / laws around startups / companies especially tech and remote.

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.

[barrell]

While I’d love there to be a European frontier model, I do very much enjoy mistral. For the price and speed it outperforms any other model for my use cases (language learning related formatting, non-code non-research).

[vessenes]

Partner in a fund that wrote a small check into this — I have no private knowledge of the deal - while I agree that one’s opinion on auto regressive models doesn’t matter, I think the fact of whether or not the auto regressive models work matters a lot, and particularly so in LeCun’s case.

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.

[jsnell]

I don't think it's "regardless", your opinion on LeCun being right should be highly correlated to your opinion on whether this is good for Europe.

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.

[vidarh]

It's sufficient to think that there is a chance that they will not be, however, for there to be a non-zero value to fund other approaches.

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.

[Tenoke]

I think LeCun has been so consistently wrong and boneheaded for basically all of the AI boom, that this is much, much more likely to be bad than good for Europe. Probably one of the worst people to give that much money to that can even raise it in the field.

[ainch]

LeCun was stubbornly 'wrong and boneheaded' in the 80s, but turned out to be right. His contention now is that LLMs don't truly understand the physical world - I don't think we know enough yet to say whether he is wrong.

[gozucito]

Could you please elaborate on what he was wrong about?

[conradkay]

He said that LLMs wouldn't have common sense about how the real world physically works, because it's so obvious to humans that we don't bother putting it into text. This seems pretty foolish honestly given the scale of internet data, and even at the time LLMs could handle the example he said they couldn't

I believe he didn't think that reasoning/CoT would work well or scale like it has

[dheera]

Just because you raise 1 billion dollars to do X doesn't mean you can't pivot and do Y if it is in the best interest of your mission.

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".

[andrepd]

It's been 6 months away for 5 years now. In that time we've seen relatively mild incremental changes, not any qualitative ones. It's probably not 6 months away.

[AStrangeMorrow]

Yeah. I feel like that like many projects the last 20% take 80% of time, and imho we are not in the last 20%

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.

[lordmathis]

It's 6 months away the same way coding is apparently "solved" now.

[HarHarVeryFunny]

I think we - in last few months - are very close to, if not already at, the point where "coding" is solved. That doesn't mean that software design or software engineering is solved, but it does mean that a SOTA model like GPT 5.4 or Opus 4.6 has a good chance of being able to code up a working version of whatever you specify, with reason.

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.

[lordmathis]

Sure, Claude and other SOTA LLMs do generate about 90% of my code but I feel like we are not closer to solving the last 10% than we were a year ago in the days of Claude 3.7. It can pretty reliably get 90% there and then I can either keep prompting it to get the rest done or just do it manually which is quite often faster.

[j4k0o]

It's interesting that people don't seem to think the likely outcome might be... capital and labour. Not capital alone.

You see this in construction - the capital is used for certain things and is operated by labour.

[mfru]

Reminds me of how cold fusion reactors are only 5 years away for decades now

[vidarh]

Cold fusion reactors haven't produced usable intermediate results. LLMs have.

[leptons]

LLMs produce slop far to often to say they are in any way better than cold fusion in terms of usable results. "AI" kind of is the cold fusion of tech. We've always been 5 or 10 years away from "AGI" and likely always will be.

[vidarh]

That's just nonsense. That they produce slop does not negate that I and many others get plenty of value out of them in their current form, while we get zero value out of fusion so far - cold or otherwise.

[basket_horse]

But I swear this time is different! Just give me another 6 months!

[andrepd]

And another 6 trillion dollars :^)

[Insanity]

Whenever I see claims about AGI being reachable through large language models, it reminds me of the miasma theory of disease. Many respectable medical professionals were convinced this was true, and they viewed the entire world through this lens. They interpreted data in ways that aligned with a miasmatic view.

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.

[visarga]

> Whenever I see claims about AGI being reachable through large language models, it reminds me of the miasma theory of disease.

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.

[awakeasleep]

If I'm understanding you, it seems like you're struck by hindsight bias. No one knew the miasma theory was wrong... it could have been right! Only with hindsight can we say it was wrong. Seems like we're in the same situation with LLMs and AGI.

[nradov]

The miasma theory of disease was "not even wrong" in the sense that it was formulated before we even had the modern scientific method to define the criteria for a theory in the first place. And it was sort of accidentally correct in that some non-infectious diseases are caused by airborne toxins.

[scarmig]

Plenty of scientific authorities believed in it through the 19th century, and they didn't blindly believe it: it had good arguments for it, and intelligent people weighed the pros and cons of it and often ended up on the side of miasma over contagionism. William Farr was no idiot, and he had sophisticated statistical arguments for it. And, as evidence that it was a scientific theory, it was abandoned by its proponents once contagionism had more evidence on its side.

It's only with hindsight that we think contagionism is obviously correct.

[0x3f]

> Only with hindsight can we say it was wrong

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.

[ainch]

It's unintuitive to me that architecture doesn't matter - deep learning models, for all their impressive capabilities, are still deficient compared to human learners as far as generalisation, online learning, representational simplicity and data efficiency are concerned.

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'.

[ordu]

> Of course you can still improve the models, but you get much more upside from data, or even better - from interactive environments.

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.

[famouswaffles]

>With only instructional materials (a 500-page reference grammar, a dictionary, and ≈400 extra parallel sentences) all provided in context, Gemini 1.5 Pro and Gemini 1.5 Flash are capable of learning to translate from English to Kalamang— a Papuan language with fewer than 200 speakers and therefore almost no online presence—with quality similar to a person who learned from the same materials

https://arxiv.org/abs/2403.05530

[ordu]

I'm not entirely sure, that I totally convinced, but yeah, it is better than me. I mean, I could do the same, but it would take me ages to go through 500 pages and to use them for the actual translation.

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.

[bethekidyouwant]

Are you asking how many books a large language model would need to read to learn a new language if it was only trained on a different language? probably just 1 (the dictionary)

[suddenlybananas]

Do you know anything about how languages work? A dictionary doesn't have sufficient information to speak a language.

[0x3f]

If model arch doesn't matter much how come transformers changed everything?

[visarga]

Luck. RNNs can do it just as good, Mamba, S4, etc - for a given budget of compute and data. The larger the model the less architecture makes a difference. It will learn in any of the 10,000 variations that have been tried, and come about 10-15% close to the best. What you need is a data loop, or a data source of exceptional quality and size, data has more leverage. Architecture games reflect more on efficiency, some method can be 10x more efficient than another.

[0x3f]

That's not how I read the transformer stuff around the time it was coming out: they had concrete hypotheses that made sense, not just random attempts at striking it lucky. In other words, they called their shots in advance.

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?

[scarmig]

The miasma theory of disease, though wrong, made lots of predictions that proved useful and productive. Swamps smell bad, so drain them; malaria decreases. Excrement in the street smells bad, so build sewage systems; cholera decreases. Florence Nightingale implemented sanitary improvements in hospitals inspired by miasma theory that improved outcomes.

It was empirical and, though ultimately wrong, useful. Apply as you will to theories of learning.

[psychoslave]

Is AGI about replicating human intelligence though? Like, human intelligence comes with its own defects, and whatever we fantasize about an intelligence free of this or that defects is done from a perspective that is full of defectiveness.

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.

[next_xibalba]

> RSI

Wait, we have another acronym to track. Is this the same/different than AGI and/or ASI?

[mietek]

Some people should definitely be getting Repetitive Strain Injury from all the hyping up of LLMs.

[robrenaud]

Recursive self improvement. It's when AI speeds up the development of the next AI.

[notnullorvoid]

Recursive Self Improvement

[crystal_revenge]

> fully ceding the research front is not a good way to keep the EU competitive

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-...

[AngryData]

Hard disagree, military might isn't going to secure anybody into the future, modern society and our economies will only get more vulnerable as time goes on and large wars or engagements will just push economies closer to collapse. And without a solid modern economy to back up the military, modern military will fall apart.

[gandalfstoe]

Right, they really need a military industrial complex to be "competitive" :eyeroll. Are you suggesting regressing to the stone age?

[crystal_revenge]

Europe doesn't want to be reliant (understandably) on the US military for defense, because if they are, as Trump has demonstrated, they will be pressured to make concessions not in their interests.

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...

[suddenlybananas]

France has nukes and is making more. They're fine.

[sofixa]

> But already unable to defend itself from Russia, EU countries

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.

[crystal_revenge]

It’s funny how you basically explain precisely why the war in Ukraine has gone on so long but refuse to recognize it.

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.

[sofixa]

> Russia (well its oligarchs and rulers) has increasingly benefited from perpetual war

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.

[gigatexal]

As an American here in Berlin, I, too welcome this. I would love for there to be many large well capitalized companies here for me to work at.

[chrisgd]

33% of the business in a seed round is nuts

[ak_111]

can you elaborate more, also isn't this necessary for a Lab that wants to compete with highly funded entities (like OpenAI, Anthropic)?

[chrisgd]

At this rate, with round 4, outside investors own 70% of company.

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.

[nailer]

> Regardless of your opinion of Yann or his views on auto regressive models being "sufficient" for what most would describe as AGI or ASI

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.

[neversupervised]

Is it good? This will almost certainly fail. Not because Yann or Europe, but because these sort of hyper-hyped projects fail. SSI and Thinking Machines haven’t lived to the hype.

[ma2rten]

Erm, ... OpenAI has hyped when it started and it took 6 years to take off. It's way to early to declare the SSI and Thinking Machines have failed.

[koakuma-chan]

They took money and haven't released anything. How are they doing?

[levocardia]

To be fair to SSI, they were very explicit about their plan: "we are going to take money and not release anything until we one-shot superintelligence."

If you invested in that you knew what you were getting yourself into!

[giancarlostoro]

I didn't really know who he was, so I went and found his wikipedia, which is written like either he wrote it himself to stroke his ego, or someone who likes him wrote it to stroke his ego:

> 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.

[pama]

You underestimate academia. Any academic that reads these two sentences only focuses on the first one: He has a named chair at Courant. In Germany, being a a Prof is added to your ID card/passport and becomes part of your official name, like knighthood in other countries.

[dr_hooo]

No true regarding the IDs, only PhD titles can be added. Not job descriptions. Source: academia person in Germany.

[DeathArrow]

It seems Germans add their PhD titles even to their nicknames. :)

[timr]

It's not comparing him to anyone. He has an endowed professorship. This is standard in academia, and you give the name because a) it's prestigious for the recipient and b) it strokes the ego of the donor.

[leoc]

Right: no-one cares about the Lucasian Chair of Mathematics https://en.wikipedia.org/wiki/Lucasian_Professor_of_Mathemat... because of Henry Lucas, it's the other way around.

[lairv]

https://cims.nyu.edu/dynamic/news/1441/

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

[stephencanon]

Yann is definitely more well-known outside of academia. Inside academia, it's going to depend a lot on your specific background and how old you are.

[bobwaycott]

That’s not a comparison to another person. That’s his job title. It is not uncommon for universities to have distinguished chairs within departments named after a notable person—in this case, the founder of NYU’s Department of Computer Science.

[g947o]

Eh, that paragraph reads perfectly normal to me.

Either you have not read enough Wikipedia pages, or you have too much to complain about. (Or both.)