[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.

[HarHarVeryFunny]

We're certainly in the "capital/robot + labor" phase of AI at the moment, which Dario Amodei is referring to as the "centaur" (half horse, half human) phase, and expects to be very short lived.

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".

[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.

[psychoslave]

> 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.

[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.

[bethekidyouwant]

Actually I do know how latent space works, If you meant achieving excellence in syntax and grammar then much like us more examples are better

[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?

[jsnell]

The premise is wrong, we are not seeing diminishing returns. By basically any metric that has a ratio scale, AI progress is accelerating, not slowing down.

[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