So at this point it does not matter what you believe about LLMs: in general, to trust LeCun words is not a good idea. Add to this that LeCun is directing an AI lab that as the same point has the following huge issues:
1. Weakest ever LLM among the big labs with similar resources (and smaller resources: DeepSeek).
2. They say they are focusing on open source models, but the license is among the less open than the available open weight models.
3. LLMs and in general all the new AI wave puts CNNs, a field where LeCun worked (but that didn't started himself) a lot more in perspective, and now it's just a chapter in a book that is composed mostly of other techniques.
Btw, other researchers that were in the LeCun side, changed side recently, saying that now "is different" because of CoT, that is the symbolic reasoning they were blabling before. But CoT is stil regressive next token without any architectural change, so, no, they were wrong, too.
How could that possibly be true?
There’s obviously a link between “[original content] is summarized as [summarized”content]
What makes it "seem to get better" and what keeps throwing people like lecun off is the training bias, the prompts, the tooling and the billions spent cherry picking information to train on.
What LLMs do best is language generation which leads to, but is not intelligence. If you want someone who was right all along, maybe try Wittgenstein.
Where I'm skeptical of LLM skepticism is that people use the term "stochastic parrot" disparagingly, as if they're not impressed. LLMs are stochastic parrots in the sense that they probabilistically guess sequences of things, but isn't it interesting how far that takes you already? I'd never have guessed. Fundamentally I question the intellectual honesty of anyone who pretends they're not surprised by this.
That's why I'm not too impressed even when he has changed his mind: he has admitted to individual mistakes, but not to the systemic issues which produced them, which makes for a safe bet that there will be more mistakes in the future.
Of course, as they learn, early in the training, the first functions they will model, to lower the error, will start being the probabilities of the next tokens, since this is the simplest function that works for the loss reduction. Then gradients agree in other directions, and the function that the LLM eventually learn is no longer related to probabilities, but to the meaning of the sentence and what it makes sense to say next.
It's not be chance that often the logits have a huge signal in just two or three tokens, even if the sentence, probabilistically speaking, could continue in much more potential ways.
So it's not so much about his incorrect predictions, but that these predictions were based on a core belief. And when the predictions turned out to be false, he didn't adjust his core beliefs, but just his predictions.
So it's natural to ask, if none of the predictions you derived from your core belief come true, maybe your core belief isn't true.
Like what?
Your timeline doesn't sound crazy outlandish. It sounds pretty normal and lines up with my thoughts as AI has advanced over the past few years. Maybe more conservative than others in the field, but that's not a reason to dismiss him entirely any more than the hypesters should be dismissed entirely because they were over promising and under delivering?
> Now reasoning models can solve problems they never saw
This is not the same as a novel question though.
> o3 did huge progresses on ARC
Is this a benchmark? O3 might be great, but I think the average person's experience with LLMs matches what he's saying, it seems like there is a peak and we're hitting it. It also matches what Ilya said about training data being mostly gone and new architectures (not improvements to existing ones) needing to be the way forward.
> LeCun is directing an AI lab that as the same point has the following huge issues
Second point has nothing to do with the lab and more to do with Meta. Your last point has nothing to do with the lab at all. Meta also said they will have an agent that codes like a junior engineer by the end of the year and they are clearly going to miss that prediction, so does that extra hype put them back in your good books?
But the point of my response was just that I find it an extremely surprising how well an idea as simple as "find patterns in sequences" actually works for the purpose of sounding human, and I'm suspicious of anyone who pretends this isn't incredible. Can we agree on this?
The idea that meaning is not impacted by language yet is somehow exclusively captured by language is just absolutely absurd
Like saying X+Y=Z but changing X or Y won’t affect Z
But enough data implies probabilities. Consider 2 sentences:
"For breakfast I had oats"
"For breakfast I had eggs"
Training on this data, how do you complete "For breakfast I had..."?
There is no best deterministic answer. The best answer is a 50/50 probability distribution over "oats" and "eggs"
He's done a lot of amazing work, but his stance on LLMs seems continuously off the mark.
If your model of reality makes good predictions and mine makes bad ones, and I want a more accurate model of reality, then I really shouldn’t just make small provisional and incremental concessions gerrymandered around whatever the latest piece of evidence is. After a few repeated instances, I should probably just say “oops, looks like my model is wrong” and adopt yours.
This seems to be a chronic problem with AI skeptics of various sorts. They clearly tell us that their grand model indicates that such-and-such a quality is absolutely required for AI to achieve some particular thing. Then LLMs achieve that thing without having that quality. Then they say something vague about how maybe LLMs have that quality after all, somehow. (They are always shockingly incurious about explaining this part. You would think this would be important to them to understand, as they tend to call themselves “scientists”.)
They never take the step of admitting that maybe they’re completely wrong about intelligence, or that they’re completely wrong about LLMs.
Here’s one way of looking at it: if they had really changed their mind, then they would stop being consistently wrong.
Are we able to prove it with output that's
1) algorithmically novel (not just a recombination)
2) coherent, and
3) not explainable by training data coverage.
No handwaving with scale...
-Lord Kelvin. 1895
> I think there is a world market for maybe five computers. Thomas Watson, IBM. 1943
> On talking films: “They’ll never last.” -Charlie Chaplin.
> This ‘telephone’ has too many shortcomings… -William Orton, Western Union. 1876
> Television won’t be able to hold any market -Darryl Zanuck, 20th Century Fox. 1946
> Louis Pasteur’s theory of germs is ridiculous fiction. -Pierre Pachet, French physiologist.
> Airplanes are interesting toys but of no military value. — Marshal Ferdinand Foch 1911
> There’s no chance the iPhone is going to get any significant market share. — Steve Ballmer, CEO Microsoft CEO. 2007
> Stocks have reached a permanently high plateau. — Irving Fisher, Economist. 1929
> Who the hell wants to hear actors talk? —Harry Warner, Warner Bros. 1927
> By 2005, it will become clear that the Internet’s impact on the economy has been no greater than the fax machine. -Paul Krugman, Economist. 1998
I want LLMs to create, but so far, every creative output I’ve seen is just a clever remix of training data. The most advanced models still fail a simple test: Restrict the domain, for example, "invent a cookie recipe with no flour, sugar, or eggs" or "name a company without using real words". Suddenly, their creativity collapses into either, nonsense (violating constraints), or trivial recombination, ChocoNutBake instead of NutellaCookie.
If LLMs could actually create, we’d see emergent novelty, outputs that couldn’t exist in the training data. Instead, we get constrained interpolation.
Happy to be proven wrong. Would like to see examples where an LLM output is impossible to map back to its training data.
In many cases the folks in question were waaaaay past their best days.
Sorry I am a little lost reading the last part about regressive next token and it is still wrong. Could someone explain a little bit? Edit: Explained here further down the thread. ( >>43594813 )
I personally went from AI skeptic ( it wont ever replace all human, at least not in the next 10 - 20 years ) to AI scary simply because of the reasoning capability it gained. It is not perfect, far from it but I can immediately infer how both algorithm improvements and hardware advance could bring us in 5 years. And that is not including any new breakthrough.
1. Weakest ever LLM? This one is really making me scratch my head. For a period of time Llama was considered to THE best. Furthermore, it's the third most used on OpenRouter (in the past month): https://openrouter.ai/rankings?view=month
2. Ignoring DeepSeek for a moment, Llama 2 and 3 require a special license from Meta if the products or services using the models have more than 700 million monthly active users. OpenAI, Claude and Gemini are not only closed source, but require a license/subscription to even get started.
(All things considered, you may be right to be suspicious of me.)
One does not follow from the other. In particular I don't "trust" anyone who is trying to make money off this technology. There is way more marketing than honest science happening here.
> and o3 did huge progresses on ARC,
It also cost huge money. The cost increase to go from 75% to 85% was two orders of magnitude greater. This cost scaling is not sustainable. It also only showed progress on ARC1, which it was trained for, and did terribly on ARC2 which it was not trained for.
> Btw, other researchers that were in the LeCun side, changed side recently,
Which "side" researchers are on is the least useful piece of information available.
if the "core belief" is that the LLM architecture cannot be the way to AGI, that is more of an "educated bet", which does not get falsified when LLMs improve but still suggest their initial faults. If seeing that LLMs seem constrained in the "reactive system" as opposed to a sought "deliberative system" (or others would say "intuitive" vs "procedural" etc.) was an implicit part of the original "core belief", then it still stands in spite of other improvements.
Which LLMs have shown you "strong summarization abilities"?
Examples of people who could not see non (in some way) dead-ends do not cancel examples of people who correctly saw dead-ends. The lists may even overlap ("if it remains that way it's a dead-end").
Not really a good measure of quality or performance but of cost effectiveness
And on the latent space bit, it's also true for classical models, and the basic idea behind any pattern recognition or dimensionality reduction. That doesn't mean it's necessarily "getting the right idea."
Again, I don't want to "think of it as a probability." I'm saying what you're describing is a probability distribution. Do you have a citation for "probability to express correctly the sentence/idea" bit? Because just having a latent space is no implication of representing an idea.
The demand for outputs that are provably untraceable to training data feels like asking for magic, not creativity. Even Gödel didn’t require “never seen before atoms” to demonstrate emergence.
Rinse and repeat.
After a while you question whether LLMs are actually a dead end
As I said, it will depend on whether the examples in question were actually substantial part of the "core belief".
For example: "But can they perform procedures?" // "Look at that now" // "But can they do it structurally? Consistently? Reliably?" // "Look at that now" // "But is that reasoning integrated or external?" // "Look at that now" // "But is their reasoning fully procedurally vetted?" (etc.)
I.e.: is the "progress" (which would be the "anomaly" in scientific prediction) part of the "substance" or part of the "form"?
"Shown are the sum of prompt and completion tokens per model, normalized using the GPT-4 tokenizer."
Also, it ranks the use of Llama that is provided by cloud providers (for example, AWS Lamda).
I get that OpenRouter is imperfect but its a good proxy to objectively make a claim that an LLM is "the weakest ever"
Using n-gram/skip-gram model over the long text you can predict probabilities of word pairs and/or word triples (effectively collocations [1]) in the summary.
[1] https://en.wikipedia.org/wiki/Collocation
Then, by using (beam search and) an n-gram/skip-gram model of summaries, you can generate the text of a summary, guided by preference of the words pairs/triples predicted by the first step.
I'm not saying that they are being bad actors, just saying this is more probable in my mind than an LLM breakthrough.
> he has admitted to individual mistakes, but not to the systemic issues which produced them, which makes for a safe bet that there will be more mistakes in the future.
What surprises me is the assumption that there's more than "find patterns in sequences" to "sounding human" i.e. to emitting human-like communication patterns. What else could there be to it? It's a tautology.
>If the recent developments don't surprise you, I just chalk it up to lack of curiosity.
Recent developments don't surprise me in the least. I am, however, curious enough to be absolutely terrified by them. For one, behind the human-shaped communication sequences there could previously be assumed to be an actual human.
o3 doing well on ARC after domain training is not a great argument. There is something significant missing from LLMs being intelligent.
I'm not sure if you watched the entire video, but there were insightful observations. I don't think anyone believes LLMs aren't a significant breakthrough in HCI and language modelling. But it is many layers with many winters away from AGI.
o3 doing well on ARC after domain training is not a great argument. There is something significant missing from LLMs being intelligent.
I'm not sure if you watched the entire video, but there were insightful observations. I don't think anyone believes LLMs aren't a significant breakthrough in HCI and language modelling. But it is many layers with many winters away from AGI.
Also, understanding human and machine intelligence isn't about sides. And CoT is not symbolic reasoning.
Like I said in another comment, I can think of a dozen statistical and computational methods where if you give me a text and its synthesis I can find a strong probabilistic link between the two.
Statistical correlation between text and synthesis undoubtedly exists, but capturing correlation does not imply you've encapsulated meaning itself. My point is precisely that: meaning isn't confined entirely within what we can statistically measure, though it may still be illuminated by it.
I am not an expert by any means but have some knowledge of the technicalities of the LLMs and my limited knowledge allows me to disagree with your statement. The models are trained on an ungodly amount of text, so they become very advanced statistical token prediction machines with magic randomness sprinkled in to make the outputs more interesting. After that, they are fine tuned on very believable dialogues, so their statistical weights are skewed in a way that when subject A (the user) tells something, subject B (the LLM-turned-chatbot) has to say something back which statistically should make sense (which it almost always does since they are trained on it in the first place). Try to paste random text - you will get a random reply. Now try to paste the same random text and ask the chatbot to summarize it - your randomness space will be reduced and it will be turned into a summary because the finetuning gave the LLM the "knowledge" what the summarization _looks like_ (not what it _means_).
Just to prove that you are wrong: ask your favorite LLM if your statement is correct and you will probably see it output that it is not.