As LLMs do things thought to be impossible before, LeCun adjusts his statements about LLMs, but at the same time his credibility goes lower and lower. He started saying that LLMs were just predicting words using a probabilistic model, like a better Markov Chain, basically. It was already pretty clear that this was not the case as even GPT3 could do summarization well enough, and there is no probabilistic link between the words of a text and the gist of the content, still he was saying that at the time of GPT3.5 I believe. Then he adjusted this vision when talking with Hinton publicly, saying "I don't deny there is more than just probabilistic thing...". He started saying: not longer just simply probabilistic but they can only regurgitate things they saw in the training set, often explicitly telling people that novel questions could NEVER solved by LLMs, with examples of prompts failing at the time he was saying that and so forth. Now reasoning models can solve problems they never saw, and o3 did huge progresses on ARC, so he adjusted again: for AGI we will need more. And so forth.
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.
I wanna believe everything you say (because you generally are a credible person) but a few things don't add up:
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.
LLMs literally are just predicting tokens with a probabilistic model. They’re incredibly complicated and sophisticated models, but they still are just incredibly complicated and sophisticated models for predicting tokens. It’s maybe unexpected that such a thing can do summarization, but it demonstrably can.
Sometimes seeing something that resembles reasoning doesn't really make it reasoning.
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.
> It was already pretty clear that this was not the case as even GPT3 could do summarization well enough, and there is no probabilistic link between the words of a text and the gist of the content, <...>
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.
> As LLMs do things thought to be impossible before
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?
>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 still regressive next token without any architectural change, so, no, they were wrong, too.
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. ( https://news.ycombinator.com/item?id=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.
> So at this point it does not matter what you believe about LLMs: in general, to trust LeCun words is not a good idea.
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.
> there is no probabilistic link between the words of a text and the gist of the content
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.
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.
o3 progress on ARC was not a zero shot. It was based on fine tuning to the particular data set. A major point of ARC is that humans do not need fine tuning more than being explained what the problem is. And a few humans working on it together after minimal explanation can achieve 100%.
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.
o3 progress on ARC was not a zero shot. It was based on fine tuning to the particular data set. A major point of ARC is that humans do not need fine tuning more than being explained what the problem is. And a few humans working on it together after minimal explanation can achieve 100%.
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.
Maybe someone can explain it to me, but isn't that slide sort of just describing what makes solving problems hard in general? That there are many more decisions which put you on an inevitable path of failure?
"Probability e that any produced [choice] takes us outside the set of correct answers .. probability that answer of length n is correct: P(correct) = (1-e)^{n}"
> Returning to the topic of the limitations of LLMs, LeCun explains, "An LLM produces one token after another. It goes through a fixed amount of computation to produce a token, and that's clearly System 1—it's reactive, right? There's no reasoning," a reference to Daniel Kahneman's influential framework that distinguishes between the human brain's fast, intuitive method of thinking (System 1) and the method of slower, more deliberative reasoning (System 2).
Many people believe that "wants" come first, and are then followed by rationalizations. It's also a theory that's supported by medical imaging.
Maybe the LLM are a good emulation of system-2 (their perfomance sugggest it is), and what's missing is system-1, the "reptilian" brain, based on emotions like love, fear, aggression, (etc.).
For all we know, the system-1 could use the same embeddings, and just work in parallel and produce tokens that are used to guide the system-2.
Personally, I trust my "emotions" and "gut feelings": I believe they are things "not yet rationalized" by my system-2, coming straight from my system-1.
I know it's very unpopular among nerds, but it has worked well enough for me!
They do not reason significantly better than autoregressive LLMs. Which makes me question “one token at a time” as the bottleneck.
Also, Lecun has been pushing his JEPA idea for years now - with not much to show for it. With his resources one could hope we would see the benefits of that over the current state of the art models.
Re the "medical imaging" reference, many of those are built on top of one famous study recording movement before conscious realization that isn't as clear-cut as it entered popular knowledge as: https://www.theatlantic.com/health/archive/2019/09/free-will...
I know there are other examples, and I'm not attacking your post; mainly it's a great opportunity to link this IMHO interesting article that interacts with many debates on HN.
I think what that shows is that in order for the fast reactions to be useful, they really have to incorporate holistic information effectively. That doesn't mean that slower conscious rational work can't lead to more precision, but does suggest that immediate reactions shouldn't necessarily be ignored. There is an analogy between that and reasoning versus non-reasoning with LLMs.
When I took cognitive science courses some years ago, one of the studies that we looked at was one where emotion-responsible parts of the brain were damaged. The result was reduction or complete failure to make decisions.
there's a bunch of stuff imperative to his thriving that has become obsolete to others 15 years ago ... maybe it's time for a few 'sabbatical' years ...
He wrote about Copycat, a program for understanding analogies ("abc is to 123 as cba is to ???"). The program worked at the symbolic level, in the sense that it hard-coded a network of relationships between words and characters. I wonder how close he was to "inventing" an LLM? The insight he needed was that instead of hard-coding patterns, he should have just trained on a vast set of patterns.
Hofstadter focused on Copycat because he saw pattern-matching as the core ability of intelligence. Unlocking that, in his view, would unlock AI. And, of course, pattern-matching is exactly what LLMs are good for.
I think he's right. Intelligence isn't about logic. In the early days of AI, people thought that a chess-playing computer would necessarily be intelligent, but that was clearly a dead-end. Logic is not the hard part. The hard part is pattern-matching.
In fact, pattern-matching is all there is: That's a bear, run away; I'm in a restaurant, I need to order; this is like a binary tree, I can solve it recursively.
I honestly can't come up with a situation that calls for intelligence that can't be solved by pattern-matching.
In my opinion, LeCun is moving the goal-posts. He's saying LLMs make mistakes and therefore they aren't intelligent and aren't useful. Obviously that's wrong: humans make mistakes and are usually considered both intelligent and useful.
I wonder if there is a necessary relationship between intelligence and mistakes. If you can solve a problem algorithmically (e.g., long-division) then there won't be mistakes, but you don't need intelligence (you just follow the algorithm). But if you need intelligence (because no algorithm exists) then there will always be mistakes.
I been thinking about something similar for a long time now. I think abstraction of patterns is at the core requirement of intelligence.
But whats critical, and I think is what's missing is a knowledge representation of events in space-time. We need something more fundamental than text or pixels, we need something that captures space and transformations in space itself.
> In fact, pattern-matching is all there is: That's a bear, run away; I'm in a restaurant, I need to order; this is like a binary tree, I can solve it recursively.
This is not correct. It does not explain creativity at all. It cannot solely be based on pattern matching. I'm not saying no AI is creative, but this logic does not explain creativity
I wouldn't call pattern matching intelligence, I would call it something closer to "trainability" or "educatable" but not intelligence. You can train a person to do a task without understanding why they have to do it like that, but when confronted with a new never-before-seen situation they have to understand the physical laws of the universe to find a solution.
Ask ChatGPT to answer something that no one on the internet has done before and it will struggle to come up with a solution.
I personally have greatly benefitted from LLM's helping me reason about problems and make progress on many diverse issues across professional, recreational and mental health difficulties. I think that asking whether it's just "text generation and search" rather than something that transcends it is as meaningful as asking whether an airplane really "flies" or just "applies thrust and generates lift".
this statement is just patently wrong, and even those are still significant value. LLMs have been significantly impacting software engineering and software prototyping.
antirez|11 months ago
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.
sorcerer-mar|11 months ago
How could that possibly be true?
There’s obviously a link between “[original content] is summarized as [summarized”content]
mbesto|11 months ago
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.
gcr|11 months ago
wat10000|11 months ago
nurettin|11 months ago
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.
pllbnk|10 months ago
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.
aprilthird2021|11 months ago
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?
ksec|11 months ago
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. ( https://news.ycombinator.com/item?id=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.
timewizard|11 months ago
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.
thesz|11 months ago
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.
belter|11 months ago
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...
daveguy|11 months ago
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.
daveguy|11 months ago
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.
charcircuit|11 months ago
No he's not.
deepfriedchokes|11 months ago
gsf_emergency_2|11 months ago
LeCun, "Mathematical Obstacles on the Way to Human-Level AI"
Slide (Why autoregressive models suck)
https://xcancel.com/ravi_mohan/status/1906612309880930641
hatefulmoron|11 months ago
"Probability e that any produced [choice] takes us outside the set of correct answers .. probability that answer of length n is correct: P(correct) = (1-e)^{n}"
greesil|11 months ago
gibsonf1|11 months ago
djoldman|11 months ago
More interesting is their research work. JEPA is what LeCun is betting on:
https://ai.meta.com/blog/v-jepa-yann-lecun-ai-model-video-jo...
redox99|11 months ago
csdvrx|11 months ago
Many people believe that "wants" come first, and are then followed by rationalizations. It's also a theory that's supported by medical imaging.
Maybe the LLM are a good emulation of system-2 (their perfomance sugggest it is), and what's missing is system-1, the "reptilian" brain, based on emotions like love, fear, aggression, (etc.).
For all we know, the system-1 could use the same embeddings, and just work in parallel and produce tokens that are used to guide the system-2.
Personally, I trust my "emotions" and "gut feelings": I believe they are things "not yet rationalized" by my system-2, coming straight from my system-1.
I know it's very unpopular among nerds, but it has worked well enough for me!
kadushka|11 months ago
They do not reason significantly better than autoregressive LLMs. Which makes me question “one token at a time” as the bottleneck.
Also, Lecun has been pushing his JEPA idea for years now - with not much to show for it. With his resources one could hope we would see the benefits of that over the current state of the art models.
sho_hn|11 months ago
I know there are other examples, and I'm not attacking your post; mainly it's a great opportunity to link this IMHO interesting article that interacts with many debates on HN.
ilaksh|11 months ago
gessha|11 months ago
https://pmc.ncbi.nlm.nih.gov/articles/PMC3032808/
unknown|11 months ago
[deleted]
bitethecutebait|11 months ago
ejang0|11 months ago
onlyrealcuzzo|11 months ago
This seems like a broken clock having a good chance of being right.
There's so much progress, it wouldn't be that surprising if something quite different completely overtakes the current trend within 5 years.
re-thc|11 months ago
Well yes in that ChatGPT 4 (current) will be replaced by ChatGPT 5 (future) etc...
GMoromisato|11 months ago
He wrote about Copycat, a program for understanding analogies ("abc is to 123 as cba is to ???"). The program worked at the symbolic level, in the sense that it hard-coded a network of relationships between words and characters. I wonder how close he was to "inventing" an LLM? The insight he needed was that instead of hard-coding patterns, he should have just trained on a vast set of patterns.
Hofstadter focused on Copycat because he saw pattern-matching as the core ability of intelligence. Unlocking that, in his view, would unlock AI. And, of course, pattern-matching is exactly what LLMs are good for.
I think he's right. Intelligence isn't about logic. In the early days of AI, people thought that a chess-playing computer would necessarily be intelligent, but that was clearly a dead-end. Logic is not the hard part. The hard part is pattern-matching.
In fact, pattern-matching is all there is: That's a bear, run away; I'm in a restaurant, I need to order; this is like a binary tree, I can solve it recursively.
I honestly can't come up with a situation that calls for intelligence that can't be solved by pattern-matching.
In my opinion, LeCun is moving the goal-posts. He's saying LLMs make mistakes and therefore they aren't intelligent and aren't useful. Obviously that's wrong: humans make mistakes and are usually considered both intelligent and useful.
I wonder if there is a necessary relationship between intelligence and mistakes. If you can solve a problem algorithmically (e.g., long-division) then there won't be mistakes, but you don't need intelligence (you just follow the algorithm). But if you need intelligence (because no algorithm exists) then there will always be mistakes.
andoando|11 months ago
But whats critical, and I think is what's missing is a knowledge representation of events in space-time. We need something more fundamental than text or pixels, we need something that captures space and transformations in space itself.
aprilthird2021|11 months ago
This is not correct. It does not explain creativity at all. It cannot solely be based on pattern matching. I'm not saying no AI is creative, but this logic does not explain creativity
guhidalg|11 months ago
Ask ChatGPT to answer something that no one on the internet has done before and it will struggle to come up with a solution.
GeorgeTirebiter|11 months ago
varelse|11 months ago
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throwuxiytayq|11 months ago
ObnoxiousProxy|11 months ago