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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 starte

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.

gcr 2y ago
Why is changing one’s mind when confronted with new evidence a negative signifier of reputation for you?
- antirez 2y ago
  Because there were plenty of evidences that the statements were either not correct or not based on enough information, at the time they were made. And to be wrong because of personal biases, and then don't clearly state you were wrong when new evidenced appeared, is not a trait of a good scientist. For instance: the strong summarization abilities where already something that, alone, without any further information, were enough to seriously doubt about the stochastic parrot mental model.
  - jaggederest 2y ago
    Here's a fun example of that kind of "I've updated my statements but not assessed any of my underlying lack of understanding" - it's a bad look on any kind of scientist. https://x.com/AukeHoekstra/status/1507047932226375688 https://x.com/AukeHoekstra/status/1507047932226375688
    - darkwater 2y ago
      Who are you referring to?
      - jaggederest 2y ago
        Yann LeCun. I think this post is a more elegant summary of what I'm trying to illustrate with this kind of thing: > 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. https://news.ycombinator.com/item?id=43594771 https://news.ycombinator.com/item?id=43594771
  - jxjnskkzxxhx 2y ago
    I don't see the contradiction between "stochastic parrot" and "strong summarisation abilities". 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.
    - antirez 2y ago
      LLMs learn from examples where the logits are not probabilities, but how a given sentence continues (only one token is set to 1). So they don't learn probabilities, they learn how to continue the sentence with a given token. We apply softmax at the logits for mathematical reasons, and it is natural/simpler to think in terms of probabilities, but that's not what happens, nor the neural networks they are composed of is just able to approximate probabilistic functions. This "next token" probability is the source of a lot misunderstanding. It's much better to imagine the logits as "To continue my reply I could say this word, more than the others, or maybe that one, a bit less, ..." and so forth. Now there are evidences, too, that in the activations producing a given token the LLM already has an idea about how most of the sentence is going to continue. 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.
- bko 2y ago
  Because he has a core belief and based on that core belief he made some statements that turned out to be incorrect. But he kept the core belief and adjusted the statements. 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.
  - mdp2021 2y ago
    I have not followed all of LeCun's past statements, but - 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.
    - bko 2y ago
      If you say LLMs are a dead end, and you give a few examples of things they will never be able to do, and a few months later they do it, and you just respond by stating that sure they can do that but they're still a dead end and won't be able to do this. Rinse and repeat. After a while you question whether LLMs are actually a dead end
      - mdp2021 2y ago
        This is a normal routine topical in Epistemology in the perspective of Lakatos. 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"?
- Maxatar 2y ago
  He hasn't fundamentally changed his mind. What he's doing is taking what he fundamentally believes and finding more and more elaborate ways of justifying it.
- danielmarkbruce 2y ago
  If you need basically rock solid evidence of X before you stop saying "this thing cannot do X", then you shouldn't be running a forward looking lab. There are only so many directions you can take, only so many resources at your disposal. Your intuition has to be really freakishly good to be running such a lab. He's done a lot of amazing work, but his stance on LLMs seems continuously off the mark.
  - SJC_Hacker 2y ago
    The list of great minds who thought that "new fangled thing is nonsense" and later turned out to be horribly wrong is quite long and distinguished
    - fragmede 2y ago
      > Heavier-than-air flying machines are impossible. -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
      - ksec 2y ago
        I am just wondering did you have this all somehow saved up or did you pull it out of somewhere? Amazing list of things. Thank You.
        fragmede 2y ago
- mordymoop 2y ago
  “Changing your mind” doesn’t really look like what LeCun is doing. 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.
- QuantumGood 2y ago
  When you limit to one framing "changing one's mind", it helps if you point it out, acknowledging that other framings can be possible, otherwise it risks seeming (not necessarily being) manipulative, and you are at least overlooking a large part of the domain. Harvard Decision group called these two of the most insidious drivers of poor decisions "frame blindness" and poor "frame choice". Give more than one frame a chance.
sorcerer-mar 2y ago
> there is no probabilistic link between the words of a text and the gist of the content How could that possibly be true? There’s obviously a link between “[original content] is summarized as [summarized”content]
- DrBenCarson 2y ago
  It’s not true 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
  - neom 2y ago
    Language is a symbolic system. From an absolute or spiritual standpoint, meaning transcends pure linguistic probabilities. Language itself emerges as a limited medium for the expression of consciousness and abstract thought. Indeed, to say meaning arises purely from language (as probability alone) or, to deny language influences meaning entirely are both overly simplistic extremes.
    - aerhardt 2y ago
      "When he to whom one speaks does not understand, and he who speaks himself does not understand, that is metaphysics." - Voltaire 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.
      - neom 2y ago
        "Not everything that counts can be counted, and not everything that can be counted counts." - Someone. 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.
  - 2y ago
nurettin 2y ago
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.
charcircuit 2y ago
>LeCun is directing an AI lab that [built LLMs] No he's not.
aprilthird2021 2y ago
> 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?
belter 2y ago
But have we established that LLMs dont just interpolate and they can create? 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...
- fragmede 2y ago
  Why is that the bar though? Imagine LLMs as a kid that has a box of lego with a hundred million blocks in it, and it can assemble those blocks into any configuration possible. Is the fact that the kid doesn't have access to ABS plastic pellets and a molding machine, and so they can't make new pieces; does that really make us think that the kid just interpolates and can't create?
  - belter 2y ago
    Actually yes...If the kid spends their whole life in the box and never invents a new block, that’s just combinatorics. We don’t call a chess engine ‘creative’ for finding novel moves, because we understand the rules. LLMs have rules too, they’re called weights. 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.
    - fragmede 2y ago
      The combinatorics on choosing 500 pieces (words) out of a bag of 1.8 million pieces (approx parameters per layer for GPT-3) with replacement, and order matters works out to be something like 10^4600. Maybe you can't call that creativity, but you've got to admit that's a pretty big number.
ksec 2y ago
>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 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.
mbesto 2y ago
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 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.
- redlock 2y ago
  Doesn't OpenRouter ranking include pricing? Not really a good measure of quality or performance but of cost effectiveness
  - mbesto 2y ago
    I mean it literally says on the page: "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"
- kristianp 2y ago
  I've found the llama 3 served by meta.ai to be quite weak for coding prompts, it gets confused by more complex tasks. Maybe its a smaller model? I agree it's weaker than others of its generation.
timewizard 2y 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. 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.
wat10000 2y ago
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.
- Workaccount2 2y ago
  The rub is that we don't know if intelligence is anything more than "just predicting next output".
  - sangnoir 2y ago
    I think we do.
    - sho 2y ago
      That's just what you were most likely to say...
      - deleted 2y ago
        [deleted]
- deleted 2y ago
  [deleted]
deepfriedchokes 2y ago
Everything is possible with math. Just ask string theorists.
thesz 2y ago
> 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. [1] https://en.wikipedia.org/wiki/Collocation 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.
daveguy 2y ago
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.
daveguy 2y ago
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.
pllbnk 2y ago
> 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.

gcr 2y ago

Why is changing one’s mind when confronted with new evidence a negative signifier of reputation for you?

antirez 2y ago
Because there were plenty of evidences that the statements were either not correct or not based on enough information, at the time they were made. And to be wrong because of personal biases, and then don't clearly state you were wrong when new evidenced appeared, is not a trait of a good scientist. For instance: the strong summarization abilities where already something that, alone, without any further information, were enough to seriously doubt about the stochastic parrot mental model.
- jaggederest 2y ago
  Here's a fun example of that kind of "I've updated my statements but not assessed any of my underlying lack of understanding" - it's a bad look on any kind of scientist. https://x.com/AukeHoekstra/status/1507047932226375688 https://x.com/AukeHoekstra/status/1507047932226375688
  - darkwater 2y ago
    Who are you referring to?
    - jaggederest 2y ago
      Yann LeCun. I think this post is a more elegant summary of what I'm trying to illustrate with this kind of thing: > 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. https://news.ycombinator.com/item?id=43594771 https://news.ycombinator.com/item?id=43594771
- jxjnskkzxxhx 2y ago
  I don't see the contradiction between "stochastic parrot" and "strong summarisation abilities". 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.
  - antirez 2y ago
    LLMs learn from examples where the logits are not probabilities, but how a given sentence continues (only one token is set to 1). So they don't learn probabilities, they learn how to continue the sentence with a given token. We apply softmax at the logits for mathematical reasons, and it is natural/simpler to think in terms of probabilities, but that's not what happens, nor the neural networks they are composed of is just able to approximate probabilistic functions. This "next token" probability is the source of a lot misunderstanding. It's much better to imagine the logits as "To continue my reply I could say this word, more than the others, or maybe that one, a bit less, ..." and so forth. Now there are evidences, too, that in the activations producing a given token the LLM already has an idea about how most of the sentence is going to continue. 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.
bko 2y ago
Because he has a core belief and based on that core belief he made some statements that turned out to be incorrect. But he kept the core belief and adjusted the statements. 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.
- mdp2021 2y ago
  I have not followed all of LeCun's past statements, but - 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.
  - bko 2y ago
    If you say LLMs are a dead end, and you give a few examples of things they will never be able to do, and a few months later they do it, and you just respond by stating that sure they can do that but they're still a dead end and won't be able to do this. Rinse and repeat. After a while you question whether LLMs are actually a dead end
    - mdp2021 2y ago
      This is a normal routine topical in Epistemology in the perspective of Lakatos. 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"?
Maxatar 2y ago
He hasn't fundamentally changed his mind. What he's doing is taking what he fundamentally believes and finding more and more elaborate ways of justifying it.
danielmarkbruce 2y ago
If you need basically rock solid evidence of X before you stop saying "this thing cannot do X", then you shouldn't be running a forward looking lab. There are only so many directions you can take, only so many resources at your disposal. Your intuition has to be really freakishly good to be running such a lab. He's done a lot of amazing work, but his stance on LLMs seems continuously off the mark.
- SJC_Hacker 2y ago
  The list of great minds who thought that "new fangled thing is nonsense" and later turned out to be horribly wrong is quite long and distinguished
  - fragmede 2y ago
    > Heavier-than-air flying machines are impossible. -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
    - ksec 2y ago
      I am just wondering did you have this all somehow saved up or did you pull it out of somewhere? Amazing list of things. Thank You.
      - fragmede 2y ago
mordymoop 2y ago
“Changing your mind” doesn’t really look like what LeCun is doing. 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.
QuantumGood 2y ago
When you limit to one framing "changing one's mind", it helps if you point it out, acknowledging that other framings can be possible, otherwise it risks seeming (not necessarily being) manipulative, and you are at least overlooking a large part of the domain. Harvard Decision group called these two of the most insidious drivers of poor decisions "frame blindness" and poor "frame choice". Give more than one frame a chance.

sorcerer-mar 2y ago

> there is no probabilistic link between the words of a text and the gist of the content How could that possibly be true? There’s obviously a link between “[original content] is summarized as [summarized”content]

DrBenCarson 2y ago
It’s not true 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
- neom 2y ago
  Language is a symbolic system. From an absolute or spiritual standpoint, meaning transcends pure linguistic probabilities. Language itself emerges as a limited medium for the expression of consciousness and abstract thought. Indeed, to say meaning arises purely from language (as probability alone) or, to deny language influences meaning entirely are both overly simplistic extremes.
  - aerhardt 2y ago
    "When he to whom one speaks does not understand, and he who speaks himself does not understand, that is metaphysics." - Voltaire 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.
    - neom 2y ago
      "Not everything that counts can be counted, and not everything that can be counted counts." - Someone. 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.
- 2y ago

nurettin 2y ago

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.

charcircuit 2y ago

>LeCun is directing an AI lab that [built LLMs] No he's not.

aprilthird2021 2y ago

> 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?

belter 2y ago

But have we established that LLMs dont just interpolate and they can create? 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...

fragmede 2y ago
Why is that the bar though? Imagine LLMs as a kid that has a box of lego with a hundred million blocks in it, and it can assemble those blocks into any configuration possible. Is the fact that the kid doesn't have access to ABS plastic pellets and a molding machine, and so they can't make new pieces; does that really make us think that the kid just interpolates and can't create?
- belter 2y ago
  Actually yes...If the kid spends their whole life in the box and never invents a new block, that’s just combinatorics. We don’t call a chess engine ‘creative’ for finding novel moves, because we understand the rules. LLMs have rules too, they’re called weights. 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.
  - fragmede 2y ago
    The combinatorics on choosing 500 pieces (words) out of a bag of 1.8 million pieces (approx parameters per layer for GPT-3) with replacement, and order matters works out to be something like 10^4600. Maybe you can't call that creativity, but you've got to admit that's a pretty big number.

ksec 2y ago

>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 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.

mbesto 2y ago

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 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.

redlock 2y ago
Doesn't OpenRouter ranking include pricing? Not really a good measure of quality or performance but of cost effectiveness
- mbesto 2y ago
  I mean it literally says on the page: "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"
kristianp 2y ago
I've found the llama 3 served by meta.ai to be quite weak for coding prompts, it gets confused by more complex tasks. Maybe its a smaller model? I agree it's weaker than others of its generation.

timewizard 2y 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. 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.

wat10000 2y ago

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.

Workaccount2 2y ago
The rub is that we don't know if intelligence is anything more than "just predicting next output".
- sangnoir 2y ago
  I think we do.
  - sho 2y ago
    That's just what you were most likely to say...
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deepfriedchokes 2y ago

Everything is possible with math. Just ask string theorists.

thesz 2y ago

> 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. [1] https://en.wikipedia.org/wiki/Collocation 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.

daveguy 2y ago

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.

daveguy 2y ago

pllbnk 2y ago

> 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.