7 ms·

He is also not very interested in LLMs, and that seems to be Zuck's top priority.

• tinco 10mo ago
  Yeah I think LeCun is underestimating the impact that LLM's and Diffusion models are going to have, even considering the huge impact they're already having. That's no problem as I'm sure whatever LeCun is working on is going to be amazing as well, but an enterprise like Facebook can't have their top researcher work on risky things when there's surefire paths to success still available.
  • raverbashing 10mo ago
    Yeah honestly I'm with the LLM people here If you think LLMs are not the future then you need to come with something better If you have a theoretical idea that's great, but take to at least GPT2 level first before writing off LLMs Theoretical people love coming up with "better ideas" that fall flat or have hidden gotchas when they get to practical implementation As Linus says, "talk is cheap, show me the code".
    • dpe82 10mo ago
      Of course the challenge with that is it's often not obvious until after quite a bit of work and refinement that something else is, in fact, better.
    • DaSHacka 10mo ago
      Do you? Or is it possible to acknowledge a plateau in innovation without necessarily having an immediate solution cooked-up and ready to go? Are all critiques of the obvious decline in physical durability of American-made products invalid unless they figure out a solution to the problem? Or may critics of a subject exist without necessarily being accredited engineers themselves?
    • hhh 10mo ago
      LLMs are the present. We will see what the future holds.
    • Seattle3503 10mo ago
      Well, we will see if Yann can.
      • deleted 10mo ago
        [deleted]
    • worldsayshi 10mo ago
      Why not both? LLM:s probably have a lot more potential than what is currently being realized but so does world models.
    • mitthrowaway2 10mo ago
      Isn't that exactly why he's starting a new company?
    • whizzter 10mo ago
      LLM's are probably always going to be the fundamental interface, the problem they solved was related to the flexibility of human languages allowing us to have decent mimikry's. And while we've been able to approximate the world behind the words, it's just full of hallucinations because the AI's lack axiomatic systems beyond much manually constructed machinery. You can probably expand the capabilties by attaching to the front-end but I suspect that Yann is seeing limits to this and wants to go back and build up from the back-end of world reasoning and then _among other things_ attach LLM's at the front-end (but maybe on equal terms with vision models that allows for seamless integration of LLM interfacing _combined_ with vision for proper autonomous systems).
      • rob_c 10mo ago
        > because the AI's lack axiomatic systems beyond much manually constructed machinery. Oh god, that is massively under-selling their learning ability. These models are able to extract and reply with why jokes are funny without even knowing basic vocab, yet there are pure-code models out there with lingual rules baked in from day one which still struggle with basic grammar. The _point_ of LLMs arguably is there ability to learn any pattern thrown at it with enough compute. With an exception to learning how logical processes work, and pure LLMs only see "time" in the sense of a paragraph begins and ends. At the least they have taught computers, "how to language", which in regards to how to interact with a machine is a _huge_ step forward. Unfortunately the financial incentives are split between agentic model usage (taking the idea of a computerised butler further), maximizing model memory and raw learning capacity (answering all problems at any time), and long-range consistency (longer ranges give better stable results due to a few reasons, but we're some way from seeing an LLM with a 128k experts and 10e18 active tokens). I think in terms of building the perfect monkey butler we already have most or all of the parts. With regard to a model which can dynamically learn on the fly... LLMs are not the end of the story and we need something to allow the models to more closely tie their LS with the context. Frankly the fact that DeepSeek gave us an LLM with LS was a huge leap since previous model attempts had been overly complex and had failed in training.
    • ActorNightly 10mo ago
      >If you think LLMs are not the future then you need to come with something better The problem isn't LLMS, the problem is that everyone is trying to build bigger/better llms or manually code agents around LLMs. Meanwhile, projects like Mu Zero are forgotten, despite being vastly more important for things like self driving.
  • hodgehog11 10mo ago
    Unless I've missed a few updates, much of the JEPA stuff didn't really bear a lot of fruit in the end.
    • HarHarVeryFunny 10mo ago
      I don't think he's given up on it. How many decades did it take for neural nets to take off? The reason we're even talking about LeCun today is because he was early in seeing the promise of neural nets and stuck with it through the whole AI winter when most people thought it was a waste of time.
      • hodgehog11 10mo ago
        But neural nets were always popular, they just went through phases of hype depending on the capacity of hardware at the time. The only limitation of neural nets at the time was computational power to scale up. AI winters came when other techniques became available that required less compute. Once GPGPU became available, all of that work became immediately viable. No similar limitations exist today for JEPA, to my knowledge.
        • HarHarVeryFunny 10mo ago
          Depends on how far back you are going. There was the whole 1969 Minsky Perceptron flap where he said ANNs (i.e Perceptrons) were useless because they can't learn XOR (and no-one at the time knew how to train multi-layer ANNs), which stiffled ANN research and funding for a while. It would then be almost 20 years until the 1986 PDP handbook published LeCun and Hinton's rediscovery of backpropagation as a way to train multi-layer ANNs thereby making them practical. The JEPA parallel is just that it's not a popular/mainstream approach (at least in terms of well funded research), but may eventually win out over LLMs in the long term. Modern GPUs provide plenty of power for almost any artifical brain type approach, but of course are expensive at scale, so lack of funding can be a barrier in of itself.
  • sebmellen 10mo ago
    While I agree with your point, “Superintelligence” is a far cry from what Meta will end up delivering with Wang in charge. I suppose that, at the end of the day, it’s all marketing. What else should we expect from an ads company :?
    • metabolian 10mo ago
      The Meta Super-Intelligence can dwell in the Metaverse with the 23 other active users there.
  • fxtentacle 10mo ago
    LLMs and Diffusion solve a completely different problem than world models. If you want to predict future text, you use an LLM. If you want to predict future frames in a video, you go with Diffusion. But what both of them lack is object permanence. If a car isn't visible in the input frame, it won't be visible in the output. But in the real world, there are A LOT of things that are invisible (image) or not mentioned but only implied (text) that still strongly affect the future. Every kid knows that when you roll a marble behind your hand, it'll come out on the other side. But LLMs and Diffusion models routinely fail to predict that, as for them the object disappears when it stops being visible. Based on what I heard from others, world models are considered the missing ingredient for useful robots and self-driving cars. If that's halfway accurate, it would make sense to pour A LOT of money into world models, because they will unlock high-value products.
    • tinco 10mo ago
      Sure, if you only consider the model they have no object permanence. However you can just put your model in a loop, and feed the previous frame into the next frame. This is what LLM agent engineers do with their context histories, and it's probably also what the diffusion engineers do with their video models. Messing with the logic in the loop and combining models has an enormous potential, but it's more engineering than researching, and it's just not the sort of work that LeCun is interested in. I think the conflict lies there, that Facebook is an engineering company, and a possible future of AI lies in AI engineering rather than AI research.
    • PxldLtd 10mo ago
      I thoroughly disagree, I believe world models will be critical in some aspect for text generation too. A predictive world model you can help to validate your token prediction. Take a look at the Code World Model for example.
    • yogrish 10mo ago
      I think World models is way to go for Super Intelligence. One of teh patent i saw already going in this direction for Autonomous mobility is https://patents.google.com/patent/EP4379577A1 https://patents.google.com/patent/EP4379577A1 where synthetic data generation (visualization) is missing step in terms of our human intelligence.
  • jll29 10mo ago
    I politely disagree - it is exactly an industry researcher's purpose to do the risky things that may not work, simply because the rest of the corporation cannot take such risks but must walk on more well-trodden paths. Corporate R&D teams are there to absorb risk, innovate, disrupt, create new fields, not for doing small incremental improvements. "If we know it works, it's not research." (Albert Einstein) I also agree with LeCun that LLMs in their current form - are a dead end. Note that this does not mean that I think we have already exploited LLMs to the limit, we are still at the beginning. We also need to create an ecosystem in which they can operate well: for instance, to combine LLMs with Web agents better we need a scalable "C2B2C" (customer delegated to business to business) micropayment infrastructure, because as these systems have already begun talking to each other, in the longer run nobody would offer their APIs for free. I work on spatial/geographic models, inter alia, which by coincident is one of the direction mentioned in the LeCun article. I do not know what his reasoning is, but mine was/is: LMs are language models, and should (only) be used as such. We need other models - in particular a knowledge model (KM/KB) to cleanly separate knowledge from text generation - it looks to me right now that only that will solve hallucination.
    • siva7 10mo ago
      > it is exactly a researcher's purpose to do the risky things that may not work Maybe at university, but not at a trillion dollar company. That job as chief scientist is leading risky things that will work to please the shareholders.
      • vintermann 10mo ago
        They knew what Yann LeCun was when they hired him. If anything, those brilliant academics who have done what they're told and loyally pursued corporate objectives the way the corporation wanted (e.g. Karpathy when he was at Tesla) haven't had great success either.
        • jack_tripper 10mo ago
          >They knew what Yann LeCun was when they hired him. Yes but he was hired in the ZIRP era where all SV companies were hiring every opinionated academic and giving them free reign and unlimited money to burn in the hopes that maybe they'll create the next big thing for them eventually. These are very different economic times right now, after the FED infinite money glitch has been patched out, so now people do need to adjust to them and start actually making some products of value for their seven figure costs to their employers, or end up being shown the door.
  • qmr 10mo ago
    > but an enterprise like Facebook can't have their top researcher work on risky things when there's surefire paths to success still available. Bell Labs
  • StopDisinfo910 10mo ago
    Hard to tell. The last time LeCun disagreed with the AI mainstream was when he kept working on neural net when everyone thought it was a dead end. He might be entirely right in his LLM scepticism. It's hardly a surefire path. He didn't prevent Meta from working on LLM anyway. The issue is more than his position is not compatible with short term investors expectations and that's fatal in a company like Meta at the position LeCun occupies.
  • netdevphoenix 10mo ago
    >the huge impact they're already having In the software development world yes, outside of that, virtually none. Yes, you can transcribe a video call in Office, yes, but that's not ground breaking. I dare you to list 10 impacts on different fields, excluding tech and including at least half blue collar fields and at least half white collar fields , at different levels from the lowest to the highest in the company hierarchy, that LLM/Diffusion models are having. Impact here specifically means a significant reduction of costs or a significant increase of revenue. Go on
    • arcticbull 10mo ago
      I'm also not sure it even drives a ton of value in software engineering. It makes the easy part easier and the hard part harder. Typing out software in your mind was never the difficult part. Figuring out what to write, how to interpret specs in context, how to make your code work within the context of a broader whole, how to be extensible, maintainable, reliable, etc. That's hard, and LLMs really don't help. Even when writing, it shifts the mental burden from an easy thing (writing code) to a very hard thing (reading that code, validating it's right, hallucination free, and then refactoring it to match your teams code style and patterns). It's great for building a first-order approximation of a tech demo app that you then throw out and build from scratch, and auto-complete. In my experience, anyways. I'm sure others have had different experiences.
    • pegasus 10mo ago
      You already mentioned two fields they have a huge impact on, software development and NLP (this latter one the most impacted so far). Another field that comes to mind is academic research is getting an important boost as well, via semantic search or more advanced stuff like Google's biological cell model which already uncovered new treatments. I'm sure I'm missing a lot of other fields I'm less familiar with (legal, for example). But just these impacts I listed are all huge and they will indirectly have a huge impact on all other areas of human industry, it's just a matter of time. "Software will eat the world" and all that.
    • antegamisou 10mo ago
      I don't think you'll find many here believing anything outside tech is worth investing into, it's schizophrenic isn't it.
  • OJFord 10mo ago
    He's quoted in OP as calling them 'useful but fundamentally limited'; that seems correct, and not at all like he's denying their utility.
  • KaiserPro 10mo ago
    > I think LeCun is underestimating the impact that LLM's and Diffusion models No, I think hes suggesting that "world models" are more impactful. The issue for him inside meta is that there is already a research group looking at that, and are wildly more successful (in terms of getting research to product) and way fucking cheaper to run than FAIR. Also LeCun is stuck weirdly in product land, rather than research (RL-R) which means he's not got the protection of Abrash to isolate him from the industrial stupidity that is the product council.
    • ainch 10mo ago
      Which other group is that?
  • anthonybsd 10mo ago
    > Facebook can't have their top researcher work on risky things when there's surefire paths to success still available. How did you determine that "surefire paths to success still available"? Most academics agree that LLMs (or LLMs alone) are not going to lead us to AGI. How are you so certain?
    • tinco 10mo ago
      I don't believe we need more academic research to achieve AGI. The sort of applications that are solving the recent AGI challenges are just severely resource constrained AGI. The only difference between those systems and human intelligence are resources and incentives. Not that I believe AGI is the measure of success, there's probably much more efficient ways to achieve company goals than simulating humans.
  • skeeter2020 10mo ago
    not sure I agree. AI seems to be following the same 3-stage path of many inventions: innovation > adoption > diffusion. LeCun and co focus on the first, and LLMs in their current form appear to be incremental at improvements; we're still using the same basis from more than ten years ago. FB and industry are signalling a focus on harvesting the innovation and that could last - but also take - many years or decades. Your fundamental researchers are not interested (or the right people) in that position.
• gdiamos 10mo ago
  The role of basic research is to get off the beaten path. LLMs aren’t basic research when they have 1 billion users