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Justifiable. There are a lot more degrees of freedom in world models. LLMs are fundamentally capped because they only learn from static text -- human communic
by A_D_E_P_T 8mo ago
Justifiable.
There are a lot more degrees of freedom in world models.
LLMs are fundamentally capped because they only learn from static text -- human communications about the world -- rather than from the world itself, which is why they can remix existing ideas but find it all but impossible to produce genuinely novel discoveries or inventions. A well-funded and well-run startup building physical world models (grounded in spatiotemporal understanding, not just language patterns) would be attacking what I see as the actual bottleneck to AGI. Even if they succeed only partially, they may unlock the kind of generalization and creative spark that current LLMs structurally can't reach.
- 10xDev 8mo agoWhether it is text or an image, it is just bits for a computer. A token can represent anything.
- A_D_E_P_T 8mo agoSure, but don't conflate the representation format with the structure of what's being represented. Everything is bits to a computer, but text training data captures the flattened, after-the-fact residue of baseline human thought: Someone's written description of how something works. (At best!) A world model would need to capture the underlying causal, spatial, and temporal structure of reality itself -- the thing itself, that which generates those descriptions. You can tokenize an image just as easily as a sentence, sure, but a pile of images and text won't give you a relation between the system and the world. A world model, in theory, can. I mean, we ought to be sufficient proof of this, in a sense...
- firecall 8mo agoIt’s worth noting how our human relationship or understanding of our world model changed as our tools to inspect and describe our world advanced. So when we think about capturing any underlying structure of reality itself, we are constrained by the tools at hand. The capability of the tool forms the description which grants the level of understanding.
- Bombthecat 8mo agoCan a token represent concentration, will?
- 10xDev 8mo agoThose sound more like emergent properties then something you can engineer.
- bsenftner 8mo agoThere will be no "unlocking of AGI" until we develop a new science capable of artificial comprehension. Comprehension is the cornucopia that produces everything we are, given raw stimulus an entire communicating Universe is generated with a plethora of highly advanceds predator/prey characters in an infinitely complex dynamic, and human science and technology have no lead how to artificially make sense of that in a simultaneous unifying whole. That's comprehension.
- rvz 8mo agoA lot more justifiable than say, Thinking Machines at least. But we will "see". World models and vision seems like a great use case for robotics which I can imagine that being the main driver of AMI.
- andy12_ 8mo agoI don't understand this view. How I see it the fundamental bottleneck to AGI is continual learning and backpropagation. Models today are static, and human brains don't learn or adapt themselves with anything close to backpropagation. World models don't solve any of these problems; they are fundamentally the same kind of deep learning architectures we are used to work with. Heck, if you think learning from the world itself is the bottleneck, you can just put a vision-action LLM on a reinforcement learning loop in a robotic/simulated body.
- zelphirkalt 8mo ago> I don't understand this view. How I see it the fundamental bottleneck to AGI is continual learning and backpropagation. Models today are static, and human brains don't learn or adapt themselves with anything close to backpropagation. Even with continuous backpropagation and "learning", enriching the training data, so called online-learning, the limitations will not disappear. The LLMs will not be able to conclude things about the world based on fact and deduction. They only consider what is likely from their training data. They will not foresee/anticipate events, that are unlikely or non-existent in their training data, but are bound to happen due to real world circumstances. They are not intelligent in that way. Whether humans always apply that much effort to conclude these things is another question. The point is, that humans fundamentally are capable of doing that, while LLMs are structurally not. The problems are structural/architectural. I think it will take another 2-3 major leaps in architectures, before these AI models reach human level general intelligence, if they ever reach it. So far they can "merely" often "fake it" when things are statistically common in their training data.
- andy12_ 8mo ago> Even with continuous backpropagation and "learning" That's what I said. Backpropagation cannot be enough; that's not how neurons work in the slightest. When you put biological neurons in a Pong environment they learn to play not through some kind of loss or reward function; they self-organize to avoid unpredictable stimulation. As far as I know, no architecture learns in such an unsupervised way. https://www.sciencedirect.com/science/article/pii/S0896627322008066 https://www.sciencedirect.com/science/article/pii/S089662732...
- energy123 8mo agowhy LLMs (transformers trained on multimodal token sequences, potentially containing spatiotemporal information) can't be a world model?
- ForHackernews 8mo agohttps://medium.com/state-of-the-art-technology/world-models-vs-word-models-why-lecun-believes-llms-will-be-obsolete-23795e729cfa https://medium.com/state-of-the-art-technology/world-models-... > One major critique LeCun raises is that LLMs operate only in the realm of language, which is a simple, discrete space compared to the continuous, complex physical world we live in. LLMs can solve math problems or answer trivia because such tasks reduce to pattern completion on text, but they lack any meaningful grounding in physical reality. LeCun points out a striking paradox: we now have language models that can pass the bar exam, solve equations, and compute integrals, yet “where is our domestic robot? Where is a robot that’s as good as a cat in the physical world?” Even a house cat effortlessly navigates the 3D world and manipulates objects — abilities that current AI notably lacks. As LeCun observes, “We don’t think the tasks that a cat can accomplish are smart, but in fact, they are.”
- energy123 8mo agoBut they don't only operate on language? They operate on token sequences, which can be images, coordinates, time, language, etc.
- kergonath 8mo agoIt’s an interesting observation, but I think you have it backwards. The examples you give are all using discrete symbols to represent something real and communicating this description to other entities. I would argue that all your examples are languages.
- samrus 8mo agoWhats the first L stand for? Thats not just vestogial, their model of the world is formed almost exclusively from language rather than a range of things contributing significantly like for humans. The biggest thing thats missing is actual feedback to their decisions. They have no "idea of that because transformers and embeddings dont model that yet. And langiage descriptions and image representations of feedback arent enough. They are too disjointed. It needs more
- Unearned5161 8mo agoI have a pet peeve with the concept of "a genuinely novel discovery or invention", what do you imagine this to be? Can you point me towards a discovery or invention that was "genuinely novel", ever? I don't think it makes sense conceptually unless you're literally referring to discovering new physical things like elements or something. Humans are remixers of ideas. That's all we do all the time. Our thoughts and actions are dictated by our environment and memories; everything must necessarily be built up from pre-existing parts.
- A_D_E_P_T 8mo agoSuno is transformer-based; in a way it's a heavily modified LLM. You can't get Suno to do anything that's not in its training data. It is physically incapable of inventing a new musical genre. No matter how detailed the instructions you give it, and even if you cheat and provide it with actual MP3 examples of what you want it to create, it is impossible. The same goes for LLMs and invention generally, which is why they've made no important scientific discoveries. You can learn a lot by playing with Suno.
- chpatrick 8mo agohttps://news.ycombinator.com/item?id=46094037 https://news.ycombinator.com/item?id=46094037
- hodgehog11 8mo agoI don't see how this is an architectural problem though. The problem is that music datasets are highly multimodal, and the training process is relying almost entirely on this dataset instead of incorporating basic musical knowledge to allow it to explore a bit further. That's what happens when computer scientists aim to "upset" a field without consulting with experts in said field.
- davidfarrell 8mo agoW Brian Arthur's book "The Nature of Technology" provides a framework for classifying new technology as elemental vs innovative that I find helpful. For example the Huntley-Mcllroy diff operates on the phenomenon that ordered correspondence survives editing. That was an invention (discovery of a natural phenomenon and a means to harness it). Myers diff improves the performance by exploiting the fact that text changes are sparse. That's innovation. A python app using libdiff, that's engineering. And then you might say in terms of "descendants": invention > innovation > engineering. But it's just a perspective.
- whiplash451 8mo agoThe term LLM is confusing your point because VLMs belong to the same bin according to Yann. Using the term autoregressive models instead might help.
- kadushka 8mo agoDiffusion models are not autoregressive but have the same limitations
- jnd-cz 8mo agoThe sum of human knowledge is more than enough to come up with innovative ideas and not every field is working directly with the physical world. Still I would say there's enough information in the written history to create virtual simulation of 3d world with all ohysical laws applying (to a certain degree because computation is limited). What current LLMs lack is inner motivation to create something on their own without being prompted. To think in their free time (whatever that means for batch, on demand processing), to reflect and learn, eventually to self modify. I have a simple brain, limited knowledge, limited attention span, limited context memory. Yet I create stuff based what I see, read online. Nothing special, sometimes more based on someone else's project, sometimes on my own ideas which I have no doubt aren't that unique among 8 billions of other people. Yet consulting with AI provides me with more ideas applicable to my current vision of what I want to achieve. Sure it's mostly based on generally known (not always known to me) good practices. But my thoughts are the same way, only more limited by what I have slowly learned so far in my life.
- daxfohl 8mo agoI guess you need two things to make that happen. First, more specialization among models and an ability to evolve, else you get all instances thinking roughly the same thing, or deer in the headlights where they don't know what of the millions of options they should think about. Second, fewer guardrails; there's only so much you can do by pure thought. The problem is, idk if we're ready to have millions of distinct, evolving, self-executing models running wild without guardrails. It seems like a contradiction: you can't achieve true cognition from a machine while artificially restricting its boundaries, and you can't lift the boundaries without impacting safety.
- jandrewrogers 8mo ago> virtual simulation of 3d world Virtual simulations are not substitutable for the physical world. They are fundamentally different theory problems that have almost no overlap in applicability. You could in principle create a simulation with the same mathematical properties as the physical world but no one has ever done that. I'm not sure if we even know how. Physical world dynamics are metastable and non-linear at every resolution. The models we do build are created from sparse irregular samples with large error rates; you often have to do complex inference to know if a piece of data even represents something real. All of this largely breaks the assumptions of our tidy sampling theorems in mathematics. The problem of physical world inference has been studied for a couple decades in the defense and mapping industries; we already have a pretty good understanding of why LLM-style AI is uniquely bad at inference in this domain, and it mostly comes down to the architectural inability to represent it. Grounded estimates of the minimum quantity of training data required to build a reliable model of physical world dynamics, given the above properties, is many exabytes. This data exists, so that is not a problem. The models will be orders of magnitude larger than current LLMs. Even if you solve the computer science and theory problems around representation so that learning and inference is efficient, few people are prepared for the scale of it. (source: many years doing frontier R&D on these problems)
- robrenaud 8mo agoWas Alphago's move 37 original? In the last step of training LLMs, reinforcement learning from verified rewards, LLMs are trained to maximize the probability of solving problems using their own output, depending on a reward signal akin to winning in Go. It's not just imitating human written text. Fwiw, I agree that world models and some kind of learning from interacting with physical reality, rather than massive amounts of digitized gym environments is likely necessary for a breakthrough for AGI.
- ml-anon 8mo agoHonestly, how do people who know so little have this much confidence to post here?
- mvc 8mo agoYou must be new here
- A_D_E_P_T 8mo agoCare to explain what led to this reaction?
- stevenhuang 8mo agohttps://news.ycombinator.com/item?id=47325940 https://news.ycombinator.com/item?id=47325940
- kypro 8mo ago> LLMs are fundamentally capped because they only learn from static text -- human communications about the world -- rather than from the world itself, which is why they can remix existing ideas but find it all but impossible to produce genuinely novel discoveries or inventions. No hate, but this is just your opinion. The definition of "text" here is extremely broad – an SVG is text, but it's also an image format. It's not incomprehensible to imagine how an AI model trained on lots of SVG "text" might build internal models to help it "visualise" SVGs in the same way you might visualise objects in your mind when you read a description of them. The human brain only has electrical signals for IO, yet we can learn and reason about the world just fine. I don't see why the same wouldn't be possible with textual IO.
- daxfohl 8mo agoYeah I don't even think you'd need to train it. You could probably just explain how SVG works (or just tell it to emit coordinates of lines it wants to draw), and tell it to draw a horse, and I have to imagine it would be able to do so, even if it had never been trained on images, svg, or even cartesian coordinates. I think there's enough world model in there that you could simply explain cartesian coordinates in the context, it'd figure out how those map to its understanding of a horse's composition, and output something roughly correct. It'd be an interesting experiment anyway. But yeah, I can't imagine that LLMs don't already have a world model in there. They have to. The internet's corpus of text may not contain enough detail to allow a LLM to differentiate between similar-looking celebrities, but it's plenty of information to allow it to create a world model of how we perceive the world. And it's a vastly more information-dense means of doing so.
- masteranza 8mo agoA few years ago I've made this simple thought experiment to convince myself that LLM's won't achieve superhuman level (in the sense of being better than all human experts): Imagine that we made an LLM out of all dolphin songs ever recorded, would such LLM ever reach human level intelligence? Obviously and intuitively the answer is NO. Your comment actually extended this observation for me sparking hope that systems consuming natural world as input might actually avoid this trap, but then I realized that tool use & learning can in fact be all that's needed for singularity while consuming raw data streams most of the time might actually be counterproductive.
- kadushka 8mo agoImagine that we made an LLM out of all dolphin songs ever recorded, would such LLM ever reach human level intelligence? It could potentially reach super-dolphin level intelligence
- hodgehog11 8mo agoI mean no offense here, but I really don't like this attitude of "I thought for a bit and came up with something that debunks all of the experts!". It's the same stuff you see with climate denialism, but it seems to be considered okay when it comes to AI. As if the people that spend all day every day for decades have not thought of this. Dataset limitations have been well understood since the dawn of statistics-based AI, which is why these models are trained on data and RL tasks that are as wide as possible, and are assessed by generalization performance. Most of the experts in ML, even the mathematically trained ones, within the last few years acknowledge that superintelligence (under a more rigorous definition than the one here) is quite possible, even with only the current architectures. This is true even though no senior researcher in the field really wants superintelligence to be possible, hence the dozens of efforts to disprove its potential existence.
- smokel 8mo ago> Imagine that we made an LLM out of all dolphin songs ever recorded, would such LLM ever reach human level intelligence? Obviously and intuitively the answer is NO. Not so fast. People have built pretty amazing thought frameworks out of a few axioms, a few bits, or a few operations in a Turing machine. Dolphin songs are probably more than enough to encode the game of life. It's just how you look at it that makes it intelligence.
- ljm 8mo agoI'm gonna be a cynic and say this is money following money and Yann LeCun is an excellent salesman. I 100% guarantee that he will not be holding the bag when this fails. Society will be protecting him. On that proviso I have zero respect for this guy.
- thinkling 8mo agoUm, why would anyone be "holding the bag" and who needs protecting by society? He's not taking out a loan, he's getting capital investment in a startup. People are gambling that he will do well and make money for them. If they gamble wrong, that's on them. Society won't be doing anything either way because investors in startups that fail don't get anything.
- roromainmain 8mo agoAgree. LLMs operate in the domain of language and symbols, but the universe contains much more than that. Humans also learn a great deal from direct phenomenological experience of the world, even without putting those experiences into words. I remember a talk by Yann LeCun where he pointed out that in just the first couple of years of life, a human baby is exposed to orders of magnitude more sensory data (vision, sound, etc.) than what current LLMs are typically trained on. This seems like a major limitation of purely language-based models.
- _s_a_m_ 8mo agoReally? As if not everyone told him the last 10 years, especially Gary Marcus which he ridiculed on Twitter at every occasion and now silently like a dog returning home switches to Gary's position. As if anyone was waiting for this, even 5 years ago this was old news, Tenenbaum is building world models for a long time. People in pop venture capital culture don't seem to know what is going on in research. Makes them easier to milk.
- jimbo808 8mo agoYou're right that world models are the bottleneck, but people underestimate the staggering complexity gap between modeling the physical world and modeling a one-dimensional stream of text. Not only is the real world high-dimensional, continuous, noisy, and vastly more information dense, it's also not something for which there is an abundance of training data.
- mountainriver 8mo agoOkay but most modern LLMs are multimodal, and it’s fairly easy to make an LLM multimodal. Also there is no evidence that novel discoveries are more than remixes. This is heavily debated but from what we’ve seen so far I’m not sure I would bet against remix. World models are great for specific kinds of RL or MPC. Yann is betting heavily on MPC, I’m not sure I agree with this as it’s currently computationally intractable at scale
- uoaei 8mo ago> There are a lot more degrees of freedom in world models. Perhaps for the current implementations this is true. But the reason the current versions keep failing is that world dynamics has multiple orders of magnitude fewer degrees of freedom than the models that are tasked to learn them. We waste so much compute learning to approximate the constraints that are inherent in the world, and LeCun has been pressing the point the past few years that the models he intends to design will obviate the excess degrees of freedom to stabilize training (and constrain inference to physically plausible states). If my assumption is true then expect Max Tegmark to be intimately involved in this new direction.
- 8bitsrule 8mo agoGotta say, good luck with that effort. Lenat started Cyc 42 years ago, and after a while it seemed to disappear. 'Understanding' the 'physical world' is something that a few -may- start to approach intuitively after a decade or five of experience. (Einstein, Maxwell, et.al.) But the idea of feeding a machine facts and equations ... and dependence on human observations ... seems unlikely to lead to 'mastering the physical world'. Let alone for $1Billon.
- mirekrusin 8mo agoThank you for not saying "language", but "text". It's true, but it's also true that text is very expressive. Programming languages (huge, formalized expressiveness), math and other formal notation, SQL, HTML, SVG, JSON/YAML, CSV, domain specific encoding ie. for DNA/protein sequences, for music, verilog/VHDL for hardware, DOT/Graphviz/Mermaid, OBJ for 3D, Terraform/Nix, Dockerfiles, git diffs/patches, URLs etc etc. The scope is very wide and covers enough to be called generic especially if you include multi modalities that are already being blended in (images, videos, sound). I'm cheering for Yann, hope he's right and I really like his approach to openness (hope he'll carry it over to his new company). At the same time current architectures do exist now and do work, by far exceeding his or anybody's else expectations and continue doing so. It may also be true they're here to stay for long on text and other supported modalities as cheaper to train.
- vidarh 8mo agoIt's just not true LLMs are limited to "static text". Data is data. Sensory input is still just data, and multimodal models has been a thing for a while. Ongoing learning and more extensive short term memory is a challenge, and so I am all for research in alternative architectures, but so much of the discourse about the limitations of LLMs act as if they have limitations they do not have.
- slibhb 8mo ago> LLMs are fundamentally capped because they only learn from static text -- human communications about the world -- rather than from the world itself, which is why they can remix existing ideas but find it all but impossible to produce genuinely novel discoveries or inventions. This seems wrong to me on a few levels. First, there is no way to "experience the world directly," all experience is indirect, and language is a very good way of describing the world. If language was a bad choice or limited in some fundamental way, LLMs wouldn't work as well as they do. Second, novel ideas are often existing ideas remixed. It's hard/impossible to point to any single idea that sprung from nowhere. Third, you can provide an LLM with real-world information and suddenly it's "interacting with the world". If I tell an LLM about the US war on Iran, I am in a very real sense plugging it into the real world, something that isn't part of its training data. Finally, modern LLMs are multi-modal, meaning they have the ability to handle images/video. My understanding is that they use some kind of adapter to turn non-text data into data that the LLM can make sense of.
- A_D_E_P_T 8mo agoRe 1: You experience the world in real time (or close enough) via your senses, which combine to form a spatiotemporal sense: A sense of being a bounded entity in space and time. The LLM has none of that. They experience the world via stale old text and text derivatives. Re 2: There's something tremendous in the fact, staring us right in the face, that LLMs are unable to meaningfully contribute to academic/medical research. I'm not saying that they need to perform on the level of a one-in-a-million Maxwell, DaVinci, or whatever. But as Dwarkesh asked one year ago: "What do you make of the fact that these things have basically the entire corpus of human knowledge memorized and they haven't been able to make a single new connection that has led to a discovery?" Re 3: Sure, you can hold it by the hand and spoonfeed it. You can also create for it a mirror reality which doesn't exist, which is pure fiction. Given how limited these systems are, I don't suppose it makes much of a difference. There's no way for it to tell. The "human in the loop" is its interaction with the world. And a pale, meager interaction it is. Re 4: Static, old images/video that they were trained on some months ago. That, too, is no way of interacting with the world.
- 8mo ago