10 ms·

Generative AI's failure to induce robust models of the world

• energy123 1y ago
  Why was Anthropic's interpretability work not discussed? Inconvenient for the conclusion? https://www.anthropic.com/news/tracing-thoughts-language-model https://www.anthropic.com/news/tracing-thoughts-language-mod...
  • lossolo 1y ago
    The same work in which they show that the LLM doesn’t know what it "thinks"? or how it arrives at its conclusions where they demonstrate that it outputs what is statistically most probable? even though the logits indicate it was something else.
• sdenton4 1y ago
  "A wandering ant, for example, tracks where it is through the process of dead reckoning. An ant uses variables (in the algebraic/computer science sense) to maintain a readout of its location, even as as it wanders, constantly updated, so that it can directly return to its home." Hm. Dead reckoning is a terrible way to navigate, and famously led to lots of ships crashed on the shore of France before good clocks allowed tracking longitude accurately. Ants lay down pheromone trails and use smell to find their way home... There's likely some additional tracking going on, but I would be surprised if it looked anything like symbolic GOFAI.
  • deadbabe 1y ago
    Even if you find a pheromone trail, it doesn’t tell you what direction is home, or what path to take at branching paths. You need dead reckoning. The trail just helps you reduce the complexity of what you have to remember.
    • deleted 1y ago
      [deleted]
    • cma 1y ago
      The trail also leads the other ants to food, hard for them to use your own dead reckoning.
    • viraptor 1y ago
      The lack of information in ant trails (beyond "it exists here") leads to death spirals https://en.m.wikipedia.org/wiki/Ant_mill https://en.m.wikipedia.org/wiki/Ant_mill
      • fmbb 1y ago
        The very first sentence of the article you linked says this happens because they lose the pheromone track.
        • viraptor 1y ago
          It does, but the original paper is not as certain. (https://digitallibrary.amnh.org/server/api/core/bitstreams/86a774f3-c264-49de-ae82-a0c86db0af86/content https://digitallibrary.amnh.org/server/api/core/bitstreams/8...) It suggests that the following is done "both chemically and tactually" in case of a circular path, although with just a minimal test.
      • deadbabe 1y ago
        How could they encode some kind of directional information into a trail?
    • sdenton4 1y ago
      You take the branch with the stronger smell to get home. The branching point is where the trail divides, as different groups branch out, and thus the way home has more pheromones. Follow the trail and you don't need to remember the direction... Many animals detect and interpret smells as chemical gradients. We don't have the hardware for it, but plenty of others do.
• vunderba 1y ago
  Speaking of chess, a fun experiment is building a few positions such as on Lichess, taking a screenshot, and asking a state-of-the-art VLM to count the number of pieces on the board. In my experience, it had a much higher error ratio in less likely or impossible board situations (three kings on the board, etc).
• extr 1y ago
  I find Gary's arguments increasingly semantic and unconvincing. He lists several examples of how LLMs "fail to build a world model", but his definition of "world model" is an informal hand-wave ("a computational framework that a system (a machine, or a person or other animal) uses to track what is happening in the world"). His examples are lifted from a variety of unclear or obsolete models - what is his opinion of O3? Why doesn't he create or propose a benchmark that researchers could use to measure progress of "world model creation"? What's more, his actual point is unclear. Even if you simply grant, "okay, even SOTA LLMs don't have world models", why do I as a user of these models care? Because the models could be wrong? Yes, I'm aware. Nevertheless, I'm still deriving subtantial personal and professional value from the models as they stand today.
  • voidhorse 1y ago
    I think the point is that category errors or misinterpreting what a tool does can be dangerous. Both statistical data generators and actual reasoning are useful in many circumstances, but there are also circumstances in which thinking that you are doing the latter when you are only doing the former can have severe consequences (example: building a bridge). If nothing else, his perspective is a counterbalance to what is clearly an extreme hype machine that is doing its utmost to force adoption through overpromising, false advertising, etc. These are bad things even if the tech does actually have some useful applications. As for benchmarks, if you fundamentally don't believe that stochastic data generation leads to reason as an emergent property, developing a benchmark is pointless. Also, not everyone has to be on the same side. It's clear that Marcus is not a fan of the current wave. Asking him to produce a substantive contribution that would help them continue to achieve their goals is preposterous. This game is highly political too. If you think the people pushing this stuff are less than estimable or morally sound, you wouldn't really want to empower them or give them more ideas.
    • NitpickLawyer 1y ago
      > If nothing else, his perspective is a counterbalance to what is clearly an extreme hype machine that is doing its utmost to force adoption through overpromising, false advertising, etc. These are bad things even if the tech does actually have some useful applications. In other words, overhyped in the short term, underhyped in the long term. Where short and long term are extremely volatile. Take programming as an example. 2.5 years ago, gpt3.5 was seen as "cute" in the programming world. Oh, look, it does poems and e-mails, and the code looks like python but it's wrong 9 times out of 10. But now a 24B model can handle end-to-end SWE tasks in 0-shot a lot of the times.
• SubiculumCode 1y ago
  I definitely would be okay if we hit an AI winter; our culture and world cannot adapt fast enough for the change we are experiencing. In the meantime, the current level of AI is just good enough to make us more productive, but not so good as to make us irrelevant.
  • bitmasher9 1y ago
    I think negative feedback loops of AIs trained on AI generated data might lead to a position where AI quality peaks and slides backwards.
    • sgt101 1y ago
      Thank goodness we have version control systems then.
      • phoe-krk 1y ago
        "Version control systems", in case of AI, mean that their knowledge will stay frozen in time, and so their usefulness will diminish. You need fresh data to train AI systems on, and since contemporary data is contaminated with generative AI, it will inevitably lead to inbreeding and eventual model collapse.
        • Mars008 1y ago
          [dead]
    • adventured 1y ago
      AI will radically leap forward in specialized function gain over the next decade. That's what everybody should be focusing on. It'll rapidly splinter and acquire dominance over the vast minutia. The intricacy of the endeavor will be led by the AI itself, as it'll fly-wheel itself on becoming an expert at every little thing far faster than we can. We're just seeding that possibility now. Not only will it not slide backwards, it'll leap a great distance forward from where it's at now. Mainframes -> desktop computers -> a computer in every hand Obese LLMs you visit -> agents riding with you whereever you are, integrated into your life and things -> everything everywhere, max specialization and distribution into every crevice, dominance over most tasks whether you're there or active or not They haven't even really started working together yet. They're still largely living in sandboxes. We're barely out of the first inning. Pick a field and it's likely hardly even at the first pitch for most of them you can name, eg aircraft/flight. In hindsight people will (jokingly?) wonder whether AI self-selected software development as one of its first conquests, as the ultimate foot in the door so it could pursue dominion over everything else (of course it had to happen in that progression; it'll prompt some chicken or the egg debates 30-50 years out).
• voidhorse 1y ago
  The whole thing is silly. Look, we know that LLMs are just really good word predictors. Any argument that they are thinking is essentially predicated on marketing materials that embrace anthropomorphic metaphors to an extreme degree. Is it possible that reason could emerge as the byproduct of being really good at predicting words? Maybe, but this depends on the antecedent claim that much if not all of reason is strictly representational and strictly linguistic. It's not obvious to me that this is the case. Many people think in images as direct sense datum, and it's not clear that a digital representation of this is equivalent to the thing in itself. To use an example another HN'er suggested, We don't claim that submarines are swimming. Why are we so quick to claim that LLMs are "reasoning"?
  • Velorivox 1y ago
    > Is it possible that reason could emerge as the byproduct of being really good at predicting words? Imagine we had such marketing behind wheels — they move, so they must be like legs on the inside. Then we run around imagining what the blood vessels and bones must look like inside the wheel. Nevermind that neither the structure nor the procedure has anything to do with legs whatsoever. Sadly, whoever named it artificial intelligence and neural networks likely knew exactly what they were doing.
  • SubiculumCode 1y ago
    I was having a discussion with Gemini. It claimed that because Gemini, as a large language model, cannot experience emotion, that the output of Gemini is less likely to be emotionally motivated. I countered that the experience of emotion is irrelevant. Gemini was trained on data written by humans who do experience emotion, who often wrote to express that emotion, and thus Gemini's output can be emotionally motivated, by proxy.
  • etaioinshrdlu 1y ago
    I don't think it's accurate anymore to say LLMs are just really good word predictors. Especially in the last year, they are trained with reinforcement learning to solve specific problems. They are functions that predict next tokens, but the function they are trained to approximate doesn't have to be just plain internet text.
• deleted 1y ago
  [deleted]
• UltraSane 1y ago
  This paper argues the opposite https://arxiv.org/abs/2506.01622 https://arxiv.org/abs/2506.01622 Are world models a necessary ingredient for flexible, goal-directed behaviour, or is model-free learning sufficient? We provide a formal answer to this question, showing that any agent capable of generalizing to multi-step goal-directed tasks must have learned a predictive model of its environment. We show that this model can be extracted from the agent's policy, and that increasing the agents performance or the complexity of the goals it can achieve requires learning increasingly accurate world models. This has a number of consequences: from developing safe and general agents, to bounding agent capabilities in complex environments, and providing new algorithms for eliciting world models from agents.
  • voidhorse 1y ago
    I only skimmed it so far, but this seems to only argue against the functional import of the OP, not its philosophical import. On my reading, the philosophical claim is that these models do not develop an actual logical, internal representation of domains. The functional import is whether or not they are able to realize specific behaviors within a domain. The paper argues that a markov process can realize the functional equivalence of the initial goal oriented picture of its domain—that is can solve goals with an error bound—but not that it develops an actual representation of the domain. Lack of an actual representation prevents such a machine from doing other things. For example, iiuc, it would be unable to solve problems in domains that are homomorphic to the original, while an explicit representation does enable this.
• Animats 1y ago
  Note that this is the same problem engineers have talking to managers. The manager may lack a mental model of the task, but tries to direct it anyway.
• Animats 1y ago
  That LLMs are a black box and that LLMs lack an underlying model are both true, but orthogonal. It's possible to have a black box system which has an underlying model. That's true of many statistical prediction methods. Early attempts at machine learning were a white box with no underlying model. This is true of most curve-fitting. The AI version was where you're trying to divide a high-dimensional space with a cutting plane to create a classifier. You can tell where the separating plane is, but not why. The lack of a world model is a very real limitation in some problem spaces, starting with arithmetic. But this argument is unconvincing.
  • comp_throw7 1y ago
    > LLMs lack an underlying model Obviously false for any useful sense by which you might operationalize "world model". But agree re: being a black box and having a world model being orthogonal.
  • seanhunter 1y ago
    “LLMs lack an underlying model” is very obviously incorrect. LLMs have an underlying model of semantics as tokens embedded into a high-dimensional vector space. The question is not whether or not they have any model at all, the question is whether the model they indisputably have (which is a model of language in terms of linear algebra) maps onto a model of the external universe (a “world model”) that emerges during training. This is pretty much an unfalsifiable question as far as I can see. There has been research that aims to show this one way or another and it doesn’t settle the question of what a “world model” even means if you permit a “world model” to mean anything other than “thinks like we do”. For example, LLMs have been shown to produce code that can make graphics somewhat in the style of famous modern artists (eg Kandinsky and Mondrian) but fail at object-stacking problems (“take a book, four wine glasses, a tennis ball, a laptop and a bottle and stack them in a stable arrangement”). Depending on the objects you choose the LLM either succeeds or fails (generally in a baffling way). So what does this mean? Clearly the model doesn’t “know” the shape of various 3-D objects (unless the problem is in their training set which it sometimes seems to be) but on the other hand seems to have shown some ability to pastiche certain visual styles. How is any of this conclusive? A baby doesn’t understand the 3-D world either. A toddler will try and fail to stack things in various ways. Are they showing the presence or lack of a world model? How do you tell?
• dist-epoch 1y ago
  The article links to a tweet about jail-braking Claude to provide a recipe for Sarin gas production: https://x.com/argleave/status/1926138376509440433 https://x.com/argleave/status/1926138376509440433 But some words are redacted. So I've uploaded the picture to Gemini and asked it what the redacted words are, and it told me. Not sure if they are correct, and some are way longer to fit in the redacted black box, but it didn't refuse the request.
• tim333 1y ago
  I usually disagree with Garry Marcus but his basic point seems fair enough if not surprising - Large Language Models model language about the world, not the world itself. For a human like understanding of the world you need some understanding of concepts like space, time, emotion, other creatures thoughts and so on, all things we pick up as kids. I don't see much reason why future AI couldn't do that rather than just focusing on language though.
  • code51 1y ago
    The underlying assumption is that language and symbols are enough to represent phenomena. Maybe we are falling for this one in our own heads as well. Understanding may not be a static symbolic representation. Contexts of the world infinite and continuously redefined. We believed we could represent all contexts tied to information, but that's a tough call. Yes, we can approximate. No, we can't completely say we can represent every essential context at all times. Some things might not be representable at all by their very chaotic nature.
    • tim333 1y ago
      I did think that human mental modeling of the world is also quite rough and often inaccurate. I don't see why AI can't become human like in it's abilities but accurately modeling all the relativistic quarks in an atom is a bit beyond anything just now.