4 ms·

The reality is that current models are simply nowhere near AGI. Next token prediction has been pushed very far, and proven to have applicability far beyond the

• ACCount37 7mo ago
  Given the mechanistic interpretability findings? I'm not sure how people still say shit like "no real world model" seriously.
  • sulam 7mo ago
    They have a _text_ model. There is some correlation between the text model and the world, but it’s loose and only because there’s a lot of text about the world. And of course robotics researchers are having to build world models, but these are far from general. If they had a real world model, I could tell them I want to play a game of chess and they would be able to remember where the pieces are from move to move.
    • ACCount37 7mo ago
      What makes you think that text is inherently a worse reflection of the world than light is? All world models are lossy as fuck, by the way. I could give you a list of chess moves and force you to recover the complete board state from it, and you wouldn't fare that much better than an off the shelf LLM would. An LLM trained for it would kick ass though.
      • abcde666777 7mo ago
        "What makes you think that text is inherently a worse reflection of the world than light is?" Come on man, did you think before you asked that one :)?
      • teeray 7mo ago
        > What makes you think that text is inherently a worse reflection of the world than light is? What does the color green look like?
        • ACCount37 7mo ago
          It doesn't look like anything to me.
        • joquarky 7mo ago
          A color without form can't look like anything.
      • RugnirViking 7mo ago
        > I could give you a list of chess moves and force you to recover the complete board state from it, and you wouldn't fare that much better than an off the shelf LLM would idk, I would expect anyone with an understanding of the rules of chess, and an understanding of whatever notation the moves are in, would be able to do it reasonably well? does that really sound so hard to you? people used to play correspondance chess. Heck I remember people doing it over email. In comparison, current ai models start to completely lose the plot after 15 or so moves, pulling out third, fourth and fifth bishops, rooks etc from thin air, claiming checkmate erroneously etc, to the point its not possible to play a game with them in a coherent manner.
        • ACCount37 7mo ago
          I would expect that off the shelf GPT-5.4 would be able to do it when prompted carefully, yes. Through reasoning - by playing every move step by step and updating the board one move at a time to arrive at a final board state. On the other hand, recovering the full board state in a single forward pass? That takes some special training. Same goes for meatbag chess. A correspondence chess aficionado might be able to take a glance at a list of moves and see the entire game unfold in his mind's eye. A casual player who only knows how to play chess at 600 ELO on a board that's in front of him would have to retrace every move carefully, and might make errors while at it.
          • sulam 7mo ago
            Try to play a simple over the board style game with 5.4 with whatever notation you chose to use (or just descriptions, literally anything). Prediction: it will start out fine, but the mid game will be very hard to keep it on track, and the endgame will make you give up.
  • famouswaffles 7mo ago
    People just overstate their understanding and knowledge, the usual human stuff. The same user has a comment in this thread that contains: 'If you actually know what models are doing under the hood to product output that...' Any one that tells you they know 'what models are dong under the hood' simply has no idea what they're talking about, and it's amazing how common this is.
    • sulam 7mo ago
      Fair, I should define what I mean by under the hood. By “under the hood” I mean that models are still just being fed a stream of text (or other tokens in the case of video and audio models), being asked to predict the next token, and then doing that again. There is no technique that anyone has discovered that is different than that, at least not that is in production. If you think there is, and people are just keeping it secret, well, you clearly don’t know how these places work. The elaborations that make this more interesting than the original GPT/Attention stuff is 1) there is more than one model in the mix now, even though you may only be told you’re interacting with “GPT 5.4”, 2) there’s a significant amount of fine tuning with RLHF in specific domains that each lab feels is important to be good at because of benchmarks, strategy, or just conviction (DeepMind, we see you). There’s also a lot work being put into speeding up inference, as well as making it cheaper to operate. I probably shouldn’t forget tool use for that matter, since that’s the only reason they can count the r’s in strawberry these days. None of that changes the concept that a model is just fundamentally very good at predicting what the next element in the stream should be, modulo injected randomness in the form of a temperature. Why does that actually end up looking like intelligence? Well, because we see the model’s ability to be plausibly correct over a wide range of topics and we get excited. Btw, don’t take this reductionist approach as being synonymous with thinking these models aren’t incredibly useful and transformative for multiple industries. They’re a very big deal. But OpenAI shouldn’t give up because Opus 4.whatever is doing better on a bunch of benchmarks that are either saturated or in the training data, or have been RLHF’d to hell and back. This is not AGI.
      • stavros 7mo ago
        Everybody says "but they just predict tokens" as if that's not just "I hope you won't think too much about this" sleight of hand. Why does predicting the next token mean that they aren't AGI? Please clarify the exact logical steps there, because I make a similar argument that human brains are merely electrical signals propagating, and not real intelligence, but I never really seem to convince people.
  • 10xDev 7mo ago
    People are finding it hard to grasp emergent properties can appear at very large scales and dimensions.
• onlyrealcuzzo 7mo ago
  How many months has it been since we were told there would be zero software engineers left in the world in 12 months?
  • xyzal 7mo ago
    >12
  • SpicyLemonZest 7mo ago
    What Dario Amodei said 12 months ago is that AI would be "writing essentially all of the code", and the job of software engineers would become guiding and reviewing the code generation process. That's come true at a number of companies. The important context I think people may miss is, this does not require AI to be 10x or 5x or even 1x as good as a human programmer. Claude is worse than me in meaningful ways at the kind of code I need to write, but it’s still doing almost all my coding because after 4.6 it’s smart enough to understand when I explain what program it should have written.
    • casey2 7mo ago
      There are 0 relevant companies where AI is writing all the code. Now, you could mislead people by taking his words hyper literally. Computers = AI, basically all companies write code using computers these days. I don't know why you would, but you could. A rent seeking mega corp that stopped writing real software over a decade ago you could literally force all your employees to use LLMs, but there is no value add for serious companies trying to solve real problems with programming.
• hintymad 7mo ago
  > It has no real world model, no ability to learn in any but superficial ways I also think so, and in the meantime I have to admit a lot of people don't learn deeply either. Take math for example, how many STEM students from elite universities truly understood the definition of limit, let alone calculus beyond simple calculation? Or how many data scientists can really intuitively understand Bayesian statistics? Yet millions of them were doing their job in a kinda fine way with the help of the stackexchange family and now with the help of AI.
  • Spivak 7mo ago
    Well part of that is because STE folks aren't typically required to take any kind of theoretical maths. It's $Math for Engineers and it eschews theoretical underpinnings for application. I don't think it's any kind of failing, it's just different. My statistics class was a dense treatise in measure theory. Anyone who took the regular stats class is almost surely way better than me at designing an experiment, but I can talk your ear off about Lebesgue measure to basically zero practical end.
    • hintymad 7mo ago
      I was not talking about theoretical foundations like Analysis or measure theory, but just basics in college-level math class. There can be other examples. The point is that many people didn’t have intuitive understanding of what they use everyday — in a way they are like AI, only slower and know less than AI
• deleted 7mo ago
  [deleted]