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It's just filled to the brim with relations between things. It's good at searching a very large meaning space and create correlations. What it does is to cover

• pyridines 2mo ago
  The very fact that it is able to search within a meaning-space demonstrates that it understands semantics, to some extent. Philosophically, that is profound, for something that is just one big matrix multiplication. Drawing connections between things in meaning-space is surely a facet of intelligence.
  • deleted 2mo ago
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  • bayindirh 2mo ago
    It’s not intelligence if you are the one who gives the correlations to the model in the pre-training. It’s Word2Vec, applied. Model doesn’t learn anything. You embed these correlations and build it from there. It just searches the space. As my AI professor said in the first lecture: “All AI is advanced search”.
    • deleted 2mo ago
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    • pyridines 2mo ago
      Okay, I guess you're right that its ability to do this is just correlational, which doesn't imply it has any understanding. However, you have to conclude that some tasks which we used to believe required intelligence don't actually require any, which is disconcerting.
      • bayindirh 2mo ago
        No, what I would say is the tasks which are handled in a passable manner by LLMs can be mathematically modeled with some reasonable accuracy. Many things are predicted by models in our planet. From weather to production and material science. Building the model needs intelligence, running the model does not. The person who came up with the formulae for CFD was intelligent. The computer running the model is not. Same for LLMs, chess engines, engine ECUs and financial prediction systems. Again, for the example’s sake; the person who came up with an algorithm is intelligent. The model mixing its training data to emit something similar is not.
        • hombre_fatal 2mo ago
          This starts to feel like you're defining the word intelligence out of any meaning and out of any way we apply that word. So when LLMs can do all human knowledge work, and do it better than humans, we'll be in the mines listening to you go on about how it's actually just autocomplete or just math, a distinction that apparently means nothing.
          • bayindirh 2mo ago
            > This starts to feel like you're defining the word intelligence out of any meaning and out of any way we apply that word. No. > So when LLMs can do all human knowledge work, and do it better than humans, we'll be in the mines listening to you go on about how it's actually just autocomplete or just math, a distinction that apparently means nothing. With a big "if" attached to it. People were saying "computers will program themselves in the near future" for, checks notes, 24 years now, as far as I'm aware. We're constantly building new knowledge and understanding things better than olden days. These models just compress our knowledge and light the blind corners we can't see well. I don't say they are useless, but I say that these things are overhyped. All they can do is regurgitate human knowledge packed into them and highlight some long-distance correlations between items, which is useful in itself, but it can't jump to somewhere where it's not present its training data, but that's something humans and only humans can do.
    • _ifton 2mo ago
      They perform tasks too. They execute functions. This has real world implications beyond search.
• rnd33 2mo ago
  Intelligence is compression, compression requires subtraction, and for some reason LLMs are not good at subtracting. To create a coherent model you kinda have to subtract correlations until only the essential parts are still there. What I don't understand is why LLMs haven't been able to do this yet, if it's the harness or some orchestration layer above the LLM that is needed. Because fundamentally if you can identify correlations then it's just another small step to prioritize and remove lower value or irrelevant correlations. I wonder if what's needed is to introduce subtraction tokens in some sense, and in post-training reward the model on that.
  • jrmg 2mo ago
    Intelligence is compression That’s a controversial statement.
  • fluoridation 2mo ago
    Intelligence is compression? What do you mean? Intuitively that doesn't seem right. >What I don't understand is why LLMs haven't been able to do this yet LLMs are just trained on what humans have said. Why is it surprising that it's still not possible to reconstruct the intelligence that wrote all that by working backwards? Think of your own work experience. When you look at a piece of code, say, are you always able to discern why the person did what they did, just from the code, with no additional context?
    • teekert 2mo ago
      I guess they mean that intelligence is being able to hold models (compressed versions of reality) internally and use them to make predictions with a probability better than chance. That last part is the definition of information.
      • fluoridation 2mo ago
        I find that highly questionable as a general description of what intelligence does. That's more like a description of a general knowledge base. When I think of someone intelligent, I think of someone who's able to draw unexpected connections between seemingly unrelated facts. In the broadest possible terms, I'd call it the ability to make abstractions and analogies. This is not just compression, but the ability to mentally operate on webs of meaning.