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Okay, I think I follow and agree legalistically with your argument. But I also think it basically only exists philosophically. In practice, we make these determ

by tel 3y ago

Okay, I think I follow and agree legalistically with your argument. But I also think it basically only exists philosophically. In practice, we make these determinations all the time. I don't see any reason why a sufficiently sophisticated representation, learned through statistical optimization, is, in practice, different from a semantic model. If there were such a thing, it'd be interesting to propose how our own minds, at least to the degree that they can be seen as statistical learners in their own right, achieve semantics. And how that thing, whatever it might be, is not itself a learned representation driven by statistical impression.

mjburgess 3y ago
We arent statistical learners. We're abductive learners. We move, and in moving, grow representations in our bodies. These representations are abstracted in cognition, and form the basis for abductive explanations of reality. We leave plato's cave by building vases of our own, inside the cave, and comparing them to shadows. We do not draw outlines around the shadows. This is all non-experimental 'empirical' statistics is: pencil marks on the cave wall.
- tel 3y ago
  So we craft experiments. If someone else crafted an experiment, and you were informed of it and then shown the results, if this was done repeatedly enough, would you be incapable of forming any sort of semantic meaning?
  - mjburgess 3y ago
    If they only showed the measures, yes. The meaning of the measures is determined by the experiment, not by the data. "Data" is itself meaningless, and statistics on data is only informative of reality because of how the experimenter creates the measurement-target relationship.
    - tel 3y ago
      Okay I think I buy that. I don’t know if I agree, but trying to argue for a position against it has been sufficiently illuminating that I just need to chew on it more. There’s no doubt in my mind that experimental learning is more efficient. Especially if you can design the experiments against your personal models at the time. At the same time, it’s not clear to me that one could not gain similar value purely by, say, reading scientific journals. Or observing videos of the experiments. At some point the prevalence of “natural experiments” becomes too low for new discover through. We weren’t going to accidentally discover an LHC hanging around. We needed giant telescopes to find examples of natural cosmological experiments. Without a doubt, thoughtful investment in experimentation becomes necessary as you push your knowledge frontier forward. But within a realm where tons of experimental data is just available? Seems very likely that a learner asked to predict new experimental results outside of things they’ve directly observed but well within the space of models they’ve observed lots of experimentation around should still find that purely as an act of compression, their statistical knowledge would predict something equivalent to the semantic theory underlying it. We even seemed to observe just this in multimodal GPT-4 where it can theorize about the immediate consequences of novel physical situations depicted in images. I find it to be weak but surprising evidence of this behavior.
      - mjburgess 3y ago
        I'd be interested in the GPT-4 case, if you have a paper (etc.) ? You are correct to observe that science, as we know it, is ending. We're way along the sigmoid of what can be known, and soon enough, will be drifting back into medieval heuristics ("this weed seems to treat this disease"). This isnt a matter of efficiency, it's a necessity. Reality is under-determined by measurement; to find out what it is like, we have to have many independent measures whose causal relationship to reality is one we can control (through direct, embodied, action). If we only have observational measures, we're trapped in a madhouse. Let's not mistake science for pseudoscience, even if the future is largely now, pseudoscientific trash.
        tel 3y ago
        I thought the examples I was thinking of were in the original GPT-4 Technical Report, but all I found on re-reading were examples of it explaining "what's funny about" a given image. Which is still a decent example of this, I think. GPT-4 demonstrates a semantic model about what entails humor.

by tel 3y ago

mjburgess 3y ago

We arent statistical learners. We're abductive learners. We move, and in moving, grow representations in our bodies. These representations are abstracted in cognition, and form the basis for abductive explanations of reality. We leave plato's cave by building vases of our own, inside the cave, and comparing them to shadows. We do not draw outlines around the shadows. This is all non-experimental 'empirical' statistics is: pencil marks on the cave wall.

tel 3y ago
So we craft experiments. If someone else crafted an experiment, and you were informed of it and then shown the results, if this was done repeatedly enough, would you be incapable of forming any sort of semantic meaning?
- mjburgess 3y ago
  If they only showed the measures, yes. The meaning of the measures is determined by the experiment, not by the data. "Data" is itself meaningless, and statistics on data is only informative of reality because of how the experimenter creates the measurement-target relationship.
  - tel 3y ago
    Okay I think I buy that. I don’t know if I agree, but trying to argue for a position against it has been sufficiently illuminating that I just need to chew on it more. There’s no doubt in my mind that experimental learning is more efficient. Especially if you can design the experiments against your personal models at the time. At the same time, it’s not clear to me that one could not gain similar value purely by, say, reading scientific journals. Or observing videos of the experiments. At some point the prevalence of “natural experiments” becomes too low for new discover through. We weren’t going to accidentally discover an LHC hanging around. We needed giant telescopes to find examples of natural cosmological experiments. Without a doubt, thoughtful investment in experimentation becomes necessary as you push your knowledge frontier forward. But within a realm where tons of experimental data is just available? Seems very likely that a learner asked to predict new experimental results outside of things they’ve directly observed but well within the space of models they’ve observed lots of experimentation around should still find that purely as an act of compression, their statistical knowledge would predict something equivalent to the semantic theory underlying it. We even seemed to observe just this in multimodal GPT-4 where it can theorize about the immediate consequences of novel physical situations depicted in images. I find it to be weak but surprising evidence of this behavior.
    - mjburgess 3y ago
      I'd be interested in the GPT-4 case, if you have a paper (etc.) ? You are correct to observe that science, as we know it, is ending. We're way along the sigmoid of what can be known, and soon enough, will be drifting back into medieval heuristics ("this weed seems to treat this disease"). This isnt a matter of efficiency, it's a necessity. Reality is under-determined by measurement; to find out what it is like, we have to have many independent measures whose causal relationship to reality is one we can control (through direct, embodied, action). If we only have observational measures, we're trapped in a madhouse. Let's not mistake science for pseudoscience, even if the future is largely now, pseudoscientific trash.
      - tel 3y ago
        I thought the examples I was thinking of were in the original GPT-4 Technical Report, but all I found on re-reading were examples of it explaining "what's funny about" a given image. Which is still a decent example of this, I think. GPT-4 demonstrates a semantic model about what entails humor.