4 ms·

> Here there is no "world" in the data, you have to make strong causal assumptions about what properties of the target cause the measures. This is not in the da

> Here there is no "world" in the data, you have to make strong causal assumptions about what properties of the target cause the measures. This is not in the data. There is no "world model" in measurement data. That's wrong. Whatever your measuring device, it is fundamentally a projection of some underlying reality, eg. a function m in m(r(x)) mapping real values to real values, where r is the function governing reality. As you've acknowledged that neural networks can learn functions, the neural network here is learning m(r(x)). Clearly the world is in the model here, and if m is invertible, then we can directly extract r. Yes, the domain of x and range of m(r(x)) is limited, so the inference will be limited for any given dataset, but it's wrong to say the world is not there at all.

mjburgess 2y ago
In the limited sense in which the world is recoverable from measures of it requires a model of how it was generated. For animals, we are born with primitive causal models of our bodies we can recurse on to build models of the world in this sense. So as toddlers we learn perception by having an internal 3d model of our bodies -- so we can ascribe distances to our optical measures. Without such assumptions there really isnt any world at all in this data. A grid of pixel patterns has no meaning as a grid of numbers. NNs are just mapping this grid to a "summary space" under supervision of how to place the points. This supervision enables a useful encoding of the data, but does not provide the kind of assumptions needed to work backwards to properties of its generation. In the case of photos, there is no such `m` -- the state of a sensor is not uniquley caused by any catness or dogness properties. Almost no photographs acquire their state from a function X -> Y, because the sensor state is "radically uncontrolled" in a causal sense. Thus the common premise of ML, that y = f(x) is false from the start -- the relevant causal graph has a near infinite number of causes that are unspecified, so f does not exist.
- naasking 2y ago
  > For animals, we are born with primitive causal models of our bodies we can recurse on to build models of the world in this sense. So as toddlers we learn perception by having an internal 3d model of our bodies -- so we can ascribe distances to our optical measures. If you agree that we evolved such an internal model from some base system that had no such abilities or assumptions, then it follows that evolving programs to do such things is also possible. If you think the base system itself already had such abilities, then you have a bootstrapping problem. > Almost no photographs acquire their state from a function X -> Y, because the sensor state is "radically uncontrolled" in a causal sense. This seems like a bizarre claim to me. A camera is clearly capturing a 2D projection of a 3D world. We can also construct 3D structures from a set of 2D pictures, as we do this all of the time with photogrammetry. It's also clear that we can infer "catness" and "dogness" of subjects despite the fact that these are not strictly defined categories, because we literally did this in our pre-genotyping scientific taxonomies which organized the animal kingdom based on morphology, and this was pretty accurate. It then follows that with enough 2D pictures, one could create a taxonomy of objects based on reconstructed 3D morphology with a high degree of statistical accuracy. I think this demonstrates that your premise is incorrect that there is no "world" that can be modelled only from pictures. Current AI systems are probably only doing a subset of this, finding statistical correlations among morphological features, but that's still a world model IMO, if a bit anemic.
foobarqux 2y ago
This is obviously false: consider a (cryptographic) pseudorandom number generator.
- naasking 2y ago
  Trivial, m is not invertible in that case. By contrast, measuring devices need to be invertible within some domain, otherwise they're not actually measuring, and we wouldn't use them.
  - foobarqux 2y ago
    You defined "m" as the measuring function which is not the pseudo-random number generator itself. I guess I don't understand your definitions. In any case it's pretty obvious you have have deterministic chaotic output from which you cannot practically (or even in theory) recover the internal workings of the system that generated them. Take just a regular pseudorandom number generator or a cellular automata.
    - naasking 2y ago
      > In any case it's pretty obvious you have have deterministic chaotic output from which you cannot practically (or even in theory) Solomonoff induction says otherwise. Of course it might take a stupendously large number of samples, but as the number of samples goes to infinity, the probability of reproducing the PRNG goes to 1.

mjburgess 2y ago

In the limited sense in which the world is recoverable from measures of it requires a model of how it was generated. For animals, we are born with primitive causal models of our bodies we can recurse on to build models of the world in this sense. So as toddlers we learn perception by having an internal 3d model of our bodies -- so we can ascribe distances to our optical measures. Without such assumptions there really isnt any world at all in this data. A grid of pixel patterns has no meaning as a grid of numbers. NNs are just mapping this grid to a "summary space" under supervision of how to place the points. This supervision enables a useful encoding of the data, but does not provide the kind of assumptions needed to work backwards to properties of its generation. In the case of photos, there is no such `m` -- the state of a sensor is not uniquley caused by any catness or dogness properties. Almost no photographs acquire their state from a function X -> Y, because the sensor state is "radically uncontrolled" in a causal sense. Thus the common premise of ML, that y = f(x) is false from the start -- the relevant causal graph has a near infinite number of causes that are unspecified, so f does not exist.

naasking 2y ago
> For animals, we are born with primitive causal models of our bodies we can recurse on to build models of the world in this sense. So as toddlers we learn perception by having an internal 3d model of our bodies -- so we can ascribe distances to our optical measures. If you agree that we evolved such an internal model from some base system that had no such abilities or assumptions, then it follows that evolving programs to do such things is also possible. If you think the base system itself already had such abilities, then you have a bootstrapping problem. > Almost no photographs acquire their state from a function X -> Y, because the sensor state is "radically uncontrolled" in a causal sense. This seems like a bizarre claim to me. A camera is clearly capturing a 2D projection of a 3D world. We can also construct 3D structures from a set of 2D pictures, as we do this all of the time with photogrammetry. It's also clear that we can infer "catness" and "dogness" of subjects despite the fact that these are not strictly defined categories, because we literally did this in our pre-genotyping scientific taxonomies which organized the animal kingdom based on morphology, and this was pretty accurate. It then follows that with enough 2D pictures, one could create a taxonomy of objects based on reconstructed 3D morphology with a high degree of statistical accuracy. I think this demonstrates that your premise is incorrect that there is no "world" that can be modelled only from pictures. Current AI systems are probably only doing a subset of this, finding statistical correlations among morphological features, but that's still a world model IMO, if a bit anemic.

foobarqux 2y ago

This is obviously false: consider a (cryptographic) pseudorandom number generator.

naasking 2y ago
Trivial, m is not invertible in that case. By contrast, measuring devices need to be invertible within some domain, otherwise they're not actually measuring, and we wouldn't use them.
- foobarqux 2y ago
  You defined "m" as the measuring function which is not the pseudo-random number generator itself. I guess I don't understand your definitions. In any case it's pretty obvious you have have deterministic chaotic output from which you cannot practically (or even in theory) recover the internal workings of the system that generated them. Take just a regular pseudorandom number generator or a cellular automata.
  - naasking 2y ago
    > In any case it's pretty obvious you have have deterministic chaotic output from which you cannot practically (or even in theory) Solomonoff induction says otherwise. Of course it might take a stupendously large number of samples, but as the number of samples goes to infinity, the probability of reproducing the PRNG goes to 1.