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This is irrelevant, and it's very frustrating that computer scientists think it is relevant. If you give a universal function approximator the task of approxim

This is irrelevant, and it's very frustrating that computer scientists think it is relevant. If you give a universal function approximator the task of approximating an abstract function, you will get an approximation. Eg., def circle(radius): ... return points() aprox_cricle = neuralnetwork(sample(circle())) if is_model_of(samples(aprox_circle), circle)): print("OF COURSE!") This is irrelevant: games, rules, shapes, etc. are all abstract. So any model of samples of these is a model of them. The "world model" in question is a model of the world. Here "data" is not computer science data, ie., numbers its measurements of the world, ie., the state of a measuring device causally induced by the target of measurement. 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. Hence the entirety of experimental science. No result based on one mathematical function succeeding in approximating another is relevant whether measurement data "contains" a theory of the world which generates it: it does not. And of course if your data is abstract, and hence constitutes the target of modelling (only applies to pure math), then there is no gap -- a model of "measures" (ie., the points on a circle) is the target. No model of actual measurement data, ie., no model in the whole family we call "machine learning", is a model of its generating process. It contains no "world model". Photographs of the night sky are compatible with all theories of the solar system in human history (including, eg., stars are angels). There is no summary of these photographs which gives information about the world over and above just summarising patterns in the night sky. The sense in which any model of measurement data is "surface statistics" is the same. Consider plato's cave: pots, swords, etc. on the outside project shadows inside. Modelling the measurement data is taking cardboard and cutting it out so it matches the shadows. Modelling the world means creating clay pots to match the ones passing by. The latter is science: you build models of the world and compare them to data, using the data to decide between them. The former is engineering (, pseudoscience): you take models of measures and reply these models to "predict" the next shadow. If you claim the latter is just a "surface shortcut" you're an engineer. If you claim its a world model you're a pseudoscientist.

deleted 2y ago
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sebzim4500 2y ago
I don't understand your objection at all. In the example, the 'world' is the grid state. Obviously that's much simpler than the real world but the point is to show that even when the model is not directly trained to input/output this world state it is still learned as a side effect of prediction the next token.
- mjburgess 2y ago
  There is no world. The grid state is not a world, there is no causal relationship between the grid state and the board. No one in this debate denies that NNs approximate functions. Since a game is just a discrete function, no one denies an NN can approximate it. Showing this is entirely irrelevant and shows a profound misunderstanding of what's at issue. The whole debate is about whether surface patterns in measurement data can be reversed by NNs to describe their generating process, ie., the world. If the "data" isnt actual measurements of the world, no one arguing about it. If there is no gap between the generating algorithm and the samples, eg., between a "circle" and "the points on a circle" -- then there is no "world model" to learn. The world is the data. To learn "the points on a cirlce" is to learn the cirlce. By taking cases where "the world" and "the data" are the same object (in the limit of all samples), you're just showing that NNs model data. That's already obvious, no ones arguing about it. That a NN can approximate a discrete function does not mean it can do science. The whole issue is that the cause of pixel distributions is not in those distributions. A model of pixel patterns is just a model of pixel patterns, not of the objects which cause those patterns. A TV is not made out of pixels. The "debate" insofar as there is one, is just some researchers being profoundly confused about what measurement data is: measurements are not their targets, and so no model of data is a model of the target. A model of data is just "surface statistics" in the sense that these statistics describe measurements, not what caused them.
bubblyworld 2y ago
> There is no summary of these photographs which gives information about the world over and above just summarising patterns in the night sky. You're stating this as fact but it seems to be the very hypothesis the authors (and related papers) are exploring. To my mind, the OthelloGPT papers are plainly evidence against what you've written - summarising patterns in the sky really does seem to give you information about the world above and beyond the patterns themselves. (to a scientist this is obvious, no? the precession of mercury, a pattern observable in these photographs, was famously not compatible with known theories until fairly recently) > Modelling the measurement data is taking cardboard and cutting it out so it matches the shadows. Modelling the world means creating clay pots to match the ones passing by. I think these are matters of degree. The former is simply a worse model than the latter of the "reality" in this case. Note that our human impressions of what a pot "is" are shadows too, on a higher-dimensional stage, and from a deeper viewpoint any pot we build to "match" reality will likely be just as flawed. Turtles all the way down.

deleted 2y ago

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sebzim4500 2y ago

I don't understand your objection at all. In the example, the 'world' is the grid state. Obviously that's much simpler than the real world but the point is to show that even when the model is not directly trained to input/output this world state it is still learned as a side effect of prediction the next token.

mjburgess 2y ago
There is no world. The grid state is not a world, there is no causal relationship between the grid state and the board. No one in this debate denies that NNs approximate functions. Since a game is just a discrete function, no one denies an NN can approximate it. Showing this is entirely irrelevant and shows a profound misunderstanding of what's at issue. The whole debate is about whether surface patterns in measurement data can be reversed by NNs to describe their generating process, ie., the world. If the "data" isnt actual measurements of the world, no one arguing about it. If there is no gap between the generating algorithm and the samples, eg., between a "circle" and "the points on a circle" -- then there is no "world model" to learn. The world is the data. To learn "the points on a cirlce" is to learn the cirlce. By taking cases where "the world" and "the data" are the same object (in the limit of all samples), you're just showing that NNs model data. That's already obvious, no ones arguing about it. That a NN can approximate a discrete function does not mean it can do science. The whole issue is that the cause of pixel distributions is not in those distributions. A model of pixel patterns is just a model of pixel patterns, not of the objects which cause those patterns. A TV is not made out of pixels. The "debate" insofar as there is one, is just some researchers being profoundly confused about what measurement data is: measurements are not their targets, and so no model of data is a model of the target. A model of data is just "surface statistics" in the sense that these statistics describe measurements, not what caused them.

bubblyworld 2y ago

> There is no summary of these photographs which gives information about the world over and above just summarising patterns in the night sky. You're stating this as fact but it seems to be the very hypothesis the authors (and related papers) are exploring. To my mind, the OthelloGPT papers are plainly evidence against what you've written - summarising patterns in the sky really does seem to give you information about the world above and beyond the patterns themselves. (to a scientist this is obvious, no? the precession of mercury, a pattern observable in these photographs, was famously not compatible with known theories until fairly recently) > Modelling the measurement data is taking cardboard and cutting it out so it matches the shadows. Modelling the world means creating clay pots to match the ones passing by. I think these are matters of degree. The former is simply a worse model than the latter of the "reality" in this case. Note that our human impressions of what a pot "is" are shadows too, on a higher-dimensional stage, and from a deeper viewpoint any pot we build to "match" reality will likely be just as flawed. Turtles all the way down.