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Re:Telling GPT-4 you're scared or under pressure improves pe... by mjburgess on Saturday November 04, 2023 @04:31PM (#38144843)
most formulas, including that one, are neural networks (such is the absurdity of the term) -- so it is trivially learnable

The issue is that to prepare the dataset from which that formula is learnt requires already knowing it. This is the triviality of applications of universal function approximators to science -- empirical data modelling isnt new, and neural networks are just one example of it; not all that special.

All observational data on the solar system, at any point in time, would not yield this formula via empirical function approximation. there really isnt "observational data" to collect, in this sense

This is what I mean about rigging -- there is no 'bare dataset' which tells you what the world is like. to construct experiments which yield data that 'presents' scientific laws as if statistical patterns requires millenia of theory-building science

science uncovers the necessary causal relations between objects and their properties, as determined by extremely controlled experiments which take millenia of engineering and theory-building to even conceive, let alone execute

stats is the dumb 'accounting' of this data -- by the time you actually have it all the science (and indeed, all the intelligence) is done

what can be automated at this point is 'stamp collecting' as rutherford said

Re:Telling GPT-4 you're scared or under pressure improves pe... by kybernetikos on Saturday November 04, 2023 @08:00PM (#38146546)
> The issue is that to prepare the dataset from which that formula is learnt requires already knowing it.

Why is that? Are you saying that universal function approximators can't invent higher level models? Isn't that what happens in the hidden layers when an neural network is trying to predict things that require those higher level models?

Re:Telling GPT-4 you're scared or under pressure improves pe... by mjburgess on Sunday November 05, 2023 @05:34AM (#38149906)
No, consider how you would collect a dataset to show F=GMm/r^2

you'd need to measure the force between planets, the distance between them, etc. no such direct measurements have taken place to my knowledge, certainly not at newton's time

All "universal fn approximator" means is that given any dataset which is sampled from a function, you can recover that function with enough data points. It does not mean, eg., that given a 2D dataset you can derive a 3D function -- you cannot, this is impossible.

So you need to be sampling from F=GMm/r^2 to find that function. So you need to know the answer before you begin. Fn approximators are only useful for empirical refinements to existing knowledge.

In order to construct such datasets you need to do science; hence experiments, etc.