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> 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 a
by kybernetikos 3y ago
> 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?
- mjburgess 3y agoNo, 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.