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> But what are "sufficient resources" in this case? How do you train a universal function approximator, even a good one, in, say "increasing GDP"? Where do you
by ShabbosGoy 8y ago
> But what are "sufficient resources" in this case? How do you train a universal function approximator, even a good one, in, say "increasing GDP"? Where do you get the examples for that?
You could specify a simpler goal like “increase economic output”, and an example might be something like optimizing the Cobb-Douglas production function. Even that very narrow goal in the context of say, manufacturing Teslas, would give me pause. Look up “instrumental convergence” to see why the above is a bad idea.
- YeGoblynQueenne 8y agoWhen I say "examples" I mean training examples- a dataset to train (and validate) on. From what I can see on wikipedia, the parameters of Cobb-Douglas are the value of goods produced, labour, capital, "total factor productivity" (as I understand it, everything other than labour and capital that might contribute to productivity) and a couple of constants. For an AI to maximise the output of the function you'd have to somehow make it possible for it to manipulate those parameters- to hire or fire personel, to spend or acquire capital and to somehow manage all those unknown factors that might be contributing to the output. The question is: how do you do that? You can certainly collect, or even auto-generate examples of the inputs and outputs of the functions, since we're just talking numerical parameters, and find a maximum of the function. But for an AI to actually improve the productivity of a business, it would have to do a lot more than that. It would need the ability to manipulate those parameters directly in the real world. Otherwise, all it would do is calculate a number. Which is not that very threatening.