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I think the current state of deep neural networking designs and research funding pouring into generalizing the simple neural nets we're working with now suggest
by AaronFriel 10y ago
I think the current state of deep neural networking designs and research funding pouring into generalizing the simple neural nets we're working with now suggests that we are, in fact, pulling them out of a hat.
Right now we're just discarding all the ones that are defective, at a stupendously high rate as we train neural nets.
I can't speak to what method would generate the first strong AI, but I suspect the overall process - if not the details - will be similar. Training, discarding, training, discarding, testing, and so on. And the first truly strong AI will likely just be the first random assemblage of parts that passes those tests.
- wyager 10y agoThat's not how neural network training works. It's not magic or guesswork; it's essentially glorified curve fitting (over a much more complicated space than the polynomials). It's also not random in any respect. The space of all neural networks plausibly generated from a training set is very, very small compared to the space of all networks of that size.
- Sean1708 10y agoI get the feeling that not many people in this thread actually know how AI and related concepts work.
- Nevermark 10y agoThe initial conditions of neural networks are almost always chosen randomly or pseudo-randomly. Data sets are sometimes, although not always, presented with random sample order or selection. Either way, the randomness in initial conditions means the solution found is one of many different solutions that could have been found, and depending on the problem, different initial conditions can result in very different solutions even on the same data.