Re:MIT AGI: Conversation with Yoshua Bengio [video]
by joe_the_user on Saturday October 20, 2018 @10:32PM
(#18266477)
NNs are not binary -- they're fundamentally analog, so for classification problems, they produce probabilities that a test case is in each possible class.
I think that's "true and false". Yes, NNs produce a "chance of being X" but no, NNs don't naturally produce a human-intuitive "degree of being X". If the NN is looking for a "red fire engine", it's not necessarily going to produce "degree of redness" as its output.
-- As to neural nets representing many functions; neural nets, Taylor series, Fourier series and so-forth can represent/approximate "any function". True but effectively irrelevant. What matters is what function a given methodology can be trained, programmed or whatever into representing.
I think that's "true and false". Yes, NNs produce a "chance of being X" but no, NNs don't naturally produce a human-intuitive "degree of being X". If the NN is looking for a "red fire engine", it's not necessarily going to produce "degree of redness" as its output.
-- As to neural nets representing many functions; neural nets, Taylor series, Fourier series and so-forth can represent/approximate "any function". True but effectively irrelevant. What matters is what function a given methodology can be trained, programmed or whatever into representing.