You could say very much the same about the brain...
> [...] the "black magic" part comes mostly from their mathematical nature and very little from them being "inteligent computers". A brain is a graph, in which a subset of neurons are "inputs", some are outputs, and others are "hidden". The nodes are interconnected between each other in a fashion, which is called the "topology" or sometimes "architecture" of the net.
The deep question about deep learning is "Why is it so bloody effective?"
I work in the field, and while some models are based on biological structures/systems, there's a lot of fuzz about them being "based on biological foundations" that is now best avoided. Yes, it is true the model is based on them, but it's a model that only covers very little of the real complexity. So in a sense, it's naive to say "put a billion neurons in there and you'll get a rat brain" (as was publicized one time).
The effectiveness comes from their non-linear nature and their ability to "learn" (store knowledge in the weights, that is derived from the training process). And black magic, of course!
If there is magic to be found, it may be in that question. Why about graphs (namely the subset that are deep neural networks) allow them to not only contain such powerful heuristics, but also allow them to be created from scratch with barely any knowledge of the problem domain.
As a side note, I was playing a board game last night (Terra Mystica I believe) and wondering if you could get 5 different neural networks to play the game and then train them against each other (and once they are good enough, against players). I wonder how quickly one could train a network that is unbeatable by humans? Maybe even scale it up to training it to play multiple board games til it is really good at all of them before setting it lose on a brand new one (with a similar genre). Maybe Google could use this to make a Go bot.
But what happens if this is used for evil instead? Say a neural network that reads a person's body language and determines how easily they can be intimidated by either a criminal or the government. Or one that is used to hunt down political dissidents. Imagine the first warrant to be signed by a judge for no reason other than a neural network saying the target is probably committing a crime...
The best Go bot approach (as of some years ago, but it's not like neural networks are a new idea) uses a very different strategy. Specifically, the strategy of "identify a few possible moves, simulate the game for several steps after each move using a very stupid move-making heuristic instead of using this actual strategy recursively, and then pick the move that yielded the best simulated board state".
The "use a stupid heuristic as part of the evaluation function" is is, in fact, also an important part of Chess AI's mode (as Quiescence Search), through for different reasons.
Before clicking I was assuming it would fail. Then read this in the summary: "When the trained convolutional network was used directly to play games of Go, without any search, it beat the traditional search program GnuGo in 97% of games, and matched the performance of a state-of-the-art Monte-Carlo tree search that simulates a million positions per move."
- They use more parameters (and fewer computations per parameter.)
- They are hierarchical (convolutions are apparently useful at different levels of abstraction of data).
- They are distributed (word2vec, thought-vectors). Not restricted to a small set of artificial classes such as parts-of-speech or parts of visual objects.
It's not really that deep, imo: a typical deep net these days has O(10^8) parameters (e.g. http://stackoverflow.com/questions/28232235/how-to-calculate...). You can store a hell of a lot of patterns in that many parameters, making them the best pattern matchers the world has ever seen. (Un)fortunately, pattern matching != intelligence. More interesting deep questions for which there is precious little theory revolve around the design of the networks themselves.
Is "pattern matching != intelligence" what occurred when the Google image recognition stuff in the news recently was shown to recognize the pattern of a "dumbbell" as always having a large muscular arm attached to it?
Seemed like a great way to highlight the limitations of patterns.
I hadn't heard about that but it sounds like what I'm talking about. With their ever expanding training corpus Google's net will eventually learn that dumbbells and arms are separate entities, but it will never deduce that on its own. And if it did it would not be able to generalize that to the fact that wedding rings and fingers are different (I hypothesize). Basically there is a whole other component of "intelligence" that feels absent from neural nets, which is why visions of AI lording over humanity don't exactly keep me up at night. (Autonomous weapons otoh...)
> [...] the "black magic" part comes mostly from their mathematical nature and very little from them being "inteligent computers". A brain is a graph, in which a subset of neurons are "inputs", some are outputs, and others are "hidden". The nodes are interconnected between each other in a fashion, which is called the "topology" or sometimes "architecture" of the net.
The deep question about deep learning is "Why is it so bloody effective?"