I am 15 years into this computers thing and this blog post made me feel like "those guys are doing black magic".
Neural networks and deep learning are truly awesome technologies.
Neural networks and deep learning are truly awesome technologies.
A neural net is a graph, in which a subset of nodes are "inputs" (that's where the net gets information), some are outputs, and there are other nodes which are called "hidden neurons".
The nodes are interconnected between each other in a fashion, which is called the "topology" or sometimes "architecture" of the net. For example I-H-O is a tipical feed forward net, in which I (inputs) is the input layer, H is the hidden layer and O the output layer. All the hidden neurons connect with all the input neurons "output", and all the output neurons connect to the hidden neurons "output". The connections are called "weights", and the training adjusts the weights of all the neuron with lots of cases until the desired output is achieved. There are also algorithms and criteria to stop before the net "learns too much" and looses the ability to generalize (this is called overfitting). In particular, a net with one hidden layer and one output layer is a universal function estimator -- that is, an estimator that can model any mathematical function of the form f(x1, x2, x3, ..., xn) = y.
Deep learning means you're using a feedforward net with lots of hidden layers (I think it's usually between 5 to 15 now), which apply convolution operators (hence the "convolutional" in the name), and lots of neurons (in the order of thousands). All this was nearly impossible until the GPGPUs came along, because of the time it took to train a modest network (minutes to hours for a net with a between 50 to 150 neurons in one hidden layer).
This is a very shortened explanation -- if you want to read more I recommend this link[1] which gives some simple Python code to illustrate and implement the innards of a basic neural network and you can learn from the inside. Once you get that you should move to more mature implementations, like Theano or Torch to get the full potential of neutral nets without worrying about implementation.
[1] http://iamtrask.github.io/2015/07/12/basic-python-network/
> [...] 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?"
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!
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...
This is likely due to the way Go works , random playout provides a rough estimate of who controls what territory ( this is how Go is scored ).
Recently two deep-learning papers showed very impressive results.
http://arxiv.org/abs/1412.3409
http://arxiv.org/abs/1412.6564
The neural networks were tasked with predicting what move an expert would make given a position.
The MCTS takes a long time 100,000 playouts are typical - once trained the neural nets are orders of magnitude faster.
The neural nets output a probability for each move ( that an expert would make that move ) - all positions are evauluated in a single forward pass.
Current work centers around combining the two approaches, MCTS evaluates the best suggestions from the neural net.
Expert Human players are still unbeatable by computer Go.
It learns to master level from self-play.
http://www0.cs.ucl.ac.uk/staff/D.Silver/web/Applications_fil...
also his lecture bootstrapping from tree based search
http://www.cse.unsw.edu.au/~cs9414/15s1/lect/1page/TreeStrap...
and Silver's overview on board game learning
http://www0.cs.ucl.ac.uk/staff/D.Silver/web/Teaching_files/g...
There was in fact a group within Google that worked on this: http://www.cs.toronto.edu/~cmaddis/pubs/deepgo.pdf
Move Evaluation in Go Using Deep Convolutional Neural Networks Chris J. Maddison, Aja Huang, Ilya Sutskever, David Silver
Seemed like a great way to highlight the limitations of patterns.
- 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.
- They are recurrent (RNN).
etc.
Oh humbug! The black magic comes from the vast resources Google drew to obtain perfect training datasets. Each step in the process took years to tune, demonstrating that data is indeed for those who dont have enough priors.
Convolutional networks are only one kind of deep learning. In particular, they generally apply only to image processing.
Two remarks. First, these guys probably don't know very well why what they are doing works so well ;) It requires a lot of trial and error, and a lot of patience and a lot of compute power (the latter being the reason why we are seeing breakthroughs only now).
Second, training a neural net requires different computing power from deploying the net. The neural network that is installed on your phone has been trained using a lot of time and/or a very large cluster. Your phone is merely "running" the network, and this requires much less compute power.