This intro is pretty good, but may be a bit high level for some readers of HN. It seems like it's written for non-technical readers. The idea is that AI, ML and DL are nested like Russian dolls, each subsequent one a subset of the other.
It might be better to explain why deep learning is so effective, in clear language:
* Deep artificial neural networks are old, relatively simple combinations of math and code that are now able to produce accurate models through brute force because we have
1) vastly more computational power thanks to NVIDIA and distributed run-times;
2) much more data, and much larger labeled datasets thanks to people like Fei-Fei Li at Stanford;
3) better algorithms thanks to the work of Hinton, LeCun, Bengio, Ng, Schmidhuber and a raft of others.
Deep is a technical term. It refers to the number of layers through which data passes in a neural net; that is, the number of mathematical operations it is subjected to, and the number of times it is recombined with other inputs.
This recombination of inputs, moving deeper into the net, is the basis of feature hierarchy, which is another way of saying: we can cluster and classify data using more complex and abstract representations.
That clustering and classification is at the heart of what deep learning does. Another way to think about it is as machine perception. So the overarching narrative in AI is that we've moved from the symbolic rules engines of the chess victors to the interpretation of complex sensory information. For a long time, people would say AI could beat a 30-year-old at chess but couldn't beat a 3-year old at basic tasks. That's no longer true. We can go around beating 3-year-olds at name games all day. AI mind, beginner's mind.
But it's important to note that deep learning actually refers to other algorithms besides artificial neural networks. Deep reinforcement learning is one example. RL is also an old set of algorithms, which are goal-oriented. RL helps agents choose the right action in a given state to maximize rewards from the environment. Basically, they learn the function that converts actions to rewards given certain conditions, and that function is non-differentiable; that is, you can't learn it simply by backpropagating error, the way neural nets do.
Deep RL is important because the most amazing algorithms, like AlphaGo, are combining deep neural nets (recognize the state of the Go board) with RL (pick the move most likely to succeed) and other components like Monte Carlo Decision Trees (limit the state space we explore).
So we're moving beyond perception to algorithms that can make strategic decisions in increasingly complex environments.
We've written more about this, and implemented many of these algorithms:
http://deeplearning4j.org/ai-machinelearning-deeplearning.ht...
http://deeplearning4j.org/reinforcementlearning.html
http://github.com/deeplearning4j/rl4j
