"Deep learning" is used a couple ways. In the loose sense employed by some journalists, deep learning is probably synonymous with both machine learning and AI.

"Deep learning" is used a couple ways. In the loose sense employed by some journalists, deep learning is probably synonymous with both machine learning and AI. It's at the cutting edge of the current hype. In the strict sense, deep learning refers to neural networks with more than one hidden layer. The depth of neural networks is equal to the longest path between input and output nodes. "Shallow" networks like a simple autoencoder might have two layers. They're not considered deep or part of deep learning. But if you stack them together, you have a deep net; e.g. many restricted Boltzmann machines form a deep-belief network. [1] As @paulsutter mentioned, one aspect of having several hidden, or intermediate, layers in a neural network is that you can combine relatively simple, granular features (like individual pixels or words) into more complex combinations. Neural networks recombine simple features automatically, and then learn which groupings should be lent significance as signals through the backpropagation of error. They're attracting all this hype because they actually do something amazing, albeit through brute force computation. Many in AI scoff at neural networks because they've been around a long time and have no particular elegance, but we're now in a historical moment where we have the hardware to make them work, and they're breaking records in almost every data type; e.g. images, sound, time series, etc. So no, it's not a rebranding, it's a thing. We can now replicate the human faculty of perception with machines in many domains, and that's going to make the future quite weird. [1] http://deeplearning4j.org/restrictedboltzmannmachine.html http://deeplearning4j.org/restrictedboltzmannmachine.html