Deep learning is the state of the art on image and sound data. It's taking over natural language processing as well. Neural networks work well because they can

Deep learning is the state of the art on image and sound data. It's taking over natural language processing as well. Neural networks work well because they can learn complicated functions from the data. With image, sound and maybe text, the data has structure that can be exploited by NNs. Convolutional neural networks take advantage of this. So instead of learning "pixel 4,773 correlates with the output", we can look at a smaller number of image patches and learn "features" from each patch that correlate with the output. We can go further and create multiple layers of these. Each small image patch extracts some features which are used by the layer above it, and so on to the final output.