Deep neural networks are quite powerful because they don't require someone to design the network specific to the task. Large deep nets will generally be able to

Deep neural networks are quite powerful because they don't require someone to design the network specific to the task. Large deep nets will generally be able to learn feature representations by themselves, which makes them extremely powerful for tasks where we aren't quite sure of exactly what features we want. However, deep nets require large amounts of training data and computational power. The fairly recent widespread adoption of general purpose GPUs has allowed much faster training. Combine this with the popularity of "big data", and you've got a perfect storm for deep neural nets. Of course, the hype may be overvaluing deep nets as the future of AI. DNNs work well in practical applications, but they're poorly defined theoretically and the AI community suspects that we're still bad at training them -- a recent paper showed that a simpler shallow net can perform as well as a deep net if a deep net is trained first[1]. We're also fairly certain that deep nets are not how the brain actually works, and thus we'll need a different architecture in order to achieve human level performance on some tasks. [1] http://arxiv.org/abs/1312.6184 http://arxiv.org/abs/1312.6184