Last time I looked there was no application of ANNs which couldn't be solved more efficiently by other algorithms
Is it possible that last time you checked was a long time ago? Deep neural networks are again all the rage now (i.e. huge teams working with them at Facebook and Google) because
- (1) They have resulted in a significant performance improvement over previously state-of-the-art algorithms in many application tasks,
- (2) Although they are computation-heavy, they are amenable to massive parallelization (modern computational power is probably the main reason why they have improved singificantly with respect to ANNs of the 80-90s, given that the main architecture itself has not changed a lot, except possibly for the "convolution" trick which effectively introduces hard-coded localization and spatial invariance).
To be fair, it always seems to me that (1) and (2) are very closely related. CNNs that won recent computer vision benchmarks are the only methods that used so much processing power so far. Not that they're less efficient than other, tough. It's just that I would love to see other methods with that many engineering, tunning, dedicated computational power and how they compare.
Also, not that when it comes to classification, the standard is to throw the last layer and train a linear SVM on the penultimate layer, which also show that CNNs alone are not enough.