[DocSavage]

It's not all hype. We are using deep learning to solve real issues in our research, things that might not get done as well due to the representational learning aspect and our relatively small manpower. It's a definite advance when it comes to improving state-of-the-art and will have impact across all sorts of things, particularly small groups who have problems but don't have hundreds of people and ten years to figure it out.

The powerplant criticism is more true of the training phase and much less true of using the resulting networks on a larger scale. The good thing about the "hype" is that more resources are being directed into the field so they'll be more efficient processing platforms (e.g., ASIC or FPGA) and better delineation of what's really needed and if there are possible shortcuts (e.g., ReLU).

The black box problem will prevent its use in some systems, but even in some areas of medicine, it will be fine because medical AI must be used in conjunction with the final decision-maker, much like how Watson is being positioned. A deep learning system that detects anomalies in patient imaging with very high precision will be useful even if it can't explain why it thought it was an anomaly. It's quality control for the radiologist, etc.

[dkarapetyan]

I think that's a good use case. Such use cases are great and I agree it's not all hype but when everyone and their grandma has an "AI" startup you have to wonder what exactly "AI" means at that point.