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by dkarapetyan
3483 days ago
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> I'll be impressed when they solve Pacman on a Raspberry Pi I think this is the thing people don't quite get when they buy into the hype. These systems are extremely inefficient. Requiring terrabytes if not petabytes of data and basically a powerplant next to a data center to power the whole thing. The work is valuable and pushing the boundary on what the hardware can do is great but so far all these things lack any kind of explanatory power and suck up a lot of energy to power the black boxes. DARPA recently put out a research program for making systems more efficient and adding explanatory capabilities to them (http://www.darpa.mil/program/explainable-artificial-intellig...). Ultimately that is the direction these things must be headed if they are to provide real value for the masses. Relying on a clever black box only takes you so far and is not beneficial in the long run because as these systems become more integrated into the institutions that drive large scale decision making they'll need to be held accountable for those decisions. |
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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.