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You are right there is a large gulf between the deep learning theory hype and the measurable improvements in physical quality of life that the hard sciences hav

You are right there is a large gulf between the deep learning theory hype and the measurable improvements in physical quality of life that the hard sciences have proved themselves capable of. However I submit a notable exception! The vision system of self driving cars is a neural network that is currently changing the world and there is no lack of explication, we know exactly how it works. These "convolutional neural network" infer object boundaries from differences in intensity among matrices of pixels by scanning different patches of the pixel matrix and cross comparing these patches. But you are right there is a lot of hype. I think part of the issue is the collective idolization of "algorithms". The limiting factor is data. And we don't have the data required to development models that are agent like in structure. Self interested agent systems, that employ subject-object language, have only been developed once. And they needed a training dataset measured in millions of years of evolution.

visarga 9y ago
I think you're glossing over many accomplishments in ML. It's not just vision that is successful. There's NLP, optimization, ranking, voice, speech, recommendations and many other tasks that work well. You say the limiting factor is data. But there has been a trend in the last 2 years to run ML on data generated from simulations (games, auto, chemical bonds, robots in VR, AlphaGo, etc). Simulations are dynamic datasets with unlimited flexibility. The better we learn to simulate, the closer we will get to reasoning. Both simulations and reasoning are based on object-relation graphs for describing the scene. My vision is that we will build better, more precise simulators that would allow an AI to input a problem and run experiments and search ("what happens if"). Just like AlphaGo, but for the real world. A combination of neural nets for "intuition" and simulators for precision would solve the problem. Simulators could be of many kinds: chemical molecule simulator, cell simulator, physical simulator, city traffic simulator, car simulator, flight simulator, and so on. Basically what's been the object of activity of supercomputing, but this time with neural nets selecting the experiments.

visarga 9y ago

I think you're glossing over many accomplishments in ML. It's not just vision that is successful. There's NLP, optimization, ranking, voice, speech, recommendations and many other tasks that work well. You say the limiting factor is data. But there has been a trend in the last 2 years to run ML on data generated from simulations (games, auto, chemical bonds, robots in VR, AlphaGo, etc). Simulations are dynamic datasets with unlimited flexibility. The better we learn to simulate, the closer we will get to reasoning. Both simulations and reasoning are based on object-relation graphs for describing the scene. My vision is that we will build better, more precise simulators that would allow an AI to input a problem and run experiments and search ("what happens if"). Just like AlphaGo, but for the real world. A combination of neural nets for "intuition" and simulators for precision would solve the problem. Simulators could be of many kinds: chemical molecule simulator, cell simulator, physical simulator, city traffic simulator, car simulator, flight simulator, and so on. Basically what's been the object of activity of supercomputing, but this time with neural nets selecting the experiments.