While I am a fan of DeepMind’s research (Alpha Go and Alpha Zero blew me away - I wrote a commercial Go program in the late 1970s for the Apple II so I apprecia

While I am a fan of DeepMind’s research (Alpha Go and Alpha Zero blew me away - I wrote a commercial Go program in the late 1970s for the Apple II so I appreciate the problem, and I used to work with DeepMind cofounder Shane Legg at Webmind), respectfully, I do have a criticism: I would like to see them use their talent and financial backing from Alphabet to tackle the hard problems of common sense in AI and in general the science of what it will take to reach AGI. Deep learning is brittle, as can be seen in the good science of finding adversarial attacks on trained models. I would like to see less accuracy on dev and test sets, smaller more general models, and especially not see convolutional models as the basis for vision systems in self driving cars. My intuition is that discarding information of relative positions and relative relationships of large features in an image is a bad idea for the self driving car problem. They have really pushed progress for an old technology (reinforcement learning) and achieved fantastic results but in my experience if there are not many failures along the way, then perhaps goals are not being set high enough.