The current state of AI and Deep Learning: A reply to Yoshua Bengio

Thanks for your note on Facebook, which I reprint below, followed by some thoughts of my own. I appreciate your taking the time to consider these issues. I concur that you and I agree more than we disagree, and as you do, I share your implicit hope that field might benefit from an articulation of both our agreements and our disagreements. "is that a simple hybrid in which the output of the deep net are discretized and then passed to a GOFAI symbolic processing system will not work. Many reasons: (1) you need learning in the system 2 component as well as in the system 1 part, (2) you need to represent uncertainty there as well…" "… it's probably not realistic to encode by hand every-thing that machines need to know. Machines are going to need to learn lots of things on their own. We might want to hand-code the fact that sharp hard blades can cut soft material, but then an AI should be able to build on that knowledge and learn how knives, cheese graters, lawn mowers, and blenders work, without having each of these mechanisms coded by hand" " formal logic of the sort we have been talking about does only one thing well: it allows us to take knowledge of which we are certain and apply rules that are always valid to deduce new knowledge of which we are also certain. If we are entirely sure that Ida owns an iPhone, and we are sure that Apple makes Iphones, then we can be sure that Ida owns something made by Apple. But what in life is absolutely certain? As Bertrand Russell once wrote, "All human knowledge is uncertain, inexact, and partial." Yet somehow we humans manage. When machines can finally do the same, representing and reasoning about that sort of knowledge -- uncertain, inexact, and partial -- with the fluidity of human beings, the age of flexible and powerful, broad AI will finally be in sight."