"What works for Go may not work for the challenging problems that DeepMind aspires to solve with AI, like cancer and clean energy. IBM learned this the hard way" Picks the only large scale symbolic AI project to illustrate the potential shortcomings of DRL... https://twitter.com/GaryMarcus/status/1161690752524550144 …pic.twitter.com/3lbLcRQ0kz
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So we build hybrid systems but are also too reliant on DRL? How can both of those be true?
It’s question of emphasis, in part, but if I were running your ship I would spend more time exploring principled ways of building hybrids, and more kinds of of hybrids, and more time on on open-ended problems.
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Other than Neural Turing Machines, AlphaGo, GraphNets, GQN, SPIRAL, etc? I'm sure you'd run things differently, but this is a far cry from the DRL centric narrative of the Wired article.
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Oh, so now AlphaGo is a hybrid? :) but yes I like a lot of that work and have advocated for some of it over time. I totally agree that DRL is not the only emphasis at DM; it’s just the largest (from what I can tell) and my least favorite and most visible, wrapped in one.
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