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by famouswaffles
781 days ago
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That was a figure of speech. I didn't literally mean games (not that GOFAI performs better than NNs in those games anyway). I simply went off your own examples - Vision, Image generation, Translation etc. >As I say in my comment above planning and scheduling, SAT, constraints, verification, theorem proving- those are still dominated by classical systems You can use NNs for all these things. It wouldn't make a lot of sense because GOFAI would be perfect and the former would be inefficient but you certainly could which is again more than I can say for GOFAI and the domains you listed. |
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As it is, your comment seems to tell me that neural nets are good at neural net things and GOFAI is good at GOFAI things, which is obvious, and is what I'm saying: neural nets can make only very limited use of expert knowledge and so suck in all domains where domain knowledge is abundant and abundantly useful, which are the same domains where GOFAI dominates. GOFAI can make very good use of expert knowledge but is traditionally not as good in domains where only tacit knowledge is available, because we don't understand the domain well enough yet, like in anything to do with pattern recognition, which is the same domains where neural nets dominate. If explicit, expert knowledge was available for those domains, then GOFAI would dominate, and neural nets would fall behind, completely contrary to what Sutton thinks.
So, the bitter lesson is only bitter for those who are not interested in what classical AI systems can do best. For those of us who are, the lesson is sweet indeed: we're making progress, algorithmic progress, progress in understanding, scientific progress, and don't need to burn through thousands of credit to train on server farms to do anything of note. That's even a running joke in my team: hey, do you need any server time? Nah, I'll run the experiment on my laptop over lunch. And then beat the RL algo (PPO) that needs three days training on GPUs. To solve mazes badly.