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The statement that the flaw remains is wrong. We included a few cyclical groups in the training data and the flaw disappeared rather quickly. It is even less of
by mafuy 2y ago
The statement that the flaw remains is wrong. We included a few cyclical groups in the training data and the flaw disappeared rather quickly. It is even less of an issue after the move to a slightly larger DCNN.
The current consensus in the community is, as far as I can tell, that there will always be some (other) flaws. But finding and exploiting them is becoming so difficult that it is perhaps impossible for human ingenuity. Automated approaches could succeed, but it is not clear that their effort is less than "simply" training a second, inherently stronger DCNN instead.
- YeGoblynQueenne 2y agoThe Go community will of course be more interested in performance on Go, but the paper mainly contributes to the debate about the real capabilities of deep neural net models. From the abstract: Our results demonstrate that even superhuman AI systems may harbor surprising failure modes. This should be read as a general observation and not just specific to Go as is made clear by reading the paper. So you can patch up Go-specific flaws but the problem remains that deep neural net performance is inconsistent and unreliable.