Everyone who has an approach that hasn't worked likes to say "the right thing to do is to combine the best of both worlds", and it's sort of tautologically true

Everyone who has an approach that hasn't worked likes to say "the right thing to do is to combine the best of both worlds", and it's sort of tautologically true that if you have orthogonal approaches that can be meaningfully combined you will do better. The unanswered implicit question is how to do this combination, and whether the actual gain will be large enough to justify a mor.complicated system. So, yes, we should try combining symbolic AI, decision trees, genetic algorithms, etc with deep learning, but the results from these combinations haven't all been super convincing.