[jedharris]

There are plenty of domains where symbolic AI has had every opportunity to use unlimited computing power, plus decades of design and experimentation, but has lost to deep learning.

a good area for examples is human games (e.g. chess, Go, Atari games, etc.) Symbolic AI has been pushed hard but has lost definitively to deep learning. Furthermore symbolic approaches had decades of investment, compared with less than a decade for the deep learning approaches.

Another good area for examples is natural language. Marcus admits that deep learning is the only viable approach to "speech understanding" (which really means transcription). He doesn't mention translation which demands a lot more "understanding" and where deep learning excels relative to symbolic approaches, again with decades of investment on the symbolic side and much less on the deep learning side.

Except for inherently symbolic problems like theorem proving, I can't think of any AI domain where deep learning doesn't dominate or seem likely to dominate symbolic approaches.