The latter of those two points is much more valid than the former. You're comment about generalisation difficulty is pretty accurate, but as for "sucks at varia

The latter of those two points is much more valid than the former. You're comment about generalisation difficulty is pretty accurate, but as for "sucks at variable length data", I think neural machine translation [0] and the fact that schemes including RNNs just won M4 [1] indicate that this is incorrect. Your point about remembering the past is true (it's hard), but people are actively working on it. Unitary neural nets, and the fast/slow weight paradigm are very different angles that seem promising. As for handling trees + graphs, this actually works very well. Thomas Kipf is pushing this area forward, GATs [2] are a nice random example of how dominant differentiable programming (eg NNs) can be in this area. Unfortunately these graph approaches don't parallelise as nicely on GPUs as CNNs. Your predictions (assistants) seem likely to me. [0] https://arxiv.org/abs/1804.09849 https://arxiv.org/abs/1804.09849 [1] https://www.m4.unic.ac.cy https://www.m4.unic.ac.cy [2] https://arxiv.org/abs/1710.10903 https://arxiv.org/abs/1710.10903