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They can both be true. There former was talking about practical limitations, whereas the latter were discussing fundamental limitations. Saying what can't be done right now is much easier than saying what can't be done (even within a certain class of methods).
It's a long list, since it's akin to describing the frontier of research. A few: 1) meta-learning requires too many tasks (and where do they come from?) 2) fine-tuning for transfer learning has no guarantees 3) RL requires a reward function, which is unrealistic for many tasks
yes, indeed, making such a list does indeed define the frontier of research, which was precisely the point of http://Rebooting.AI . Since our list doesn't overlap with yours, I will be curious to see which of those on our list you do and do not agree with. [+1 to yours.]
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