My point was that you've underestimated the problem difficulty, when the signal is not of a smooth manifold and where the dynamics are far from stationary, it is incorrect to infer that techniques which have worked well in stable and smooth scenarios will continue to work well. In fact they have not and it looks, not for a while yet either.
> Citing Newton as a typical example of the superiority of human inferential abilities perhaps represents a case of cherry picking
Are hyperparameter searches across a sea of AWS instances cherry picking?
Anyways, I don't think I cherry picked. There is not a single machine that can do the same yet. And more importantly, a learning algorithm that notices model failure and works out the surgery required to correct or even completely replace it.
Newton is an example of the human brain at its best. But he was far from the only: Maxwell, Green, Einstein, Noether, Archimedes and so on the list goes. But we don't need to go that far since Crows are already capable of generalization that out matches what machines can do for the near future.
> Citing Newton as a typical example of the superiority of human inferential abilities perhaps represents a case of cherry picking
Are hyperparameter searches across a sea of AWS instances cherry picking?
Anyways, I don't think I cherry picked. There is not a single machine that can do the same yet. And more importantly, a learning algorithm that notices model failure and works out the surgery required to correct or even completely replace it.
Newton is an example of the human brain at its best. But he was far from the only: Maxwell, Green, Einstein, Noether, Archimedes and so on the list goes. But we don't need to go that far since Crows are already capable of generalization that out matches what machines can do for the near future.