As much of a pity as that after 3 billion years, all life is still based upon selection bias and random genetic mutations, a brute force trial and error?
The algorithms which allow AlphaGo or the Libratus Poker AIs to achieve superhuman play have a direct correspondence with natural selection (and learning rate with selection strength). There is idea sharing between evolutionary game theory and learning, up to algorithms to play extensive form games with imperfect information such as this, based on replicator dynamics: http://dl.acm.org/citation.cfm?id=2617448
Evolution, per genome trajectory, also improves in its ability to evolve, as seen in evolvability.
Returning to the grandparent's original lament on stochastic gradient descent, I do agree with them. I suspect the need for better has not been seen due to non-supervised and incremental learning remaining minor areas of study. Artificial separation between learning and prediction allows "hacks" like batch-norm to somewhat suffice. It does seem unlikely that we will never need to take into account curvature of information manifolds while learning. Note that evolution uses curvature too.
Anything order than SGD has proven expensive but there are promising approximations, as found in KFAC or Projected Natural Gradient Descent, allowing me to close this post with yet another link between learning and evolution.
The algorithms which allow AlphaGo or the Libratus Poker AIs to achieve superhuman play have a direct correspondence with natural selection (and learning rate with selection strength). There is idea sharing between evolutionary game theory and learning, up to algorithms to play extensive form games with imperfect information such as this, based on replicator dynamics: http://dl.acm.org/citation.cfm?id=2617448
Evolution, per genome trajectory, also improves in its ability to evolve, as seen in evolvability.
Returning to the grandparent's original lament on stochastic gradient descent, I do agree with them. I suspect the need for better has not been seen due to non-supervised and incremental learning remaining minor areas of study. Artificial separation between learning and prediction allows "hacks" like batch-norm to somewhat suffice. It does seem unlikely that we will never need to take into account curvature of information manifolds while learning. Note that evolution uses curvature too.
Anything order than SGD has proven expensive but there are promising approximations, as found in KFAC or Projected Natural Gradient Descent, allowing me to close this post with yet another link between learning and evolution.