Re:Deep Learning - The Biggest Data Science Breakthrough of ...
by pilooch on Friday March 15, 2013 @03:24AM
(#5379547)
Deep learning is so attractive to any AI and machine learning practitioner! The results are beautiful to witness or read about. This is clearly another step in a direction that many of us have been waiting (or working on) for a long time!
That said, AI, like every other sciences, experiences trends and bubbles. If you give a decent look to usages and problem solving with machine learning, deep learning techniques are not exactly the final answer. Typically, they're slow to train, to my knowledge there is no good 'online' algorithm yet to train them (i.e. for autoencoders, recursive autoencoders, Boltzman machines). Many applications, and a trend toward 'lifelong learning'[1], require fast incremental learning that yields results in near real-time, or at least in minutes rather than days.
I've compared a couple of unsupervised machine learning algorithms with recursive autoencoders: the latter can learn deeper, very often, but at a computational costs (days vs seconds). Deep learning computation will improve, for sure though.
That said, AI, like every other sciences, experiences trends and bubbles. If you give a decent look to usages and problem solving with machine learning, deep learning techniques are not exactly the final answer. Typically, they're slow to train, to my knowledge there is no good 'online' algorithm yet to train them (i.e. for autoencoders, recursive autoencoders, Boltzman machines). Many applications, and a trend toward 'lifelong learning'[1], require fast incremental learning that yields results in near real-time, or at least in minutes rather than days.
I've compared a couple of unsupervised machine learning algorithms with recursive autoencoders: the latter can learn deeper, very often, but at a computational costs (days vs seconds). Deep learning computation will improve, for sure though.
[1] http://cs.brynmawr.edu/~eeaton/AAAI-SSS13-LML/