Is AI Riding a One-Trick Pony?
Funny thing, the problems with deep learning sketched in the article are pretty much exactly the same in nature as problems with machine learning 10 years ago (when other algorithms such as SVM, LVQ and others outperformed NNs for a bit), except of course that the examples of what a ML algorithm could do back then were much less impressive.<p>That lack of real-world knowledge, understanding and conceptualisation feeding back on itself has always been a big unknown roadblock standing in the way of AI. And of course now, with the modern and improving impressive results of deep learning, there appears to be less and less cool stuff to solve before we finally have to face this roadblock.<p>But it's the same roadblock.<p>But, maybe the advances in deep learning will provide some tools to chip away at it. That wordvector stuff seems promising, if it can do (Paris - France + Italy) ~= (Rome), that's a good stab at realworld knowledge, it seems.<p>I used to study Machine Learning at university until 2009 (until personal circumstances forced me to abandon it). But even after that, when I read the first papers and talks about deep learning (back when it was still about Boltzman networks) I got very excited and have been following it closely. Except for the part where I haven't yet played around with it myself apart from some very tiny experiments :) (I only recently acquired hardware to have a stab at it, so maybe soon. The libraries available seem easy enough to use, and many of the concepts I learned in ML are still applicable).