Interesting link, but I still feel AI hasn't had much success: Do we really have much more today that A* search, neural nets with backpropagation, and HMMs/SVM/etc, which were all developed in the 1960s? The successes of AI that I see (eg OCR/speech recognition and Chess/Jeopardy) use these same old algorithms with only marginal improvements and more CPU. There have been no new major techniques or insights. I'm not an expert though, correct me if I'm wrong.
> The successes of AI that I see (eg OCR/speech recognition and Chess/Jeopardy) use these same old algorithms with only marginal improvements and more CPU.
There have been huge improvements in algorithms since the 1960s. The only things around back then were a few speculative papers on analytic methods. The current state of the art in learning algorithms is a huge advance over just having some ideas about the mathematical properties of learning and a few analytic tricks in obscure papers.
Huh, thank you for the informative reply. I will reconsider my view, which I had perhaps overstated before to make a point.
Wikipedia gives a citation for backpropagation going back to 1963 by the way, but looking more carefully you are right that the 1986 paper is important.
Of course research is iterative -- you wouldn't say other fields of math or science haven't had success/breakthroughs just because they are relying on old techniques.
That said, some more recent work comes to mind.
In terms of new algos: planning algorithms, deep learning architectures (ANNs without backprop), reinforcement learning, alife and multi-agent systems.
In terms of applications (which you already hint at): Deep Blue and Watson, both of which are great examples that shouldn't be regarded so trivially. Is the only difference between the "old algorithms" from the 1960s and Watson challenging people on Jeopardy is a matter of margin? No. It's not as if we were nearly there in the 60s and only needed to crank up CPU or RAM speed/storage. Read IBM's paper on it -- it took a complex architecture spanning natural language processing, databases, search, and machine learning. As for Deep Blue, even in the early 90s people said there would never be an AI to beat the best human Chess players. Once it happened, the paradigm shifted and "of course" AI can beat humans at Chess, as if there hadn't been who denied it was possible.
Some of the coolest more recent applications are in the realm of machine learning: self-driving cars, robots that learn to navigate or perform tasks, and image recognition (which has made an immense leap in the past ~2 years).
