There has been some buzz recently around a few articles that demonstrate machine learning in the video game domain.
Here's one writeup:
Artificial intelligence bests humans at classic arcade gameshttp://news.sciencem...classic-arcade-games
For the academically inclines, I would recommend:
Playing Atari with Deep Reinforcement Learninghttp://www.cs.toront.../~vmnih/docs/dqn.pdf
Which talks in detail about the methods used.
The use of the term "deep" seems to me to be as much about coming up with a catchy term that has gone viral and is being hyped like mad -- and has little innovation behind it -- but the new wave of practitioners using neural networks for large scale problems are getting undeniably impressive results.
Again, getting back to the video game results:
There is nothing particularly novel in the approach -- the domain is wonderful, and the basic focus on using the same architecture and parameters to tackle a large collection of learning problems -- and using large dimensional raw input, is great. And the results are impressive. Again -- in my mind this is more a story of the new wave of practioners who are getting very good at leveraging fairly standard neural network techniques on larger and larger problems.
Having said that, this line of work offers little qualitative improvement on the hard problems in AI -- on serious multiscale hierarchical planning, scene recognition, etc. For that we are still waiting for some paradigm shifts.