I read http://arxiv.org/abs/1412.6564 earlier this year and tldr they trained a deep net bot that managed to hold about 1-2d on KGS. Since there's no searching going on, that's pretty good, though not as good as CrazyStone or other MCTS bots. (I hadn't heard they went from 6d to 5d, but sure enough http://www.gokgs.com/graphPage.jsp?user=CrazyStone I wonder what happened?) I suspect combining the two methods will yield even stronger programs, I've heard some folks at Facebook and Google are each working on their own Go bots based on deep learning. If some company spent a few years building dedicated hardware like Deep Blue I even bet they could beat 9d pros with current methods. Go is close to falling.
a big, discontinuous jump in rankings like that suggests a couple of things 1) the bot was taken offline and updated with a different engine hence different rank and/or 2) KGS recalibrated it's relative kyu/stone ranks (which it does from time to time)
It's worth pointing out that Monte Carlo tree search is a relatively 'dumb' algorithm. It turns out that getting a computer to be good at Go doesn't reveal anything deep or interesting about: Humans, Computers, Intelligence or Go.
On Kgs where I play, the highest rated computers are at 5 dan. That is basically better than 99% of the players on the server which I think is the largest server in the English speaking world. Computer go has made considerable strides since 2009 [1]. The main impetus for this has been Monte Carlo Tree search[2]. Computers have not yet reached the level of professional Go player unlike Chess but progress from existing methods seems quite possible.
[1] https://en.wikipedia.org/wiki/Computer_Go [2] https://en.wikipedia.org/wiki/Monte_Carlo_tree_search