This is a big deal, and it is another sign that AGI is near.

Intelligence boils down to inference. Go is an interesting case because good play for both humans and bots like AlphaGo requires two specialized types of inference operating over very different timescales:

• rapid combinatoric inference over move sequences during a game(planning). AlphaGo uses MCT search for this, whereas the human brain uses a complex network of modules involving the basal ganglia, hippocampus, and PFC.
• slow deep inference over a huge amount of experience to develop strong pattern recognition and intuitions (deep learning). AlphaGo uses deep supervised and reinforcement learning via SGD over a CNN for this. The human brain uses the cortex.

Machines have been strong in planning/search style inference for a while. It is only recently that the slower learning component (2nd order inference over circuit/program structure) is starting to approach and surpass human level.

Critics like to point out that DL requires tons of data, but so does the human brain. A more accurate comparison requires quantifying the dataset human pro go players train on.

A 30 year old asian pro will have perhaps 40,000 hours of playing experience (20 years * 50 * 40 hrs/week). The average game duration is perhaps an hour and consists of 200 moves. In addition, pros (and even fans) study published games. Reading a game takes less time, perhaps as little as 5 minutes or so.

So we can estimate very roughly that a top pro will have absorbed between 100,000 games to 1 million games, and between 20 to 200 million individual positions (around 200 moves per game) .

AlphaGo was trained on the KGS dataset: 160,00 games and 29 million positions. So it did not train on significantly more data than a human pro. The data quantities are actually very similar.

Furthermore, the human's dataset is perhaps of better quality for a pro, as they will be familiar with mainly pro level games, whereas the AlphaGo dataset is mostly amateur level.

The main difference is speed. The human brain's 'clockrate' or equivalent is about 100 hz, whereas AlphaGo's various CNNs can run at roughly 1000hz during training on a single machine, and perhaps 10,000 hz equivalent distributed across hundreds of machines. 40,000 hours - a lifetime of experience - can be compressed 100x or more into just a couple of weeks for a machine. This is the key lesson here.

The classification CNN trained on KGS was run for 340 million steps, which is about 10 iterations per unique position in the database.

The ANNs that AlphaGo uses are much much smaller than a human brain, but the brain has to do a huge number of other tasks, and also has to solve complex vision and motor problems just to play the game. AlphaGO's ANNs get to focus purely on Go.

A few hundred TitanX's can muster up perhaps a petaflop of compute. The high end estimate of the brain is 10 petaflops (100 trillion synapses * 100 hz max firing rate). The more realistic estimate is 100 teraflops (100 trillion synapes * 1 hz avg firing rate), and the lower end is 1/10 that or less.

So why is this a big deal? Because it suggests that training a DL AI to master more economically key tasks, such as becoming an expert level programmer, could be much closer than people think.

The techniques used here are nowhere near their optimal form yet in terms of efficiency. When Deep Blue beat Kasparov in 1996, it required a specialized supercomputer and a huge team. 10 years later chess bots written by individual programmers running on modest PC's soared past Deep Blue - thanks to more efficient algorithms and implementations.