In March 2016, AlphaGo beat Lee Sodol, one of the world’s top players of the Chinese game Go, by 4-1, marking the end of what was probably the last great board game AI had to conquer.

What makes Go a particularly difficult problem for computers to at least in the traditional (simplistic) approach is that the number of possible combination moves number more than the atoms in the universe. So it’s impossible to brute force it with the entirety of the computing power we have. Thus it was a good problem for finding alternative approaches.

“It works like the brain.” So? Because I’m so into artificial intelligence, I’ll start with a post on it. Here we’re reminded that just because something is described as being like the brain doesn’t mean that it is important or even revolutionary.

But here’s the thing: this “like the brain” label usually isn’t a lie — it’s just not very informative. There are many ways a system can be like the brain, but only a fraction of these will prove important. We know so much that is true about the brain, but the defining issue in theoretical neuroscience today is, simply put, we don’t know what matters when it comes to understanding how the brain computes. The debate is wide open, with plausible guesses about the fundamental unit, ranging from quantum phenomena all the way to regions spanning millimeters of brain tissue.