They Took Our Jobs

This past year a huge story in the tech world was that Google's Go playing AI AlphaGo was able to top the best Go player in the world. A year later, the most beautiful fallout of this story is how the Go community has responded.

AlphaGo's greatest strength is not any one move or sequence, but rather the unique perspective that it brings to every game. While Go style is difficult to encapsulate, one could say that AlphaGo's strategy embodies a spirit of flexibility and open-mindedness: a lack of preconceptions that allows it to find the most effective line of play. As the following two games will show, this philosophy often leads AlphaGo to discover counterintuitive yet powerful moves.
Although Go is a game of territory, most decisive battles hinge on the balance of power between groups, and AlphaGo excels in shaping this balance. Specifically, AlphaGo makes masterful use of "influence," or the effect of existing stones on surrounding areas. Although influence cannot be measured exactly, AlphaGo's value network enables it to consider all stones on the board at once, endowing its judgment with subtlety and precision. These abilities let AlphaGo convert local regions of influence into coordinated global advantages.

I remember reading this Wired article vividly back in 2014. At the time, the best AI Go software was being run on 5 and 10 year old MacBooks and the algorithms were being created by individuals in their free time. One thing in the article that caught my eye was this claim:

I was surprised to hear from programmers that the eventual success of these programs will have little to do with increased processing power. It is still the case that a Go program’s performance depends almost entirely on the quality of its code. Processing power helps some, but it can only get you so far. Indeed, the UEC lets competitors use any kind of system, and although some opt for 2048-processor-core super-computers, Crazy Stone and Zen work their magic on commercially available 64-core hardware.

Mild error in that sentence, 64-core is not, and certainly was not, commercially available. The author almost certainly intended to say 64-bit processors, which is is mostly a distinction on how much memory the processor can access at once and not the same thing as running dozens to thousands of computers simultaneously as in the example given of a super computer. Still, it seemed weird to me that processing power wasn't being viewed as a differentiating factor. That said, following these guidelines even the most optimistic timelines had AI losing to humans for at least another decade.

Even with Monte Carlo, another ten years may prove too optimistic. And while programmers are virtually unanimous in saying computers will eventually top the humans, many in the Go community are skeptical. “The question of whether they’ll get there is an open one,” says Will Lockhart, director of the Go documentary The Surrounding Game. “Those who are familiar with just how strong professionals really are, they’re not so sure.”

The Wired article published on May 14th, 2014. Because reality loves coincidences, on May 15th, 2016 AlphaGo successfully finished it's best of 7 victory over Lee Sedol, the world's current Go grandmaster, in a mere 5 matches. 

AlphaGo took the Go community completely off guard. No one expected AI to be able to compete with the best players in the world in a fair match on that timeline, let alone win handily against the best in the world. 

What was humanity's response? To approach the game with a whole new mentality. This story sums up why I think cyborgs are the logical implementation of our AI and robotics future. Human brains are simply too resilient to be dropped completely, but clearly computers are advancing too quickly to be ignored.