Arya Sharma/Catch News
Let's observe a moment of silence for Lee Se-dol. Lee is a man who dedicated his life to something few people even care about, the ancient Chinese board game 'Go'. Being South Korean he doubtless fought against the odds to pursue his passion. Probably even as his parents looked on in massive disappointment as his next door neighbour Cho joined a mass-brand tech-company.
Still Lee soldiered on, accompanied I imagine by an uplifting musical score and a harsh but lovable mentor until finally, he was the world's best... Until today, when he was beaten by Google's artificial intelligence (AI).
You'd imagine that in a world where AI is capable of producing articles, giving you legal advice and even identifying and assassinating enemies, beating humans at a board game would be a cinch. After all, IBM's Deep Blue computer beat Garry Kasparov way back in 1997. That being the case, AlphaGo's victory shouldn't seem amazing but it is, because Go has, up until now, remained the only board game where humans can beat computers.
Why? Because the complexity of Go is, quite simply, mental.
It's so complex that there are more possible games of Go than the total number of subatomic particles in the universe. If you had to represent it in numbers it would be a 10 followed by about 300 zeros. I believe the scientific term for this figure is a shit ton. I may be wrong.
And it's this complexity that makes AlphaGo's victory of Lee so amazing. While a computer is able to map every possible ramification of a chess move, the complexity of Go makes it near impossible to apply the same computing to it. In fact, as one of the leading minds behind AlphaGo explained in a video, the biggest hindrance to a computer playing Go is that players often made moves intuitively, something a computer cannot do.
Google's subsidiary DeepMind, which specialises in artificial intelligence research though, thought otherwise. Since they realised that evaluating every possible move was not feasible they approached the problem differently.
The geniuses at DeepMind created two computerised neural networks modelled on the human brain. One of the neural networks, the "value network", analyses the computer's board positions while the other, the "policy network", decides where to move. Thus, the computer settles on a few moves it deems likely to be great moves, rather than trying to think of every possible one.
It's this learning component that will likely mean that humans will only struggle more against AI as time passes. As the CEO of DeepMind, Demi Hassabis, said in a video by Nature, "Humans have a limitation in terms of the actual number of Go games that they're able to process in a lifetime. AlphaGo can play through millions of games every single day."
Go was long held to be one of the impenetrable challenges in AI research, but with AlphaGo's success, we now have proof that machines will one day be able to perform tasks we believe impossible without human involvement. This may be clinching proof that we could soon be obsolete. May as well have fun during our time here on earth. Go on, play a game.
Edited by Payal Puri