of Go. Hassabis and his team at DeepMind decided they were ready to create a new program that could take it on.
It was at this moment that Hassabis decided to sell the company to Google. ‘We weren’t planning to, but three years in, focused on fundraising, I had only ten per cent of my time for research,’ he explained in an interview in Wired at the time. ‘I realised that there’s maybe not enough time in one lifetime to both build a Google-sized company and solve AI. Would I be happier looking back on building a multi-billion business or helping solve intelligence? It was an easy choice.’ The sale put Google’s firepower at his fingertips and provided the space for him to create code to realise his goal of solving Go … and then intelligence.
First blood
Previous computer programs built to play Go had not come close to playing competitively against even a pretty good amateur, so most pundits were highly sceptical of DeepMind’s dream to create code that could get anywhere near an international champion of the game. Most people still agreed with the view expressed in The New York Times by the astrophysicist Piet Hut after DeepBlue’s success at chess in 1997: ‘It may be a hundred years before a computer beats humans at Go – maybe even longer. If a reasonably intelligent person learned to play Go, in a few months he could beat all existing computer programs. You don’t have to be a Kasparov.’
Just two decades into that hundred years, the DeepMind team believed they might have cracked the code. Their strategy of getting algorithms to learn and adapt appeared to be working, but they were unsure quite how powerful the emerging algorithm really was. So in October 2015 they decided to test-run their program in a secret competition against the current European champion, the Chinese-born Fan Hui.
AlphaGo destroyed Fan Hui five games to nil. But the gulf between European players of the game and those in the Far East is huge. The top European players, when put in a global league, rank in the 600s. So, although it was still an impressive achievement, it was like building a driverless car that could beat a human driving a Ford Fiesta round Silverstone then trying to challenge Lewis Hamilton in a Grand Prix.
Certainly when the press in the Far East heard about Fan Hui’s defeat they were merciless in their dismissal of how meaningless the win was for AlphaGo. Indeed, when Fan Hui’s wife contacted him in London after the news got out, she begged her husband not to go online. Needless to say he couldn’t resist. It was not a pleasant experience to read how dismissive the commentators in his home country were of his credentials to challenge AlphaGo.
Fan Hui credits his matches with AlphaGo with teaching him new insights into how to play the game. In the following months his ranking went from 633 to the 300s. But it wasn’t only Fan Hui who was learning. Every game AlphaGo plays affects its code and changes it to improve its play next time around.
It was at this point that the DeepMind team felt confident enough to offer their challenge to Lee Sedol, South Korea’s eighteen-time world champion and a formidable player of the game.
The match was to be played over five games scheduled between 9 and 15 March 2016 at the Four Seasons hotel in Seoul, and would be broadcast live across the internet. The winner would receive a prize of a million dollars. Although the venue was public, the precise location within the hotel was kept secret and was isolated from noise – not that AlphaGo was going to be disturbed by the chitchat of the press and the whispers of curious bystanders. It would assume a perfect Zen-like state of concentration wherever it was placed.
Sedol wasn’t fazed by the news that he was up against a machine that had beaten Fan Hui. Following Fan Hui’s loss he had declared: ‘Based on its level seen … I think I will win the game by a near landslide.’
Although he was aware of the fact that the machine he would be playing was learning and evolving, this did not concern him. But as the match approached, you could hear doubts beginning to creep into his view of whether AI will ultimately be too powerful for humans to defeat it even in the game of Go. In February he stated: ‘I have heard that DeepMind’s AI is surprisingly strong and getting stronger, but I am confident that I can win … at least this time.’
Most people still felt that despite great inroads into programming, an AI Go champion was still a distant goal. Rémi Coulom, the creator of Crazy Stone, the only program to get close to playing Go at any high standard, was still predicting another decade before computers would beat the best humans at the game.
As the date for the match approached, the team at DeepMind felt they needed someone to really stretch AlphaGo and to test it for any weaknesses. So they invited Fan Hui back to play the machine going into the last few weeks. Despite having suffered a 5–0 defeat and being humiliated by the press back in China, he was keen to help out. Perhaps a bit of him felt that if he could help make AlphaGo good enough to beat Sedol, it would make his defeat less humiliating.
As Fan Hui played he could see that AlphaGo was extremely strong in some areas but he managed to reveal a weakness that the team was not aware of. There were certain configurations in which it seemed to completely fail to assess who had control of the game, often becoming totally delusional that it was winning when the opposite was true. If Sedol tapped into this weakness, AlphaGo wouldn’t just lose, it would appear extremely stupid.
The DeepMind team worked around the clock trying to fix this blind spot. Eventually they just had to lock down the code as it was. It was time to ship the laptop they were using to Seoul.
The stage was set for a fascinating duel as the players, or at least one player, sat down on 9 March to play the first of the five games.
‘Beautiful. Beautiful. Beautiful’
It was with a sense of existential anxiety that I fired up the YouTube channel broadcasting the matches that Sedol would play against AlphaGo and joined 280 million other viewers to see humanity take on the machines. Having for years compared creating mathematics to playing the game of Go, I had a lot on the line.
Lee Sedol picked up a black stone and placed it on the board and then waited for the response. Aja Huang, a member of the DeepMind team, would play the physical moves for AlphaGo. This, after all, was not a test of robotics but of artificial intelligence. Huang stared at AlphaGo’s screen, waiting for its response to Sedol’s first stone. But nothing came.
We all stared at our screens wondering if the program had crashed! The DeepMind team was also beginning to wonder what was up. The opening moves are generally something of a formality. No human would think so long over move 2. After all, there was nothing really to go on yet. What was happening? And then a white stone appeared on the computer screen. It had made its move. The DeepMind team breathed a huge sigh of relief. We were off! Over the next couple of hours the stones began to build up across the board.
One of the problems I had as I watched the game was assessing who was winning at any given point in the game. It turns out that this isn’t just because I’m not a very experienced Go player. It is a characteristic of the game. Indeed, this is one of the main reasons why programming a computer to play Go is so hard. There isn’t an easy way to turn the current state of the game into a robust scoring system of who leads by how much.
Chess, by contrast, is much easier to score as you play. Each piece has a different numerical value which gives you a simple first approximation of who is winning. Chess is destructive. One by one pieces are removed so the state of the board simplifies as the game proceeds. But Go increases in complexity as you play. It is constructive. The commentators kept up a steady stream of observations but struggled to say if anyone was in the lead right up until the final moments of the game.
What they
