The Creativity Code: How AI is learning to write, paint and think

Having lost the match, Sedol started game 4 playing far more freely. It was as if the heavy burden of expectation had been lifted, allowing him to enjoy his game. In sharp contrast to the careful, almost cautious play of game 3, he launched into a much more extreme strategy called ‘amashi’. One commentator compared it to a city investor who, rather than squirrelling away small gains that accumulate over time, bet the whole bank.

Sedol and his team had stayed up all of Saturday night trying to reverse-engineer from AlphaGo’s games how it played. It seemed to work on a principle of playing moves that incrementally increase its probability of winning rather than betting on the potential outcome of a complicated single move. Sedol had witnessed this when AlphaGo preferred lazy moves to win game 3. The strategy they’d come up with was to disrupt this sensible play by playing the risky single moves. An all-or-nothing strategy might make it harder for AlphaGo to score so easily.

AlphaGo seemed unfazed by this line of attack. Seventy moves into the game, commentators were already beginning to see that AlphaGo had once again gained the upper hand. This was confirmed by a set of conservative moves that were AlphaGo’s signal that it had the lead. Sedol had to come up with something special if he was going to regain the momentum.

If move 37 of game 2 was AlphaGo’s moment of creative genius, move 78 of game 4 was Sedol’s retort. He’d sat there for thirty minutes staring at the board, staring at defeat, when he suddenly placed a white stone in an unusual position, between two of AlphaGo’s black stones. Michael Redmond, who was commentating on the YouTube channel, spoke for everyone: ‘It took me by surprise. I’m sure that it would take most opponents by surprise. I think it took AlphaGo by surprise.’

It certainly seemed to. AlphaGo appeared to completely ignore the play, responding with a strange move. Within several more moves AlphaGo could see that it was losing. The DeepMind team stared at their screens behind the scenes and watched their creation imploding. It was as if move 78 short-circuited the program. It seemed to cause AlphaGo to go into meltdown as it made a whole sequence of destructive moves. This apparently is another characteristic of the way Go algorithms are programmed. Once they see that they are losing they go rather crazy.

Silver, the chief programmer, winced as he saw the next move AlphaGo was suggesting: ‘I think they’re going to laugh.’ Sure enough, the Korean commentators collapsed into fits of giggles at the moves AlphaGo was now making. Its moves were failing the Turing Test. No human with a shred of strategic sense would make them. The game dragged on for a total of 180 moves, at which point AlphaGo put up a message on the screen that it had resigned. The press room erupted with spontaneous applause.

The human race had got one back. AlphaGo 3 Humans 1. The smile on Lee Sedol’s face at the press conference that evening said it all. ‘This win is so valuable that I wouldn’t exchange it for anything in the world.’ The press cheered wildly. ‘It’s because of the cheers and the encouragement that you all have shown me.’

Gu Li, who was commentating on the game in China, declared Sedol’s move 78 as the ‘hand of god’. It was a move that broke the conventional way to play the game and that was ultimately the key to its shocking impact. Yet this is characteristic of true human creativity. It is a good example of Boden’s transformational creativity, whereby breaking out of the system you can find new insights.

At the press conference, Hassabis and Silver could not explain why AlphaGo had lost. They would need to go back and analyse why it had made such a lousy move in response to Sedol’s move 78. It turned out that AlphaGo’s experience in playing humans had led it to totally dismiss such a move as something not worth thinking about. It had assessed that this was a move that had only a one in 10,000 chance of being played. It seems as if it just had not bothered to learn a response to such a move because it had prioritised other moves as more likely and therefore more worthy of response.

Perhaps Sedol just needed to get to know his opponent. Perhaps over a longer match he would have turned the tables on AlphaGo. Could he maintain the momentum into the fifth and final game? Losing 3–2 would be very different from 4–1. The last game was still worth competing in. If he could win a second game, then it would sow seeds of doubt about whether AlphaGo could sustain its superiority.

But AlphaGo had learned something valuable from its loss. You play Sedol’s one in 10,000 move now against the algorithm and you won’t get away with it. That’s the power of this sort of algorithm. It learns from its mistakes.

That’s not to say it can’t make new mistakes. As game 5 proceeded, there was a moment quite early on when AlphaGo seemed to completely miss a standard set of moves in response to a particular configuration that was building. As Hassabis tweeted from backstage: ‘#AlphaGo made a bad mistake early in the game (it didn’t know a known tesuji) but now it is trying hard to claw it back … nail-biting.’

Sedol was in the lead at this stage. It was game on. Gradually AlphaGo did claw back. But right up to the end the DeepMind team was not exactly sure whether it was winning. Finally, on move 281 – after five hours of play – Sedol resigned. This time there were cheers backstage. Hassabis punched the air. Hugs and high fives were shared across the team. The win that Sedol had pulled off in game 4 had suddenly re-engaged their competitive spirit. It was important for them not to lose this last game.

Looking back at the match, many recognise what an extraordinary moment this was. Some immediately commented on its being an inflexion point for AI. Sure, all this machine could do was play a board game, and yet, for those looking on, its capability to learn and adapt was something quite new. Hassabis’s tweet after winning the first game summed up the achievement: ‘#AlphaGo WINS!!!! We landed it on the moon.’ It was a good comparison. Landing on the moon did not yield extraordinary new insights about the universe, but the technology that we developed to achieve such a feat has. Following the last game, AlphaGo was awarded an honorary professional 9 dan rank by the South Korean Go Association, the highest accolade for a Go player.

From hilltop to mountain peak

Move 37 of game 2 was a truly creative act. It was novel, certainly, it caused surprise, and as the game evolved it proved its value. This was exploratory creativity, pushing the limits of the game to the extreme.

One of the important points about the game of Go is that there is an objective way to test whether a novel move has value. Anyone can come up with a new move that appears creative. The art and challenge are in making a novel move that has some sort of value. How should we assess value? It can be very subjective and time-dependent. Something that is panned critically at the time of its release can be recognised generations later as a transformative creative act. Nineteenth-century audiences didn’t know what to make of Beethoven’s Symphony no. 5, and yet it is central repertoire now. During his lifetime Van Gogh could barely sell his paintings, trading them for food or painting materials, but now they go for millions. In Go there is a more tangible and immediate test of value: does it help you win the game? Move 37 won AlphaGo game 2. There was an objective measure that we could use to value the novelty of this move.

AlphaGo had taught the world a new way to play an ancient game. Analysis since the match has resulted in new tactics. The fifth line is now played early on, as we have come to understand that it can have big implications for the endgame. AlphaGo has gone on to discover still more innovative strategies. DeepMind revealed at the beginning of 2017 that its latest iteration had played online anonymously against a range of top-ranking professionals under two pseudonyms: Master and Magister. Human players were unaware that they were playing a machine. Over a few weeks it had played a total of sixty complete games. It won all sixty games.

But it was the analysis of the games that was truly insightful. Those games are now regarded as a treasure trove of new ideas. In several games AlphaGo played moves that beginners would have their wrists slapped for by their Go master. Traditionally you do not play a stone in the intersection of the third column and third row. And yet AlphaGo showed how to use such a move to your advantage.

Hassabis describes how the game of Go had got stuck on what mathematicians like to call a local maximum. If you look at the landscape I’ve drawn here (#ulink_7f8966d3-6bdf-5805-bd07-e485fc0cb2a8) then you might be at the top of peak A. From this height there is nowhere higher to go. This is called a local maximum. If there were fog all around you, you’d think you were at the highest point in the land. But across the valley is a higher peak. To know this, you need the fog to clear. You need to descend from your peak, cross the valley and climb the higher peak.

The trouble with modern Go is that conventions had built up about ways to play that had ensured players hit peak A. But by breaking those conventions AlphaGo had cleared the fog and revealed an even higher peak B. It’s even possible to measure the difference. In Go, a player using the conventions of peak A will in general lose by two stones to the player using the new strategies discovered by AlphaGo.

This rewriting of the conventions of how to play Go has happened at a number of previous points in history. The most recent was the innovative game play introduced by the legendary Go Seigen in the 1930s. His experimentation with ways of playing the opening moves revolutionised the way the game is played. But Go players now recognise that AlphaGo might well have launched an even greater revolution.

The Chinese Go champion Ke Jie recognises that we are in a new era: ‘Humanity has played Go for thousands of years, and yet, as AI has shown us, we have not yet even scratched the surface. The union of human and computer players will usher in a new era.’

Ke Jie’s compatriot Gu Li, winner of the most Go world titles, added: ‘Together, humans and AI will soon uncover the deeper mysteries of Go.’ Hassabis compares the algorithm to the Hubble telescope. This illustrates the way many view this new AI. It is a tool for exploring deeper, further, wider than ever before. It is not meant to replace human creativity but to augment it.

And yet there is something that I find quite depressing about this moment. It feels almost pointless to want to aspire to be the world champion at Go when you know there is a machine that you will never be able to beat. Professional Go players have tried to put a brave face on it, talking about the extra creativity that it has unleashed in their own play, but there is something quite soul-destroying about knowing that we are now second best to the machine. Sure, the machine was programmed by humans, but that doesn’t really seem to make it feel better.

AlphaGo has since retired from competitive play. The Go team at DeepMind has been disbanded. Hassabis proved his Cambridge lecturer wrong. DeepMind has now set its sights on other goals: health care, climate change, energy efficiency, speech recognition and generation, computer vision. It’s all getting very serious.

Given that Go was always my shield against computers doing mathematics, was my own subject next in DeepMind’s cross hairs? To truly judge the potential of this new AI we are going to need to look more closely at how it works and dig around inside. The crazy thing is that the tools DeepMind is using to create the programs that might put me out of a job are precisely the ones that mathematicians have created over the centuries. Is this mathematical Frankenstein’s monster about to turn on its creator?

4 (#ulink_b7db296c-191d-5acf-ba31-7d6811ec7763)

ALGORITHMS, THE SECRET TO MODERN LIFE (#ulink_b7db296c-191d-5acf-ba31-7d6811ec7763)

The Analytical Engine weaves algebraic patterns, just as the Jacquard loom weaves flowers and leaves.

Ada Lovelace

Our lives are completely run by algorithms. Every time we search for something on the internet, plan a journey with our GPS, choose a movie recommended by Netflix or pick a date online, we are being guided by an algorithm. Algorithms are steering us through the digital age, yet few people realise that they predate the computer by thousands of years and go to the heart of what mathematics is all about.

The birth of mathematics in Ancient Greece coincides with the development of one of the very first algorithms. In Euclid’s Elements, alongside the proof that there are infinitely many prime numbers, we find a recipe that, if followed step by step, solves the following problem: given two numbers, find the largest number that divides them both.

It may help to put the problem in more visual terms. Imagine that the floor of your kitchen is 36 feet long by 15 feet wide. You want to know the largest square tile that will enable you to cover the entire floor without cutting any tiles. So what should you do? Here is the 2000-year-old algorithm that solves the problem:

Suppose you have two numbers, M and N (and suppose N is smaller than M). Start by dividing M by N and call the remainder N

. If N

is zero, then N is the largest number that divides them both. If N

is not zero, then divide N by N

and call the remainder N

. If N

is zero, then N

is the largest number that divides M and N. If N

is not zero, then do the same thing again. Divide N

by N

and call the remainder N

. These remainders are getting smaller and smaller and are whole numbers, so at some point one must hit zero. When it does, the algorithm guarantees that the previous remainder is the largest number that divides both M and N. This number is known as the highest common factor or greatest common divisor.

Now let’s return to our challenge of tiling the kitchen floor. First we find the largest square tile that will fit inside the original shape. Then we look for the largest square tile that will fit inside the remaining part – and so on, until you hit a square tile that finally covers the remaining space evenly. This is the largest square tile that will enable you to cover the entire floor without cutting any tiles.

If M = 36 and N = 15, then dividing N into M gives you a remainder of N

= 6. Dividing N

into N we get a remainder of N