When machines beat men, they make us seriously think about our place in a technologically advanced world and we tend to overestimate machines and underestimate men
Two men sitting on the opposite sides of a table at a luxury hotel in Seoul, taking turns to place black and white coins on a board, were the centre of attraction for the artificial intelligence (AI) community across the world in the last few days.
One of them was Lee Se-dol, one of the best Go players in the world. The other was an operator who merely followed instructions from a computer next to him that was running a program called AlphaGo.
In many ways, the match was a replay of another board game played 19 years ago in New York, when Deep Blue, a program running on an IBM supercomputer, defeated Garry Kasparov, the reigning chess champion. As in 1997, the present game involved a tech giant. AlphaGo was developed by DeepMind, owned by Google since 2014. As in 1997, this match too has a tremendous symbolic value, pitching man against machine. And as in 1997, the machine won. AlphaGo won four out of five games.
AlphaGo’s victory was not expected for another 10 to 15 years. Go is a more complex game than chess, even though it’s based on a simpler set of rules. This quality had attracted some of the most powerful minds to it: Albert Einstein, Alan Turing, a key figure in AI, and US astronaut Daniel Barry, among others.
In chess, on average, there are 35 legal moves at any point, each branching out to 35 more options. In Go, there are 250 options at any point, each branching out to 250 more. This means, in chess, a computer could use brute force to look at all possible options in some depth before selecting the best—which is what Deep Blue did. This approach won’t work in Go, because of the enormity of the options it needs to consider. Again, in chess, an expert can look at a half-played board and reasonably take a call on who would win. In Go, an expert won’t be able to do that. It’s more intuitive, the strategic implications of a tactical move would be too hard to calculate.
AlphaGo developers tried to tackle this by making the program first choose a limited number of promising moves (using an algorithm that learnt from millions of moves and thousands of games) and then sending those to another algorithm that would evaluate their statistical probability of success. AlphaGo learnt the game by looking at millions of games to arrive at its own strategy and tactics. In Deep Blue’s case, the developers could tweak (and in fact they did tweak) the programme during the course of the match, to make it play differently. AlphaGo’s developers didn’t have that luxury. It has to play thousands of games before its behaviour changes.
Despite this difference, both Deep Blue and AlphaGo have similar lessons to offer. One, they throw light on how human beings see and relate to technology. Two, they give a sense of how fast technology can advance. Finally, they make us seriously think about our own place in a technologically advanced world.
In chess or Go, all the information that one may need is right there on the board, at least in theory. In practice, though, a lot depends on what’s outside the board. The players are informed by the previous games of the opponent, they spend a lot of time figuring out their opponent’s strategy and state of mind. During the game, body language matters. However, when playing against a machine, you don’t have these benefits. A machine is a black box, you don’t know what’s happening inside. Both Kasparov and Lee mentioned this lack of information as a key challenge.
In Kasparov’s case, this turned ugly. Watching the clippings of the 1997 match, one could see that Kasparov felt that something was seriously amiss. Deep Blue didn’t play like a machine. (It made a move that made sense to Kasparov.) Kasparov demanded that IBM share the rationale behind each of Deep Blue’s moves (IBM didn’t). Kasparov refused to accept he lost.
The passage of time made no difference. A 2003 documentary, ‘Game Over: Kasparov and the Machine’, directed by Vikram Jayanti, looked at the match from Kasparov’s point of view. Its central metaphor was The Turk, a fake mechanical chess player from 18th century. It was fake because the moves were in fact made by chess masters hiding inside the instrument. Deep Blue’s principal architect Feng-hsiung Hsu in a 2002 book on how the machine was built (‘Behind Deep Blue—Building the Computer that Defeated the World Chess Champion’), looked at it differently: “Garry’s accusations of cheating both during and after the 1997 match confirmed that Deep Blue passed the chess version of the Turing Test (a blind test to tell whether you are interacting with a human or a computer)." More recently, Nate Silver, in ‘The Signal and the Noise: Why so Many Predictions Fail—but Some Don’t’, attributed Deep Blue’s random moves to a bug.
The key lesson though is that human beings tend to look at machines with suspicion. It will get increasingly more difficult to convince sceptics, as technology gets deeper into the dark box that is AI. One way to tackle this is to do exactly what Kasparov demanded: explain the rationale behind its action.
Deep Blue and AlphaGo also stand as a testament to how fast the technology can progress. A year before the 1997 match, Deep Blue played against Kasparov—and lost. It took just a year to better him. When AlphaGo defeated Fan Hui, Europe’s Go champion, in 2015, no one saw its success as an indicator that it would beat Lee Se-dol. But AlphaGo was improving at exponential rates, learning from thousands of games and by playing against two versions of itself, over and over again.
It might be tempting to think of AlphaGo as the new Deep Blue. However, a better comparison would be IBM’s Watson, which was developed specifically to beat human opponents in the TV quiz show ‘Jeopardy’, and which it did in 2011, but didn’t stop there. Today, Watson is used in a range of areas including pharmaceutical research and development, healthcare and retail. It’s used by doctors at Memorial Sloan Kettering Cancer Center and Cleveland Clinic, among others. It’s a billion-dollar business within IBM today.
DeepMind seems to have bigger ambitions. In a speech, DeepMind co-founder Demis Hassabis said the company’s ambition was to solve intelligence first, and using that solve everything else. His comment on AlphaGo is an indicator of that ambition. He called it “just a prototype".
Where would all this leave human beings? This question in the context of AlphaGo or Deep Blue comes with an inherent bias. It’s the same bias that creeps in when we discuss it in the context of movies such as ‘The Terminator’ and ‘The Matrix’. In these films, machines are pitched against men. The machines can destroy human lives, or at least, their jobs. It’s a pessimistic view. A recent study, released during the World Economic Forum earlier this year, puts the job loss due to automation at 5 million.
There is another way to look at the question. Steve Jobs liked to think of computers as bicycles for the mind. (In fact, in Apple’s early days, he wanted to rename Macintosh as the Bicycle.) A bicycle can make man more efficient in movement than even a condor in flight, and computers, he believed, can make mind many times more efficient. It’s an optimistic view. Here, technology becomes a tool in the hands of man.
Neither is false, for there will be winners and losers. However, it’s too early to speculate on the exact shape it will take. During the early days of industrialization, many feared mass unemployment, as machines outperformed human beings. It’s true that many lost their jobs. Ultimately though, it didn’t turn out badly for human beings.
When machines beat men, we tend to overestimate machines and underestimate men. It’s easy to forget that for all its complexity, AlphaGo didn’t have to consider group dynamics, didn’t have to worry about vague goals or unclear rules that human beings face in their daily lives. Machines are good at what they are, and humans are good at what they are.
Thus, a useful question to ask would be what are the skills and competencies that are unique to humans, that cannot be copied by machines. In ‘Humans Are Underrated—What High Achievers Know That Brilliant Machines Never Will’, Geoff Colvin argues that these skills will get more and more important, as machines get better and better at performing ever more complex tasks. These skills, he says, are to do with human interaction—empathy, creativity and teamwork.
Watching Lee Se-dol in the press conference, with his broad smile and polite manners, one cannot help but think that he is in many ways the opposite of Garry Kasparov. But they are alike in one important way. They represent what humans are capable of.
In 1997, after losing to Deep Blue, Kasparov came across as aggressive, even rude, unable to accept the fact that he lost. But then, it’s those very same qualities that made him stand up against someone as powerful as Vladimir Putin in Russia (Kasparov is a member of The Other Russia, a coalition that opposes Putin’s policies). And, Lee Se-dol, in his own gentle manner, pointed out to something that’s easy to forget if we get too caught up in the Man vs Machine narrative. He said, “I would like to express my respect to the programmers for making such an amazing program."