21 Lessons for the 21st Century(11)

Another possible objection is that it is unclear how the algorithm could establish its emotional goal. If you just fought with your boyfriend, should the algorithm aim to make you sad or joyful? Would it blindly follow a rigid scale of ‘good’ emotions and ‘bad’ emotions? Maybe there are times in life when it is good to feel sad? The same question, of course, could be directed at human musicians and DJs. Yet with an algorithm, there are many interesting solutions to this puzzle.

One option is to just leave it to the customer. You can evaluate your emotions whichever way you like, and the algorithm will follow your dictates. Whether you want to wallow in self-pity or jump for joy, the algorithm will slavishly follow your lead. Indeed, the algorithm may learn to recognise your wishes even without you being explicitly aware of them.

Alternatively, if you don’t trust yourself, you can instruct the algorithm to follow the recommendation of whichever eminent psychologist you do trust. If your boyfriend eventually dumps you, the algorithm may walk you through the official five stages of grief, first helping you deny what happened by playing Bobby McFerrin’s ‘Don’t Worry, Be Happy’, then whipping up your anger with Alanis Morissette’s ‘You Oughta Know’, encouraging you to bargain with Jacques Brel’s ‘Ne me quitte pas’ and Paul Young’s ‘Come Back and Stay’, dropping you into the pit of depression with Adele’s ‘Someone Like You’ and ‘Hello’, and finally aiding you to accept the situation with Gloria Gaynor’s ‘I Will Survive’.

The next step is for the algorithm to start tinkering with the songs and melodies themselves, changing them ever so slightly to fit your quirks. Perhaps you dislike a particular bit in an otherwise excellent song. The algorithm knows it because your heart skips a beat and your oxytocin levels drop slightly whenever you hear that annoying part. The algorithm could rewrite or edit out the offending notes.

In the long run, algorithms may learn how to compose entire tunes, playing on human emotions as if they were a piano keyboard. Using your biometric data the algorithms could even produce personalised melodies, which you alone in the entire universe would appreciate.

It is often said that people connect with art because they find themselves in it. This may lead to surprising and somewhat sinister results if and when, say, Facebook begins creating personalised art based on everything it knows about you. If your boyfriend leaves you, Facebook will treat you to an individualised song about that particular bastard rather than about the unknown person who broke the heart of Adele or Alanis Morissette. The song will even remind you of real incidents from your relationship, which nobody else in the world knows about.

Of course, personalised art might never catch on, because people will continue to prefer common hits that everybody likes. How can you dance or sing together to a tune nobody besides you knows? But algorithms could prove even more adept at producing global hits than personalised rarities. By using massive biometric databases garnered from millions of people, the algorithm could know which biochemical buttons to press in order to produce a global hit which would set everybody swinging like crazy on the dance floors. If art is really about inspiring (or manipulating) human emotions, few if any human musicians will have a chance of competing with such an algorithm, because they cannot match it in understanding the chief instrument they are playing on: the human biochemical system.

Will all this result in great art? That depends on the definition of art. If beauty is indeed in the ears of the listener, and if the customer is always right, then biometric algorithms stand a chance of producing the best art in history. If art is about something deeper than human emotions, and should express a truth beyond our biochemical vibrations, biometric algorithms might not make very good artists. But nor do most humans. In order to enter the art market and displace many human composers and performers, algorithms won’t have to begin by straightaway surpassing Tchaikovsky. It will be enough if they outperform Britney Spears.





New jobs?


The loss of many traditional jobs in everything from art to healthcare will partly be offset by the creation of new human jobs. GPs who focus on diagnosing known diseases and administering familiar treatments will probably be replaced by AI doctors. But precisely because of that, there will be much more money to pay human doctors and lab assistants to do groundbreaking research and develop new medicines or surgical procedures.12

AI might help create new human jobs in another way. Instead of humans competing with AI, they could focus on servicing and leveraging AI. For example, the replacement of human pilots by drones has eliminated some jobs but created many new opportunities in maintenance, remote control, data analysis and cyber security. The US armed forces need thirty people to operate every unmanned Predator or Reaper drone flying over Syria, while analysing the resulting harvest of information occupies at least eighty people more. In 2015 the US Air Force lacked sufficient trained humans to fill all these positions, and therefore faced an ironic crisis in manning its unmanned aircraft.13

If so, the job market of 2050 might well be characterised by human–AI cooperation rather than competition. In fields ranging from policing to banking, teams of humans-plus-AIs could outperform both humans and computers. After IBM’s chess program Deep Blue beat Garry Kasparov in 1997, humans did not stop playing chess. Rather, thanks to AI trainers human chess masters became better than ever, and at least for a while human–AI teams known as ‘centaurs’ outperformed both humans and computers in chess. AI might similarly help groom the best detectives, bankers and soldiers in history.14