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weight them to reach a certain goal. This method in some sense mimics how we learn as
children. The results from these new approaches are amazing.
Such a deep-learning program was used to teach a computer to play Go, a game
that only a few years ago was thought to be beyond the reach of AI because it was so
hard to calculate how well you were doing. It seemed that top Go players relied a great
deal on intuition and a feel for position, so proficiency was thought to require a
particularly human kind of intelligence. But the AlphaGo program produced by
DeepMind, after being trained on thousands of high-level Go games played by humans
and then millions of games with itself, was able to beat the top human players in short
order. Even more amazingly, the related AlphaGo Zero program, which learned from
scratch by playing itself, was stronger than the version trained initially on human games!
It was as though the humans had been preventing the computer from reaching its true
potential. The same method has recently been generalized: Starting from scratch, within
just twenty-four hours, an equivalent AlphaZero chess program was able to beat today’s
top “conventional” chess programs, which in turn have beaten the best humans.
Progress has not been restricted to games. Computers are significantly better at
image and voice recognition and speech synthesis than they used to be. They can detect
tumors in radiographs earlier than most humans. Medical diagnostics and personalized
medicine will improve substantially. Transportation by self-driving cars will keep us all
safer, on average. My grandson may never have to acquire a driver’s license, because
driving a car will be like riding a horse today—a hobby for the few. Dangerous
activities, such as mining, and tedious repetitive work will be done by computers.
Governments will offer better targeted, more personalized and efficient public services.
AI could revolutionize education by analyzing an individual pupil’s needs and enabling
customized teaching, so that each student can advance at an optimal rate.
Along with these huge benefits, of course, will come alarming risks. With the
vast amounts of personal data, computers will learn more about us than we may know
about ourselves; the question of who owns data about us will be paramount. Moreover,
data-based decisions will undoubtedly reflect social biases: Even an allegedly neutral
intelligent system designed to predict loan risks, say, may conclude that mere
membership in a particular minority group makes you more likely to default on a loan.
While this is an obvious example that we could correct, the real danger is that we are not
always aware of biases in the data and may simply perpetuate them.
Machine learning may also perpetuate our own biases. When Netflix or Amazon
tries to tell you what you might want to watch or buy, this is an application of machine
learning. Currently such suggestions are sometimes laughable, but with time and more
data they will get increasingly accurate, reinforcing our prejudices and likes and dislikes.
Will we miss out on the random encounter that might persuade us to change our views by
exposing us to new and conflicting ideas? Social media, given its influence on elections,
is a particularly striking illustration of how the divide between people on different sides
of the political spectrum can be accentuated.
We may have already reached the stage where most governments are powerless to
resist the combined clout of a few powerful multinational companies that control us and
our digital future. The fight between dominant companies today is really a fight for
control over our data. They will use their enormous influence to prevent regulation of
data, because their interests lie in unfettered control of it. Moreover, they have the

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