What Is Artificial Intelligence?
The Social Singularity
By Max Borders
An Excerpt From "The Social Singularity"
IN 2016, Google AI’s AlphaGo program won its third go match against Lee Sedol, one of the game’s most dominant players. The ancient game of go has long been considered uncrackable in terms of AI programming. Not only did Google crack go, it was the first time a program had won so decisively. For many, it portended the end of human domination of planet Earth.
To hand wring is human. If you’ve seen any of the predictions of robot apocalypse, you’ll find at minimum that Homo sapiens still tells the scariest stories.
One popular theme is that the robots will take our jobs.
Not only are artificial intelligence and automation are likely to displace people across a number of industries, they say, but the displaced will not be able to find new jobs. Technology has already started eating up roles currently occupied by the poorest among us, they warn, jobs like checkout clerk and fast food order taker.
One of the tidiest summaries of our collective hyperventilation about AI comes from writer James Surowiecki:
Over the past few years, it has become conventional wisdom that dramatic advances in robotics and artificial intelligence have put us on the path to a jobless future. We are living in the midst of a “second machine age,” . . . in which routine work of all kinds—in manufacturing, sales, book- keeping, food prep—is being automated at a steady clip, and even complex analytical jobs will be superseded before long. A widely cited 2013 study by researchers at the University of Oxford, for instance, found that nearly half of all jobs in the US were at risk of being fully automated over the next 20 years. The endgame, we’re told, is inevitable: The robots are on the march, and human labor is in retreat.
So people who have come to depend on these jobs to support themselves and their families will be cast into the streets, forming legions of the dispossessed.
The rationale is simple: Why hire a factory worker at twelve dollars per hour, when you can install a robot for ten bucks per hour? Why bring on a janitor if C3PO can do the job better, faster, and cheaper? Who needs an accountant when an AI bot can manage the books as part of a software bundle? The answers to such questions add up to massive unemployment.
In the scariest of these scenarios, we’re expected to accept certain assumptions. These are not totally unreasonable assumptions, but there’s room for pushback. Indeed, the good news is no iron laws of economics lead one to project permanent displacement. It is only creative destruction, which Joseph Schumpeter described as the “process of industrial mutation that incessantly revolutionizes the economic structure from within, incessantly destroying the old one, incessantly creating a new one.”
Unless a meteor hits Earth, we can count on that. Otherwise none of these dire predictions is as inevitable as gravity. In fact, there are a lot of things we already know about economics that offer us a glimmer of hope.
Liberating Labor and Capital
Economic history since before the time of Ned Ludd is a story of people finding ways to liberate labor and capital. Something as basic as a broom spares hours of work compared to sweeping with one’s hands. The mechanization of the plow has reduced a lot of back pain, not to mention time spent cleaning up mule manure. The microprocessor has done wonders for writers, who once used Wite-Out instead of delete keys. While we might be nostalgic about churned butter and wine grapes squashed underfoot, we can now go straight to eating and drinking and leave churning and smashing to machines.
Henry Hazlitt, in his famous book Economics in One Lesson, urges us not to take for granted the blessings of progress:
“The belief that machines cause unemployment, when held with any logical consistency, leads to preposterous conclusions” Hazlett avers. “Not only must we be causing unemployment with every technological improvement we make today, but primitive man must have started causing it with the first efforts he made to save himself from needless toil and sweat.”
That’s great, but Economics in One Lesson was published in 1946. The fear is: It’s different this time around. Hazlett could not have anticipated cognitive computers like IBM’s Watson.
As I write, I wonder if my work is next. An AI consumes all the masters of non-fiction. Then literature. Then it tracks biological and other responses from readers as they read, mapping these responses against tropes, devices, and formulas until . . . For a moment I’m consumed by the fear that a sufficiently advanced AI could write these very words. But could it?