Our automated future
Uber is already introducing a driverless car in San Francisco. If it works, there go the jobs for taxi drivers and Uber drivers. When driverless trucks arrive in a few years, truckers will be out of work. We really should be planning for this. Elizabeth Kolbert offers some thoughts in the New Yorker:
There are many accounts of the genesis of Watson. The most popular, which is not necessarily the most accurate—and this is the sort of problem that Watson himself often stumbled on—begins in 2004, at a steakhouse near Poughkeepsie. One evening, an I.B.M. executive named Charles Lickel was having dinner there when he noticed that the tables around him had suddenly emptied out. Instead of finishing their sirloins, his fellow-diners had rushed to the bar to watch “Jeopardy!” This was deep into Ken Jennings’s seventy-four-game winning streak, and the crowd around the TV was rapt. Not long afterward, Lickel attended a brainstorming session in which participants were asked to come up with I.B.M.’s next “grand challenge.” The firm, he suggested, should take on Jennings.
I.B.M. had already fulfilled a similar “grand challenge” seven years earlier, with Deep Blue. The machine had bested Garry Kasparov, then the reigning world chess champion, in a six-game match. To most people, beating Kasparov at chess would seem a far more impressive feat than coming up with “Famous First Names,” say, or “State Birds.” But chess is a game of strictly defined rules. The open-endedness of “Jeopardy!”—indeed, its very goofiness—made it, for a machine, much more daunting.
Lickel’s idea was batted around, rejected, and finally resurrected. In 2006, the task of building an automated “Jeopardy!” champion was assigned to a team working on question-answering technology, or QA. As Stephen Baker recounts in his book about the project, “Final Jeopardy,” progress was, at first, slow. Consider the following (actual) “Jeopardy!” clue: “In 1984, his grandson succeeded his daughter to become his country’s Prime Minister.” A person can quickly grasp that the clue points to the patriarch of a political family and, with luck, summon up “Who is Nehru?” For a computer, the sentence is a quagmire. Is what’s being sought a name? If so, is it the name of the grandson, the daughter, or the Prime Minister? Or is the question about geography or history?
Watson—basically a collection of processing cores—could be loaded with whole Wikipedias’ worth of information. But just to begin to search this enormous database Watson had to run through dozens of complicated algorithms, which his programmers referred to as his “parsing and semantic analysis suite.” This process yielded hundreds of “hypotheses” that could then be investigated.
After a year, many problems with Watson had been solved, but not the essential one. The computer took hours to generate answers that Jennings could find in an instant.
A year turned into two and then three. Watson’s hardware was upgraded. Benefitting from algorithms that allowed him to learn from his own mistakes, he became more proficient at parsing questions and judging the quality of potential answers. In 2009, I.B.M. began to test the machine against former, sub-Jennings “Jeopardy!” contestants. Watson defeated some, lost to others, and occasionally embarrassed his creators. In one round, in response to a question about nineteenth-century British literature, the computer proposed the eighties pop duo Pet Shop Boys when the answer was Oliver Twist. In another round, under the category “Just Say No,” Watson offered “What is fuck?” when the right response was “What is nein?”
I.B.M.’s aspirations for Watson went way beyond game shows. A computer that could cope with the messiness and the complexity of English could transform the tech world; one that could improve his own performance in the process could upend nearly everything else. Firms like Google, Microsoft, and Amazon were competing with I.B.M. to dominate the era of intelligent machines, and they continue to do so. For the companies involved, hundreds of billions of dollars are at stake, and the same could also be said for the rest of us. What business will want to hire a messy, complex carbon-based life form when a software tweak can get the job done just as well?
Ken Jennings, who might be described as the first person to be rendered redundant by Watson, couldn’t resist a dig at his rival when the two finally, as it were, faced off. In January, 2011, Jennings and another former champion, Brad Rutter, played a two-game match against the computer, which was filmed in a single day. Heading into the final “Final Jeopardy!,” the humans were so far behind that, for all intents and purposes, they were finished. All three contestants arrived at the correct response to the clue, which featured an obscure work of geography that inspired a nineteenth-century novelist. Beneath his answer—“Who is Bram Stoker?”—Jennings added a message: “I for one welcome our new computer overlords.”
How long will it be before you, too, lose your job to a computer? This question is taken up by a number of recent books, with titles that read like variations on a theme: “The Industries of the Future,” “The Future of the Professions,” “Inventing the Future.” Although the authors of these works are employed in disparate fields—law, finance, political theory—they arrive at more or less the same conclusion. How long? Not long.
“Could another person learn to do your job by studying a detailed record of everything you’ve done in the past?” Martin Ford, a software developer, asks early on in “Rise of the Robots: Technology and the Threat of a Jobless Future” (Basic Books). “Or could someone become proficient by repeating the tasks you’ve already completed, in the way that a student might take practice tests to prepare for an exam? If so, then there’s a good chance that an algorithm may someday be able to learn to do much, or all, of your job.”
Later, Ford notes, “A computer doesn’t need to replicate the entire spectrum of your intellectual capability in order to displace you from your job; it only needs to do the specific things you are paid to do.” He cites a 2013 study by researchers at Oxford, which concluded that nearly half of all occupations in the United States are “potentially automatable,” perhaps within “a decade or two.” (“Even the work of software engineers may soon largely be computerisable,” the study observed. ) . . .