ARE WE THINKING MORE LIKE MACHINES?

ZIYAD MARAR

Global publishing director, SAGE; author, Intimacy: Understanding the Subtle Power of Human Connection

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There’s something old-fashioned about visions of the future. The majority of predictions, like three-day weeks, personal jet packs, and the paperless office, tell us more about the times in which they were proposed than about contemporary experience. When people point to the future, we’d do well to run an eye back up the arm to see who’s doing the pointing.

The possibility of artificial general intelligence has long invited such crystal-ball gazing, whether utopian or dystopian in tone. Yet speculations on this theme have reached such a pitch and intensity in the last few months alone (enough to trigger an Edge Question, no less) that this may reveal something about ourselves and our culture today.

We’ve known for some time that machines can outthink humans in a narrow sense. The question is whether they do so in any way that could or should ever resemble the baggier mode of human thought. Even when dealing with as tame a domain as chess, the computer and the human diverge widely.

“Tame” problems (like establishing the height of a mountain), which are well formulated and have clear solutions, are good grist for the mill of narrow, brute-force thinking. Sometimes even narrower thinking is called for, when huge data sets can be mined for correlations, leaving aside the distraction of thinking about underlying causes.

But many of the problems we face—from challenging inequality to choosing the right school for our children—are “wicked,” in that they don’t have right or wrong answers (though we hope they have better or worse ones). They’re uniquely contextual and have complex overlapping causes that change based on the level of explanation used. Those problems don’t suit narrow, computational thinking well. In blurring facts with values, they resemble the messy emotion-riddled thinking that reflects the human minds that conjured them up.

To tackle wicked problems requires peculiarly human judgment, even if these judgments are illogical in some sense—especially in the moral sphere. Notwithstanding Joshua Greene and Peter Singer’s logical urging of a consequentialist frame of mind, one that a computer could reproduce, the human tendency to distinguish acts from omissions and to blur intentions with outcomes (as in the principle of double effect) means we need solutions that will satisfy the instincts of human judges if they’re to be stable over time.

And that very feature of human thinking (shaped by evolutionary pressures) points to the widest gulf between machine and human thinking. Thinking is not motivated without preferences, and machines don’t have those on their own. Only minds that comprehend cause and effect conjure up motives. So if goals, wants, values are features of human minds, then why predict that artificial superintelligences will become more than tools in the hands of those who program in those preferences?

If the welter of prognostications about AI and machine learning tells us anything, I don’t think it’s about how a machine will emulate a human mind anytime soon. We can do that easily enough just by having more children and educating them. Rather, it tells us that our appetites are shifting.

We’re understandably awed by what sheer computation has achieved and will achieve; I’m happy to jump on the driverless virtual-reality bandwagon that careens off into that overpredicted future. But this awe is leading to a tilt in our culture. The digital republic of letters is yielding up engineering as the thinking metaphor of our time. In its wake lies the once complacent, now anxious figure with a more literary, less literal cast of mind. We’re cleaning up our act, embarrassed by the fumbling inconclusiveness of messy thinking. It’s unsurprising to hear that the United Kingdom’s education secretary recently advised teenagers to steer away from arts and humanities in favor of STEM disciplines if they’re to flourish. The sheer obviousness of a certain kind of progress has made narrow thinking gleam with a new and addictive luster.

But something’s lost as whole fields of inquiry succeed or fail by the standard of narrow thinking, and a new impediment is created. Alongside the true, we need to think well about the good and the beautiful—and, indeed, the wicked. This requires vocabularies that better reflect our crooked timber (whether thought of, by turns, as bug or feature). Meanwhile, the understandable desire to upgrade those wicked problems to mere tame ones is leading us to tame ourselves.