Associate professor of psychology, UC Berkeley
The everyday objects we mark as “machines”—washing machines, sewing machines, espresso machines—have their roots in the mechanical. They move liquids and objects around, they transform matter from one manifestation to another. Clothes become clean, fabrics become connected, coffee is brewed. But “thinking machines” have changed the way we think about machines. Many of today’s prototypical machines—laptops, smartphones, tablets—have their roots in the digital. They move information around, they transform ideas. Numbers become sums, queries produce answers, goals generate plans.
As the way we think about machines has changed, has the way we think about thinking undergone a comparable transformation?
One version of this question isn’t new and the answer is yes. The technology of a given time and place has often provided a metaphor for thinking about thought, whether it’s hydraulic, mechanical, digital, or quantum. But there’s more to how we think about thinking, and it stems from the standards we implicitly import in assessments of what does and doesn’t count as thinking in the first place.
Does your washing machine think? Does your smartphone? We might be more willing to attribute thought to the latter—and to its more sophisticated cousins—not only because it’s more complex but also because it seems to think more like we do. Our own experience of thinking isn’t mechanical and isn’t restricted to a single task. We—adult humans—seem to be the standard against which we assess what does and doesn’t count as thinking.
Psychologists have already forced us to stretch, defend, and revise the way we think about thinking. Cultural psychologists have challenged the idea that Western adults provide an optimal population for the study of human thinking. Developmental psychologists have raised questions about whether and how preverbal infants think. Comparative psychologists have long been interested in whether and how nonhuman animals think. And philosophers, of course, have considered these questions too. Across these disciplines, one advance in how we think about thinking has come from recognizing and abandoning the idea that “thinking like I do” is the only way to think about thinking, or that “thinking like I do” is always the best or most valuable kind of thinking. We’ve benefited from scrutinizing the implicit assumptions that often slip into discussions of thinking, and from abandoning a particular kind of thinking chauvinism.
With thinking machines, we face many of the same issues. Two sets of basic assumptions are tempting to adopt, but we must be careful not to do so uncritically. One is the idea that the best, or only, kind of thinking is adult human thinking. For example, “intelligent” computer systems are sometimes criticized for not thinking but instead relying too heavily on a brute-force approach, on raw horsepower. Are these approaches an alternative to thinking? Or do we need to broaden the scope of what counts as thinking?
The second idea deserving scrutiny is the opposite extreme: that the best, or only, kind of thinking is reflected by the way our thinking machines happen to think right now. For example, there’s evidence that emotions influence human thinking, and sometimes for the better. And there’s evidence that we sometimes outsource our thinking to our social and physical environment, relying on experts and gadgets to support effective interactions with the world. It might be tempting to reject this messy reality in favor of an emotionless, self-contained entity as the basic unit of thinking—something like a personal computer, which doesn’t feel compassion and can happily chug away without peers.
Somewhere between the human chauvinist standard for thinking and the 1990s laptop approach is likely to be the best way to think about thinking—one that recognizes some diversity in the means and ends that constitute it. Recent advances in artificial intelligence are already compelling us to rethink some of our assumptions—not just making us think differently, and with different tools, but changing the way we think about thinking itself.