The everyday objects we mark as "machines"—washing machines, sewing machines, espresso machines—have their roots in the mechanical. They move around liquids and objects, they transform matter from one manifestation to another. Clothes become clean, fabrics become connected, coffee is served. 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 around information, 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 because it seems to think more like us. Our own experience of thinking isn't mechanical, and it isn't restricted to a single task. We—adult humans—seem to be the standard against which we assess what does, and what does not, 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 a privileged population from which to study human thinking. Developmental psychologists have raised questions about whether and how preverbal infants can think. Comparative psychologists have long been interested in whether and how non-human animals can think. And philosophers, or course, have considered these questions along the way. 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. In other words, 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 very same issues, but the target of study has shifted from humans and other animals to machines of our own creation. As we move forward, there are two sets of basic assumptions that 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 really thinking, but 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 that deserves scrutiny is the opposite extreme: the idea 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 thinking. Recent advances in artificial intelligence are already compelling us to rethink some of our assumptions about thinking. They aren't just making us think differently and with different tools, but changing the way we think about thinking itself.