• Choose a category
• All
• Classic-Fiction

By Root 1063 0

the hallmark of commonsense knowledge that I discussed in the previous chapter. And in practice, it rarely occurs to us that the ease with which we make decisions disguises any sort of complexity. As the philosopher Daniel Dennett points out, when he gets up in the middle of the night to make himself a midnight snack, all he needs to know is that there is bread, ham, mayonnaise, and beer in the fridge, and the rest of the plan pretty much works itself out. Of course he also knows that “mayonnaise doesn’t dissolve knives on contact, that a slice of bread is smaller than Mount Everest, that opening the refrigerator doesn’t cause a nuclear holocaust in the kitchen” and probably trillions of other irrelevant facts and logical relations. But somehow he is able to ignore all these things, without even being aware of what it is that he’s ignoring, and focus on the few things that matter.16

But as Dennett argues, there is a big difference between knowing what is relevant in practice and being able to explain how it is that we know it. To begin with, it seems clear that what is relevant about a situation is just those features that it shares with other comparable situations—for example, we know that how much something costs is relevant to a purchase decision because cost is something that generally matters whenever people buy something. But how do we know which situations are comparable to the one we’re in? Well, that also seems clear: Comparable situations are those that share the same features. All “purchase” decisions are comparable in the sense that they involve a decision maker contemplating a number of options, such as cost, quality, availability, and so on. But now we encounter the problem. Determining which features are relevant about a situation requires us to associate it with some set of comparable situations. Yet determining which situations are comparable depends on knowing which features are relevant.

This inherent circularity poses what philosophers and cognitive scientists call the frame problem, and they have been beating their heads against it for decades. The frame problem was first noticed in the field of artificial intelligence, when researchers started trying to program computers and robots to solve relatively simple everyday tasks like, say, cleaning a messy room. At first they assumed that it couldn’t be that hard to write down everything that was relevant to a situation like this. After all, people manage to clean their rooms every day without even really thinking about it. How hard could it be to teach a robot? Very hard indeed, as it turned out. As I discussed in the last chapter, even the relatively straightforward activity of navigating the subway system requires a surprising amount of knowledge about the world—not just about subway doors and platforms but also about maintaining personal distance, avoiding eye contact, and getting out of the way of pushy New Yorkers. Very quickly AI researchers realized that virtually every everyday task is difficult for essentially the same reason—that the list of potentially relevant facts and rules is staggeringly long. Nor does it help that most of this list can be safely ignored most of the time—because it’s generally impossible to know in advance which things can be ignored and which cannot. So in practice, the researchers found that they had to wildly overprogram their creations in order to perform even the most trivial tasks.17

The intractability of the frame problem effectively sank the original vision of AI, which was to replicate human intelligence more or less as we experience it ourselves. And yet there was a silver lining to this defeat. Because AI researchers had to program every fact, rule, and learning process into their creations from scratch, and because their creations failed to behave as expected in obvious and often catastrophic ways—like driving off a cliff or trying to walk through a wall—the frame problem was impossible to ignore. Rather than trying to crack the problem, therefore, AI researchers took a different approach entirely—one that