Watson Still Can't Think - NYTimes.com - 0 views
opinionator.blogs.nytimes.com/...watson-still-cant-think
technology social consciousness cognitive computers intelligence artificial
![](/images/link.gif)
-
Fish argued that Watson “does not come within a million miles of replicating the achievements of everyday human action and thought.” In defending this claim, Fish invoked arguments that one of us (Dreyfus) articulated almost 40 years ago in “What Computers Can’t do,” a criticism of 1960s and 1970s style artificial intelligence.
-
At the dawn of the AI era the dominant approach to creating intelligent systems was based on finding the right rules for the computer to follow.
- ...12 more annotations...
-
the dominant paradigm in AI research has largely “moved on from GOFAI to embodied, distributed intelligence.” And Faustus from Cincinnati insists that as a result “machines with bodies that experience the world and act on it” will be “able to achieve intelligence.”
-
The new, embodied paradigm in AI, deriving primarily from the work of roboticist Rodney Brooks, insists that the body is required for intelligence. Indeed, Brooks’s classic 1990 paper, “Elephants Don’t Play Chess,” rejected the very symbolic computation paradigm against which Dreyfus had railed, favoring instead a range of biologically inspired robots that could solve apparently simple, but actually quite complicated, problems like locomotion, grasping, navigation through physical environments and so on. To solve these problems, Brooks discovered that it was actually a disadvantage for the system to represent the status of the environment and respond to it on the basis of pre-programmed rules about what to Do, as the traditional GOFAI systems had. Instead, Brooks insisted, “It is better to use the world as its own model.”
-
although they respond to the physical world rather well, they tend to be oblivious to the global, social moods in which we find ourselves embedded essentially from birth, and in virtue of which things matter to us in the first place.
-
the embodied AI paradigm is irrelevant to Watson. After all, Watson has no useful bodily interaction with the world at all.
-
The statistical machine learning strategies that it uses are indeed a big advance over traditional GOFAI techniques. But they still fall far short of what human beings do.
-
“The illusion is that this computer is doing the same thing that a very good ‘Jeopardy!’ player would do. It’s not. It’s doing something sort of different that looks the same on the surface. And every so often you see the cracks.”
-
Watson doesn’t understand relevance at all. It only measures statistical frequencies. Because it is relatively common to find mismatches of this sort, Watson learns to weigh them as only mild evidence against the answer. But the human just doesn’t do it that way. The human being sees immediately that the mismatch is irrelevant for the Erie Canal but essential for Toronto. Past frequency is simply no guide to relevance.
-
The fact is, things are relevant for human beings because at root we are beings for whom things matter. Relevance and mattering are two sides of the same coin. As Haugeland said, “The problem with computers is that they just don’t give a damn.” It is easy to pretend that computers can care about something if we focus on relatively narrow domains — like trivia games or chess — where by definition winning the game is the only thing that could matter, and the computer is programmed to win. But precisely because the criteria for success are so narrowly defined in these cases, they have nothing to do with what human beings are when they are at their best.
-
Far from being the paradigm of intelligence, therefore, mere matching with no sense of mattering or relevance is barely any kind of intelligence at all. As beings for whom the world already matters, our central human ability is to be able to see what matters when.
-
But, as we show in our recent book, this is an existential achievement orders of magnitude more amazing and wonderful than any statistical treatment of bare facts could ever be. The greatest danger of Watson’s victory is not that it proves machines could be better versions of us, but that it tempts us to misunderstand ourselves as poorer versions of them.