Page 43 - Foundations of Cognitive Psychology : Core Readings

Can Machines Think? 41

the rest of the city’s treasures. I would cheerfully run the minuscule risk of
having my bluffcalled. Obviously, thetestitems arenot allthatI care aboutin
a city. In fact, some of them I don’t care about at all. I just think they would be
cheap and easy ways of assuring myself that the subtle things I do care about in
cities are present. Similarly, I think it would be entirely unreasonable to sup-
pose that Alan Turing had an inordinate fondness for party games, or put too
high a value on party game prowess in his test. In both the Turing and the
Dennett test, a very unrisky gamble is being taken:the gamble that the quick-
probe assumption is, in general, safe.
But two can play this game of playing the odds. Suppose some computer
programmer happens to be, for whatever strange reason, dead set on tricking
me into judging an entity to be a thinking, intelligent thing when it is not. Such
a trickster could rely as well as I can on unlikelihood and take a few gambles.
Thus,if the programmer canexpectthatitisnot remotely likely that I, as the
judge, will bring up the topic of children’s birthday parties, or baseball, or
moon rocks, then he or she can avoid the trouble of building world knowledge
on those topics into the data base. Whereas if I do improbably raise these
issues, the system will draw a blank and I will unmask the pretender easily. But
given all the topics and words that I might raise, such a savings would no doubt
be negligible. Turn the idea inside out, however, and the trickster would have a
fighting chance. Suppose the programmer has reason to believe that I will ask
only about children’s birthday parties, or baseball, or moon rocks—all other
topics being, for one reason or another, out of bounds. Not only does the task
shrink dramatically, but there already exist systems or preliminary sketches of
systems in artificial intelligence that can do a whiz-bang job of responding with
apparent intelligence on just those specialized topics.
William Wood’s LUNAR program, to take what is perhaps the best example,
answers scientists’ questions—posed in ordinary English—about moon rocks.
In one test it answered correctly and appropriately something like 90 percent
of the questions that geologists and other experts thought of asking it about
moon rocks. (In 12 percent of those correct responses there were trivial, cor-
rectable defects.) Of course, Wood’s motive in creating LUNAR was not to trick
unwary geologists into thinking they were conversing with an intelligent being.
And if that had been his motive, his project would still be a long way from
success.
For it is easy enough to unmask LUNAR without ever straying from the
prescribed topic of moon rocks. Put LUNAR in one room and a moon rock
specialist in another, and then ask them both their opinion of the social value of
the moon-rocks-gathering expeditions, for instance. Or ask the contestants their
opinion of the suitability of moon rocks as ashtrays, or whether people who
have touched moon rocks are ineligible for the draft. Any intelligent person
knows a lot more about moon rocks than their geology. Although it might be
unfair to demand this extra knowledge of a computer moon rock specialist, it
would be an easy way to get it to fail the Turing test.
But just suppose that someone could extend LUNAR to cover itself plausibly
on such probes, so long as the topic was still, however indirectly, moon rocks.
We might come to think it was a lot more like the human moon rocks specialist
than it really was. The moral we should draw is that as Turing test judges we