CAMPO DE TRATAMIENTO

Philosophically, the game playing techniques are not very satisfying. Can one really say that a computer us- ing exhaustive search is displaying any intelligence? While major chess computers search through millions of boards for each play, a human grandmaster searches through merely hundreds of moves and still performs as well. One cannot accurately say that a computer is actually reasoning as a human does.

7.2.1 Turing test

Alan Turing, a British mathematician working with the first computers back in the 1940’s, struggled with this question of what constitutes artificial intellegence. Eventually he proposed the following way of testing whether an entity was intelligent.

The name alpha-beta search is purely historical. In early descriptions of the algorithm, these two Greek letters were important variables.

7.2 Nature of intelligence 75

A C

B

To see if a computer (

) is intelligent, we place it and a human ( ) behind a screen, each

connected via a communication wire to a human tester ( ) in front. asks questions of and

in an attempt to determine which is the human and which is the computer. If can’t reliably tell which of and

is a human, then

must be intelligent.

This is called the Turing test. Many accept this aim as the ultimate AI goal.

When the conversation is restricted to the domain of game playing, computers appear close to passing the Turing test when restricted to games alone. After playing a historic match with a chess computer in 1996, world chess champion Garry Kasparov said of his opponent, “I could feel — I could smell — a new kind of intelligence across the table.” Although he won the series then, he lost to an improved version the next year. Yet computers have not completed even this reduced version of the Turing test: Kasparov maintains that the computer has a distinctive style of play, and this indicates that the champion computer would not pass the Turing test, if only because it plays too well.

The general Turing test, though, is a much more difficult goal. It’s not something that we’re likely to reach soon, but it is something that indicates how we know when we’re there.

7.2.2 Searle’s Chinese Room experiment

Some people disagree that the Turing test is a good way to evaluate artificial intelligence. It’s somewhat irritating that the Turing test is so output-oriented, they say: That computer could be doing anything, and we’d be saying that it is intelligent.

Such is the stance taken by philosopher John Searle in 1980. Searle proposed the following thought experiment called the Chinese room experiment to illustrate his stance: Suppose that everybody commu- nicates via Chinese, and that the human behind the screen ( ) doesn’t know any Chinese. In principle, can still appear intelligent, simply by having a vast phrasebook listing each possible input with some

English instructions of how to respond, including a corresponding Chinese-symbol output. The book could omit a translation of what the Chinese means, so that doesn’t understand what is going on. Even so, if the

phrasebook is vast enough, then will appear intelligent to . But if has no idea of what is happening,

Searle asks, can we say that is behaving intelligently? (The practicality of such a phrasebook is beside

the point. Searle is trying to illustrate the test’s shortcoming in principle.)

Searle is saying that the Turing test is flawed — intelligence cannot be defined as simply appearing to be intelligent, however convenient that may be to a scientist. To be intelligent, something must actually work intelligently. We cannot define intelligence functionally; the method also matters.

Searle’s argument is not universally accepted, but it stands as a credible argument against the Turing test.

7.2.3 Symbolic versus connectionist AI

Searle’s problems with the Turing test bears some similarity to a long-standing debate within the artificial intelligence community, a split between those advocating symbolic AI and those advocating connectionist AI. The symbolic AI camp contends that the best way toward intelligence is to achieve behavior that appears intelligent, by any means possible. And the easiest programs to write are those that manipulate symbols (and thus they take the name symbolists). The minimax search technique for game playing is a symbolist’s technique: It is a no-holds-barred approach to playing games.

Connectionists assert that this technique is flawed — although you may succeed on some simple prob- lems, they say, such a program will never exceed the specific algorithms plugged into it. The program will always be brittle, breaking as soon as we move away from the restricted problem that the program was de- signed to solve. Instead, connectionists argue, our work on AI should focus on programs that resemble how the human brain works.

One of the arguments of connectionists is that the human brain does not resemble symbolic AI at all, so it’s difficult to see how symbolic programs are solid steps toward intelligence. They might point to studies of human chess grandmasters, who can play dozens of simultaneous timed games with many different people, winning all of the games. Obviously, though beginners might play by searching through a variety of pos- sible moves, human chess mastery involves something different than becoming more efficient at searching through moves. When we work on the minimax search technique, which relies solely on evaluating vast numbers of possible moves, we’re chasing up the wrong tree.

Let’s review how the brain works. Researchers don’t understand it entirely, but they’ve done enough experimentation to understand the simplest pieces, which are simple cells called neurons. Each neuron has several dendrites, connected to other neurons via connections called synapses. Other neurons can send electrochemical signals through the synapses through the dendrites. Occasionally, the signals may become so intense that the neuron becomes excited and sends its own electrochemical signals down its axon, which are relayed through synapses to the dendrites of other neurons.

The connectionists’ idea is to simulate the human brain within the computer. (They are called connec- tionists because the systems they develop rely on the connections between “neurons.”) Since the human brain is a mechanical system, they argue, this plan can only result in success. Symbolists don’t disagree with them; they simply feel that this is the difficult road to AI, with little room for intermediate success along the way.

Incidentally, Searle buys into none of this. He certainly does not agree with the symbolists but neither does he accept the connectionists’ position. In fact, Searle argues (outside his Chinese room experiment) that AI is impossible. Other philosophers, too, counter that AI researchers have no chance of success. There are a variety of arguments that various philosophers propose for AI’s impossibility. Some arguments are based on the assertion that AI requires a materialist view of humanity, where human behavior is understood entirely as a physical phenomenon. Philosophers who reject this materialist view (believing instead in a soul-like entity that affects humans’ behavior) thus often reject the possibility of true artificial intelligence. AI advocates tend to have a materialist view of humanity, discounting the possibility that humans may have some nonmaterial being.

There are also some philosophers who accept the materialist view, but they still argue against the possi- bility of artificial intelligence. For example, a philosopher might argue that computers can’t simulate reality perfectly — simulating quantum mechanics perfectly, for example, is seemingly impossible for a computer, but conceivably the human brain’s behavior may depend on the intricacies of quantum mechanics.