Complexity_ A Guided Tour - Melanie Mitchell [88]
In contrast, we have modern-day computers, which are anything but sensitive to context. My computer supposedly has a state-of-the-art spam filter, but sometimes it can’t figure out that a message with a “word” such as V!a&®@ is likely to be spam. As a similar example, a recent New York Times article described how print journalists are now learning how to improve the Web accessibility of their stories by tailoring headlines to literal-minded search engines instead of to savvy humans: “About a year ago, the Sacramento Bee changed online section titles. ‘Real Estate’ became ‘Homes,’ ‘Scene’ turned into ‘Lifestyle,’ and dining information found in newsprint under ‘Taste,’ is online under ‘Taste/Food.’ ”
This is, of course, not to say that computers are dumb about everything. In selected, narrow domains they have become quite intelligent. Computer-controlled vehicles can now drive by themselves across rugged desert terrain. Computer programs can beat human doctors at diagnosing certain diseases, human mathematicians at solving complex equations, and human grand masters at chess. These are only a few examples of a surge of recent successes in artificial intelligence (AI) that have brought a new sense of optimism to the field. Computer scientist Eric Horvitz noted, “At conferences you are hearing the phrase ‘human-level AI,’ and people are saying that without blushing.”
Well, some people, perhaps. There are a few minor “human-level” things computers still can’t do, such as understand human language, describe the content of a photograph, and more generally use common sense as in the preceding examples. Marvin Minsky, a founder of the field of artificial intelligence, concisely described this paradox of AI as, “Easy things are hard.” Computers can do many things that we humans consider to require high intelligence, but at the same time they are unable to perform tasks that any three-year-old child could do with ease.
Making Analogies
An important missing piece for current-day computers is the ability to make analogies.
The term analogy often conjures up people’s bad memories of standardized test questions, such as “Shoe is to foot as glove is to _____?” However, what I mean by analogy-making is much broader: analogy-making is the ability to perceive abstract similarity between two things in the face of superficial differences. This ability pervades almost every aspect of what we call intelligence.
Consider the following examples:
A child learns that dogs in picture books, photographs, and real life are all instances of the same concept.
A person is easily able to recognize the letter A in a vast variety of printed typefaces and handwriting.
Jean says to Simone, “I call my parents once a week.” Simone replies “I do that too,” meaning, of course, not that she calls Jean’s parents once a week, but that she calls her own parents.
A woman says to her male colleague, “I’ve been working so hard lately, I haven’t been able to spend enough time with my husband.” He replies, “Same here”—meaning not that he is too busy to spend enough time with the woman’s husband, but that he has little time to spend with his girlfriend.
An advertisement describes Perrier as “the Cadillac of bottled waters.” A newspaper article describes teaching as “the Beirut of professions.” The war in Iraq is called “another Vietnam.”
Britain and Argentina go to war over the Falklands (or las Malvinas), a set of small islands located near the coast of Argentina and populated by British settlers. Greece sides with Britain because of its own conflict with Turkey over Cyprus, an island near the coast of Turkey, the majority of whose population is ethnically Greek.
A classical music lover hears an