Story of Psychology - Morton Hunt [362]
For a third, the mind knows the meaning of words and other symbols, but the computer does not; to it they’re only labels. Nor does anything about the computer resemble the unconscious or all that goes on in it.
These are only a few of the differences that have been obvious since the first experiments in computer reasoning. Yet, no less an authority than Herbert Simon categorically asserted that mind and machine were kin. In 1969, in a series of lectures published as The Sciences of the Artificial, he argued that the computer and the human mind are both “symbol systems”—physical entities that process, transform, elaborate, and generally manipulate symbols of various kinds.
Throughout the 1970s, small cadres of dedicated psychologists and computer scientists at MIT, Carnegie-Mellon, Stanford, and a handful of other universities, possessed of a zealotlike belief that they were on the verge of a great breakthrough, developed programs that were both theories of how the mind works and machine versions of human thinking. By the 1980s the work had spread to scores of universities and to the laboratories of a number of major companies. The programs carried out such varied activities as playing chess, parsing sentences, deducing the laws of planetary motion from a mass of raw data, translating elementary sentences from one language to another, and inferring the structure of molecules from mass spectrographic data.94
The enthusiasts saw no limit to the ability of IP theory to explain how the mind works and of AI to verify those explanations by carrying out the same processes—and eventually doing so far better than human beings. In 1981 Robert Jastrow, director of the Goddard Institute for Space Studies, predicted that “around 1995, according to current trends, we will see the silicon brain as an emergent form of life, competitive with man.”95
But some psychologists felt that the computer was only a mechanical simulation of certain aspects of the mind and that the computational model of mental processing was a poor fit. The eminent cognitivist Ulric Neisser had become “disillusioned” with information-processing models by 1976, when he published Cognition and Reality. Here, much influenced by James Gibson and his “ecological” psychology, Neisser made the case that IP models were narrow and far removed from real-life perception, cognition, and purposeful activity, and fail to take into account the richness of experience and information we continually receive from the world around us.96
A number of other psychologists, though not saying they were disillusioned, sought to broaden the IP view to include the mind’s use of schemas, shortcuts, and intuitions, and its ability to function simultaneously on both the conscious and unconscious levels to conduct simultaneous processes in parallel (a critical issue we shall hear more of in a moment).
Still others challenged the notion that computors programmed to think like humans actually think. AI, they maintained, isn’t anything like human intelligence, and though it may vastly outperform the human mind at calculations, it would never do easily, or at all, many things the human mind does routinely and effortlessly.
The most important difference is the computer’s inability to understand what it is thinking about. John Searle and Hubert Dreyfus, both philosophy professors at Berkeley, the computer scientist Joseph Weizenbaum at MIT, and others argued that computers, even when programmed to reason, merely manipulate symbols without having any idea what they mean and imply. General Problem Solver, for instance, may have figured out how the father and two sons could get across the river, but only in terms of algebraic symbols; it did not know what a father, son, or boat were, what “sink” meant, what would happen if they sank, or anything else