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  3. The Love of a Robot Can computers ever really be like us, and if not, why not? The similarities are obvious. We can both wor

The Love of a Robot Can computers ever really be like us, and if not, why not? The similarities are obvious. We can both wor

admin2013-07-02  24
问题                         The Love of a Robot
    Can computers ever really be like us, and if not, why not? The similarities are obvious. We can both work out certain problems and apparently engage in dialogue, but the differences are striking, too. Marvin Minsky, one of the founding fathers of artificial intelligence at the Massachusetts Institute of Technology, confesses that the more he tries to imitate the human brain, the more wonderful he finds it.

    Computers can engage in dialogue and even simulate speech, but it will be a very long time indeed before they indulge in metaphor, jokes or slang — the things that human beings manage so effortlessly, and reprimand(谴责)their children for doing too much.
    Yet the differences between human and computer "thinking" do not lie simply in the kinds of things that each is good at. The strategy is different. Computers are logical; they are tolerable to work with only because they do what they do so blindingly fast, processing billions of bits a second. The brains of humans, like those of all animals, are survival machines that use a variety of strategies, of which logic is only one, and not usually dominant. We think our way through life with roles of thumb, making guesses and taking chances based on past successes. Computers would find us intolerable, too, if they had opinions.
    Besides, humans do not merely think and solve immediate problems. We have consciousness, whatever that is. We are emotional. Taken all in all, we have "mind". Nobody supposes that present-day computers possess consciousness or feeling, and, with neither, they surely cannot be "mindful".
    Many artificial intelligence enthusiasts claim that the differences are only those of complexity. Consciousness is nothing more than the brain looking at itself, thinking about its own thinking. Computers could surely acquire such ability with suitable circuitry. It may not be a matter simply of making them more complex; perhaps there must be new computer architecture, with the different parts of the circuit interacting in ways not yet conceived. But time will sort this out. Already, the latest robots have e-motion built into them. Without emotion, they have no motivation at all and remain inactive. The human brain, in the end, is an electrical circuit. Why should a silicon-based circuit not imitate a carbonbased circuit, if that is what it is required to do?
    The first great modern computer scientist, Alan Turing, said that, in principle, functional computers could be made out of anything. Turing is too clever to argue with and we must concede that computers can indeed be made of anything at all. But we know that computers, at least of the present day, do not do all that brains do. The human brain is not designed at all. It evolved by natural selection. Evolved systems have tremendous strengths. They encapsulate(压缩)solutions to all the problems that have been posed by the environment over many millions of years. Those problems are more various than any mere designer could consider; and the systems that evolve to cope with them are more complicated and subtle than any designer could conceive.
    But evolved systems have their weaknesses, too. Natural selection is opportunist, but not creative. Each new generation is limited in materials and form by what was available to the generation before. It cannot simply seize what it needs from the surroundings, as a designer can. Hence the solutions to the problems posed by life often have a rough-and-ready quality. Solutions to old problems remain as a visible trace. Evolved systems cannot exhaustively be understood. After all, the major way to understand how living things work is by "reverse engineering": looking at what they do, and then inferring the problems they are solving. But the problems they are really solving may be hidden deep in their history. It’s not like reverse-engineering an enemy plane that has crash-landed in your back garden.
    Computers, however, are designed and the process of designing has strengths and weaknesses of its own. The strength is in the flexibility: when designers make a mistake, they can go back to the drawing board, which natural selection can never do. The weakness is that the problems that need to be solved cannot be predicted completely in advance. In practice, consumers discover their weaknesses and find out what they can really do. Computers intended for one purpose often succeed, as animals do, by applying themselves to something completely different. Future computers will design themselves and, however much we may initially make them in our image, they will increasingly grow away from us.
    Science does not progress in steady, logical steps, as conventionally conceived. Machines are innately impulsive and unpredictable, too. As soon as computer programs become even a little complex, it becomes theoretically impossible to predict all that they are capable of. The social relationships between unpredictable human beings and advanced, innately unpredictable robots are beyond guessing.
    The less imaginative scientists assume that all outstanding questions can be answered within their existing paradigm, that more of the same researches will provide whatever answers are lacking. The great scientists, however, think beyond the paradigm. Historians tend to argue that Newton gave up experimental physics in the late 17th century because he ran out of ideas. Surely, though, he ran out of physics: he knew that his mechanics was not adequate, but he also knew that 17th-century data and maths could not lead to better understanding.
    Today’s physicists, it has been suggested, may face the same problem: they have developed the idea of "superstrings", as the most fundamental of all fundamental entities in the universe, but they may need 23rd-century maths to understand them. This surely is the case also with the problems of mind and consciousness, and of whether computers can truly partake of them. We just don’t have the data or the means of thinking about what we do have. To understand the human brain we need a new paradigm. We should not assume that it will simply extend the present one, which involves neurology and pharmacology. It may well include new physics, or elements of eastern mysticism.
    The next few centuries will surely bring us supertoys. They will also bring insights. Whether they bring the enlightenment we seek remains to be seen.
What is the weakness of evolved systems?
选项 A、The systems that evolve to cope with the problems are complicated.
B、The problems that need to be solved cannot be predicted completely in advance.
C、They encapsulate solutions to all the problems that have been posed.
D、They are not creative and cannot exhaustively be understood.
答案D
解析 本段开头说进化系统也有缺点，自然选择是机会主义者，但是它不具有创造性。第七句说进化系统不能被彻底理解，故[D]项符合。[A][C]项是进化系统的优点，[B]项是电脑设计过程的缺点，不是进化系统的缺点。
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