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What can the computer information help to save
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Passage 4 In the 1950s, the pioneers of artificial intelligence (AI) predicted that, by the end of this century, computers would be conversing with us at work and robots would be performing our housework. But as useful as computers are, they’re nowhere close to achieving remotely resembling these early aspirations for humanlike behavior. Never mind something as complex as conversation: the most powerful computers struggle to reliably recognize the shape of an object, the most elementary of a ten-month-old kid. A growing group of AI researchers think they know where the field went wrong. The problem, the scientists say, is that AI has been trying to separate the highest, most abstract levels of thought, like language and mathematics, and to duplicate them with logical, step-by-step programs. A new movement in AI, on the other hand, takes a closer look at the more roundabout way in which nature came up with intelligence. Many of these researchers study evolution and natural adaptation instead of formal logic and conventional computer programs. Rather than digital computers and transistors, some want to work with brain cells and proteins. The results of these early efforts are as promising as they are peculiar, and the new nature-based Al movement is slowly but surely moving to the forefront of the field. Imitating the brain’s neural network is a huge step in the right direction, says computer Scientist and biophysicist Michael Conrad, but it still misses an important aspect of natural intelligence. "People tend to treat the brain as if it were made up of color-coded transistors", he explains." But it’s not simply a clever network of switches. There are lots of important things going on inside the brain cells themselves." Specifically, Conrad believes that many of the brain’s capabilities stem from the pattern-recognition proficiency of the individual molecules that make up each brain cell. The best way to build an artificially intelligent device, he claims, would be to build it around the same sort of molecular skills. Right now, the notion that conventional computers and software are fundamentally incapable of matching the processes that take place in the brain remains controversial. But if it proves true, then the efforts of Conrad and his fellow AI rebels could turn out to be the only game in town. Which of the following is closest in meaning to the phrase "the only game in town"(Line 3, Para. 4)
Questions 11 -13 are based on the following passage. You now have 15 seconds to read questions 11- 13. From the passage we can infer that ______.
10岁儿童，发热伴左腿痛3天，此前一周曾有咽炎，按扁桃体炎治疗，咽痛好转，查体：体温39℃，左股骨下端肿胀，皮温高，并有深压痛，血白细胞17×109/L，中性粒白细胞90%，血沉60mm/h。 下列哪一种是最可能的诊断
Passage 2 The more time scientists spend designing computers, the more they marvel at the human brain. Tasks that stump the most advanced supercomputer--recognizing a face, reading a handwritten note--are child’s play for the 3-lb. organ. Most important, unlike any conventional computer, the brain can learn from its mistakes. Researchers have tried for years to program computers to imitate the brain’s abilities, but without success. Now a growing number of designers believe they have the answer: if a computer is to function more like a person and less like an over-grown calculator, it must be built more like a brain, which distributes information across a vast interconnected web of nerve cells, or neurons. Conventional computers function by following a chainlike sequence of detailed instructions. Although very fast, their processors can perform only one task at a time. This approach works best in solving problems that can be broken down into simpler logical pieces. The processors in a neural-network computer, by contrast, form a grid much like the nerve cells in the brain. Since these artificial neurons are interconnected, they can share information and perform tasks at the same time. This two-dimensional approach works best at recognizing patterns. Instead of programming a neural-network computer to make decisions, its maker trains it to recognize the patterns in any solution to a problem by repeatedly feeding examples to the machine. This process is like a process that prepares all possible moves in a chess game and try to find a best approach to it. If the examples are not sufficient or complete, the computer will be in for trouble, after all, it can not respond to something it can not recognize as a pattern in its memory. Neural networks come in all shapes and size. The new networks will make things which were simply impossible completely feasible in the near future. What the users need to do is to wait and see. Developers are experimenting with new equipment and hopefully they will succeed. Until now most existed as software simulations because redesigning computer chips took a lot of time and money. By experimenting with different approaches through software rather than hardware, scientists have been able to avoid costly mistakes. What does the word "stump" in paragraph 1, line 2, mean
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