Verbal Assessment

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Passage 1 An expert system (ES) is a computer program that attacks problems formerly relegated to human experts. ES’s already help to find oil for petroleum companies, teach children arithmetic, and advise investors about the stock market. They also analyze chemicals, diagnose diseases, and design other computer systems. An ES must have an interface, that is, a way of communicating with those who enter information and those who want advice. Most ES’s guide users with questions about their goals and resources and allow users to answer by typing appropriate answers. The second part of an ES is a huge database that consists of facts, which can be updated as frequently as necessary. For example, the database of an ES that gives stock-market advice would include daily market reports, prices of products and commodities, and current interest rates. The third part is a knowledge base, consisting of thousands of facts – the specialized knowledge that experts have. Factual knowledge is organized into structures called frames, which have slots where the computer stores important features. Frames for companies listed on the stock market would include slots for each company’s products or services, the ratio of its stock price to its earnings, the names of its parent or subsidiary organizations, its suppliers, and its customers. The knowledge base also includes hundreds of rules that relate to each other. These rules apply to any company, component, or product; by plugging in particular facts, the system can deduce new facts. Rules for an ES that provides stock advice might include “If component prices rise, product prices rise”; “If product prices rise, sales drop”; “If sales drop, profits drop”; and “If profits drop, stock prices are likely to drop.” The “intelligent” part of an ES is the inference engine, a set of rules for searching the data and knowledge bases for useful combinations of rules. Because the inference engine must select these useful items from masses of information, it relies not on exhaustive searches of all combinations, which would take too long, but on rules of thumb such as “Find facts and rules that contain words used in the user’s question.” An ES that provides stock-market advice might start with a computer manufacturer ABC; the system would find chip manufacturers in ABC’s supplier slot, scan its databases, and note a steady rise in chip prices. Then, by plugging increased chip prices into the first rule above, the ES would predict a rise in prices of ABC’s products. By linking the other intelligently selected rules, it would predict a drop in the price of ABC’s stock. Skeptics argue that ES’s are so complex that users simply exchange problem-area experts for computer experts. Skeptics also claim that the potentially enormous amount of time required to enter information into databases and knowledge bases makes ES’s inefficient. Finally, skeptics maintain that sometimes there is no substitute for the human expert’s “intuition.” ES designers reply that “friendly” interfaces already exist and need only be attached to existing ES’s, that most useful data are already in computers and can be transferred into ES’s automatically, and that if intuition is real, programmers can build it into their systems. Designers also point out that, as computers do more tasks once thought impossible for machines, critics continually redefine intelligent to mean whatever computers can’t do – yet.

Transcript of Verbal Assessment

Page 1: Verbal Assessment

Passage 1

An expert system (ES) is a computer program that attacks problems formerly relegated to human experts. ES’s already help to find oil for petroleum companies, teach children arithmetic, and advise investors about the stock market. They also analyze chemicals, diagnose diseases, and design other computer systems.

An ES must have an interface, that is, a way of communicating with those who enter information and those who want advice. Most ES’s guide users with questions about their goals and resources and allow users to answer by typing appropriate answers.

The second part of an ES is a huge database that consists of facts, which can be updated as frequently as necessary. For example, the database of an ES that gives stock-market advice would include daily market reports, prices of products and commodities, and current interest rates.

The third part is a knowledge base, consisting of thousands of facts – the specialized knowledge that experts have. Factual knowledge is organized into structures called frames, which have slots where the computer stores important features. Frames for companies listed on the stock market would include slots for each company’s products or services, the ratio of its stock price to its earnings, the names of its parent or subsidiary organizations, its suppliers, and its customers. The knowledge base also includes hundreds of rules that relate to each other. These rules apply to any company, component, or product; by plugging in particular facts, the system can deduce new facts. Rules for an ES that provides stock advice might include “If component prices rise, product prices rise”; “If product prices rise, sales drop”; “If sales drop, profits drop”; and “If profits drop, stock prices are likely to drop.”

The “intelligent” part of an ES is the inference engine, a set of rules for searching the data and knowledge bases for useful combinations of rules. Because the inference engine must select these useful items from masses of information, it relies not on exhaustive searches of all combinations, which would take too long, but on rules of thumb such as “Find facts and rules that contain words used in the user’s question.” An ES that provides stock-market advice might start with a computer manufacturer ABC; the system would find chip manufacturers in ABC’s supplier slot, scan its databases, and note a steady rise in chip prices. Then, by plugging increased chip prices into the first rule above, the ES would predict a rise in prices of ABC’s products. By linking the other intelligently selected rules, it would predict a drop in the price of ABC’s stock.

Skeptics argue that ES’s are so complex that users simply exchange problem-area experts for computer experts. Skeptics also claim that the potentially enormous amount of time required to enter information into databases and knowledge bases makes ES’s inefficient. Finally, skeptics maintain that sometimes there is no substitute for the human expert’s “intuition.” ES designers reply that “friendly” interfaces already exist and need only be attached to existing ES’s, that most useful data are already in computers and can be transferred into ES’s automatically, and that if intuition is real, programmers can build it into their systems. Designers also point out that, as computers do more tasks once thought impossible for machines, critics continually redefine intelligent to mean whatever computers can’t do – yet.

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Passage II

The leaf of a plant is not only a manufacturing unit: it is the power supply for most of the world. Coal is nothing but the remains of past vegetation laid down in Carboniferous times, two hundred and seventy million years ago. Oil is of uncertain origin, but it is thought to be derived, at least in part, from plants. When we burn coal, we are burning the carbonized residues of past forests, and petrol in the tank of a motor car was once seaweed. Most of our electricity is derived from coal or oil power, both products of photosynthesis by plants. The only power that does not come from plants is found in the windmill and the water-wheel, plus a little that is derived from nuclear energy. Whenever we travel by road, rail or aeroplane we are propelled by the work of plants which long ago trapped the energy in sunlight and stored it up for future use…

We do not know how long the miracle of photosynthesis has been in operation, but it must be as old as the plant kingdom. The first plants had chlorophyll, or something like it, and were able to feed themselves by taking the carbon dioxide out of the air and building it into sugar with the aid of sunlight, chlorophyll acting as an energy trap to steal the energy from the light and make it do chemical work. We can say with confidence, therefore, that photosynthesis has been going on for at least two thousand million years, and probably longer than that.

In most plants known to us today the leaf is the main seat of photosynthesis…; but it was not always so. The first land plants had no leaves. One of our most ancient fossil land plants is Zosterophyllum, found in Silurian beds in Australia, which are about three hundred and fifty million years old. Zosterophyllum was a small plant, consisting of a tangled mass of branches which were probably underground… From this underground system rose erect branches which were evidently aerial, for they were covered with an epidermis and a cuticle, clearly designed to prevent the plant from drying up when exposed to the air. These branches were about a twelfth of an inch across, and were leafless, so that it must be presumed that they carried out the work of photosynthesis.

Leafless though the first land plants may have been, the leaf was not long in making its appearance and in taking over the main work of photosynthesis. In the same Australian beds that yield Zosterophyllum are found the remains of a plant called Baragwanathia. This was a larger plant than Zosterophyllum, with stems up to nearly three inches in diameter. These stems bore numerous closely set leaves…. Each leaf had a single vascular strand… to bring water to the leaf and to carry the products of photosynthesis away.

Since the fossils of Baragwanathia are found in the same Silurian beds as Zosterophyllum the two plants must have been of comparable age. The evolution of the leaf cannot therefore have been long delayed once plants had established themselves on dry land.

As the principal organ of photosynthesis, the leaf is beautifully constructed to serve its purpose. It is usually thin and flat, thus exposing a large area of surface not only for the intake of carbon dioxide but also for the sun to illuminate…. In addition, a leaf has… abundant vascular strands which not only bring water to the leaf, but also carry away in their phloem the organic compounds formed by photosynthesis. Lastly, a leaf is well covered with epidermis and cuticle, these preventing undue evaporation of water, and yet has in its stomata means of ready access of carbon dioxide to the internal air spaces.

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Passage III

Many people who have been resuscitated after having been declared clinically dead report having had near-death experience (NDE). Although no two reports of NDEs are exactly alike, the reports are remarkably similar. Many people report that, after hearing their attending physicians declare them dead, they floated out of and hovered above their own bodies, watching the resuscitation efforts. They claim to have passed through a dark tunnel and to have seen spirits of dead loved ones, followed by an encounter with a “being of light” with whom there was nonverbal communication as they reviewed together their lives’ major events. Finally, filled with joy and tranquility, they reached an ethereal barrier separating the earthly life from the next, only to learn that their time to die had not yet come. Although reluctant to leave the afterlife, they somehow reunited with their bodies and abruptly rejoined the living.

Some people believe that NDEs provide convincing evidence that humans survive death and that God awaits us in the afterlife, but NDEs do not justify these conclusions. First, although those who report NDEs were declared clinically dead, they were never biologically dead; that is, although heartbeat and respiration may have temporarily ceased, they were resuscitated before their brains died.

Second, estimates are that only 15 percent of those who have been “near death” have reported having had NDEs. If only 15 percent of the frightened few who witnessed a bank robbery reported that the robber wore a mask (while disagreeing among themselves about the color of the mask), it would be unreasonable to conclude that the robber did in fact wear a mask. Thus, it is as unreasonable to base a belief in an afterlife on the NDEs of a small percentage of the few who survived “near death.” One who believes in an afterlife on the basis of NDEs might reply that 85 percent who fail to report NDEs simply forget their NDEs upon awakening after the resuscitation efforts. Although this may be true, there could be no evidence that this is true, because the hypothesis is unverifiable.

Researchers report that 95 percent of those who have had NDEs claim that their experiences seemed “too real” to have been dreams. There is also no correlation between having had an NDE and one’s religiousness. However, one should not conclude from this that there is an afterlife or that both the religious and nonreligious actually perceive God.

It is just as reasonable to believe that NDEs are nothing more than extraordinarily vivid hallucinations. After all, unlike normal dreamers, those who have had NDEs have had them under extraordinary circumstances: their cardiac and respiratory functions had ceased. For all anyone knows, the human brain produces NDEs when it suffers oxygen deprivation. That dying human brains may produce such unusual experiences is consistent with the close similarities among NDE reports and with the fact that both religious and nonreligious people alike have had NDEs.

Finally, those who have had NDEs report utterly bizarre, fantastic events: hovering above one’s own body, seeing dead loved ones, having telepathic communication with a “being of light.” Whenever a reported happening is improbable, violating laws of nature and ordinary sensory experience, one should distrust reports that the event occurred. Thus, it is unreasonable to count NDEs as compelling evidence for the existence of God or an afterlife.

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Passage IV

There seem to be two ways of knowing, or knowing about, another person. One is associated with existentialism, phenomenology, and structuralism. It is a matter of knowing what a person is, or what he is like, or what he is coming to be or becoming. We try to know another person in this sense as we know ourselves. We share his feelings through sympathy or empathy. Through intuition we discover his attitudes, intentions, and other states of mind. We communicate with him in the etymological sense of making ideas and feelings common to both of us. We do so more effectively if we have established good interpersonal relations. This is a passive, contemplative kind of knowing: If we want to predict what a person does or is likely to do, we assume that he, like us, will behave according to what he is; his behavior, like ours, will be an expression of his feelings, states of mind, intentions, attitudes, and so on.

The other way of knowing is a matter of what a person does. We can usually observe this as directly as any other phenomenon in the world; no special kind of knowing is needed. We explain why a person behaves as he does by turning to the environment rather than to inner states or activities. The environment was effective during the evolution of the species, and we call the result the human genetic endowment. A member of the species is exposed to another part of that environment during his lifetime, and from it he acquires a repertoire of behavior which converts an organism with a genetic endowment into a person. By analyzing these effects of the environment, we move toward the prediction and control of behavior.

But can this formulation of what a person does neglect any available information about what he is? There are gaps in time and space between behavior and the environmental events to which it is attributed, and it is natural to try to fill them with an account of the intervening state of the organism. We do this when we summarize a long evolutionary history by speaking of genetic endowment. Should we not do the same for a personal history? An omniscient physiologist should be able to tell us, for example, how a person is changed when a bit of his behavior is reinforced, and what he thus becomes should explain why he subsequently behaves in a different way. We argue in such a manner, for example, with respect to immunization. We begin with the fact that vaccination makes it less likely that a person will contract a disease at a later date. We say that he becomes immune, and we speak of a state of immunity, which we then proceed to examine. An omniscient physiologist should be able to do the same for comparable states in the field of behavior. He should also be able to change behavior by changing the organism directly rather than by changing the environment. Is the existentialist, phenomenologist, or structuralist not directing his attention precisely to such a mediating state?

A thoroughgoing dualist would say no, because for him what a person observes through introspection and what a physiologist observes with his special techniques are in different universes. But it is a reasonable view that what we feel when we have feelings are states of our own bodies, and that the states of mind we perceive through introspection are other varieties of the same kinds of things. Can we not, therefore, anticipate the appearance of an omniscient physiologist and explore the gap between environment and behavior by becoming more keenly aware of what we are?

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Passage V

First of all, with respect to pushing formal instruction into kindergarten, some cross-cultural data is instructive. In Denmark, formal reading instruction is not introduced until the second grade. Before that, children have a rich exploratory and manipulative language experience; they are read to and talked to, encouraged to dictate their own stories and learn sight words. Denmark has almost 100 percent literacy.

In France, there is a state-mandated reading program that is begun in kindergarten. French children are thus exposed to formal instruction in reading at age five. In contrast to Denmark, France has some 30 percent of children with reading problems. In Japan, formal instruction in reading is also begun early, but there are fewer reading problems than in France because spelling is phonetic and thus eliminates some of the logical difficulties inherent in languages without totally phonetic spelling systems, such as French and English. In a phonetic language, each symbol has only one sound attached to it, and thus learning this language is easier than learning a language where the same sound can be represented by different letters and the same letter can represent different sounds. In a sense, a phonetic language is to a nonphonetic, or only partially phonetic, language what a digital clock is to a regular clock face…

There is also evidence that individual children who are exposed to formal instruction (symbolic and derived learning) too early experience both short-term and long-term negative effects. A number of studies have found that children who enter kindergarten before the age of five are more likely to do poorly academically and to drop out of high school than children who are older than age five when they enter kindergarten.

In a recent doctoral dissertation, McCarty examined the effects of promoting and not promoting developmentally young… kindergarten children. Her study is one of the few to look at the effects of nonpromotion over a long time interval. McCarty found that after eight years the nonpromoted youngsters were significantly ahead of the promoted in peer acceptance, classroom adjustment, and academic achievement. It is reasonable to assume that the nonpromoted had acquired a healthier sense of industry than the promoted. Again, this is attributable to the exposure of the promoted youngsters to formal instruction before they left the manipulative and fundamental learning mode.

There is growing evidence that while symbolic and derived instruction has positive short-term effects, it may also have negative long-term results. In one of the most carefully conducted experiments of its kind, "The Louisville experiment 1983,” the investigators compared the short- and long-term effects of four different preschool programs. Two programs used didactic (symbolic-derived) curricula, whereas the other two employed age-appropriate (manipulative-fundamental) materials. Although the children who participated in the didactic programs made short-term gains, these were not maintained. When the children were followed up at eighth grade, boys from the age-appropriate programs had a twelve-month advantage in reading and a ten-month advantage in math compared to boys from the didactic programs. The effects were not the same for girls; by eighth grade, girls from the didactic programs performed slightly better in both reading and math than did girls from age-appropriate programs. For boys in particular, then, the immediate gains of didactic programs are apparently purchased at the price of long-term losses.

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