Просмтор этого файла доступен только зарегистрированным пользователям. Но у нас супер быстрая регистрация: достаточно только электронной почты!

Текст из файла (страница 2)

The First Generation of Computers

The first generation of computers prevailed in the 1940s and in the early 1950s. They used vacuum tubes for calculation and control. Vacuum tubes were bulky, unreliable, energy consuming and generated large amounts of heat. As long as computers were tied down to vacuum tube technology, they could only be cumbersome and expensive. First generation computers used binary notation to represent numbers.

The Second Generation of Computers

In the late 1950s the transistors became available. They replaced vacuum tubes. Transistors were ready to work quickly and far fewer failures. They provided much more power than vacuum tubes did, generated little heat and draw a very little amount of electricity.

At about the same time the magnetic-core memory was introduced. This consisted of a latticework of wires on which were strung tiny rings, called cores. Electric current flowing in the wires stored information by magnetizing the cores. Information could be stored in core memory or retrieved from it in about a millionth of a second.

Transistors and relatively low-cost magnetic core memory made it possible to build smaller, more powerful computers. At the same time faster, more efficient input devices (like card readers) and output devices (like printers) were developed.

The Third Generation of Computers

The early 1960s saw the introduction of integrated circuits which incorporated hundreds of transistors on a single silicon chip. When the chip arrived, it reduced even further the size of computers while increasing their speed.

Another development that changed the way people used computers was time-sharing, a feature of some IBM 360 models. Time-sharing made it possible for several people to use computer resources simultaneously. A time-sharing computer allows many users, each working at a separate input/output terminal, to use it at the same time.

Because the design of third-generation computers was so different from that of second-generation computers, most of the second generation software was incompatible with the new machines. Much software had to be rewritten and many programmers had to be retrained.

The Forth Generation of Computers

The next jump in computer technology was the introduction of large-scale integrated circuits. Whereas the older integrated circuits contained hundreds of transistors, the new ones contained thousands or ten thousands of transistors on a single silicon chip. It was a large-scale integrated circuit that made possible the microprocessor and microcomputer. They also made possible compact, inexpensive, high-speed, high-capacity integrated-circuit memory.

The Fifth Generation of Computers

Fifth generation computers aim to be able to solve highly complex problems, ones which require reasoning, intelligence and expertise when solved by people. They are intended to be able to cope with large subsets of natural languages, and draw on very large knowledge bases. In spite of their complexity, fifth generation computers are being designed to be used by people who are not necessarily computer experts.

In order to achieve these very ambitious aims, fifth generation computers will not have a single processor or a small number of tightly coupled processors as computers do today. They are being designed to contain a large number of processors, grouped into three major subsystems: a knowledge base system, an inference mechanism and an intelligent user interface.

The knowledge base system has a very large store of knowledge, with a set of processors which access and update the knowledge. It is likely that knowledge bases will evolve from current work in relational databases. Operations on knowledge bases require the manipulation of large numbers of individual elements: this manipulation will be done in parallel by the arrays of knowledge processing elements.

The intelligent user interface is the point of contact between a fifth generation computer and its user. Many of these will be based on communication in a large subset of a natural language. Others will make extensive use of advanced graphics, including image processing. The intention is to build a user interface which is close to the natural way of thinking of the user, rather than close to the way of working of the computer, as in the case with contemporary user interfaces. The intelligent interface will contain its own set of processing elements – image processing systems may have an array of processors, one per pixel of the display.

Ex. 1. Answer the following questions:

1. What typical characteristics did the first generation computers have?

2. What were the drawbacks of vacuum tubes?

3. What innovation did the second generation of computers bring?

4. What are the advantages of transistors over vacuum tubes?

5. What electronic advancement moved computers into the third generation?

6. What does time-sharing mean?

7. What became possible with the advent of large-scale integrated circuits?

8. What is the aim of fifth generation computers?

9. What are the major subsystems within the processors?

10. In what way will the new computers deal with natural languages and knowledge bases?

Ex. 2. Give the main ideas of the text in logical order.

Ex. 3. Translate in writing:

Первая ЭВМ в Европе

В 1947 году в Киеве небольшая группа ученых под руководством академика Сергея Алексеевича Лебедева начала работать над созданием первой в мире электронно-вычислительной машины.

Невозможно представить себе трудности, с которыми столкнулись ученые в процессе работы. Все нужно было начинать с нуля: не было опыта подобных работ, негде было прочитать или узнать о них. Дело в том, что уже работавшая в то время в США вычислительная машина ЭНИАК применялась в военных целях и могла решать только одну задачу – задачу встречи летящего снаряда (ракеты) и движущегося корабля.

ЭВМ, созданная академиком С. А. Лебедевым, была способна решать не только военные, но и мирные задачи и почти не отличались от современных ЭВМ. Эта была очень сложная машина. Подумать только! В этой ЭВМ работало около 6000 электронных ламп. Ее начали выпускать в 1951 году и назвали МЭСМ – малая элетронно-счетная машина.

Ex. 4. Topics for discussion.

1. The evolution of computers in terms of generations.

2. Describe the technological features of each computer generation.

3. What technological developments made microcomputers possible.

UNIT 3

Algorithms

Key vocabulary

Eliminate v.– устранять, исключать,

уничтожать

Execute v.– выполнять

Furthermore adv.– кроме того, более того

Concept n.–понятие, идея, концепция

Appreciate v.–ценить, оценивать

(по достоинству)

Equation n.– уравнение, равенство

Apply v.– применять, употреблять

Employ v.– использовать, применять

Procedure n.– процедура

Property n.– свойство

Notion n.– понятие, представление,

идея

Experience n.– опыт

Solvability n.– разрешимость

Decision-making – принятие решения

Rapid adj.– быстрый

Represent v.– представлять

Representation n.– представление,

утверждение

Prove v.– доказать, доказывать

Knowledge n.– знание, познание,

эрудиция

Rigorous adj.– строгий, точный

Hitherto adv.– до сих пор, до

настоящего момента

Twenty or more years ago the word “algorithm” was unknown to most educated people; indeed, it was scarcely necessary. The rapid rise of computer science, which has the study of algorithms as its focal point has changed all that; the word is now essential. There are some other words that almost, but not quite, capture the concept that is needed: procedure, recipe, process, routine, method, rigmarole. Like these things an algorithm is a set of rules or directions (instructions) for getting a specific output from a specific input. The distinguishing feature of an algorithm is that all vagueness must be eliminated; the rules must describe operations that are so simple and well-defined that they can be executed by a machine. Furthermore, an algorithm must always terminate after a finite number of steps.

A computer programme is the statement of an algorithm in some well-defined language, although the algorithm itself is a mental concept that exists independently of any representation. Anyone who has prepared a computer programme will appreciate the fact that an algorithm must be very precisely defined, with attention to detail that is unusual in comparison with other things people do. Programmes for numerical problems were written as early as 1800 B.C. when Babylonian mathematicians gave rules for solving many types of equations. The rules were as step-by-step procedures applied systematically to particular numerical examples. The word “algorithm” itself originated in the Middle East, although at a much later time. Curiously enough it comes from the Latin version of the last name of the Persian scholar Abu Jafar Mohammed ibn Musa al-Khowaresmi (Algorithmi) whose textbook on arithmetic ( с. 825 A.D.) employed for the first time Hindu positional decimal notation and gave birth to algebra as an independent branch of mathematics. It was translated into Latin in the 12^th century and had a great influence for many centuries on the development of computing procedures. The name of the textbook’s author became associated with computations in general and used as a term “algorithm”.

Originally algorithms were concerned solely with numerical calculations; Euclid’s algorithm for finding the greatest common divisor of two numbers is the best illustration. There are many properties of Euclid’s powerful algorithm which has become a basic tool in modern algebra and number theory. Nowadays the concept of an algorithm is one of the most fundamental notions not only in mathematics but in science and engineering. Experience with computers has shown that the data manipulated by programmers can represent virtually anything. In all branches of mathematics the task to prove the solvability or unsolvability of any problem requires a precise algorithm. In computer science the emphasis has now shifted to the study of various structures by which information can be represented and to the branching or decision-making aspects of algorithms, which allow them to fall on one or another sequence of operations depending on the state of affairs at the time. It is precisely these features of algorithms that sometimes make algorithmic models more suitable than traditional mathematical models for the representation and organization of knowledge.

The concept of algorithms is perhaps almost as old as human civilization. The famous Euclid’s algorithm is more than 2000 years old. Angle trisection, solving diophantine equations are some well known examples of algorithmic questions. However until the 1930s the notion of algorithms was used informally (or rigorously but in a limited context).

A machine model proposed by Turing in 1936 is of particular interest. It has come to be known as a Turing machine.

Turing machines are basic abstract symbol-manipulating devices which, despite their simplicity, can be adapted to simulate the logic of any computer algorithm. Turing machines are not intended as a practical computing technology, but a thought experiment about the limits of mechanical computation. Thus, they were not actually constructed. Studying their abstract properties yields many insights into computer science and complexity theory.

A Turing machine that is able to simulate any other Turing machine is called a Universal Turing Machine (UTM, or simply universal machine). A more mathematically-oriented definition with a similar “universal” nature was introduced by Alonzo Church, whose work on lambda calculus intertwined with Turing’s in a formal theory of computation, is known as the Church-Turing thesis. The thesis states that Turing machines indeed capture the informal notion of effective method in logic and mathematics, and provide a precise definition of an algorithm or ‘mechanical procedure’.

This particular achievement has had numerous significant consequences. It is widely acknowledged that the development of a universal Turing machine was prophetic of the modern all-purpose digital computer and played a key role in the thinking of pioneers in the development of modern computers.

A more interesting consequence was that it was now possible to show the nonexistence of algorithms, hitherto impossible due to their elusive nature. In addition, many apparently different definitions of an algorithm proposed by different researchers in different continents turned out to be equivalent. This equivalence led to the widely held hypothesis known as the Church-Turing thesis that mechanical solvability is the same as solvability on a Turing machine.

Turing’s notion of mechanical computation was based on identifying the basic steps of such computations. He reasoned that an operation such as multiplication is not primitive because it can be divided into more basic steps such as digit-by-digit multiplication, shifting, and adding. Addition itself can be expressed in terms of more basic steps such as add the lowest digits, compute, carry, and move to the next digit, etc. Turing thus reasoned that the most basic features of mechanical computation are the abilities to read and write on a storage medium (which he chose to be a linear tape divided into cells or squares) and to make some simple logical decisions. He also restricted each type cell to hold only one among a finite number of symbols (which we call the tape alphabet).

The decision step enables the computer to control the sequence of actions. To make things simple, Turing restricted the next action to be performed on a cell neighboring the one on which the current action occurred. He also introduced an instruction that told the computer to stop. To sum up, Turing proposed a model to characterize mechanical computation as being carried out as a sequence of instructions of the form: write a symbol (such as 0 or 1) on the tape cell, move to the next cell, observe the symbol currently scanned and choose the next step accordingly, or stop.

Характеристики

Список файлов книги