Computer Science

In the 19th century, the term computer referred to people who performed mathematical computations. But mechanical tabulating machines and calculators began to appear in the late 19th century and early 20th century, and in 1946, engineers J. Presper Eckert (1919-95) and John Mauchly (1907-80) built one of the first contemporary electronic computers, known as the Electronic Numerical Integrator and Computer (Eniac). Eniac was an prominent advance but had some disadvantages - it was the size of a room, ran slowly, and often suffered failures in its electrical components. But since the 1940s, computers have evolved into fast and sufficient machines that fill almost every niche in today's society.

Hand Held Credit Card Machine

The expanding role of computers has begun to encroach on tasks that wish enormous plan - at least for a person. For example, in 1997, a computer called Deep Blue defeated Garry Kasparov, the reigning World Chess Champion at the time, in a chess match. Chess-playing computer programs have been routinely defeating novice chess players since the 1970s, but Deep Blue beat one of the best.

No one is sure how much more superior - and maybe keen - computers will come to be in the 21st century. Computer Science, one volume of the multivolume Frontiers of Science set, explores six prominent topics in computer science investigate that address issues about the capacity of computers and their applications.

Although a computer may achieve keen tasks, the doing of most machines today reflects the skill of computer engineers and programmers. None of the applications mentioned above would have been inherent without the efforts of computer engineers who build the machines, and computer programmers who write the programs to supply the important instructions. Most computers today achieve a series of easy steps, and must be told exactly which steps to achieve and in what order. Deep Blue, for example, did not think as a person does, but instead ran a schedule to crusade for the best move, as thought about by complicated formulas. A fast computer such as Deep Blue can zip straight through these instructions so speedily that it is capable of impressive feats of "intelligence."

But some computer scientists are working on production computers smarter - and more like humans. The human brain consists of complicated neural networks that process sensory information, passage prominent features, and solve problems.

Speedy computations are important in many of these operations, and fast computers can find solutions to complicated problems. Deep Blue's program, for instance, churned straight through millions of instructions every second to find the optimal chess move. But sure kinds of problems have remained intractable, even with the fastest computers. Many of these problems, such as factoring integers or looking the shortest distances in sure routes, have prominent practical applications for engineering and science, as well as for computer networks and economics. people can schedule computers to address these problems on a small scale - factoring a small amount such as 20, or looking a route with only three cities to visit - but problems keen larger numbers wish too much time.

An sufficient method to solve these problems, if one is ever found, would have a enormous impact, especially on the Internet. Personal and confidential information, such as prestige card numbers, gets passed from computer to computer every day on the Internet. This facts must be protected by production the facts unreadable to all except the intended recipient. The science of writing and reading secret messages is called cryptology, and many techniques today could be broken - and their secrets exposed.

One of the most prominent human senses is vision. Images supply a wealth of facts that is difficult or cumbersome to put into words. These days, images are often processed in digital form - arrays of numbers that computers can store and process. As computers come to be faster and smarter, people have started using these machines to achieve functions similar to human vision, such as reading.

Searching for patterns is an integral part of many computer appli- cations - for example, looking for clues to crack a secret message, or sifting straight through the features of an image to find a exact object. Biologists have recently amassed a huge quantity of data keen genetics. Patterns in this kind of facts include vital clues about how organisms develop, what traits they have, and how sure diseases arise and progress. Overwhelmed by the sheer size of these data, which is the equivalent of thousands of encyclopedia volumes, biologists have turned to computer science for help.

Computers have made life easier in many ways, relieving people of boring and time-consuming tasks, but computers have also made life more complicated, forcing people to keep up with technological developments.

A fundamental element of investigate in computer science is the computer itself. Despite the efficiency of today's machines, the computer remains a frontier of science. The surmise for this is the same as it was while the early years of computational technology.

In 1790, marshals of the newly formed government of the United States set out on horseback to achieve the prominent mission of counting the country's population. Taking an exact census was important in order to apportion the amount of congressional delegates for each district, as specified by the U.S. Constitution. Agreeing to the U.S. Census Bureau, the census-takers manually compiled a list of 3,929,214 people in less than a year. Officials took someone else census each decade, and by 1880 the people had grown to 50,155,783. But census-takers had reached the breaking point - it took them almost the whole decade to desist tabulating the 1880 census, and the country prolonged to grow at an remarkable rate. Government officials feared that the 1890 census would not be completed before they had to begin the 1900 census.

The explication to this qoute was automation. In response to a competition sponsored by the Bureau of the Census, Herman Hollerith (1860-1929), a young engineer, designed an self-acting "census counting machine." Census personnel collected data - the plural of a Latin word, datum, meaning facts - and encoded the facts in the positions of holes punched in cards. These cards were the same size as dollar bills of the time, meaning that a stack of cards conveniently fit into boxes used by the Treasury Department. When operators inserted the cards into the machine, an electromechanical process automatically tabulated the people figures. Using Hollerith's machines, the 1890 census of 62,979,766 people was counted within a few months, and the statistical tables were completed two years later.

Hollerith formed a company, the Tabulating engine Company, in 1896. The company changed its name in 1924 to International company Machines (Ibm) Corporation. Ibm thrived, and is presently one of the world's largest companies.

Computational machines have also thrived. The need for speed and efficiency - the same needs of the 1890 census - motivated the improvement of computers into the ubiquitous machines they are today. Computers are in homes, offices, cars, and even spacecraft, and people carry movable computers known as notebooks or laptops whenever they travel. Yet the evolution of computers is by no means finished. One of the most active frontiers of computer science is the improvement of faster and more sufficient computers, which may ultimately transform the world as drastically as their predecessors did.

Computer Science
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