V. Answer the questions.

1. How did the Internet begin?

2. What were two main goals for the initial project?

3. What software was developed in Europe in 1991 by a programmer Tim Berners-Lee?

4. What links are called hyperlinks and what do they allow you to do?

5. How is the collection of hyperlinked documents accessible on the Internet called?

6. How are Internet locations that contain hyperlinked documents called?

7. What is Web browser software?

8. How can you use a Web browser to view pages on the WWW?

9. By what program is the Internet connection established?

10. How is the Web page designated?

11. What is one of the original features of the Internet?

12. How do you to receive e-mail on the Internet?

13. What is an Internet mailbox address?

14. What do we call a user-ID?

Topic for Discussion.

Consider some familiar technological developments such as the automobile, airplane, and the Internet. List the effects of each development on society. Which effects are harmful, which are beneficial, and which are controversial – considered beneficial by some and harmful by others?

Artificial Intelligence

Artificial intelligence (AI) is a broad field, and means different things to different people. It is concerned with getting computers to do tasks that require human intelligence. However, there are many tasks, which computers can do very easily. Conversely, there are many tasks that people do without even thinking - such as recognising a face - which are extremely complex to automate. AI is concerned with these difficult tasks, which seem to require complex and sophisticated reasoning processes and knowledge.

People might want to automate human intelligence for a number of different reasons. One reason is simply to understand human intelligence better. For example, we may be able to test and refine psychological and linguistic theories by writing programs, which attempt to simulate aspects of human behaviour. Another reason is simply so that we have smarter programs. We may not care if the programs accurately simulate human reasoning, but by studying human reasoning, we may develop useful techniques for solving difficult problems.

AI is a field that overlaps with computer science rather than being a strict subfield. Different areas of AI are more closely related to psychology, philosophy, logic, linguistics, and even neurophysiology.

Artificial intelligence research makes the assumption that human intelligence can be reduced to the manipulation of symbols, and that it does not matter what medium is used to manipulate these symbols - it does not have to be a biological brain! This assumption does not go unchallenged among philosophers etc. Some argue that true intelligence can never be achieved by a computer, but requires some human property, which cannot be simulated. There are endless philosophical debates on this issue, brought recently to public attention again in Penrose’s book.

The most well known contributions to the philosophical debate are Turing’s “Turing test” paper, and Searle’s “Chinese room”. The best way to gauge the intelligence of a machine is British computer scientist Alan Turing's test. He stated that a computer would deserve to be called intelligent if it could deceive a human into believing that it was human. Very roughly, Turing considered how you would be able to conclude that a machine was intelligent. He argued that the only reasonable way was to do a test. The test involves a human communicating with a human and with a computer in other rooms, using a computer for the communication. The first human can ask the other human/computer any questions they like, including very subjective questions like “What do you think of this Poem”. If the computer answers so well that the first human cannot tell which of the two others is human, then we say that the computer is intelligent.

Searle argued that just behaving intelligently was not enough. He tried to demonstrate this by suggesting a thought experiment (the “Chinese room”). Imagine that you do not speak any Chinese, but that you have a huge rulebook, which allows you to look up Chinese sentences, and tells you how to reply to them in Chinese. You do not understand Chinese, but can behave in an apparently intelligent way. He claimed that computers, even if they appeared intelligent, would not really be, as they would be just using something like the rulebook of the Chinese room. Many people go further than Searle, and claim that computers will never even be able to appear to be really intelligent (so will never pass the Turing test). There are therefore a number of positions that you might adopt:

- Computers will never even appear to be really intelligent, though they might do a few useful tasks that conventionally require intelligence.

- Computers may eventually appear to be intelligent, but in fact they will just be simulating intelligent behaviour, and not really be intelligent.

- Computers will eventually be really intelligent.

- Computers will not only be intelligent, they will be conscious and have emotions.

Though computers can clearly behave intelligently in performing certain limited tasks, full intelligence is a very long way off. However, these philosophical issues rarely impinge on AI practice and research. It is clear that AI techniques can be used to produce useful programs that conventionally require human intelligence, and that this work helps us understand the nature of our own intelligence.

Cognitive Science. This area of AI is based on research in biology, neurology, psychology mathematics and many allied disciplines. It focuses on researching how the human brain works and how human can think and learn. The results of such research in human information processing are the basis for the development of a variety of computer-based applications in AI. Applications in the cognitive science area of AI include the development of expert systems and other knowledge-based systems that add a knowledge base and some reasoning capability to information systems. Also included are adaptive learning systems that can modify their behaviors based on information they acquire as they operate. Chess-playing systems are primitive examples of such applications, though many more applications are being implemented. Fuzzy logic systems can process data that are incomplete or ambiguous, that is, fuzzy data. Neural network software can learn by processing sample problems and their solutions. As neural nets start to recognize patterns, they can begin to program themselves to solve such problems on their own. Genetic algorithm software uses Darvinian (survival of the fittest), randomizing and other mathematical functions to simulate evolutionary process that can generate increasingly better solutions to problems. And intelligent agents use expert systems and other AI technologies to serve as software surrogates for a variety of end user applications.

Comments:

assumption припущення, допущення

conscious обізнаний, свідомий

to impinge on приходити у зіткнення

Cognitive Science пізнавальна наука

fuzzy logic нечітка логіка//концепція часткової правди.

дозволяє уникнути однозначності відповіді на

запитання. Часто застосовують в експертних

і самонавчальних системах, системах керування

пристроями й технологічними процесами, а

також у системах розпізнавання образів

neural network нейронна мережа

expert systems експертна система//система, яка використовує

базу знань (правил) для вирішення завдань у

певній предметній області