Artificial Intelligence

(https://en.wikipedia.org)

Artificial Intelligence (AI) is the intelligence of machines and robots and the branch of computer science that aims to create it. AI textbooks define this field as " the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success. John McCarthy, who coined the term in 1955, defines it as " the science and engineering of making intelligent machines".

AI research is highly technical and specialized, deeply divided into subfields that often fail to communicate with each other. Some of the division is due to social and cultural factors: subfields have grown up around particular institutions and the work of individual researchers. AI research is also divided by several technical issues. There are subfields which are focused on the solution of specific problems, on one of several possible approaches, on the use of widely differing tools and towards the accomplishment of particular applications. The central problems of AI include such traits as reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects.

General intelligence (or " strong AI") is still among the field's long term goals. Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. There are an enormous number of tools used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics, and many others.

Early AI researchers developed algorithms that imitated the step-by-step reasoning that humans use when they solve puzzles or make logical deductions. By the late 1980s and '90s, AI research had also developed highly successful methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.

For difficult problems, most of these algorithms can require enormous computational resources – most experience a " combinatorial explosion": the amount of memory or computer time becomes astronomical when the problem goes beyond a certain size. The search for more efficient problem-solving algorithms is a high priority for AI research.

Human beings solve most of their problems using fast, intuitive judgements rather than the conscious, step-by-step deduction that early AI research was able to model. AI has made some progress at imitating this kind of " sub-symbolic" problem solving: embodied agent approaches emphasize the importance of sensorimotor skills to higher reasoning; neural net research attempts to simulate the structures inside the brain that give rise to this skill; statistical approaches to AI mimic the probabilistic nature of the human ability to guess.

Knowledge representation and knowledge engineering are central to AI research. Many of the problems will require extensive knowledge about the world. Among the things that AI needs to represent are: objects, properties, categories and relations between objects; situations, events, states and time; causes and effects; knowledge about knowledge (what we know about what other people know); and many other, less well researched domains.

Intelligent agents must be able to set goals and achieve them. They need a way to visualize the future (they must have a representation of the state of the world and be able to make predictions about how their actions will change it) and be able to make choices that maximize the utility (or " value") of the available choices.

Слова для сдачи наизусть:

1. artif i cial int e lligence - искусственный интеллект

2. the branch of computer science - отрасль информатики / теории вычислительных систем

3. to perc ei ve [pə `si: v] - чувствовать; воспринимать

4. to coin the term - создать термин

5. to divide into subfields - делить / подразделять на отдельные области

6. technical i ssue - технический вопрос

7. widely d i ffering tools инструменты, сильно отличающиеся друг от друга

8. acc o mplishment of application - реализация практического применения

9. reasoning - аргументация; объяснение; мотивировка

10. long term goal - долгосрочная цель

11. comput a tional int e lligence - вычислительный интеллект

12. probab i lity - вероятность

13. to make l o gical ded u ctions - делать логические выводы

14. to require - требовать

15. combinat o rial expl o sion - комбинаторный взрыв, лавинообразное увеличение

затрат машинного времени при незначительном усложнении задачи

16. intuitive judgement - интуитивная оценка

17. sensorimotor skills - сенсорно-двигательные способности

18. neural net - нейронная компьютерная сеть

19. ext e nsive knowledge - обширные знания

20. causes and effects - причины и последствия

21. domain - область; сфера

22. to be able to set goals - быть способным ставить цели

23. to achieve goals - достигать цели

24. the state of the world - состояние мира / вселенной

25. to make predictions - делать прогнозы; предсказывать

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