Give the summary of the text using the key terms. Topics for essays (you might need additional information):

 

Topics for essays (you might need additional information):

 

· History and development of computer graphics

· 3D modeling

· Computer animation

 

ARTIFICIAL INTELLIGENCE: OVERVIEW

DEFINITIONS

 

Read the following words and word combinations and use them for understanding and translation of the text:

 

to impart - наделять

attribute - определение

highest good – высшее благо

offhand – импровизированный, сделанный на скорую руку

enclosed surface – замкнутое пространство

behavior pattern – модель поведения

observable – наблюдаемый

insufficient definition – неполное определение

tersely and concisely – сжато и кратко

to lose relevance – терять актуальность

human reasoning – мышление человека, человеческое мыш-ление

to adjust – приспосабливаться

productive approach - плодотворный подход

to a limited extent – в определенных пределах

chatterbot – чатбот, программа «виртуальный собеседник»

 

The term artificial intelligence stirs emotions. For one thing there is our fascination with intelligence, which seemingly imparts to us humans a special place among life forms. Questions arise such as “What is intelligence?”, “How can one measure intelligence?” or “How does the brain work?” All these questions are meaningful when trying to understand artificial intelligence. However, the central question for the engineer, especially for the computer scientist, is the question of the intelligent machine that behaves like a person, showing intelligent behavior. The attribute artificial might awaken much different associations. It brings up fears of intelligent cyborgs. It recalls images from science fiction novels. It raises the question of whether our highest good, the soul, is something we should try to understand, model, or even reconstruct. With such different offhand interpretations, it becomes difficult to define the term artificial intelligence or AI simply and robustly.

In 1955, John McCarthy, one of the pioneers of AI, was the first to define the term artificial intelligence, roughly as follows: The goal of AI is to develop machines that behave as though they were intelligent.

To test this definition, imagine the following scenario. Fifteen or so small robotic vehicles are moving on an enclosed square surface. One can observe various behavior patterns. Some vehicles form small groups with relatively little movement. Others move peacefully through the space and gracefully avoid any collision. Still others appear to follow a leader. Aggressive behaviors are also observable. Is what we are seeing intelligent behavior? According to McCarthy’s definition these robots can be described as intelligent, thus it is clear that this definition is insufficient.

In the Encyclopedia Britannica one finds a definition that goes like: AI is the ability of digital computers or computer controlled robots to solve problems that are normally associated with the higher intellectual processing capabilities of humans... But this definition also has weaknesses. It would admit, for example, that a computer that can save a long text and retrieve it on demand displays intelligent capabilities, for memorization of long texts can certainly be considered a higher intellectual processing capability of humans, as can, for example, the quick multiplication of two 20-digit numbers. According to this definition, then, every computer is an AI system. This dilemma is solved elegantly by the following definition by Elaine Rich: Artificial Intelligence is the study of how to make computers do things at which, at the moment, people are better.

Rich, tersely and concisely, characterizes what AI researchers have been doing for the last 50 years. Even in the year 2050, this definition will be up to date.

Tasks such as the execution of many computations in a short amount of time are the strong points of digital computers. In this regard they outperform humans by many multiples. In many other areas, however, humans are far superior to machines. For instance, a person entering an unfamiliar room will recognize the surroundings within fractions of a second and, if necessary, just as swiftly make decisions and plan actions. To date, this task is too demanding for autonomous robots. According to Rich’s definition, this is, therefore, a task for AI. In fact, research on autonomous robots is an important, current theme in AI. Construction of chess computers, on the other hand, has lost relevance because they already play at or above the level of grandmasters.

It would be dangerous, however, to conclude from Rich’s definition that AI is only concerned with the pragmatic implementation of intelligent processes. Intelligent systems, in the sense of Rich’s definition, cannot be built without a deep understanding of human reasoning and intelligent action in general, because of which neuroscience is of great importance to AI. This also shows that the other cited definitions reflect important aspects of AI. A particular strength of human intelligence is adaptivity. We are capable of adjusting to various environmental conditions and change our behavior accordingly through learning. Precisely because our learning ability is so vastly superior to that of computers, machine learning is, according to Rich’s definition, a central subfield of AI.

In 1950, computer pioneer Alan M. Turing suggested a productive approach to evaluating claims of artificial intelligence in what became known as the Turing test. He gave a definition of an intelligent machine, in which the machine in question must pass the following test. The test person Alice sits in a locked room with two computer terminals. One terminal is connected to a machine, the other with a non-malicious person Bob. Alice can type questions into both terminals. She is given the task of deciding, after five minutes, which terminal belongs to the machine. The machine passes the test if it can trick Alice at least 30% of the time.

Computer programs have been able to pass the Turing test to a limited extent. The AI pioneer and social critic JosephWeizenbaum developed a program named Eliza, which is meant to answer a test subject’s questions like a human psychologist. He was in fact able to demonstrate success in many cases. Supposedly his secretary often had long discussions with the program. Today in the internet there are many so-called chatterbots, some of whose initial responses are quite impressive. After a certain amount of time, however, their artificial nature becomes apparent.

 

 

Notes:

John McCarthy (1927 - 2011) was a legendary computer scientist at Stanford University who developed time-sharing, invented LISP, and founded the field of Artificial Intelligence.

Elaine Rich works as Distinguished Senior Lecturer at the University of Texas at Austin. Books: Automata, Computability and Complexity: Theory and Applications (author), Artificial Intelligence (co-author).

Joseph Weizenbaum (1923 - 2008) was a German-American computer scientist who is famous for his development of the Eliza program in 1966 and for his views on the ethics of artificial intelligence. He became sceptical of artificial intelligence and a leading critic of the AI field following the response of users to the Eliza program.

 

 

Assignments

 

1. Translate the sentences from the texts into Russian in writing paying attention to the underlined words and phrases:

 

1. The term artificial intelligence stirs emotions. For one thing there is our fascination with intelligence, which seemingly imparts to us humans a special place among life forms.

2. With such different offhand interpretations, it becomes difficult to define the term artificial intelligence or AI simply and robustly.

3. AI is the ability of digital computers or computer controlled robots to solve problems that are normally associated with the higher intellectual processing capabilities of humans.

4. In this regard they outperform humans by many multiples. In many other areas, however, humans are far superior to machines.

5. It would be dangerous, however, to conclude from Rich’s definition that AI is only concerned with the pragmatic implementation of intelligent processes.

 

2. Answer the following questions:

 

1. What is the key AI problem to be addressed by computer scientists?

2. Why is McCarthy’s definition called “insufficient”?

3. What is wrong with the definition of AI in the Encyclopedia Britannica?

4. Where do machines outperform humans? Where do people win?

5. What is the essence of the Turing test?

 

3. Translate into English:

 

Эрик Браун, 45-летний исследователь из IBM, отвечает за мозг суперкомпьютера Ватсон, который в 2011 г. получил известность победами над людьми в популярной телевик­торине. Самая большая трудность для Брауна, как настав­ника машины, не в том, чтобы впихнуть в Ватсона как можно больше знаний, но в том, чтобы придать тонкость его пониманию языка. Например, научить слэнгу.

Как проверить, может ли компьютер «мыслить»? Клас­сический тест — так называемый тест Тьюринга — прост: он предполагает способность вести светскую беседу. Если бы компьютер сумел бы не выдать свою двоичную сущ­ность в непринужденном разговоре, он бы доказал свое ин­теллектуальное превосходство. Но пока ни одной машине это не удалось.

Два года назад Браун попытался натаскать Ватсона с помощью популярного веб-сайта Urban Dictionary. Словар­ные статьи на сайте составляются обычными пользовате­лями и редактируются добровольными редакторами по достаточно произвольным правилам. Тут есть всевозмож­ные актуальные аббревиатуры как bb (англ. bye bye) — пока, hf (англ. have fun) — отлично повеселиться, w8 (англ. wait) — жди. В том числе и огромное количество всяких слэнговых конструкций, таких, как hot mess — «горячая штучка».

Но Ватсон не мог различить салонную лексику и слэн­говую — которой в Urban Dictionary хватает. Кроме того, из-за чтения Википедии Ватсон приобрел некоторые дурные привычки. В ответах на вопросы исследователя в тестах он использовал малоцензурные словечки.

В конечном счете команда Брауна разработала фильтр, чтобы отцеживать брань Ватсона, и выскребла Urban Dictionary из его памяти. Это испытание доказывает, на­сколько тернист будет путь любого железного интеллек­туала к “лёгкой болтовне”. Теперь Браун подготавливает Ватсона к использованию в качестве диагностического ин­струмента в больнице: там знание всяких модных аббре­виатур не потребуется.