Научная электронная библиотека
Монографии, изданные в издательстве Российской Академии Естествознания
Unit 7. ARTIFICIAL INTELLIGENCE
WARM UP
What is artificial intelligence?
1. Discuss your answers in small groups and then check your answers. Below are the definitions of artificial intelligence.
American Heritage Dictionary:
artificial intelligence
n. (Abbr. AI)
1. The ability of a computer or other machine to perform those activities that are normally thought to require intelligence.
2. The branch of computer science concerned with the development of machines having this ability.
Britannica Concise Encyclopedia:
artificial intelligence
Ability of a machine to perform tasks thought to require human intelligence. Typical applications include game playing, language translation, expert systems, and robotics. Although pseudo-intelligent machinery dates back to antiquity, the first glimmerings of true intelligence awaited the development of digital computers in the 1940s. AI, or at least the semblance of intelligence, has developed in parallel with computer processing power, which appears to be the main limiting factor. Early AI projects, such as playing chess and solving mathematical problems, are now seen as trivial compared to visual pattern recognition, complex decision making, and the use of natural language
SPEAKING ACTIVITIES
2. Skim the text from McGraw-Hill Science & Technology Encyclopedia about artificial intelligence and answer the following questions.
1. What activities involve intelligent action?
2. What is artificial intelligence primarily concerned with?
3. What are the foundations of artificial intelligence?
4. Give some examples of artificial intelligence.
5. What are expert systems?
ARTIFICIAL INTELLIGENCE
The subfield of computer science is concerned with understanding the nature of intelligence and constructing computer systems capable of intelligent action. It embodies the dual motives of furthering basic scientific understanding and making computers more sophisticated in the service of humanity.
Many activities involve intelligent action – problem solving, perception, learning, planning and other symbolic reasoning, creativity, language, and so forth – and therein lie an immense diversity of phenomena. Scientific concern for these phenomena is shared by many fields, for example, psychology, linguistics, and philosophy of mind, in addition to artificial intelligence.
The starting point for artificial intelligence is the capability of the computer to manipulate symbolic expressions that can represent all manner of things, including knowledge about the structure and function of objects and people in the world, beliefs and purposes, scientific theories, and the programs of action of the computer itself.
Artificial intelligence is primarily concerned with symbolic representations of knowledge and heuristic methods of reasoning, that is, using common assumptions and rules of thumb. Two examples of problems studied in artificial intelligence are planning how a robot, or person, might assemble a complicated device, or move from one place to another; and diagnosing the nature of a person’s disease, or of a machine’s malfunction, from the observable manifestations of the problem. In both cases, reasoning with symbolic descriptions predominates over
calculating.
The foundations of artificial intelligence are divided into representation, problem-solving methods, architecture, and knowledge. To work on a task, a computer must have an internal representation in its memory, for example, the symbolic description of a room for a moving robot, or a set of features describing a person with a disease. The representation also includes all the knowledge, including basic programs, for testing and measuring the structure, plus all the programs for transforming the structure into another one in ways appropriate to the task. Changing the representation used for a task can make an immense difference, turning a problem from impossible to trivial.
Given the representation of a task, a method must be adopted that has some chance of accomplishing the task. Artificial intelligence has gradually built up a stock of relevant problem-solving methods (the so-called weak methods) that apply extremely generally.
An important feature of all the weak methods is that they involve search. One of the most important generalizations to arise in artificial intelligence is the ubiquity of search. It appears to underlie all intelligent action. In the worst case, the search is blind. In heuristic search extra information is used to guide the search.
Some of the weak methods are generate-and-test (a sequence of candidates is generated, each being tested for solution hood); hill climbing (a measure of progress is used to guide each step); means-ends analysis (the difference between the desired situation and the present one is used to select the next step); impasse resolution (the inability to take the desired next step leads to a subgoal of making the step feasible); planning by abstraction (the task is simplified, solved, and the solution used as a guide); and matching (the present situation is represented as a schema to be mapped into the desired situation by putting the two in correspondence).
In artificial intelligence, the basic paradigm of intelligent action is that of search through a space of partial solutions (called the problem space) for a goal situation. Each step offers several possibilities, leading to a cascading of possibilities that can be represented as a branching tree. The search is thus said to be combinatorial or exponential. For example, if there are 10 possible actions in any situation, and it takes a sequence of 12 steps to find a solution (a goal state), then there are 1012 possible sequences in the exhaustive search tree. What keeps the search under control is knowledge, which suggests how to choose or narrow the options at each step. Thus the fourth fundamental concern is how to represent knowledge in the memory of the system so it can be brought to bear on the search when relevant.
An intelligent agent will have immense amounts of knowledge. This implies another major problem that of discovering the relevant knowledge as the solution attempt progresses. Although this search does not include the combinatorial explosion characteristic of searching the problem space, it can be time consuming and hard. However, the structure of the database holding the knowledge (called the knowledge base) can be carefully tailored to suit the architecture in order to make the search efficient. This knowledge base, with its accompanying problems of encoding and access, constitutes the final ingredient of an intelligent system.
An example of artificial intelligence is computer perception. Perception is the formation, from a sensory signal, of an internal representation suitable for intelligent processing. Though there are many types of sensory signals, computer perception has focused on vision and speech. Perception might seem to be distinct from intelligence, since it involves incident time-varying continuous energy distributions prior to interpretation in symbolic terms. However, all the same ingredients occur: representation, search, architecture, and knowledge.
A class of artificial intelligence programs called expert systems attempt to accomplish tasks by acquiring and incorporating the same knowledge that human experts have. Many attempts to apply artificial intelligence to medicine, government, and other socially significant tasks take the form of expert systems. Even though the emphasis is on knowledge, all the standard ingredients are present.
As computers become smaller and less expensive, more and more intelligence is built into automobiles, appliances, and other machines, as well as computer software, in everyday use.
3. Now scan the text and find the following:
• the starting point for artificial intelligence;
• two examples of problems studied in artificial intelligence;
• an important feature of all the weak methods;
• some of the weak methods;
• what computer perception is;
• one important lesson learned from incorporating artificial intelligence software into ongoing practice.
4. Finish the sentences from the text and translate them.
1. The subfield of computer science concerned with …
2. Scientific concern for these phenomena is shared by many fields, for example, …
3. Artificial intelligence is primarily concerned with …
4. The foundations of artificial intelligence are divided into …
5. In artificial intelligence, the basic paradigm of intelligent action is …
6. An example of artificial intelligence is …
5. Comment on the following statement about artificial intelligence. Agree or disagree with the statement. Give your reasons to support your opinion.
As computers become smaller and less expensive, more and more intelligence is built into automobiles, appliances, and other machines, as well as computer software, in everyday use.
6. Learn more about artificial intelligence. Read the news from Science Daily magazine and answer the following questions.
Suggested online resource:
http://www.sciencedaily.com/news/computers_math/artificial_intelligence/
1. What are Kilobots? What are their characteristics?
2. Where were they created?
3. What is the key to achieve high-value applications for multi-robot systems in the future?
4. What can Kilobots do?
Kilobots Are Leaving the Nest
www.sciencedaily.com/releases/2011/11/111122112020.htm
ScienceDaily (Nov. 22, 2011) – The Kilobots are coming. Computer scientists and engineers at Harvard University have developed and licensed technology that will make it easy to test collective algorithms on hundreds, or even thousands, of tiny robots.
1. What are the basic features of Mask-bot?
2. Where was it designed?
3. What was a talking head animation engine developed for?
4. Can Mask-bot understand much of the spoken word?
5. What is the cost of the first prototype and for the successor model?
Mask-Bot: A Robot With a Human Face
www.sciencedaily.com/releases/2011/11/111107161758.htm
ScienceDaily (Nov. 7, 2011) – Robotics researchers in Munich have joined forces with Japanese scientists to develop an ingenious technical solution that gives robots a human face. By using a projector to beam the 3D image of a face onto the back of a plastic mask, and a computer to control voice and facial expressions, the researchers have succeeded in creating Mask-bot, a startlingly human-like plastic head. Yet even before this technology is used to give robots of the future a human face, it may well soon be used to create avatars for participants in video conferences.
7. Make a presentation «Robots Application in Modern Life».
LISTENING ACTIVITIES
Can a Robot be Your Friend?
MIT researchers have created some of the most emotionally engaging robots in the world. Watch this video about friendly robots. Can a Robot be Your Friend?
Suggested online resource:
http://videos.howstuffworks.com/wgbh-nova/2351-can-a-robot-be-your-friend-video.htm