Definitions of 'Artificial Intelligence'

    
Contents:
A. Definition of “Artificial Intelligence”
B. Foundations of “Artificial Intelligence”

A. Definition of “Artificial Intelligence”

Different people in the history of AI have tried to provide definitions for it and these definitions can organized into four categories:

1.1. Thinking Humanly

“The exciting new effort to make computers think... machines with minds, in the full and literal sense.” (Haugeland, 1985) 

“The automation of activities that we associate with human thinking, activities such as decision-making, problem solving, learning...” (Bellman, 1978)

1.2. Thinking Humanly [1]

Thinking humanly means trying to understand and model how the human mind works.
There are (at least) two possible routes that humans use to find the answer to a question:
1.2.A. We reason about it to find the answer. This is called “introspection”.
1.2.B. We conduct experiments to find the answer, drawing upon scientific techniques to conduct controlled experiments and measure change.
The field of ‘Cognitive Science’ focuses on modeling how people think.

1.3. Thinking humanly: The cognitive modeling approach [2]

If we are going to say that a given program thinks like a human, we must have some way of determining how humans think. We need to get inside the actual workings of human minds. There are three ways to do this: 

a. through introspection—trying to catch our own thoughts as they go by; 
b. through psychological experiments—observing a person in action; and 
c. through brain imaging—observing the brain in action. 

Once we have a sufficiently precise theory of the mind, it becomes possible to express the theory as a computer program. If the program’s input–output behavior matches corresponding human behavior, that is evidence that some of the program’s mechanisms could also be operating in humans. For example, Allen Newell and Herbert Simon, who developed GPS, the “General Problem Solver” (Newell and Simon, 1961), were not content merely to have their program solve problems correctly. They were more concerned with comparing the trace of its reasoning steps to traces of human subjects solving the same problems. The interdisciplinary field of cognitive science brings together computer models from AI and experimental techniques from psychology to construct precise and testable theories of the human mind.
Cognitive science is a fascinating field in itself, worthy of several textbooks and at least one encyclopedia (Wilson and Keil, 1999). We will occasionally comment on similarities or differences between AI techniques and human cognition. Real cognitive science, however, is necessarily based on experimental investigation of actual humans or animals. We will leave that for other books, as we assume the reader has only a computer for experimentation. 
In the early days of AI there was often confusion between the approaches: an author would argue that an algorithm performs well on a task and that it is therefore a good model of human performance, or vice versa. Modern authors separate the two kinds of claims; this distinction has allowed both AI and cognitive science to develop more rapidly. The two fields continue to fertilize each other, most notably in computer vision, which incorporates neurophysiological evidence into computational models.

2. Thinking Rationally

2.a. The study of mental faculties through the use of computational models. (Charniak and McDermott, 1985)

2.b. The study of the computations that make it possible to perceive, reason, and act. (Winston, 1992)

2.1. Thinking Rationally

• Trying to understand how we actually think is one route to AI.
• But another approach is to model how we should think.
• The “thinking rationally” approach to AI uses symbolic logic to capture the laws of rational thought as symbols that can be manipulated.
• Reasoning involves manipulating the symbols according to well-defined rules, kind of like algebra.
• The result is an idealized model of human reasoning. This approach is attractive to theoretists, i.e., modeling how humans should think and reason in an ideal world.

2.2. Thinking rationally: The “laws of thought” approach

The Greek philosopher Aristotle was one of the first to attempt to codify “right thinking,” that is, irrefutable reasoning processes. His syllogisms provided patterns for argument structures that always yielded correct conclusions when given correct premises—for example, “Socrates is a man; all men are mortal; therefore, Socrates is mortal.” These laws of thought were LOGIC supposed to govern the operation of the mind; their study initiated the field called logic.
Logicians in the 19th century developed a precise notation for statements about all kinds of objects in the world and the relations among them. (Contrast this with ordinary arithmetic notation, which provides only for statements about numbers.) By 1965, programs existed that could, in principle, solve any solvable problem described in logical notation. (Although  if no solution exists, the program might loop forever.) The so-called logicist tradition within artificial intelligence hopes to build on such programs to create intelligent systems.
There are two main obstacles to this approach. First, it is not easy to take informal knowledge and state it in the formal terms required by logical notation, particularly when the knowledge is less than 100% certain. Second, there is a big difference between solving a problem “in principle” and solving it in practice. Even problems with just a few hundred facts can exhaust the computational resources of any computer unless it has some guidance as to which reasoning steps to try first. Although both of these obstacles apply to any attempt to build computational reasoning systems, they appeared first in the logicist tradition.

3. Acting Humanly

3.a. “The art of creating machines that perform functions that require intelligence when performed by people.” (Kurzweil, 1990) 

3.b. “The study of how to make computers do things at which, at the moment, people are better.” (Rich and Knight, 1991)

3.1 Acting Humanly: Turing Test

• This is a problem that has greatly troubled AI researchers for years. They ask the question “when can we count a machine as being intelligent?”

• The most famous response is attributed to Alan Turing, a British mathematician and computing pioneer. The famous “Turing Test” was named after him, based on ideas he expressed in a paper published in 1950. Human interrogates entity via teletype for 5 minutes. If, after 5 minutes, human cannot tell whether entity is human or machine, then the entity must be counted as intelligent.

• To date, no program has yet passed the Turing Test! However, there is the annual Loebner Prize which awards scientists for getting close. See http://www.loebner.net/Prizef/loebner-prize.html for more information.

• In order to pass the Turing Test, a program that succeeded would need to be capable of:speech recognition, natural language understanding and generation, and speech synthesis; knowledge representation; learning; and automated reasoning and decision making. (Note: that the basic Turing Test does not specify a visual or aural component.)

3.2. Acting Humanly: Searle’s Chinese Room

• Another famous test is called the “Chinese Room” which was proposed by John Searle in a paper published in 1980.

• Suppose you have a computer in a room that reads Chinese characters as input, follows a program and outputs (other) Chinese characters. Suppose this computer does this so well that it passes the Turing Test (convinces a human Chinese speaker that it is talking to another human Chinese speaker). Does the computer understand Chinese?

• Suppose Searle is in the room, and he uses a dictionary to translate the input characters from Chinese to English; he then constructs his answer to the question, translates that back into Chinese and delivers the output—does Searle understand Chinese?
• Of course not.

• This is Searle’s argument: the computer doesn’t understand it either, because all it is doing is translating words (symbols) from one language (representation) to another.

3.3. Acting humanly: The Turing Test approach [2]

The Turing Test, proposed by Alan Turing (1950), was designed to provide a satisfactory operational definition of intelligence. A computer passes the test if a human interrogator, after posing some written questions, cannot tell whether the written responses come from a person or from a computer. 

For now, we note that programming a computer to pass a rigorously applied test provides plenty to work on. The computer would need to possess the following capabilities:

• Natural Language Processing to enable it to communicate successfully in English;
• Knowledge Representation to store what it knows or hears;
• Automated Reasoning to use the stored information to answer questions and to draw new conclusions;
• Machine Learning to adapt to new circumstances and to detect and extrapolate patterns.

Turing’s test deliberately avoided direct physical interaction between the interrogator and the computer, because physical simulation of a person is unnecessary for intelligence. However, the so-called total Turing Test includes a video signal so that the interrogator can test the subject’s perceptual abilities, as well as the opportunity for the interrogator to pass physical objects “through the hatch.” To pass the total Turing Test, the computer will need:

• Computer Vision to perceive objects, and
• Robotics to manipulate objects and move about.

These six disciplines compose most of AI, and Turing deserves credit for designing a test that remains relevant 60 years later. Yet AI researchers have devoted little effort to passing the Turing Test, believing that it is more important to study the underlying principles of intelligence than to duplicate an exemplar. The quest for “artificial flight” succeeded when the Wright brothers and others stopped imitating birds and started using wind tunnels and learning about aerodynamics. Aeronautical engineering texts do not define the goal of their field as making “machines that fly so exactly like pigeons that they can fool even other pigeons.”

4. Acting Rationally

4.a. “Computational Intelligence is the study of the design of intelligent agents.” (Poole et al., 1998)

4.b. “AI... is concerned with intelligent behavior in artifacts.” (Nilsson, 1998)

4.1. Acting Rationally

• Acting rationally means acting to achieve one’s goals, given one’s beliefs or understanding about the world. An agent is a system that perceives an environment and acts within that environment. An intelligent agent is one that acts rationally with respect to its goals. For example, an agent that is designed to play a game should make moves that increase its chances of winning the game.

• When constructing an intelligent agent, emphasis shifts from designing the theoretically best decision-making procedure to designing the best decision-making procedure possible within the circumstances in which the agent is acting.

• Logical approaches may be used to help find the best action, but there are also other approaches.

• Achieving so-called “perfect rationality”, making the best decision theoretically possible, is not usually possible due to limited resources in a real environment (e.g., time, memory, computational power, uncertainty, etc.).

• The trick is to do the best with the information and resources you have. This represents a shift in the field of AI from optimizing (early AI) to satisfying (more recent AI).

4.2. Acting rationally: The rational agent approach [2]

An agent is just something that acts. Of course, all computer programs do something, but computer agents are expected to do more: operate autonomously, perceive their environment, persist over a prolonged time period, adapt to change, and create and pursue goals. A rational agent is one that acts so as to achieve the best outcome or, when there is uncertainty, the best expected outcome. In the “laws of thought” approach to AI, the emphasis was on correct inferences. Making correct inferences is sometimes part of being a rational agent, because one way to act rationally is to reason logically to the conclusion that a given action will achieve one’s goals and then to act on that conclusion. On the other hand, correct inference is not all of rationality; in some situations, there is no provably correct thing to do, but something must still be done. There are also ways of acting rationally that cannot be said to involve inference. For example, recoiling from a hot stove is a reflex action that is usually more successful than a slower action taken after careful deliberation.

All the skills needed for the Turing Test also allow an agent to act rationally. Knowledge representation and reasoning enable agents to reach good decisions. We need to be able to generate comprehensible sentences in natural language to get by in a complex society. We need learning not only for erudition, but also because it improves our ability to generate effective behavior.

The rational-agent approach has two advantages over the other approaches. First, it is more general than the “laws of thought” approach because correct inference is just one of several possible mechanisms for achieving rationality. Second, it is more amenable to scientific development than are approaches based on human behavior or human thought. The standard of rationality is mathematically well defined and completely general, and can be “unpacked” to generate agent designs that provably achieve it. Human behavior, on the other hand, is well adapted for one specific environment and is defined by, well, the sum total of all the things that humans do. The Peter Norvig book therefore concentrates on general principles of rational agents and on components for constructing them. 

One important point to keep in mind: We will see before too long that achieving perfect rationality—always doing the right thing—is not feasible in complicated environments. The computational demands are just too high. For most of the book, however, we will adopt the working hypothesis that perfect rationality is a good starting point for analysis. It simplifies the problem and provides the appropriate setting for most of the foundational material in the field. At times, one needs to deal explicitly with the issue of limited rationality—acting appropriately when there is not enough time to do all the computations one might like.

B. Foundations of “Artificial Intelligence”  

In this section, we provide ideas, viewpoints, and techniques from different disciplines that contributed towards AI. 
We certainly would not wish to give the impression that these questions are the only ones the disciplines address or that the disciplines have all been working toward AI as their ultimate fruition.

B.1. Philosophy
B.2. Mathematics
B.3. Economics
B.4. Neuroscience
B.5. Psychology
B.6. Computer engineering
B.7. Control theory and cybernetics
B.8. Linguistics

B.1. Philosophy

• Can formal rules be used to draw valid conclusions?
• How does the mind arise from a physical brain?
• Where does knowledge come from?
• How does knowledge lead to action?

B.2. Mathematics

• What are the formal rules to draw valid conclusions?
• What can be computed?
• How do we reason with uncertain information?

B.3. Economics

• How should we make decisions so as to maximize payoff?
• How should we do this when others may not go along?
• How should we do this when the payoff may be far in the future?

Most people think of economics as being about money, but economists will say that they are really studying how people make choices that lead to preferred outcomes. When McDonald’s offers a hamburger for a dollar, they are asserting that they would prefer the dollar and hoping that customers will prefer the hamburger.

B.4. Neuroscience
• How do brains process information?

B.5. Psychology
• How do humans and animals think and act?

B.6. Computer engineering
• How can we build an efficient computer?

For artificial intelligence to succeed, we need two things: intelligence and an artifact. The computer has been the artifact of choice. The modern digital electronic computer was invented independently and almost simultaneously by scientists in three countries embattled in World War II. The first operational computer was the electromechanical Heath Robinson, built in 1940 by Alan Turing’s team for a single purpose: deciphering German messages. In 1943, the same group developed the Colossus, a powerful general-purpose machine based on vacuum tubes. (In the postwar period, Turing wanted to use these computers for AI research—for example, one of the first chess programs (Turing et al., 1953). His efforts were blocked by the British government.)

The first operational programmable computer was the Z-3, the invention of Konrad Zuse in Germany in 1941. Zuse also invented floating-point numbers and the first high-level programming language, Plankalk¨ul. The first electronic computer, the ABC, was assembled by John Atanasoff and his student Clifford Berry between 1940 and 1942 at Iowa State University. Atanasoff’s research received little support or recognition; it was the ENIAC, developed as part of a secret military project at the University of Pennsylvania by a team including John Mauchly and John Eckert, that proved to be the most influential forerunner of modern computers.

B.7. Control theory and cybernetics
• How can artifacts operate under their own control?

B.8. Linguistics
• How does language relate to thought?