Artificial Intelligence 3

Thinking humanly: The cognitive modelling approach
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 two ways to do this: through intro
spection- trying to catch our own thoughts as they go by - and through
psychological experiments. 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 and timing behaviors match
corresponding human behaviors, that is evidence that some of the programs
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 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 toge
ther computer models from AI and experimental techniques from psychology
to try to construct precise and testable theories of the workings of the
human mind.
Cognitive science is a fascinating field worthy of an encyclopedia in
itself (Wilson and Keil, 1999). We will not attempt to describe what is
known of human cognition in this article. We will occassionally 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, and we assume that the reader
has access only to 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, especially in the areas of
vision and natural language. Vision in particular has recently made
advances via an integrated approach that considers neurophysiological
evidence and computational models.

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Artificial Intelligence 2

WHAT IS AI?
We have claimed that AI is exciting but we have not said what it is. Some
textbook definitions on AI vary along two main dimensions. Some Textbooks
define AI based on thought processes and reasoning, whereas others define
it based on behavior.

The Turing Test, proposed by Alan Turing (1950), was designed to provide
a satisfactory operational definition of intelligence. Rather than
proposing a long and perhaps controversial list of qualifications requi
red for intelligence, he suggested a test based on indistinguishability
from undeniably intelligent entities- human beings. The computer passes
the test if a human interrogator, after posing some written questions,
cannot tell whether the written responses come from a person or not.
For now, we note that programming a computer to pass the test 
provides plenty to work on. The computer would need to possess the
following capabilities:
1. natural language processing to enable it to communicate successfully in
English.
2. Knowledge representation to store what it knows or hears;
3. Automated reasoning to use the stored information to answer questions
and to draw new conclusions;
4. Machine learning to adapt to new circumstances and to detect and
extrapolate patterns.
Turing's test deliberately avoided direct pysical interaction between
the interrogator and the computer, because physical simulation of a per
son is unnecessary for the 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
5. Computer vision to perceive objects, and
6. Robotics to manipulate objects and move about.
These six discplines compose most of AI, and Turing deserves credit for
designing a test that remains relevant 50 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" succee
ded when the Wright brothers and others stopped imitating birds and
leaned 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"

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Artificial Intelligence

We call ourselves Homo Sapiens - man the wise - because our mental capacities
are so important to us. For thousands of years, we have tried to under
stand how we think; that is, how a mere handful of stuff can perceive,
understand, predict, and manipulate a worldd far larger and more complicated
than itself. The field of artificial intelligence, or A1, goes further
still: it attempts not just to understand but also to build intelligent
entities.
Artificial intelligence (AI) is one of the current sciences. Work
started in earnest soon agter World War II, and the name itself was 
coined in 1956. Along with molecular biology, AI is reguarly cited as
the "field I would most like to be in" by scientists in other disciplines. A
student in physics might reasonably feel that all the good ideas have already
been taken by Galileo, Newton, Einstein, and the rest.
AI, on the other hand, still has openings for several full-time
Einsteins. AI currently encompasses a huge variety of subfields, ranging
from general-purpose areas, such as learning and perception to such 
specific tasks as playing chess, proving mathematical theorems, writing
poetry, and diagnosing diseases. AI systematizes and automates intellectual
tasks and is therefore potentially relevant to any sphere of human intellectual
activity. In this sense, it is truly a universal field.

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