ARTIFICIAL INTELLIGENCE

ARTIFICIAL INTELLIGENCE

Overview of Artificial Intelligence

What is AI ?

=> Artificial Intelligence (AI) is a branch of Science which deals with helping machines find solutions to complex problems in a more human-like fashion.

=> This generally involves borrowing characteristics from human intelligence, and applying them as algorithms in a computer friendly way.

=> A more or less flexible or efficient approach can be taken depending on the

requirements established, which influences how artificial the intelligent behavior

appears

=> Artificial intelligence can be viewed from a variety of perspectives.

=> From the perspective of intelligence

artificial intelligence is making machines "intelligent" -- acting as we would

expect people to act.

-> The inability to distinguish computer responses from human responses

is called the Turing test.

-> Intelligence requires knowledge

-> Expert problem solving - restricting domain to allow including

significant relevant knowledge

=> From a business perspective AI is a set of very powerful tools, and

methodologies for using those tools to solve business problems.

=> From a programming perspective, AI includes the study of symbolic

programming, problem solving, and search.

=> Typically AI programs focus on symbols rather than numeric

processing.

=> Problem solving - achieve goals.

=> Search - seldom access a solution directly. Search may include a

variety of techniques.

=> AI programming languages include:

– LISP, developed in the 1950s, is the early programming language

strongly associated with AI. LISP is a functional programming language with

procedural extensions. LISP (LISt Processor) was specifically designed for processing heterogeneous lists -- typically a list of symbols. Features of LISP

are run- time type checking, higher order functions (functions that have other

functions as parameters), automatic memory management (garbage collection)

and an interactive environment.

– The second language strongly associated with AI is PROLOG.

PROLOG was developed in the 1970s. PROLOG is based on first order logic.

PROLOG is declarative in nature and has facilities for explicitly limiting the

search space.

– Object-oriented languages are a class of languages more recently used

for AI programming. Important features of object-oriented languages include:

concepts of objects and messages, objects bundle data and methods for

manipulating the data, sender specifies what is to be done receiver decides

how to do it, inheritance (object hierarchy where objects inherit the attributes

of the more general class of objects). Examples of object-oriented languages

are Smalltalk, Objective C, C++. Object oriented extensions to LISP (CLOS -

Common LISP Object System) and PROLOG (L&O - Logic & Objects) are

also used.

Artificial Intelligence is a new electronic machine that stores large amount of

information and process it at very high speed

=> The computer is interrogated by a human via a teletype It passes if the human cannot tell if there is a computer or human at the other end

=> The ability to solve problems

=> It is the science and engineering of making intelligent machines, especially intelligent

computer programs. It is related to the similar task of using computers to understand

human intelligence

Importance of AI

=> Game Playing

You can buy machines that can play master level chess for a few hundred dollars.

There is some AI in them, but they play well against people mainly through brute

force computation--looking at hundreds of thousands of positions. To beat a world

champion by brute force and known reliable heuristics requires being able to look at

200 million positions per second.

=> Speech Recognition

In the 1990s, computer speech recognition reached a practical level for limited purposes. Thus United Airlines has replaced its keyboard tree for flight information by a system using speech recognition of flight numbers and city names. It is quite convenient. On the other hand, while it is possible to instruct some computers using speech, most users have gone back to the keyboard and the mouse as still more

convenient.

=> Understanding Natural Language

Just getting a sequence of words into a computer is not enough. Parsing sentences is not enough either. The computer has to be provided with an understanding of the

domain the text is about, and this is presently possible only for very limited domains.

=> Computer Vision

The world is composed of three-dimensional objects, but the inputs to the human eye and computers' TV cameras are two dimensional. Some useful programs can work

solely in two dimensions, but full computer vision requires partial three-dimensional

information that is not just a set of two-dimensional views. At present there are only limited ways of representing three-dimensional information directly, and they are not

as good as what humans evidently use.

=> Expert Systems

A ``knowledge engineer'' interviews experts in a certain domain and tries to embody

their knowledge in a computer program for carrying out some task. How well this

works depends on whether the intellectual mechanisms required for the task are

within the present state of AI. When this turned out not to be so, there were many

disappointing results. One of the first expert systems was MYCIN in 1974, which

diagnosed bacterial infections of the blood and suggested treatments. It did better than

medical students or practicing doctors, provided its limitations were observed.

Namely, its ontology included bacteria, symptoms, and treatments and did not include

patients, doctors, hospitals, death, recovery, and events occurring in time. Its

interactions depended on a single patient being considered. Since the experts

consulted by the knowledge engineers knew about patients, doctors, death, recovery,

etc., it is clear that the knowledge engineers forced what the experts told them into a

predetermined framework. The usefulness of current expert systems depends on their

users having common sense.

=> Heuristic Classification

One of the most feasible kinds of expert system given the present knowledge of AI is to put some information in one of a fixed set of categories using several sources of information. An example is advising whether to accept a proposed credit card

purchase. Information is available about the owner of the credit card, his record of

payment and also about the item he is buying and about the establishment from which

he is buying it (e.g., about whether there have been previous credit card frauds at this

establishment).

Early work in AI

=> “Artificial Intelligence (AI)" is the part of computer science concerned with designing intelligent computer systems, that is, systems that exhibit characteristics we associate with intelligence in human behaviour – understanding language, learning, reasoning, solving problems, and so on.”

=> Scientific Goal To determine which ideas about knowledge representation, learning,

rule systems, search, and so on, explain various sorts of real intelligence.

=> Engineering Goal To solve real world problems using AI techniques such as

knowledge representation, learning, rule systems, search, and so on.

=> Traditionally, computer scientists and engineers have been more interested in the

engineering goal, while psychologists, philosophers and cognitive scientists have been

more interested in the scientific goal.

=> The Roots - Artificial Intelligence has identifiable roots in a number of older

disciplines, particularly:

=> Philosophy

=> Logic/Mathematics

=> Computation