ARTIFICIAL INTELLIGENCE
INTRODUCTION:
The term “artificial intelligence “ coined by John McCarthy, a math
professor in 1956 is a complex science, which is adopting a global posture that centers on
logic, learning stimulating the operation of a human brain.
The most celebrated result of artificial intelligence research is the expert system, a computer
program that purports to mimic the ability of a human expert in some narrow field of
knowledge to reason about and solve difficult problems.
AREAS OF INVESTIGATION:
Artificial intelligence is closely related with the following areas of investigation:

Robotics:
It is a complete artificial stimulation of a human being, presumably including
both mental and physical attributes.

Cognitive science:
It is a study of cognition, or the thought processes, structures, and mechanics
used by human beings.

Intelligent software:
It is the art of writing experimental programs that display intelligent
behavior.
HISTORY OF ARTIFICIAL INTELLIGENCE:
Some important events in the modern history of AI.
1945 Von Neumann conceives “Stored program” design for serial Digital computers.
1955 Bernstein develops first working chess program.
1956 McCarthy coins the term Artificial Intelligence.
1957 McCarthy invents Lisp (LIS Processor), the first Popular AI
programming language.
Feigenbaum develops DENDRAL, the first Expert System.
1965 Quillan invents the semantic network.
1966 Greeblatt develops machack, the first competent Chess program.
1972. MYCIN , first practical exert system to use production rules, developed.
Wino grad completes SHRDLU natural language processing program.
1975 The first specialized AI computer, invented at MIT.
1980 Japanese “Fifth Generation” AI research effort begins.
1986 Thinking Machines Corporation introduces the Connection Machine.
TURING TEST:
This test suggested by the scientist Turing was an important one in
artificial intelligence.
In the Turing test, a human examiner, another human being and the computer are placed in
separate chambers.
There are Teletype links among them as indicated below:
HUMAN
BEING
MACHINE
EXAMINER
The examiner poses a series of questions to both the human being and the computer
attempted to discriminate between them based on their responses.
 The Turing (TT) only tests the ability to communicate intelligently.
 The TT cannot test perceptual abilities like speech and vision.
Later, Steven Harnad, proposed the Total Turing Test(TTT) to get around this limitation.
THE COMPUTER AND THE HUMAN BRAIN
Information processing model is essentially an abstraction of the features of
brain and computer to a sufficiently high level that we can view them as identical.
But the problem remains to implement the model in real computer systems. One
way to model the brain’s performance is to model its structure, especially its fundamental
unit, the neuron. However its enormous complexity restricts us from doing so.
This achieved by the new science of connectionism that offers a mathematical
formalism with which we can make a computational analysis of so-called neural networks.
AXIOM OF AI:
Information is not knowledge and throwing more and more data into an AI
program may slow down the AI program and reduces its performance.
Brain smith has formulated the fundamental axiom of AI that connects
the structures of an intelligent agent with its processes: It states:
“ Any mechanically embodied intelligent process will be comprised of structural
ingredients that:

We as external observers naturally take to represent a prepositional account of the
knowledge that the overall process exhibits,and

Independent of such external semantical attribution, play a formal but causal and
essential role in engendering the behavior that manifests that knowledge."
SEMANTIC NETWORKS:
The semantic network is a useful representation formalism for such knowledge,
allowing the easy expression of taxonomies and properties.
Quillian’s thesis introduced the semantic network formalism for representing
common sense knowledge.
Architecture of semantic networks:
A semantic network consists of a network of labeled nodes and links in that network.
The meanings of the nodes, links and labels are completely determined by the
procedures that manipulate the network.
We can fudge the distinction between syntax and semantics by using so called
semantic grammar.
FRAME SYSTEMS:
Frame systems improve upon semantic networks by adding slots
and filters, as well as advanced capabilities like defaults, cancellation, and procedure
attachment.
The problem is the question of:
“ Can a system that receives new knowledge know which pieces of its existing knowledge
are rendered invalid or inconsistent by the new information?”
A clean solution for this frame problem is an elusive goal for research into reasoning with
knowledge bases.
NATURAL LANGUAGE PROCESSING:
Natural language processing (NLP), the action being performed by a
computer program that can accept some non-trivia inputs in a human language, is actually a
difficult task that is far from being fully realized.
 HAL (Human access language) is a famous one developed by lotus.
 ELIZA program by Joseph Weizenbaum of MIT could carry on a sort of stylized
conversation with a user typing at a terminal. This program was designed to act like a
psychologist, seemingly attempting to probe the thoughts and feelings of the user-patient.
ART OF NLP :
NLP relies on a primitive technique called keyword analysis to select certain words in the
input sentence and insert them in precreated responses.
APPLICATIONS:
NLP capabilities is becoming essential to applications like database management and
query in order to make them as easy to use as possible.
The most common application of natural language processing techniques is to user
interface design.
ARCHITECTURE OF A DBMS WITH A NLI:
N
SEMANTIC
GRAMMAR
L
LEXICON
P
EXPERT SYSTEMS:
TRADITIONAL
(non-AI) SYSTEM
Expert systems are computing systems, or programs, that offer adviser solve problems by
reasoning with bodies of knowledge highly specific to particular domain.
One of the first expert systems to undergo performance testing was MYCIN in1974.
CAPABILITIES AND COMPONENTS OF AN EXPERT SYSTEM:
A true expert system must be capable of performing three basic services:

Not only problem solving but also interactive explanation and knowledge acquisition.

It should be fast, easy to maintain

User friendly
MODULES OF EXPERT SYSTEMS:
It is essential that the user’s input should be prompted with a list of the applicable
possibilities.
In order to deliver this performance, expert systems are normally organized into four
modules:

The knowledge base

The working memory

The inference engine

Some user interface and support software
The inference engine is the heart of the expert system, embodying the main control structures
and algorithms.
Architecture of a typical expert system:
Working
Memory
Knowledge
Base
User
User
Interface
Inference
engine
FUTURE DIRECTIONS:
The logical next step in AI is perception, the ability to
receive the input from the external world in its own media: sights, sounds, smells, and so on.
While odor processing may be beyond today’s AI and engineering techniques, both speech
and image understanding have been extensively pursued since the late11960s. Commercial
applications that have evolved since then include voice-activated word processors, optical
text scanning and robots that sort containers.
VISUAL PROBLEMS AND EDGE DETECTION:
The visual process is one of converting the very low-level information provided
as raw input from the “senses’ into a very high level abstraction of the relevant and useful
objects encoded in the input.
To make vision problems more tractable, first the domain is restricted to the
familiar block world, resulting in scenes of only straight-edged geometric figures, perhaps
with occlusions or shadows to complicate things. Second we skip several of the intermediate
representation, intending to produce a conceptual description more directly from the input.
Edge detection aims to eliminate noisy data from the bitmap and transform it into a line
drawing.
APPLICATIONS:
ARTIFICIAL INTELLIGENCE:
AI was moving at a faster pace. In 1986,us sales of AI related hardware and
software surged to $425millions.
The applications of AI are vast .yet some of them discussed here:
 The military put AI based hardware to the test of war during “desert storm”.
 AI based systems were used in missile systems.
 Heads-up-displays.
 Applications for the apple Macintosh and IBM computer such as voice and character
recognition have been available.
Here is a list of AI programs with their corresponding applications:

Problem solvers
 SHRDLU and the block microworld
 The general problem solver

Human computer interaction
 Eliza
 Psychotherapist
 Parry

Other notable programs
 Hacker, the program with writing programs.
 Sam and comprehension
 The frump, summary
EXPERT SYSTEMS:
Its applications widens in the following fields:
Chemistry:
Heuristic DENDRAL developed in 1965 assist chemists in determining the exact or likely
structures of molecules based on their chemical formulae.
Mathematics:
Programs like SAINT, SIN, MATHLAB etc, were able to compete in symbolic integration,
the solution of definite integrals, they were also able to solve certain numerical problems.
Exploration:
PROSPECTOR created at SRI international assist geologists with a variety of mineral
exploration problems.
Electronics:
Randall Davis of MIT developed a program, that is able to diagnose failures in digital
electronic circuits .
Medicine:
MYCIN is a very famous system for blood disease diagnosis, begun in the early 1970s.
CONCLUSION:
As a science, AI is still in its infancy, and just like other fields in their youth, it
has its share of critics, disbeliveers and heretics. Having concluded that a knowledge-based
approach is the right one for our study of AI and its applications, we should pause to consider
some of the main criticisms of our efforts. A part forms a healthy “Loyal opposition” who
intend their criticisms to be purely constructive, three individuals have taken it upon
themselves to expose AI as a fraud, an illegitimate science, or a potential social ill.
- IT Acumens & Team