Artificial Intelligence
Chapter 1
What is Artificial Intelligence?
Intelligence : the ability to learn and solve problems
AI Defined
AI may be defined as the branch of computer science that is concerned
with the automation of intelligent behavior
Other definitions:
The exciting new effort to make computers think … machines with
minds
The automation of activities that we associate with human thinking
(e.g., decision-making, learning…)
The art of creating machines that perform functions that require
intelligence when performed by people
The study of mental faculties through the use of computational
models
A field of study that seeks to explain and emulate intelligent
behavior in terms of computational processes
The study of how to make programs/computers do things that
people do better
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What is Artificial Intelligence?
Some Definitions
Weak AI: AI develops useful, powerful
applications.
Strong AI: claims machines have cognitive minds
comparable to humans.
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What is Artificial Intelligence?
Operational Definition of AI
(Turing Test):
In 1950 Turing proposed an
operational definition of intelligence by
using a Test composed of :
An interrogator (a person who will ask
questions)
a computer (intelligent machine !!)
A person who will answer to questions
A curtain (separator)
Alan Turing
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Turing Test
Mathematician Alan If the interrogator
Turing devised a test cannot tell which is the
for defining artificial human and which is the
intelligence: computer, then the
an interrogator poses computer passes the
questions to two entities, Turing Test and should
a human and a computer be considered
intelligent
Turing first called this
the Imitation game but
has since been renamed
the Turing Test – a test
for machine intelligence
What is Artificial Intelligence ?
Goals of AI:
AI began as an attempt to understand the nature of
intelligence, but it has grown into a scientific and
technological field affecting many aspects of commerce
and society. The main goals of AI are:
Engineering: solve real-world problems using
knowledge and reasoning. AI can help us solve
difficult, real-world problems, creating new
opportunities in business, engineering, and many other
application areas
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What is Artificial Intelligence ?
Goals of AI (cont’d)
Scientific: use computers as a platform for studying
intelligence itself. Scientists design theories
hypothesizing aspects of intelligence then they can
implement these theories on a computer.
Even as AI Technology becomes integrated into the fabric
of everyday life. AI researchers remain focused on the grand
challenges of automating intelligence.
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What is Artificial Intelligence ?
Examples of AI Application
systems:
Game Playing
TDGammon, the world champion
backgammon player, built by Gerry
Tesauro of IBM research
Deep Blue chess program beat world
champion Gary Kasparov
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What is Artificial Intelligence ?
Natural language processing (NLP) gives machines
the ability to read and understand the languages that
humans speak.
Natural Language Understanding
AI Translators – spoken to and prints what
one wants in foreign languages.
Natural language understanding (spell
checkers, grammar checkers)
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What is Artificial Intelligence ?
Examples of AI Application Systems:
Expert Systems:
Expert System: An expert system is a computer system that emulates
the decision-making ability of a human expert. Expert systems are
designed to solve complex problems by reasoning about knowledge,
like an expert, and not by following the procedure of a developer as is
the case in conventional programming.
Diagnostic Systems
• Pathfinder, a medical diagnosis system (suggests tests and makes
diagnosis) developed by Heckerman and other Microsoft research
• MYCIN system for diagnosing bacterial infections of the blood and
suggesting treatments
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What is Artificial Intelligence ?
Examples of AI Application Systems:
Expert Systems:
Financial Decision Making
• Credit card providers, banks, mortgage companies use AI systems to
detect fraud and expedite financial transactions.
Configuring Hardware and Software
• AI systems configure custom computer, communications, and
manufacturing systems, guaranteeing the purchaser maximum
efficiency and minimum setup time.
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What is Artificial Intelligence ?
Examples of AI Application Systems:
Robotics:
Robotics becoming increasing important in various areas like: games, to
handle hazardous conditions and to do tedious jobs among other things. For
examples:
- automated cars, ping pong player
- mining, construction, agriculture
- garbage collection
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What is Artificial Intelligence ?
Examples of AI Application systems:
Other examples:
Handwriting recognition (US postal service zip code readers)
Automated theorem proving
• use inference methods to prove new theorems
Web search Engines
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Notes
Machine learning is the study of computer algorithms that improve
automatically through experience and has been central to AI research
since the field’s inception.
Unsupervised learning is the ability to find patterns in a stream of input.
Supervised learning includes both classification and numerical
regression.
Classification is used to determine what category something belongs in,
after seeing a number of examples of things from several categories.
Regression is the attempt to produce a function that describes the
relationship between inputs and outputs and predicts how the outputs
should change as the inputs change.
In reinforcement learning the agent is rewarded for good responses and
punished for bad ones.
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Voice Recognition
In computer science, voice recognition (VR) is the
translation of spoken words into text.
It is also known as “automatic speech recognition”,
“ASR”, “computer speech recognition”, “speech to
text”, or just “STT”. Some SR systems use “training”
where an individual speaker reads sections of text
into the SR system
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Artificial Intelligence –
Vision
Computer vision is a field that includes methods for
acquiring, processing, analyzing, and understanding
images and, in general, high dimensional data from
the real world in order to produce numerical or
symbolic information, e.g., in the forms of decisions
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Questions
State whether the following statements are true or false:
1. Natural language processing (NLP) gives machines the ability to read and
understand the languages that humans speak.
2. Unsupervised learning includes both classification and numerical
regression.
3. Voice recognition (VR) is also known as “automatic speech recognition” or
“speech to text”.
4. Robots are one of the applications of AI.
5. AI can be defined as “the science and engineering of making intelligent
machines”.
6. Artificial Intelligence has no branch.
7. Banks use artificial intelligence systems to organize operations, invest in
stocks and manage properties.
8. AI deals with the types of problem-solving and decision-making that
humans continually face in dealing with the world.
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AI Programming Languages
General programming languages such as C++ and
Java are often used because these are the languages
with which most computer scientists have
experience. There also exist two programming
languages that have features that make them
particularly useful for programming Artificial
Intelligence projects
PROLOG
LISP
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AI Topics:
Problem solving by searching
(Uninformed search, heuristic search …)
Knowledge-based systems
(expert systems …)
Machine learning
(neural networks, RL …)
Artificial Life <Modern AI>
(cellular automata, GAs …)
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Problem Solving by Searching
Why search ?
Early works of AI was mainly towards
• proving theorems
• solving puzzles
• playing games
All AI is search!
Not totally true (obviously) but more true than you might think.
Finding a good/best solution to a problem amongst many possible
solutions.
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Classic AI search problems
Map searching (navigation)
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Problem definition
Initial State : Arad
Actions(state) :
Results ( state , action)
Goal test
Path cost (s1S2,…,Sn)
Note that:
Step_Cost: (s,a,s’)
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3*3*3 Rubik’s Cube
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8-Puzzle
2 1 3 1 2 3
4 7 6 4 5 6
5 8 7 8
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Knowledge-based system
expert system (or knowledge-based system): a program
which encapsulates knowledge from some domain,
normally obtained from a human expert in that domain
components:
Knowledge base (KB): repository of rules, facts
(productions)
working memory: (if forward chaining used)
inference engine: the deduction system used to infer
results from user input and KB
user interface: interfaces with user
external control + monitoring: access external databases,
control,...
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Knowledge-based system
Why use expert systems:
commercial viability: whereas there may be only a few experts whose time
is expensive and rare, you can have many expert systems
expert systems can be used anywhere, anytime
expert systems can explain their line of reasoning
commercially beneficial: the first commercial product of AI
Weaknesses:
expert systems are as sound as their KB; errors in rules mean errors in
diagnoses
automatic error correction, learning is difficult (although machine learning
research may change this)
the extraction of knowledge from an expert, and encoding it into machine-
inferrable form is the most difficult part of expert system implementation
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Questions
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