The document provides a comprehensive overview of artificial intelligence (AI), including its definitions, applications, and historical development. It discusses various approaches to AI, such as acting and thinking humanly, and the foundational disciplines that contribute to the field, including philosophy, psychology, and neuroscience. The history of AI is traced from early theoretical work to the establishment of AI as a distinct field, highlighting key milestones and the evolution of intelligent agents.

1. Artificial Intelligence
Stuart J. Russell and Peter Norvig

2. Introduction
Intelligence is important to humans and we have tried to understand ‘how we think’ since thousand
of years.
John McCarthy coined the term ―Artificial Intelligence which he would define as ―the science and
engineering of making intelligent machines.
The field of artificial intelligence, or AI, goes further still:
it attempts not just to understand but also to build intelligent entities.
Intelligence as ‘the ability to learn and understand, to solve problems and to make decisions’.
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.

3. Application of AI

4. 1.1 What is AI? Four Main Views/ Approaches
1. AI means acting humanly.
 Concerned with only actions.
2. AI means thinking humanly.
 Concerned with modelling the thinking process.
3. AI means thinking rationally.
 Modelling thinking as a logical process, draw conclusion.
4.AI means acting rationally.
 Performing actions that increase the value of the state of the agent or environment in which the agent is acting. Example
playing chess game.

5. Acting humanly: The turing test approach
 The turing Test, proposed by Alan Turing (1950), was designed to provide a satisfactoryoperational definition
of intelligence.
Instead of asking whether machine can think, we should ask whether machines can pass a behavioral
intelligence test- Alan turing.

6. Six disciplines/ capabilies : computer need to pass test
Natural language processing: 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.
Computer vision to perceive objects.
Robotics to manipulate objects and move about.

7. Thinking humanly: The cognitive modeling approach
For us to say that program thinks like a human, we must have some way of determining how humans
think.
There are two ways to understand how human thinks
through introspector-trying to catch our own thoughts as they go by.
 through psychological experiments- observing a person in action.
sufficiently precise theory of mind==express the theory as program.
Allen newell and Herbet simon developed GPS program. concerned with comparing the trace of its
reasoning steps to traces of human
subjects solving the same problems.
cognitive science brings together computer models from AI and experimental techniques from
psychology to try to construct precise and testable theories of the workings of the human mind.

8. Thinking rationally: The "laws of thought" approach
Aristotle was one of the first to attempt to codify "right thinking,“
Syllogisms example, "Socrates is a man; all men are mortal; therefore, Socrates is mortal.“
Mind operates using these laws of thoughts –logic
We will be able to write programs that take sentences and they come up with conclusions.
There are two main obstacles to this approach
1. 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.
2. there is a big difference between being able to solve a problem "in principle" and doing so in
practice

9. Acting rationally: The rational agent approach
Agent is just something that acts.
Agent vs program- programs do somethings. Agents are expected to do more :
operate autonomously, perceive environment, adapt to change, create and
pursue goal.
Rational agent is one that acts so as to achieve the best outcome or, when
there is uncertainty, the best expected outcome.
Acting rational but not thinking rationally. Example reflex action hot stove
Modern AI focus on designing agent that acts rationally.

11. FOUNDATIONS OF AI
The foundation provides the disciplines that contributed ideas, viewpoints and techniques to AI.
1. Philosophy
2. Mathematics
3. Economics
4. Neuroscience
5. Psychology
6. Computer engineering
7. Control theory and cybernetics
8. Linguistics

12. 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?
Aristotle (384–322 B.C.), was the first to formulate a precise set of laws governing
the rational part of the mind.
He developed an informal system of syllogisms for proper reasoning.
which in principle allowed one to generate conclusions mechanically, given initial
premises. All dogs are animals. Animals have 4 legs.

13. consider the mind as a physical system.
1. Dualism- there is a part of the human mind (or soul or spirit) that is outside of nature, exempt
from physical laws.
2. Materialism- which holds that the brain's operation according to the laws of physics
constitutes the mind.
3. Empiricism- Nothing is in the understanding, which was not first in the senses.
4. Induction: deriving generalizations or patterns based on observations of repeated
associations between elements or events.
5. Logical positivism: all knowledge can be characterized by logical theories connected,
ultimately, to observation sentences that correspond to sensory inputs.
6. Confirmation theory: attempt to understand how knowledge can be acquired from
experience.

14. 2. Mathematics
• What are the formal rules to draw valid conclusions?
• What can be computed?
• How do we reason with uncertain information?
Formulations- three fundamental areas: logic, computation, and probability.
George Boole- worked out the details of propositional, or Boolean, logic.
Euclid algorithm- computing GCD.

15. • undecidability: Incompleteness theory there are true statements that are
undecidable i.e. they have no proof within the theory.
“ a line can be extended infinitely in both directions”
• intractability: A problem is called intractable if the time required to solve
instances of the problem grows exponentially with the size of the instance.
• Probability: Predicting the future.

16. 3. Economics
When we are developing AI product, we should make decision for when to invest, how
much, where to????
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?
 Adam Smith was first to treat economic as science.
Not just about money- Economists say it’s a studying how people make choices that lead
to preferred outcomes
Decision theory: It combines probability theory with utility theory, provides a formal and
complete framework for decisions made under uncertainty.

17. 4.Neuroscience
 How do brain process information?
 Neuroscience is the study of the nervous system, particularly the brain.
Hans Berger in 1929 invented electroencephalogram (EEG), is a medical test used to measure the
electrical activity of the brain.
 The recent development of functional magnetic resonance imaging (fMRI) - detailed images of brain
activity.

18. Each neuron consists of a cell body, or soma, that contains a cell nucleus. Branching out from
the cell body are a number of fibers called dendrites and a single long fiber called the axon.
Neuron makes connections with 10 to 100,000 other neurons at junctions called synapses.
Signals are propagated from neuron to neuron by a complicated electrochemical reaction.
These mechanisms are thought to form the basis for learning in the brain

19. Behaviorism movement, led by John Watson Behaviorists insisted on
studying only objective measures of the percepts(stimulus) given to an animal and its
resulting actions(or response). Behaviorism discovered a lot about rats and pigeons
but had less success at understanding human.
Cognitive psychology, views the brain as an information processing device.
Common view among psychologist that a cognitive theory should be like a computer
program.
It should describe a detailed information processing mechanism whereby some cognitive
function might be implemented.

20. 5. Psychology
 How do humans and animals think and act?
 Contributes to AI's understanding of human behavior, cognition, and perception,
influencing the development of human-centric AI applications.
The origins of scientific psychology- German physicist Hermann and his student applied
the scientific method to the study of human vision.
 Behaviorism movement/ folk psychology
Cognitive Psychology

21. 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(object) of choice.
• The first operational computer was the electromechanical Heath Robinson,
built in 1940 by Alan Turing's team for a single purpose: deciphering German
messages.
• The first operational programmable computer was the Z-3, the invention of
KonradZuse in Germany in 1941.

22. The first electronic computer, the ABC, was assembled between 1940 and 1942
 The first programmable machine was a jacquard loom, devised in 1805 by Joseph Marie Jacquard
(1752-1834) that used punched cards to store instructions for the pattern to be woven.

23. 7. Control Theory and Cybernetics
How can artifacts operate under their own control?
Control theory deals with the behavior of dynamical systems and how to
manipulate their inputs to achieve desired outputs or states.
Cybernetics is a field that looks at how different systems, like machines,
animals, or even organizations, control and communicate with each other.
Ktesibios of Alexandria (c. 250 B.c.) built the first self-controlling machine:
a water clock with a regulator.

24. Modern control theory, especially the branch known as stochastic optimal
control, has as its goal the design of systems that maximize an objective function
over time.
Calculus and matrix algebra- the tools of control theory
The tools of logical inference and computation allowed AI researchers to
consider problems such as language, vision, and planning

25. 8. Linguistic
How does language relate to thought?
In 1957, Skinner published verbal behavior. behaviorist approach to language learning. He
believed that language, like any other behavior, is learned through a process of trial and
error.
Noam Chomsky- published a book on his own theory, Syntactic Structures. Chomsky
showed how the behaviorist theory did not address the notion of creativity in language-it did
not explain how a child could understand and make up sentences that he or she had never
heard before.
Chomsky theory - humans are born with an innate ability to understand and produce
language.
Modem linguistics and AI, then, were "born" at about the same time, and grew up
together, intersecting in a hybrid field called computational linguistics or natural language
processing.

26. The problem of understanding language soon turned out to be considerably more
complex than it seemed in 1957.
Understanding language requires an understanding of the subject matter and context, not just
an understanding of the structure of sentences.

27. HISTORY OF AI
1. The Gestation of AI -the early theoretical and conceptual work carried out.
2. The Birth of AI-Dartmouth conference
3. Early enthusiasm, great expectations- the earliest AI programs developed.
4. A dose of reality
5. Knowledge based systems
6. AI becomes an industry.
7. The return of neural networks
8. AI becomes a science
9. The emergence of intelligent agents

28. History of AI
The gestation of artificial intelligence (1943-1955)
Year 1943: The first work in AI done by Warren McCulloch and Walter pits in
1943. They proposed a model of artificial neurons.
Year 1949: Donald Hebb demonstrated an updating rule for modifying the
connection strength between neurons. His rule is now called Hebbian learning.
Year 1950: Alan Turing publishes "Computing Machinery and Intelligence“.

29. The birth of artificial intelligence (1956)
Dartmouth Conference in 1956: organized by John McCarthy brought together
researchers from various disciplines to discuss the potential of artificial
intelligence.
Conference led to establishment of AI as a field of study.
why it was necessary for AI to become a separate field.
1. AI main idea of duplicating human faculties.
2. methodology: AI is the only one of these fields that is clearly a branch of
computer science and AI is the only field to attempt to build machines that
will function autonomously in complex, changing environments.

30. Early enthusiasm, great expectations (1952-1969)
First AI Programs: During the 1950s and 1960s, researchers developed some of
the earliest AI programs
Symbolic AI and Logical Reasoning: General Problem Solver (GPS) developed
by Allen Newell.
Herbert Gelernter (1959) constructed the Geometry Theorem Prover.
McCarthy defined the high-level language Lisp.(1958)

31. Tom Evans's ANALOGY program (1968): The ANALOGY program was designed to solve geometric
analogy problems, which are a type of problem commonly found in IQ tests.
 Microworld which consists of a set of solid blocks placed on a tabletop. A typical task in this world is
to rearrange the blocks in a certain way, using a robot hand that can pick up one block at a time

32. Dose of reality(1966-1973)
AI researchers made predictions of coming successes in the field of AI.
3 kinds of difficulties
most early programs contained little or no knowledge of their subject.
The second kind of difficulty was the intractability of many of the problems that AI was attempting to
solve.
some fundamental limitations on the basic structures being used to generate intelligent behavior.

33. Knowledge based systems: the key to power(1969-1979)
Weak method: Early AI researcher used general-purpose search mechanism trying to string
together elementary reasoning steps to find complete solutions.
The alternative to weak methods is to use more powerful, domain-specific knowledge that allows
larger reasoning steps.
Examples include the MYCIN system for diagnosing bacterial infections.
The DENDRAL program: first successful knowledge-intensive system. DENDRAL to help figure out
the chemical components of unknown substances.
The Prolog language became popular in Europe.
Minsky's idea of frames (1975 allowed AI systems to represent knowledge in a more organized and
flexible way, Instead of just storing facts in a flat list.

34. AI becomes an industry(1980-present)
In 1982 : The first successful commercial expert system, R1.
Companies had implemented 100 AI systems in their operations and had an additional 500 AI systems
in development.
In 1981, the Japanese announced the "Fifth Generation" project, a 10-year plan to build intelligent
computers running Prolog.
Overall, the AI industry boomed from a few million dollars in 1980 to billions of dollars in 1988. Soon
after that came a period called the "AI Winter," in which many companies suffered as they failed to
deliver promising system.

35. The return of neural networks(1986-present)
The period from 1986 to the present is characterized by the resurgence and dominance of neural
networks in the field of artificial intelligence (AI). This era is marked by significant advancements
in the development of neural network architectures, training algorithms, and the widespread
adoption of deep learning techniques.

36. AI becomes a Science(1987-present)
Building on existing theory, Rigorous Theorems and Experimental Evidence, Focus
on Real-World Applications.
The shift towards a more scientific approach in AI research.
hidden Markov models (HMMs) have come to dominate the area.
Data mining technology has spawned a vigorous new industry.
normative expert systems: ones that act rationally according to the laws of decision
theory.

37. The emergence of intelligent agents(1995-present)
emergence and evolution of intelligent agents in the field of artificial
intelligence (AI). Intelligent agents are autonomous entities that perceive their
environment, make decisions, and take actions to achieve goals.

38. Module 1 Introduction : what isAI?
Foundation ofAI and History ofAI
IntelligentAgents:Agents and environment, concept of rationality,
The nature of environment, The structure of agents