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Introduction to AI
- By Sheetal Jain
Before We Begin
Studying AI is Valuable but before delving into the Study of AI,
defining is important.
For years, we human thought how we humans think and why
only a few living organisms can perceive, understand,
manipulate, and predict.But AI goes beyond it, it doesn’t just
understand but also builds intelligent entities.
“Artificial Intelligence is a technology that can perform tasks
which require human cognition”
1.1 What is AI ?
Scientists have approached artificial intelligence in
various ways, looking at it from different angles.
Some focus on making machines smart like humans,
while others aim to create systems that can perform
tasks intelligently. These approaches are as follows:
Approaches to AI
Thought Process and
Reasoning
Human Behavior
Acting Rationally
(The Rational
Agent Approach)
Acting Humanly
(Turing Test)
Thinking
Rationally
(The Laws of
Thought)
Thinking
Humanly
(Cognitive
Modeling)
1.1.1 Thinking Humanly: Cognitive Modeling
● This approach first requires to understand how human brain works on
problems.
● We can understand how human brain works through introspection,
psychological experiments, and brain imaging
● If we grasp how the human brain works, we can write a computer program
on the same principles.
● Now, we need to ensure that the program input-output are correspond
those of human brain.
Experimental
tools of
Psychology
Precise
Theory of
Human Brain
AI Modeling
1.1.2 Thinking Rationally : The laws of thought approach
● Aristotle was the first person to codify “Right Thinking Process” or “Irrefutable
Reasoning Process”.
● He gave idea of Syllogism
● This study of laws of thought led to the birth to the term “Logic”.
● Logist tradition of AI hopes to build program to create intelligent system
● Challenges encountered in this approach are:
(i) Notation of the informal knowledge in formal term using logical notation.
(ii) Big difference between solving problems in principle and solving problem in
practical
1.1.3 Acting Humanly : Turing Test
● The Turing Test, devised by British mathematician Alan Turing, suggests that
if a person cannot reliably tell whether they are communicating with a
computer or a human through terminals, then the machine demonstrates
human-like behavior.
● Following are the process under turing tests that a machine need to pass:
- Natural Language Processing: Understanding of Language
- Knowledge Representation: Storing information
- Automatic Reasoning: using stored data to draw conclusion
- Machine Learning: Adopting new situation and drawing patterns
- Computer Vision: To perceive object
- Robotics: To manipulate object physically
1.1.4 Acting Rationally: The Rational Agent Approach
● The word agent is a latin word which means “To do” and computer agents are expected
to do more: Operate Automatically, Perceive Environment, Persist over a prolonged
period, adopt changes, create and pursue goals.
● The word “Rational” means “Act to achieve best or best expected outcome”.
● Combining above two points, the rational agent focuses on course of action.
● All the skills required in turing test also allow an agent to act rationally.
● Knowledge representation and reasoning will enable good decision making and then
we require to generate natural general sentences to cope with complex society.
● Rational Agent approach has two advantages over other approaches to AI: The
solution will be more general and this approach is acceptable to scientific
development. This is because the standard of acting rationally is well defined
mathematically and in more general way.
1.2
The Foundation
Of Artificial
Intelligence
This section provide a brief history of
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
1.2.1 Philosophy
● In ancient times (around 4th century B.C.), Aristotle created basic rules for
thinking and made a system for logical reasoning called syllogisms.
● Aristotle's system allowed people to draw conclusions in a mechanical way from
starting ideas.
● Ramon Lull later thought that machines could do logical thinking in a mechanical
way.
● In the 17th century, Thomas Hobbes compared thinking to doing math, suggesting
a link between mental processes and calculations.
● Around the year 1500, Leonardo da Vinci designed a working mechanical
calculator, showing early progress in automation.
● Wilhelm Schickard (1623) and Blaise Pascal (1642) made machines that could do
calculations, with Pascal saying they act a lot like thought.
● Gottfried Wilhelm Leibniz (1646–1716) made a machine that could do operations
on ideas, going beyond basic math.
● Descartes (1596–1650) proposed rationalism, dualism, and materialism.
● The empiricism movement, led by Bacon and Locke, said knowledge comes from experiences
with our senses.
● Logical positivism, created by the Vienna Circle, mixed ideas from thinking and experiences,
connecting knowledge to what we observe.
Rationalism
Dualism Materialism
The power of
reasoning in
understanding
world
There is a part
of human
brain which is
out of nature
Brain operation,
according to
the physics law,
constitute brain
● Hence, the philosophical picture of the mind is constituted by connection between knowledge and actions
1.2.2 Mathematics
Philosopher gave fundamental idea of AI but formal science required mathematical
formalization. There are three areas of focus under this discipline:
Logic
George Boole (1815–1864)
initiated the development of
propositional, or Boolean, logic,
which serves as the basis for
logical reasoning. In 1879,
Gottlob Frege (1848–1925)
expanded Boole's work,
introducing first-order logic that
is widely used today. Alfred
Tarski (1902–1983) contributed
by presenting a theory of
reference, facilitating the
connection between logical
entities and real-world objects.
Computation
In AI, computation is fundamental,
relying on logical operations and
algorithms. By manipulating
symbols and data, computers mimic
intelligent behavior, facilitating
problem-solving, learning, and
decision-making across different
fields. The essence of AI
computation lies in mathematical
models, algorithms, and data
processing, replicating cognitive
functions and advancing the
creation of intelligent systems and
machines.
Probability
Probability, the third big math idea
in AI, started with Gerolamo
Cardano and was developed by
Blaise Pascal and others. It began
with gambling but became crucial
for dealing with uncertainty in
sciences. People like James
Bernoulli, Pierre Laplace, and
Thomas Bayes improved the
theory and introduced ways to use
statistics. Thomas Bayes
suggested a rule for updating
probabilities with new evidence,
which is a key part of how AI
systems handle uncertainty today.
1.2.3 Economics
● Adam Smith gave birth to economics by launching his book An Inquiry into the nature
and the cause of wealth of nations . Smith was the first person to treat economic as
science. Economists always thought that economy is always about money, but it was
about how people make choices that lead to "preferred outcome". The mathematical
treatment or Utility of "Preferred outcome" was formalised by Leon Walras, and was
further improved by Frank Ramsey, John von Neumann and Oskar Morgenstern in the
book, "The Theory of Games and Economic Behavior".
● Decision Theory combines probability and Utility Theory,
applicable to large economies where individual decisions have
no bearing on others.
● In small organizations, individual decisions significantly
impact others, leading to the development of Game Theory by
Von Neumann and Morgenstern.
● Unlike Decision Theory, Game Theory doesn't prescribe clear
actions.
Probability Utility Theory Decision Theory
● Economists haven't addressed the question of making rational decisions when payoffs
result from a sequence of actions. This was pursued in the field of operations research,
which emerged during WWII.
● Richard Bellman introduced a sequential process, Markov Decision Process, to
formalize a class of sequential problems in the field of operations research.
Pursued in
1.2.4 Neuroscience
● Neuroscience is the study of brain. Though, exact working of human brain
remained mysterious but later it was called a seat of consciousness
● Paul Broca’s study of speech deficit in a damaged-brain patient showed
the existence of localized areas in brain responsible for different function.
The left hemisphere of the brain is responsible for speech production
● The brain is made up of small nerves called neurons. Camillo Golgi
developed a technique to study individual neurons, which Santiago used to
understand the brain's neuron structure. Nicolas was the first to apply a
mathematical model to comprehend how the brain's neurons work.
● Brain cannot use all of its neurons simultaneously but a computer can. But
brain has advantage of storing unlimited information
● A simple collection of cells lead to thought, action, and consciousness or
we can say brain causes mind.
● The most information
processing goes in
cerebral cortex, the outer
layer of the brain.
● When several neurons
sends signal to each other
and communicate at a
junction, this happens
because of
electrochemical reaction.
The signals control brain
activity and this
mechanism is thought to
be the process of learning
in the brain.
1.2.5 Psychology
● Scientific psychology traces its origins to Hermann von Helmholtz
(1821–1894) and his student Wilhelm Wundt (1832–1920).
● Helmholtz applied the scientific method to human vision, creating a
fundamental treatise on vision.
● In 1879, Wundt established the first laboratory of experimental
psychology at the University of Leipzig, emphasizing controlled
experiments and introspection.
● Behaviorism, led by John Watson (1878–1958), rejected mental
processes, focusing on objective measures of stimulus and response.
● Cognitive psychology, viewing the brain as an information-processing
device, can be traced back to William James (1842–1910).
● Helmholtz believed perception involved unconscious logical inference, a
viewpoint later revived in cognitive psychology.
● Frederic Bartlett's Applied Psychology Unit at Cambridge fostered cognitive
modeling, challenging behaviorism.
● Kenneth Craik (1943) outlined three key steps for a knowledge-based agent:
translating stimulus, cognitive manipulation, and retranslation into action.
● Donald Broadbent continued Craik's work, modeling psychological
phenomena as information processing.
● Cognitive science emerged in the U.S., influenced by computer modeling and
key presentations in a 1956 MIT workshop by Miller, Chomsky, and Newell-
Simon.
______________
1.2.6 Computer Engineering
● Essentials for AI Success: AI success relies on combining intelligence with a computing
artifact, with the computer being the primary tool.
● World War II Contributions: The first operational computers emerged during World War
II, including Heath Robinson and Colossus by Alan Turing's team, Z-3 by Konrad Zuse,
and ENIAC by John Mauchly and John Eckert.
● Evolution of Computer Performance: Computer performance has evolved,
emphasizing parallelism since 2005, after which the focus shifted from
increasing clock speed to multiplying CPU cores.
● Calculating Devices Before Computers: Automated machines from the
17th century preceded electronic computers, with Joseph Marie
Jacquard's programmable loom in 1805 and Charles Babbage's
ambitious Analytical Engine in the mid-19th century.
● Babbage's Unfinished Machines: In 1991, Charles
Babbage designed the Difference Engine for
mathematical computations. He also designed the
Analytical Engine, the first artifact capable of
universal computation.
● Ada Lovelace's Contribution: Ada Lovelace,
Babbage's colleague, is considered the world's first
programmer. She had written programs for the
unfinished Analytical Engine.
● Debt to Computer Science: AI owes a debt to
computer science for operating systems,
programming languages, and tools. However, AI
has also contributed significantly to mainstream
computer science with ideas like time sharing,
interactive interpreters, and more.
● AI Pioneering Ideas: AI has pioneered concepts adopted in mainstream computer
science, including personal computers with windows and mice, rapid development
environments, linked list data type, automatic storage management, and key
concepts of symbolic, functional, declarative, and object-oriented programming.
● Software Side Contribution: The software side of computer science has played a
vital role in providing tools and languages for writing modern programs and papers
about them.
● Reciprocal Impact: The relationship between AI and computer science is reciprocal,
with both fields influencing and benefiting from each other's advancements.
1.2.7 Control theory and Cybernetics
● Control theory and cybernetics contributed to AI by providing frameworks for
understanding and regulating the behavior of systems.
● Cybernetics is the interdisciplinary study of the structure, function, and dynamics of
systems, particularly those that involve communication and control. It explores the
principles of feedback, information, and regulation in various types of systems,
including biological, mechanical, and social systems.Cybernetic principles, such as
goal-oriented feedback, played a crucial role in the development of learning
algorithms in AI.
● Control theory offered insights into system stability, helping AI engineers
design robust and reliable autonomous systems.
● The integration of cybernetic ideas into AI allowed for the creation of self-
regulating systems capable of adapting to changing environments.
● The study of control mechanisms in biological systems inspired the design
of adaptive algorithms in AI, mirroring natural learning processes.
● Cybernetics influenced the development of intelligent agents, enabling them
to perceive, reason, and act in a manner analogous to how living organisms
interact with their environment.
● Control theory and cybernetics continue to shape AI research, providing
theoretical foundations and practical tools for designing efficient and
responsive artificial systems.
__________________
1.2.8 Linguistics
● B.F. Skinner's "Verbal Behavior" (1957) presented behaviorism in language learning.
● Noam Chomsky's critique questioned behaviorism's inability to explain language
creativity.
● Chomsky's own theory, based on syntactic models, offered a programming
potential.
● Modern linguistics and AI emerged simultaneously, forming computational
linguistics.
● Language understanding complexity extends beyond sentence structure to context
and subject matter.
● Early knowledge representation work in AI was closely linked to language and
linguistics.
● The intersection of philosophy and language influenced linguistic research and AI
development.
_________________
1.3
1943-55
The gestation
of Artificial
Intelligence
1956
The birth of
artificial
intelligence
1952-69
Early
enthusiasm,
great
expectations
1966-73
A dose of
reality
1969-79
Knowledge
Based
System
1980-
Present
1986–
present
1987–
present
1995–
present
2001–
present
AI
Becomes
an Industry
The return of
neural
network
AI Adopts
Scientific
Method
The
emergence of
intelligent
agent
The
availability
of large data
sets
1.3.1 The Gestation of AI
In 1943, Warren McCulloch and Walter
Pitts laid the foundation for artificial
intelligence (AI) by creating a model of
artificial neurons inspired by brain
physiology, propositional logic, and
Turing's theory of computation. They
demonstrated that networks of these
neurons could compute any function
and implement logical operations.
Donald Hebb (1949) introduced
Hebbian learning to modify connection
strengths between neurons, a concept
still influential today.
Alan Turing's 1950 article introduced key AI concepts, including the Turing Test,
machine learning, genetic algorithms, and reinforcement learning. Turing also
proposed the Child Programme idea, simulating a child's mind instead of an
adult's.
In 1950, Harvard
students Marvin
Minsky and Dean
Edmonds built the first
neural network
computer, SNARC.
Minsky later explored
universal computation
in neural networks at
Princeton.
1.3.2 The Birth of AI
For the next 20 years, AI was shaped by these people and their connections at
MIT, CMU, Stanford, and IBM. The Dartmouth proposal highlighted that AI
focuses on imitating human abilities, using computer science as its method. AI
became its own field because it had unique goals and methods, unlike control
theory, operations research, or decision theory.
In 1951, John McCarthy, an important person in
AI, finished his PhD at Princeton. Later, in 1956,
he organized a workshop at Dartmouth, which is
considered the starting point of AI. The goal was
to figure out how to make machines simulate
human intelligence. Attendees included famous
researchers like Allen Newell and Herbert Simon.
The workshop didn't bring big breakthroughs, but
it united key people.
1.3.3 Early Enthusiasm, great expectations
● In the early days of AI, with basic
computers, pioneers like John
McCarthy and others amazed people
by making computers do clever things.
● Allen Newell and Herbert Simon made
the General Problem Solver, a program
that solved problems like humans. It
sparked the idea that intelligence
involves manipulating symbols.
Outline of General Problem Solver
● At IBM, Herbert Gelernter and Arthur Samuel created AI programs.
● in 1958, McCarthy made Lisp, a key programming language for AI.
● McCarthy later started the AI lab at Stanford to emphasize logic.
● They explored "microworlds" like the blocks world to solve limited but
smart tasks.
● Early work on neural networks, inspired by McCulloch and Pitts, also
advanced.
● All these achievements set the stage for the future of AI.
_________
1.3.4 A dose of reality
● In 1957, Herbert Simon said machines
would think and learn fast. But early AI
had problems.
● Translating languages failed because
computers lacked knowledge.
● Thinking faster with better hardware
didn't work for complex AI challenges.
● In 1973, the Lighthill report criticized AI,
reducing support.
● In 1969, Minsky showed that basic
structures for smart behavior had limit.
● New learning methods came later, but
early AI struggled with big expectations
and real-world difficulties.
"AI Winter"
symbolizes a period
marked by reduced
enthusiasm and
backing for
advancements in
artificial intelligence.
1.3.5 Knowledge Based System
● In early day of AI, AI researchers used weak methods or general searches for
solutions.
● DENDRAL ,an expert system, broke ground using specific knowledge for
molecular structure. It replaced exhaustive searches with chemists' pattern
recognition, making it more efficient.
● DENDRAL was knowledge-intensive and used specialized rules.
● MYCIN, another expert system, was a backward chaining expert system that
used AI to identify microorganisms causing severe diseases like bacteremia
and meningitis and propose antibiotics based on patient weight.
● Since then, domain knowledge became crucial in natural language
understanding. While early systems like SHRDLU had limitations, Roger
Schank's work at Yale emphasized knowledge representation and reasoning
for language understanding.
● Real-world applications led to different languages, from logic-based Prolog to
Minsky's frame-based approach.
1.3.6 AI Becomes An Industry
In the early 1980s, the first successful
commercial expert system, R1, operated
at Digital Equipment Corporation, saving
millions of dollars. By 1988, major
corporations like DEC and DuPont had
deployed numerous expert systems,
resulting in significant cost savings. The
AI industry grew rapidly, reaching billions
of dollars with companies developing
expert systems, vision systems, robots,
and specialized software and hardware.
However, the period known as the "AI
Winter" followed, marked by companies
failing to fulfill grand promises, leading to
a downturn in the AI industry.
1.3.7 The Return of Neural Network
In the 1980s, researchers rediscovered a learning algorithm called back-
propagation, first found in 1969. They applied it to solve learning problems
in computer science and psychology. Some thought that connectionist
models, which emphasize neural networks, could challenge symbolic and
logic-based approaches in AI. There was a debate about whether
manipulating symbols played a crucial role in human thinking. Nowadays,
we see both connectionist and symbolic approaches as working together,
not competing. Current neural network research has two branches: one
focuses on designing effective systems, and the other studies the
properties of real neurons.
1.3.8 AI Adopts Scientific Method
● In recent years, there has been a significant shift in artificial intelligence (AI)
towards building on existing theories, rigorous experimentation, and real-world
applications.
● AI, once isolated, is now integrating with fields like control theory and
statistics.
● The scientific method is firmly applied and, now, AI requires hypotheses to
undergo empirical experiments and statistical analysis.
● Recent dominance by hidden Markov models (HMMs) is due to their rigorous
theory and training on real speech data.
● Similar trends are seen in machine translation and neural networks, which now
benefit from improved methodology and theoretical frameworks.
● Judea Pearl's work in probabilistic reasoning led to a new acceptance of
probability and decision theory, with Bayesian networks dominating
uncertain reasoning in AI.
● Normative expert systems, acting rationally based on decision theory,
have become prominent.
● Similar revolutions have occurred in robotics, computer vision, and
knowledge representation, as increased formalization and integration
with machine learning prove effective in solving complex problems.
______________
1.3.9 The emergence of Intelligent Agent
Researchers are looking again at the "whole agent" challenge in AI, like the SOAR
architecture. The Internet is a big deal for smart agents, used in things like search
engines.
Creating complete agents shows the need to shake up AI fields and handle
uncertainties in sensory systems. AI now works closely with areas like control theory
and economics, especially in things like controlling robotic cars.
● Despite successes, some AI leaders like McCarthy, Minsky,
Nilsson, and Winston weren't happy.
● They wanted AI to go back to its original goal of making human-
like AI (HLAI), focusing on machines that think, learn, and create.
● Another idea was Artificial General Intelligence (AGI), aiming for a
universal way of learning and acting in any situation rightly and
making sure AI is friendly and not a worry in this journey.
__________________
1.3.10 The availability of large data sets.
In the past 60 years of computer science, people mostly focused on creating
algorithms. But now, in AI, we're realizing that for many problems, it's more useful to
focus on the data instead
of getting too caught up
in which algorithm to use.
This change is because
we have a lot of data
available, like trillions of
English words or billions
of web images.
An important study by Yarowsky showed that, for tasks like figuring out
the meaning of a word in a sentence, you can do it really well without
human-labeled examples. Another study by Banko and Brill found that
having more data is often more helpful than choosing a specific
algorithm.
For instance, Hays and Efros improved a photo-filling tool by using a
bigger collection of photos. This shift in thinking suggests that in AI,
where we need a lot of knowledge, we might rely more on learning from
data instead of manually coding everything.
With the rise of new AI applications, some say we're moving from "AI
Winter" to a new era, “AI Summer”, as AI becomes a fundamental part of
many industries, as noted by Kurzweil.
1.4 The State of The Art
AI today does various tasks:
1. Robotic Vehicles: Driverless cars like
STANLEY navigate terrains using
cameras and sensors.
2. Speech Recognition: Systems guide
conversations, like booking flights with
an automated phone system.
3. Autonomous Planning: NASA's
Remote Agent autonomously plans
spacecraft operations.
4. Game Playing: IBM's DEEP BLUE
beat the world chess champion, Garry
Kasparov.
5. Spam Fighting: Learning algorithms classify
over a billion messages daily to identify and filter
spam.
6. Logistics Planning: During the Persian Gulf
crisis, AI tools like DART automated complex
logistics planning for the U.S. forces.
7. Robotics: iRobot's Roomba vacuum and
PackBot handle various tasks, from cleaning
homes to hazardous materials disposal.
8. Machine Translation: Programs translate
languages, like Arabic to English, using
statistical models trained on vast text examples.
These are real applications of AI, showing its
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Introduction to Artificial Intelligence and History of AI

  • 1. Introduction to AI - By Sheetal Jain
  • 2. Before We Begin Studying AI is Valuable but before delving into the Study of AI, defining is important. For years, we human thought how we humans think and why only a few living organisms can perceive, understand, manipulate, and predict.But AI goes beyond it, it doesn’t just understand but also builds intelligent entities. “Artificial Intelligence is a technology that can perform tasks which require human cognition”
  • 3. 1.1 What is AI ? Scientists have approached artificial intelligence in various ways, looking at it from different angles. Some focus on making machines smart like humans, while others aim to create systems that can perform tasks intelligently. These approaches are as follows: Approaches to AI Thought Process and Reasoning Human Behavior Acting Rationally (The Rational Agent Approach) Acting Humanly (Turing Test) Thinking Rationally (The Laws of Thought) Thinking Humanly (Cognitive Modeling)
  • 4. 1.1.1 Thinking Humanly: Cognitive Modeling ● This approach first requires to understand how human brain works on problems. ● We can understand how human brain works through introspection, psychological experiments, and brain imaging ● If we grasp how the human brain works, we can write a computer program on the same principles. ● Now, we need to ensure that the program input-output are correspond those of human brain. Experimental tools of Psychology Precise Theory of Human Brain AI Modeling
  • 5. 1.1.2 Thinking Rationally : The laws of thought approach ● Aristotle was the first person to codify “Right Thinking Process” or “Irrefutable Reasoning Process”. ● He gave idea of Syllogism ● This study of laws of thought led to the birth to the term “Logic”. ● Logist tradition of AI hopes to build program to create intelligent system ● Challenges encountered in this approach are: (i) Notation of the informal knowledge in formal term using logical notation. (ii) Big difference between solving problems in principle and solving problem in practical
  • 6. 1.1.3 Acting Humanly : Turing Test ● The Turing Test, devised by British mathematician Alan Turing, suggests that if a person cannot reliably tell whether they are communicating with a computer or a human through terminals, then the machine demonstrates human-like behavior. ● Following are the process under turing tests that a machine need to pass: - Natural Language Processing: Understanding of Language - Knowledge Representation: Storing information - Automatic Reasoning: using stored data to draw conclusion - Machine Learning: Adopting new situation and drawing patterns - Computer Vision: To perceive object - Robotics: To manipulate object physically
  • 7. 1.1.4 Acting Rationally: The Rational Agent Approach ● The word agent is a latin word which means “To do” and computer agents are expected to do more: Operate Automatically, Perceive Environment, Persist over a prolonged period, adopt changes, create and pursue goals. ● The word “Rational” means “Act to achieve best or best expected outcome”. ● Combining above two points, the rational agent focuses on course of action. ● All the skills required in turing test also allow an agent to act rationally. ● Knowledge representation and reasoning will enable good decision making and then we require to generate natural general sentences to cope with complex society. ● Rational Agent approach has two advantages over other approaches to AI: The solution will be more general and this approach is acceptable to scientific development. This is because the standard of acting rationally is well defined mathematically and in more general way.
  • 8. 1.2 The Foundation Of Artificial Intelligence This section provide a brief history of 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
  • 9. 1.2.1 Philosophy ● In ancient times (around 4th century B.C.), Aristotle created basic rules for thinking and made a system for logical reasoning called syllogisms. ● Aristotle's system allowed people to draw conclusions in a mechanical way from starting ideas. ● Ramon Lull later thought that machines could do logical thinking in a mechanical way. ● In the 17th century, Thomas Hobbes compared thinking to doing math, suggesting a link between mental processes and calculations. ● Around the year 1500, Leonardo da Vinci designed a working mechanical calculator, showing early progress in automation. ● Wilhelm Schickard (1623) and Blaise Pascal (1642) made machines that could do calculations, with Pascal saying they act a lot like thought. ● Gottfried Wilhelm Leibniz (1646–1716) made a machine that could do operations on ideas, going beyond basic math.
  • 10. ● Descartes (1596–1650) proposed rationalism, dualism, and materialism. ● The empiricism movement, led by Bacon and Locke, said knowledge comes from experiences with our senses. ● Logical positivism, created by the Vienna Circle, mixed ideas from thinking and experiences, connecting knowledge to what we observe. Rationalism Dualism Materialism The power of reasoning in understanding world There is a part of human brain which is out of nature Brain operation, according to the physics law, constitute brain ● Hence, the philosophical picture of the mind is constituted by connection between knowledge and actions
  • 11. 1.2.2 Mathematics Philosopher gave fundamental idea of AI but formal science required mathematical formalization. There are three areas of focus under this discipline: Logic George Boole (1815–1864) initiated the development of propositional, or Boolean, logic, which serves as the basis for logical reasoning. In 1879, Gottlob Frege (1848–1925) expanded Boole's work, introducing first-order logic that is widely used today. Alfred Tarski (1902–1983) contributed by presenting a theory of reference, facilitating the connection between logical entities and real-world objects. Computation In AI, computation is fundamental, relying on logical operations and algorithms. By manipulating symbols and data, computers mimic intelligent behavior, facilitating problem-solving, learning, and decision-making across different fields. The essence of AI computation lies in mathematical models, algorithms, and data processing, replicating cognitive functions and advancing the creation of intelligent systems and machines. Probability Probability, the third big math idea in AI, started with Gerolamo Cardano and was developed by Blaise Pascal and others. It began with gambling but became crucial for dealing with uncertainty in sciences. People like James Bernoulli, Pierre Laplace, and Thomas Bayes improved the theory and introduced ways to use statistics. Thomas Bayes suggested a rule for updating probabilities with new evidence, which is a key part of how AI systems handle uncertainty today.
  • 12. 1.2.3 Economics ● Adam Smith gave birth to economics by launching his book An Inquiry into the nature and the cause of wealth of nations . Smith was the first person to treat economic as science. Economists always thought that economy is always about money, but it was about how people make choices that lead to "preferred outcome". The mathematical treatment or Utility of "Preferred outcome" was formalised by Leon Walras, and was further improved by Frank Ramsey, John von Neumann and Oskar Morgenstern in the book, "The Theory of Games and Economic Behavior".
  • 13. ● Decision Theory combines probability and Utility Theory, applicable to large economies where individual decisions have no bearing on others. ● In small organizations, individual decisions significantly impact others, leading to the development of Game Theory by Von Neumann and Morgenstern. ● Unlike Decision Theory, Game Theory doesn't prescribe clear actions. Probability Utility Theory Decision Theory
  • 14. ● Economists haven't addressed the question of making rational decisions when payoffs result from a sequence of actions. This was pursued in the field of operations research, which emerged during WWII. ● Richard Bellman introduced a sequential process, Markov Decision Process, to formalize a class of sequential problems in the field of operations research. Pursued in
  • 15. 1.2.4 Neuroscience ● Neuroscience is the study of brain. Though, exact working of human brain remained mysterious but later it was called a seat of consciousness ● Paul Broca’s study of speech deficit in a damaged-brain patient showed the existence of localized areas in brain responsible for different function. The left hemisphere of the brain is responsible for speech production ● The brain is made up of small nerves called neurons. Camillo Golgi developed a technique to study individual neurons, which Santiago used to understand the brain's neuron structure. Nicolas was the first to apply a mathematical model to comprehend how the brain's neurons work. ● Brain cannot use all of its neurons simultaneously but a computer can. But brain has advantage of storing unlimited information ● A simple collection of cells lead to thought, action, and consciousness or we can say brain causes mind.
  • 16. ● The most information processing goes in cerebral cortex, the outer layer of the brain. ● When several neurons sends signal to each other and communicate at a junction, this happens because of electrochemical reaction. The signals control brain activity and this mechanism is thought to be the process of learning in the brain.
  • 17. 1.2.5 Psychology ● Scientific psychology traces its origins to Hermann von Helmholtz (1821–1894) and his student Wilhelm Wundt (1832–1920). ● Helmholtz applied the scientific method to human vision, creating a fundamental treatise on vision. ● In 1879, Wundt established the first laboratory of experimental psychology at the University of Leipzig, emphasizing controlled experiments and introspection. ● Behaviorism, led by John Watson (1878–1958), rejected mental processes, focusing on objective measures of stimulus and response. ● Cognitive psychology, viewing the brain as an information-processing device, can be traced back to William James (1842–1910).
  • 18. ● Helmholtz believed perception involved unconscious logical inference, a viewpoint later revived in cognitive psychology. ● Frederic Bartlett's Applied Psychology Unit at Cambridge fostered cognitive modeling, challenging behaviorism. ● Kenneth Craik (1943) outlined three key steps for a knowledge-based agent: translating stimulus, cognitive manipulation, and retranslation into action. ● Donald Broadbent continued Craik's work, modeling psychological phenomena as information processing. ● Cognitive science emerged in the U.S., influenced by computer modeling and key presentations in a 1956 MIT workshop by Miller, Chomsky, and Newell- Simon. ______________
  • 19. 1.2.6 Computer Engineering ● Essentials for AI Success: AI success relies on combining intelligence with a computing artifact, with the computer being the primary tool. ● World War II Contributions: The first operational computers emerged during World War II, including Heath Robinson and Colossus by Alan Turing's team, Z-3 by Konrad Zuse, and ENIAC by John Mauchly and John Eckert.
  • 20. ● Evolution of Computer Performance: Computer performance has evolved, emphasizing parallelism since 2005, after which the focus shifted from increasing clock speed to multiplying CPU cores. ● Calculating Devices Before Computers: Automated machines from the 17th century preceded electronic computers, with Joseph Marie Jacquard's programmable loom in 1805 and Charles Babbage's ambitious Analytical Engine in the mid-19th century.
  • 21. ● Babbage's Unfinished Machines: In 1991, Charles Babbage designed the Difference Engine for mathematical computations. He also designed the Analytical Engine, the first artifact capable of universal computation. ● Ada Lovelace's Contribution: Ada Lovelace, Babbage's colleague, is considered the world's first programmer. She had written programs for the unfinished Analytical Engine. ● Debt to Computer Science: AI owes a debt to computer science for operating systems, programming languages, and tools. However, AI has also contributed significantly to mainstream computer science with ideas like time sharing, interactive interpreters, and more.
  • 22. ● AI Pioneering Ideas: AI has pioneered concepts adopted in mainstream computer science, including personal computers with windows and mice, rapid development environments, linked list data type, automatic storage management, and key concepts of symbolic, functional, declarative, and object-oriented programming. ● Software Side Contribution: The software side of computer science has played a vital role in providing tools and languages for writing modern programs and papers about them. ● Reciprocal Impact: The relationship between AI and computer science is reciprocal, with both fields influencing and benefiting from each other's advancements.
  • 23. 1.2.7 Control theory and Cybernetics ● Control theory and cybernetics contributed to AI by providing frameworks for understanding and regulating the behavior of systems. ● Cybernetics is the interdisciplinary study of the structure, function, and dynamics of systems, particularly those that involve communication and control. It explores the principles of feedback, information, and regulation in various types of systems, including biological, mechanical, and social systems.Cybernetic principles, such as goal-oriented feedback, played a crucial role in the development of learning algorithms in AI.
  • 24. ● Control theory offered insights into system stability, helping AI engineers design robust and reliable autonomous systems. ● The integration of cybernetic ideas into AI allowed for the creation of self- regulating systems capable of adapting to changing environments. ● The study of control mechanisms in biological systems inspired the design of adaptive algorithms in AI, mirroring natural learning processes. ● Cybernetics influenced the development of intelligent agents, enabling them to perceive, reason, and act in a manner analogous to how living organisms interact with their environment. ● Control theory and cybernetics continue to shape AI research, providing theoretical foundations and practical tools for designing efficient and responsive artificial systems. __________________
  • 25. 1.2.8 Linguistics ● B.F. Skinner's "Verbal Behavior" (1957) presented behaviorism in language learning. ● Noam Chomsky's critique questioned behaviorism's inability to explain language creativity. ● Chomsky's own theory, based on syntactic models, offered a programming potential. ● Modern linguistics and AI emerged simultaneously, forming computational linguistics. ● Language understanding complexity extends beyond sentence structure to context and subject matter. ● Early knowledge representation work in AI was closely linked to language and linguistics. ● The intersection of philosophy and language influenced linguistic research and AI development. _________________
  • 26. 1.3
  • 27. 1943-55 The gestation of Artificial Intelligence 1956 The birth of artificial intelligence 1952-69 Early enthusiasm, great expectations 1966-73 A dose of reality 1969-79 Knowledge Based System 1980- Present 1986– present 1987– present 1995– present 2001– present AI Becomes an Industry The return of neural network AI Adopts Scientific Method The emergence of intelligent agent The availability of large data sets
  • 28. 1.3.1 The Gestation of AI In 1943, Warren McCulloch and Walter Pitts laid the foundation for artificial intelligence (AI) by creating a model of artificial neurons inspired by brain physiology, propositional logic, and Turing's theory of computation. They demonstrated that networks of these neurons could compute any function and implement logical operations. Donald Hebb (1949) introduced Hebbian learning to modify connection strengths between neurons, a concept still influential today.
  • 29. Alan Turing's 1950 article introduced key AI concepts, including the Turing Test, machine learning, genetic algorithms, and reinforcement learning. Turing also proposed the Child Programme idea, simulating a child's mind instead of an adult's. In 1950, Harvard students Marvin Minsky and Dean Edmonds built the first neural network computer, SNARC. Minsky later explored universal computation in neural networks at Princeton.
  • 30. 1.3.2 The Birth of AI For the next 20 years, AI was shaped by these people and their connections at MIT, CMU, Stanford, and IBM. The Dartmouth proposal highlighted that AI focuses on imitating human abilities, using computer science as its method. AI became its own field because it had unique goals and methods, unlike control theory, operations research, or decision theory. In 1951, John McCarthy, an important person in AI, finished his PhD at Princeton. Later, in 1956, he organized a workshop at Dartmouth, which is considered the starting point of AI. The goal was to figure out how to make machines simulate human intelligence. Attendees included famous researchers like Allen Newell and Herbert Simon. The workshop didn't bring big breakthroughs, but it united key people.
  • 31. 1.3.3 Early Enthusiasm, great expectations ● In the early days of AI, with basic computers, pioneers like John McCarthy and others amazed people by making computers do clever things. ● Allen Newell and Herbert Simon made the General Problem Solver, a program that solved problems like humans. It sparked the idea that intelligence involves manipulating symbols. Outline of General Problem Solver
  • 32. ● At IBM, Herbert Gelernter and Arthur Samuel created AI programs. ● in 1958, McCarthy made Lisp, a key programming language for AI. ● McCarthy later started the AI lab at Stanford to emphasize logic. ● They explored "microworlds" like the blocks world to solve limited but smart tasks. ● Early work on neural networks, inspired by McCulloch and Pitts, also advanced. ● All these achievements set the stage for the future of AI. _________
  • 33. 1.3.4 A dose of reality ● In 1957, Herbert Simon said machines would think and learn fast. But early AI had problems. ● Translating languages failed because computers lacked knowledge. ● Thinking faster with better hardware didn't work for complex AI challenges. ● In 1973, the Lighthill report criticized AI, reducing support. ● In 1969, Minsky showed that basic structures for smart behavior had limit. ● New learning methods came later, but early AI struggled with big expectations and real-world difficulties. "AI Winter" symbolizes a period marked by reduced enthusiasm and backing for advancements in artificial intelligence.
  • 34. 1.3.5 Knowledge Based System ● In early day of AI, AI researchers used weak methods or general searches for solutions. ● DENDRAL ,an expert system, broke ground using specific knowledge for molecular structure. It replaced exhaustive searches with chemists' pattern recognition, making it more efficient. ● DENDRAL was knowledge-intensive and used specialized rules. ● MYCIN, another expert system, was a backward chaining expert system that used AI to identify microorganisms causing severe diseases like bacteremia and meningitis and propose antibiotics based on patient weight. ● Since then, domain knowledge became crucial in natural language understanding. While early systems like SHRDLU had limitations, Roger Schank's work at Yale emphasized knowledge representation and reasoning for language understanding. ● Real-world applications led to different languages, from logic-based Prolog to Minsky's frame-based approach.
  • 35. 1.3.6 AI Becomes An Industry In the early 1980s, the first successful commercial expert system, R1, operated at Digital Equipment Corporation, saving millions of dollars. By 1988, major corporations like DEC and DuPont had deployed numerous expert systems, resulting in significant cost savings. The AI industry grew rapidly, reaching billions of dollars with companies developing expert systems, vision systems, robots, and specialized software and hardware. However, the period known as the "AI Winter" followed, marked by companies failing to fulfill grand promises, leading to a downturn in the AI industry.
  • 36. 1.3.7 The Return of Neural Network In the 1980s, researchers rediscovered a learning algorithm called back- propagation, first found in 1969. They applied it to solve learning problems in computer science and psychology. Some thought that connectionist models, which emphasize neural networks, could challenge symbolic and logic-based approaches in AI. There was a debate about whether manipulating symbols played a crucial role in human thinking. Nowadays, we see both connectionist and symbolic approaches as working together, not competing. Current neural network research has two branches: one focuses on designing effective systems, and the other studies the properties of real neurons.
  • 37. 1.3.8 AI Adopts Scientific Method ● In recent years, there has been a significant shift in artificial intelligence (AI) towards building on existing theories, rigorous experimentation, and real-world applications. ● AI, once isolated, is now integrating with fields like control theory and statistics. ● The scientific method is firmly applied and, now, AI requires hypotheses to undergo empirical experiments and statistical analysis. ● Recent dominance by hidden Markov models (HMMs) is due to their rigorous theory and training on real speech data. ● Similar trends are seen in machine translation and neural networks, which now benefit from improved methodology and theoretical frameworks.
  • 38. ● Judea Pearl's work in probabilistic reasoning led to a new acceptance of probability and decision theory, with Bayesian networks dominating uncertain reasoning in AI. ● Normative expert systems, acting rationally based on decision theory, have become prominent. ● Similar revolutions have occurred in robotics, computer vision, and knowledge representation, as increased formalization and integration with machine learning prove effective in solving complex problems. ______________
  • 39. 1.3.9 The emergence of Intelligent Agent Researchers are looking again at the "whole agent" challenge in AI, like the SOAR architecture. The Internet is a big deal for smart agents, used in things like search engines. Creating complete agents shows the need to shake up AI fields and handle uncertainties in sensory systems. AI now works closely with areas like control theory and economics, especially in things like controlling robotic cars.
  • 40. ● Despite successes, some AI leaders like McCarthy, Minsky, Nilsson, and Winston weren't happy. ● They wanted AI to go back to its original goal of making human- like AI (HLAI), focusing on machines that think, learn, and create. ● Another idea was Artificial General Intelligence (AGI), aiming for a universal way of learning and acting in any situation rightly and making sure AI is friendly and not a worry in this journey. __________________
  • 41. 1.3.10 The availability of large data sets. In the past 60 years of computer science, people mostly focused on creating algorithms. But now, in AI, we're realizing that for many problems, it's more useful to focus on the data instead of getting too caught up in which algorithm to use. This change is because we have a lot of data available, like trillions of English words or billions of web images.
  • 42. An important study by Yarowsky showed that, for tasks like figuring out the meaning of a word in a sentence, you can do it really well without human-labeled examples. Another study by Banko and Brill found that having more data is often more helpful than choosing a specific algorithm. For instance, Hays and Efros improved a photo-filling tool by using a bigger collection of photos. This shift in thinking suggests that in AI, where we need a lot of knowledge, we might rely more on learning from data instead of manually coding everything. With the rise of new AI applications, some say we're moving from "AI Winter" to a new era, “AI Summer”, as AI becomes a fundamental part of many industries, as noted by Kurzweil.
  • 43. 1.4 The State of The Art AI today does various tasks: 1. Robotic Vehicles: Driverless cars like STANLEY navigate terrains using cameras and sensors. 2. Speech Recognition: Systems guide conversations, like booking flights with an automated phone system. 3. Autonomous Planning: NASA's Remote Agent autonomously plans spacecraft operations. 4. Game Playing: IBM's DEEP BLUE beat the world chess champion, Garry Kasparov.
  • 44. 5. Spam Fighting: Learning algorithms classify over a billion messages daily to identify and filter spam. 6. Logistics Planning: During the Persian Gulf crisis, AI tools like DART automated complex logistics planning for the U.S. forces. 7. Robotics: iRobot's Roomba vacuum and PackBot handle various tasks, from cleaning homes to hazardous materials disposal. 8. Machine Translation: Programs translate languages, like Arabic to English, using statistical models trained on vast text examples. These are real applications of AI, showing its

Editor's Notes

  1. Introspection : catching your own thoughts, psychological experiments: Observing actions of human, brain imaging: observing actions of brain