Bachelor of Science (Honours) in Data Science and Artificial Intelligence

DA109: AI Basics
Learning Objectives

➢ Understand the Foundations of Artificial Intelligence

➢ Explore Problem-Solving Techniques

➢ Master Knowledge Representation and Reasoning

➢ Introduction to Learning

2
Course Structure

➢ Complete course is divided into 12 modules

❑ Module 1: Introduction to AI

❑ Module 2: Agents and Environment

❑ Module 3: Problems in AI

❑ Module 4: Problem Solving by Uninformed Strategies

❑ Module 5: Problem Solving by Informed Strategies

❑ Module 6: Knowledge Representation

3
Course Structure

➢ Complete course is divided into 12 modules

❑ Module 7: Automated Planning

❑ Module 8: Uncertain Knowledge and Reasoning: Quantifying Uncertainty

❑ Module 9: Uncertain Knowledge and Reasoning: Probabilistic Reasoning

❑ Module 10: Introduction to Learning

❑ Module 11: Multi-armed Bandit

❑ Module 12: Q Learning

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5
Bachelor of Science (Honours) in Data Science and Artificial Intelligence

DA109: AI Basics

Module 1

Introduction to AI
Learning objective of Module 1

➢ Understand why AI is important with Foundations and History of AI

7
Parts
➢ Applications
❑ Brief overview of the day-to-day life AI applications.

➢ What is AI?
❑ a comprehensive understanding of what AI is, its historical context, core concepts.

➢ The Foundations of AI
❑ A brief history of the disciplines that contributed ideas, viewpoints, and techniques to AI.

➢ The History of AI
❑ Aim to provide development of AI itself

➢ The State of the Art

❑ Detailed discussion about what AI can do?
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 Part - I
AI Applications

9
Applications of AI

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Applications of AI
➢ Healthcare

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Applications of AI
➢ Healthcare

AI based pipeline for healthcare application

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Applications of AI
➢ Agriculture

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Applications of AI
➢ Agriculture

AI based pipeline for agriculture application

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Applications of AI
➢ Transportation

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Applications of AI
➢ Movie

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Applications of AI
➢ Games

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Applications of AI

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 Part - II
What is AI?

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What is Intelligence?
➢ What is Intelligence?

❑ “the capacity to learn and solve problems” (Websters dictionary)

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What’s involved in Intelligence?

➢ Ability to interact with the real world

❑ to perceive, understand, and act
❑ e.g., speech recognition and understanding and synthesis
❑ e.g., image understanding
❑ e.g., ability to take actions, have an effect

➢ Reasoning and Planning

❑ modeling the external world, given input
❑ solving new problems, planning, and making decisions
❑ ability to deal with unexpected problems, uncertainties

➢ Learning and Adaptation

❑ we are continuously learning and adapting
❑ our internal models are always being “updated”
❑ e.g., a baby learning to categorize and recognize animals
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What is AI?

➢ AI stands for Artificial Intelligence.

➢ What is Intelligence?
❑ “the capacity to learn and solve problems” (Websters dictionary)
❑ in particular,
❖ the ability to solve novel problems
❖ the ability to act rationally
❖ the ability to act like humans

➢ What is Artificial Intelligence?

❑ build and understand intelligent entities or agents
❑ 2 main approaches: “engineering” versus “cognitive modeling”
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What is AI?

➢ Artificial Intelligence (AI) refers to the simulation of human intelligence in machines,

enabling them to perform tasks that typically require human intelligence.

➢ AI techniques aim to replicate human-like cognitive functions, such as perception,

reasoning, learning, and decision-making, in machines.

➢ Some researchers provided definition of AI with human and ideal performance.

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Approaches towards AI Development: Thinking Humanly

❑ “The exciting new effort to make computers think . . . machines with minds, in the full and
literal sense.” (Haugeland, 1985)

❑ “The automation of activities that we associate with human thinking, activities such as
decision-making, problem solving, learning . . .” (Bellman, 1978)

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Approaches towards AI Development: Acting Humanly

❑ “The art of creating machines that perform functions that require intelligence when
performed by people.” (Kurzweil, 1990)

❑ “The study of how to make computers do things at which, at the moment, people
are better.” (Rich and Knight, 1991)

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Approaches towards AI Development: Thinking Rationally

❑ “The study of mental faculties through the use of computational models.” (Charniak and
McDermott, 1985)

❑ “The study of the computations that make it possible to perceive, reason, and act.”
(Winston, 1992)

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Approaches towards AI Development: Acting Rationally

❑ “Computational Intelligence is the study of the design of intelligent agents.” (Poole

et al., 1998)

❑ “AI . . . is concerned with intelligent behavior in artifacts.” (Nilsson, 1998)

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Approaches towards AI Development

➢ Thought processes
❑ “The exciting new effort to make computers think .. Machines with minds, in the full and
literal sense” (Haugeland, 1985)

➢ Behavior
❑ “The study of how to make computers do things at which, at the moment, people are
better.” (Rich, and Knight, 1991)

➢ Activities
❑ The automation of activities that we associate with human thinking, activities such as
decision-making, problem solving, learning… (Bellman)

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Types of AI?

➢ Hard or Strong AI
❑ Generally, artificial intelligence research aims to create AI that can replicate human
intelligence completely.

❑ Strong AI refers to a machine that approaches or supersedes human intelligence,

❖ If it can do typically human tasks,
❖ If it can apply a wide range of background knowledge and
❖ If it has some degree of self-consciousness.

❑ Strong AI aims to build machines whose overall intellectual ability is indistinguishable

from that of a human being.

❑ Hard AI aims to develop AI systems that possess consciousness, self-awareness, and

the ability to understand and learn from diverse contexts, similar to humans.
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Types of AI?

➢ Soft or Weak AI
❑ Weak AI refers to the use of software to study or accomplish specific problem solving
or reasoning tasks that do not encompass the full range of human cognitive abilities.

AI systems that simulate human cognitive functions, such as perception, learning,

reasoning, and decision-making, to some extent

❑ Weak AI does not achieve self-awareness; it demonstrates wide range of human-level

cognitive abilities; it is merely an intelligent, a specific problem-solver.

❑ Soft AI is more aligned with the concept of narrow or weak AI, focusing on solving
particular problems without striving for general intelligence.

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What is Machine?

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What is Machine?

➢ ENIAC (Electronic Numerical Integrator and Computer) was one of the earliest
electronic general-purpose computers

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What is Machine?

➢ ENIAC (Electronic Numerical Integrator and Computer) was one of the earliest
electronic general-purpose computers

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What is Machine?

➢ ENIAC (Electronic Numerical Integrator and Computer) was one of the earliest
electronic general-purpose computers
❑ Development:

❑ Architecture:

❑ Purpose:

❑ Operation:

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What is Machine?

➢ ENIAC (Electronic Numerical Integrator and Computer) was one of the earliest
electronic general-purpose computers
❑ Development: Developed by John Mauchly and J. Presper Eckert at the University of
Pennsylvania's Moore School of Electrical Engineering from 1943 to 1946.

❑ Architecture:

❑ Purpose:

❑ Operation:

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What is Machine?

➢ ENIAC (Electronic Numerical Integrator and Computer) was one of the earliest
electronic general-purpose computers
❑ Development: Developed by John Mauchly and J. Presper Eckert at the University of
Pennsylvania's Moore School of Electrical Engineering from 1943 to 1946.

❑ Architecture: Used around 17,468 vacuum tubes, 70,000 resistors, 10,000 capacitors,
1,500 relays, and hundreds of thousands of soldered joints. It covered about 1,800
square feet of floor space and weighed around 30 tons.

❑ Purpose:

❑ Operation:

37
What is Machine?

➢ ENIAC (Electronic Numerical Integrator and Computer) was one of the earliest
electronic general-purpose computers
❑ Development: Developed by John Mauchly and J. Presper Eckert at the University of
Pennsylvania's Moore School of Electrical Engineering from 1943 to 1946.

❑ Architecture: Used around 17,468 vacuum tubes, 70,000 resistors, 10,000 capacitors,
1,500 relays, and hundreds of thousands of soldered joints. It covered about 1,800
square feet of floor space and weighed around 30 tons.

❑ Purpose: Primarily designed for calculating ballistic trajectories for artillery. Its ability to
perform complex calculations at a much faster rate than human "computers" made it
invaluable for military applications during World War II.

❑ Operation:

38
What is Machine?

➢ ENIAC (Electronic Numerical Integrator and Computer) was one of the earliest
electronic general-purpose computers
❑ Development: Developed by John Mauchly and J. Presper Eckert at the University of
Pennsylvania's Moore School of Electrical Engineering from 1943 to 1946.

❑ Architecture: Used around 17,468 vacuum tubes, 70,000 resistors, 10,000 capacitors,
1,500 relays, and hundreds of thousands of soldered joints. It covered about 1,800
square feet of floor space and weighed around 30 tons.

❑ Purpose: Primarily designed for calculating ballistic trajectories for artillery. Its ability to
perform complex calculations at a much faster rate than human "computers" made it
invaluable for military applications during World War II.

❑ Operation: Programmed using a combination of plugboard wiring and switches. Its

architecture allowed for parallel processing, enabling it to perform calculations much
faster than earlier mechanical computers.
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 Part - III
The Foundations of Artificial Intelligence

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The Foundations of Artificial Intelligence

➢ In this section, we provide a brief history of the disciplines that contributed ideas,
viewpoints, and techniques to AI.
❑ Philosophy

❑ Mathematics

❑ Economics

❑ Neuroscience

❑ Psychology

❑ Computer engineering

❑ Control theory and cybernetics

❑ Linguistics
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The Foundations of Artificial Intelligence

➢ 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 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.

➢ Thomas Hobbes (1588–1679) proposed that reasoning was like numerical computation, that “we add
and subtract in our silent thoughts.”

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The Foundations of Artificial Intelligence

➢ Mathematics
❑ What are the formal rules to draw valid conclusions?
❑ What can be computed?
❑ How do we reason with uncertain information?

➢ Philosophers staked out some of the fundamental ideas of AI, but the leap to a formal science required a
level of mathematical formalization in three fundamental areas: logic, computation, and probability.

➢ The first nontrivial algorithm is thought to be Euclid’s algorithm for computing greatest common divisors.

➢ This fundamental result can also be interpreted as showing that some functions on the integers cannot be
represented by an algorithm—that is, they cannot be computed.

➢ Although decidability and computability are important to an understanding of computation, the notion of
tractability has had an even greater impact.

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The Foundations of Artificial Intelligence

➢ Economics
❑ 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?

➢ The science of economics got its start in 1776, when Scottish philosopher Adam Smith (1723–1790) published
An Inquiry into the Nature and Causes of the Wealth of Nations.

➢ Most people think of economics as being about money, but economists will say that they are really studying
how people make choices that lead to preferred outcomes.

➢ The mathematical treatment of “preferred outcomes” or utility was first formalized by L´eon Walras
(pronounced “Valrasse”) (1834-1910) and was improved by Frank Ramsey (1931) and later by John von
Neumann and Oskar Morgenstern in their book The Theory of Games and Economic Behavior (1944).

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The Foundations of Artificial Intelligence

➢ Neuroscience
❑ How do brains process information?

➢ Neuroscience is the study of the nervous system, particularly the brain. Although the exact way in which the
brain enables thought is one of the great mysteries of science, the fact that it does enable thought has been
appreciated for thousands of years because of the evidence that strong blows to the head can lead to mental
incapacitation.

➢ Paul Broca’s (1824–1880) study of aphasia (speech deficit) in brain-damaged patients in 1861 demonstrated
the existence of localized areas of the brain responsible for specific cognitive functions. In particular, he
showed that speech production was localized to the portion of the left hemisphere now called Broca’s area.
By that time, it was known that the brain consisted of nerve cells, or neurons, but it was not until 1873 that
Camillo Golgi (1843–1926) developed a staining technique allowing the observation of individual neurons in
the brain

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The Foundations of Artificial Intelligence

➢ Neuroscience
➢ Neuron: The parts of a nerve cell or neuron. 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. The axon stretches out for a long distance,
much longer than the scale in this diagram indicates.
Typically, an axon is 1 cm long (100 times the diameter
of the cell body), but can reach up to 1 meter. A 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. The signals control brain activity in the short
term and also enable long-term changes in the
connectivity of neurons. These mechanisms are thought
to form the basis for learning in the brain.
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The Foundations of Artificial Intelligence

➢ Neuroscience

A crude comparison of the raw computational resources available to the IBM BLUE GENE supercomputer, a typical personal
computer of 2008, and the human brain. The brain’s numbers are essentially fixed, whereas the supercomputer’s numbers
have been increasing by a factor of 10 every 5 years or so, allowing it to achieve rough parity with the brain. The personal
computer lags behind on all metrics except cycle time.

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The Foundations of Artificial Intelligence

➢ Psychology
❑ How do humans and animals think and act?

➢ The origins of scientific psychology are usually traced to the work of the German physicist Hermann von
Helmholtz (1821–1894) and his student Wilhelm Wundt (1832–1920).

➢ Helmholtz applied the scientific method to the study of human vision, and his Handbook of Physiological
Optics is even now described as “the single most important treatise on the physics and physiology of
human vision”

➢ In 1879, Wundt opened the first laboratory of experimental psychology, at the University of Leipzig. Wundt
insisted on carefully controlled experiments in which his workers would perform a perceptual or associative
task while introspecting on their thought processes

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The Foundations of Artificial Intelligence

➢ Psychology
❑ How do humans and animals think and act?

➢ Cognitive psychology, which views the brain as an information-processing device, can be traced back at
least to the works of William James (1842–1910).

➢ The Nature of Explanation, by Bartlett’s student and successor Kenneth Craik (1943), forcefully reestablished
the legitimacy of such “mental” terms as beliefs and goals, arguing that they are just as scientific as, say,
using pressure and temperature to talk about gases, despite their being made of molecules that have neither.

➢ After Craik’s death in a bicycle accident in 1945, his work was continued by Donald Broadbent, whose book
Perception and Communication (1958) was one of the first works to model psychological phenomena as
information processing.

➢ Meanwhile, in the United States, the development of computer modeling led to the creation of the field of
cognitive science. 50
The Foundations of Artificial Intelligence

➢ Computer engineering
❑ How can we build an efficient computer?
➢ For artificial intelligence to succeed, we need two things: intelligence and an artifact. The modern digital
electronic computer was invented independently and almost simultaneously by scientists in three countries
embattled in World War II.

➢ The first operational computer was the electromechanical Heath Robinson, built in 1940 by Alan Turing’s
team for a single purpose: deciphering German messages. In 1943, the same group developed the
Colossus, a powerful general-purpose machine based on vacuum tubes.

➢ The first operational programmable computer was the Z-3, the invention of Konrad Zuse in Germany in
1941. Zuse also invented floating-point numbers and the first high-level programming language, Plankalkul.

➢ The first electronic computer, the ABC, was assembled by John Atanasoff and his student Clifford Berry
between 1940 and 1942 at Iowa State University.

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The Foundations of Artificial Intelligence

➢ Linguistics
❑ How does language relate to thought?

➢ In 1957, B. F. Skinner published Verbal Behavior. This was a comprehensive, detailed account of the
behaviorist approach to language learning, written by the foremost expert in the field.

➢ Modern 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.

➢ 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. This might seem obvious, but it was not widely appreciated
until the 1960s.

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 Part - IV
The History of Artificial Intelligence

54
The History of Artificial Intelligence

55
The History of Artificial Intelligence

➢ Maturation of Artificial Intelligence (1943-1952)

❑ Year 1943: The first work which is now recognized as AI was 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: The Alan Turing who was an English mathematician and pioneered Machine
learning in 1950. Alan Turing publishes "Computing Machinery and Intelligence" in which he
proposed a test. The test can check the machine's ability to exhibit intelligent behavior
equivalent to human intelligence, called a Turing test.

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The History of Artificial Intelligence

➢ The birth of Artificial Intelligence (1952-1956)

❑ Year 1955: An Allen Newell and Herbert A. Simon created the "first artificial intelligence
program” Which was named as "Logic Theorist". This program had proved 38 of 52
Mathematics theorems, and find new and more elegant proofs for some theorems.

❑ Year 1956: The word "Artificial Intelligence" first adopted by American Computer scientist John
McCarthy at the Dartmouth Conference. For the first time, AI coined as an academic field.

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The History of Artificial Intelligence

➢ The golden years-Early enthusiasm (1956-1974)

❑ Year 1966: The researchers emphasized developing algorithms which can solve mathematical
problems. Joseph Weizenbaum created the first chatbot in 1966, which was named as ELIZA.

❑ Year 1972: The first intelligent humanoid robot was built in Japan which was named as WABOT-1.

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The History of Artificial Intelligence

➢ The first AI winter (1974-1980)

❑ The duration between years 1974 to 1980 was the first AI winter duration. AI winter refers to the
time period where computer scientist dealt with a severe shortage of funding from government for
AI researches.

❑ During AI winters, an interest of publicity on artificial intelligence was decreased.

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The History of Artificial Intelligence

➢ A boom of AI (1980-1987)

❑ Year 1980: After AI winter duration, AI came back with "Expert System". Expert systems were
programmed that emulate the decision-making ability of a human expert.

❑ In the Year 1980, the first national conference of the American Association of Artificial
Intelligence was held at Stanford University.

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The History of Artificial Intelligence

➢ The second AI winter (1987-1993)

❑ The duration between the years 1987 to 1993 was the second AI Winter duration.

❑ Again Investors and government stopped in funding for AI research as due to high cost but not
efficient result. The expert system such as XCON was very cost effective.

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The History of Artificial Intelligence

➢ The emergence of intelligent agents (1993-2011)

❑ Year 1997: In the year 1997, IBM Deep Blue beats world chess champion, Gary Kasparov, and
became the first computer to beat a world chess champion.

❑ Year 2002: for the first time, AI entered the home in the form of Roomba, a vacuum cleaner.

❑ Year 2006: AI came in the Business world till the year 2006. Companies like Facebook, Twitter,
and Netflix also started using AI.

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The History of Artificial Intelligence

➢ Deep learning, big data and artificial general intelligence (2011-present)

❑ Year 2011: In the year 2011, IBM's Watson won jeopardy, a quiz show, where it had to solve the
complex questions as well as riddles. Watson had proved that it could understand natural
language and can solve tricky questions quickly.

❑ Year 2012: Google has launched an Android app feature "Google now", which was able to provide
information to the user as a prediction.

❑ Year 2014: In the year 2014, Chatbot "Eugene Goostman" won a competition in the infamous
"Turing test.“

❑ Year 2018: The "Project Debater" from IBM debated on complex topics with two master debaters
and also performed extremely well.

❑ Google has demonstrated an AI program "Duplex" which was a virtual assistant and which had
taken hairdresser appointment on call, and lady on other side didn't notice that she was talking
with the machine. 63
AI Failure

➢ 1974-1980: Winter #1
❑ Failure of Machine Translation
❑ Negative results in Neural nets
❑ Poor speech understanding

➢ 1987-1993: Winter #2
❑ Decline of list processing (LISP)
❑ Decline of specialized hardware for expert systems

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AI Success

➢ 1977: Deep Blue ends Human Supremacy in Chess

❑ Its development and victory against world chess champion Garry Kasparov was a
landmark achievement in the field of computer science.

❑ I could feel human-level intelligence across the room

-Gary Kasparov, world chess champion (human) 65
66
 Part - V
The State of the Art

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The State of the Art
➢ What can AI do today?

❑ Robotic Vehicles:

❖ A driverless robotic car named STANLEY sped through the rough terrain of the Mojave
dessert at 22 mph, finishing the 132-mile course first to win the 2005 DARPA Grand
Challenge.

❖ STANLEY is a Volkswagen Touareg outfitted with cameras, radar, and laser rangefinders
to sense the environment and onboard software to command the steering, braking,
and acceleration (Thrun, 2006).

❖ The following year CMU’s BOSS won the Urban Challenge, safely driving in traffic
through the streets of a closed Air Force base, obeying traffic rules and avoiding
pedestrians and other vehicles.

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The State of the Art
➢ What can AI do today?

❑ Speech Recognition:

❖ A traveler calling United Airlines to book a flight can have the entire conversation
guided by an automated speech recognition and dialog management system.

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The State of the Art
➢ What can AI do today?

❑ Autonomous planning and scheduling:

❖ A hundred million miles from Earth, NASA’s Remote Agent program became the first
on-board autonomous planning program to control the scheduling of operations for a
spacecraft (Jonsson et al., 2000).

❖ REMOTE AGENT generated plans from high-level goals specified from the ground
and monitored the execution of those plans—detecting, diagnosing, and recovering
from problems as they occurred.

❖ Successor program MAPGEN (Al-Chang et al., 2004) plans the daily operations for
NASA’s Mars Exploration Rovers, and MEXAR2 (Cesta et al., 2007) did mission
planning—both logistics and science planning—for the European Space Agency’s
Mars Express mission in 2008.
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The State of the Art
➢ What can AI do today?

❑ Game playing:

❖ IBM’s DEEP BLUE became the first computer program to defeat the world champion
in a chess match when it bested Garry Kasparov by a score of 3.5 to 2.5 in an
exhibition match (Goodman and Keene, 1997).

❖ Kasparov said that he felt a “new kind of intelligence” across the board from him.

❖ Newsweek magazine described the match as “The brain’s last stand.” The value of
IBM’s stock increased by $18 billion.

❖ Human champions studied Kasparov’s loss and were able to draw a few matches in
subsequent years, but the most recent human-computer matches have been won
convincingly by the computer.
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The State of the Art
➢ What can AI do today?

❑ Spam fighting:

❖ Each day, learning algorithms classify over a billion messages as spam, saving the
recipient from having to waste time deleting what, for many users, could comprise
80% or 90% of all messages, if not classified away by algorithms.

❖ Because the spammers are continually updating their tactics, it is difficult for a static
programmed approach to keep up, and learning algorithms work best (Sahami et al.,
1998; Goodman and Heckerman, 2004).

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The State of the Art
➢ What can AI do today?

❑ Logistics planning:

❖ During the Persian Gulf crisis of 1991, U.S. forces deployed a Dynamic Analysis and
Re-planning Tool, DART (Cross and Walker, 1994), to do automated logistics planning
and scheduling for transportation.

❖ This involved up to 50,000 vehicles, cargo, and people at a time, and had to account
for starting points, destinations, routes, and conflict resolution among all parameters.

❖ The AI planning techniques generated in hours a plan that would have taken weeks
with older methods.

❖ The Defense Advanced Research Project Agency (DARPA) stated that this single
application more than paid back DARPA’s 30-year investment in AI.
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The State of the Art
➢ What can AI do today?

❑ Robotics:

❖ The iRobot Corporation has sold over two million Roomba robotic vacuum cleaners for
home use.

❖ The company also deploys the more rugged PackBot to Iraq and Afghanistan, where it
is used to handle hazardous materials, clear explosives, and identify the location of
snipers.

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The State of the Art
➢ What can AI do today?

❑ Machine Translation:

❖ A computer program automatically translates from Arabic to English, allowing an English

speaker to see the headline “Ardogan Confirms That Turkey Would Not Accept Any
Pressure, Urging Them to Recognize Cyprus.”

❖ The program uses a statistical model built from examples of Arabic-to-English translations
and from examples of English text totaling two trillion words (Brants et al., 2007).

❖ None of the computer scientists on the team speak Arabic, but they do understand
statistics and machine learning algorithms.

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The State of the Art
➢ What can AI do today?

❑ Face Detection & Face Recognition Technologies:

❖ Face detection technologies are based on

human detection and object detection.
These technologies have a wide range of
applications from demographic analysis to
security.

❖ Facial recognition technologies enable

direct identification and frequently used in
residential and commercial buildings to
provide access to pre-registered visitors,
family members, and authorized staff while
restricting entry to unauthorized individuals.
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The State of the Art
➢ What can AI do today?

❑ Emotion Recognition

❖ Emotion recognition is emerging as a very

important AI feature in business development.

❖ For example, if you have a retail store, you

can make the necessary changes and
updates thanks to these technologies that
provide you with a report on how the
customers who come here react and what
they feel while looking at which parts of the
store, which products and the showcase, and
you can also have detailed information about
your customer profile.
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The State of the Art
➢ What can AI do today?

❑ Telecommunications

❖ In the field of telecommunications, State of the Art is focused on the development of 5G

networks, which will provide faster and more reliable mobile internet access, as well as
the use of software-defined networking (SDN) and network function virtualization (NFV)
to improve network flexibility and scalability.

❖ Additionally, there is a growing focus on the use of satellite-based technologies and

low-Earth orbit (LEO) networks to provide internet access to remote and underserved
areas.

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The State of the Art
➢ What can AI do today?

❑ Environmental Science

❖ In the field of environmental science, State of the Art is focused on the development of
new technologies and policies to address issues such as climate change, pollution, and
resource depletion.

❖ This includes the use of renewable energy sources, such as solar, wind, and hydropower,
as well as the use of energy-efficient technologies and green building design.

❖ Additionally, there is a growing focus on the use of big data and machine learning
techniques to improve our understanding of the Earth’s ecosystems and to develop more
effective conservation and management strategies.

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The State of the Art
➢ What can AI do today?

❑ AI-Powered Chatbots

❖ One of the most popular applications of AI is in the form of chatbots. These are computer
programs that are designed to simulate human conversation.

❖ Chatbots can handle customer queries, provide support and even make sales. Thanks to
their 24/7 availability and rapid response times, chatbots are an invaluable asset for
businesses.

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The State of the Art
➢ What can AI do today?

❑ Computer Vision

❖ Computer vision is a branch of AI that deals with how computers interpret and understand
digital images.

❖ This technology is used in many fields, such as security, automotive and healthcare.

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The State of the Art
➢ What can AI do today?

❑ Natural Language Processing

❖ Natural language processing (NLP) is a form of AI that interprets and understands human
language.

❖ NLP is used in various applications, such as voice recognition, text analysis and machine
translation.

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Summary
➢ This chapter defines AI and establishes the cultural background against which it has developed.

➢ Some of the important points are as follows

❑ Philosopher made AI conceivable by considering the ideas that the mind is in some ways like a machine,
that it operates on knowledge encoded in some internal language.
❑ Mathematicians provided the tools to manipulate statements of logical certainty as well as uncertain,
probabilistic statements.
❑ Economists formalized the problem of making decisions that maximize the expected outcome to the
decision maker.
❑ Neuroscientists discovered some facts about how the brain works and the ways in which it is similar to
and different from computers.
❑ Psychologists adopted the idea that humans and animals can be considered information processing
machines. Linguists showed that language use fits into this model.
❑ Computer engineers provided the ever-more-powerful machines that make AI applications possible.
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