Artificial intelligence (AI) involves developing intelligent machines that can perform tasks normally requiring human intelligence. This document provides an overview of key concepts in AI including definitions of intelligence, AI, and artificial intelligence. It discusses different approaches to AI such as systems that act like humans by passing the Turing test, systems that think like humans through cognitive modeling, systems that think rationally using logical reasoning, and systems that act rationally as intelligent agents. The document also briefly outlines the history and foundations of the AI field.

1. Introduction to AI/ML

2. What is Intelligence?
• Is there a “holistic” definition for intelligence?
• Here are some definitions:
• the ability to comprehend; to understand and profit from experience
• a general mental capability that involves the ability to reason, plan, solve problems, think abstractly,
comprehend ideas and language, and learn
• is effectively perceiving, interpreting and responding to the environment
• perceive, reason and infer, solve problems, learn and adapt, apply common sense, apply analogy,
recall, apply intuition, reach emotional states, achieve self-awareness

3. What is AI?
• The study of how to make programs/computers do things that
people do better
• The study of how to make computers solve problems which
require knowledge and intelligence
• The exciting new effort to make computers think … machines with
minds
• The automation of activities that we associate with human
thinking (e.g., decision-making, learning…)
• The art of creating machines that perform functions that require
intelligence when performed by people
• The study of mental faculties through the use of computational
models
• A field of study that seeks to explain and emulate intelligent
behavior in terms of computational processes
• The branch of computer science that is concerned with the
automation of intelligent behavior
Thinking
machines or
machine
intelligence
Studying
cognitive
faculties
Problem
Solving and CS

4. Systems that act like humans
• You enter a room which has a computer
terminal. You have a fixed period of time to
type what you want into the terminal, and
study the replies. At the other end of the line is
either a human being or a computer system.
• If it is a computer system, and at the end of the
period you cannot reliably determine whether
it is a system or a human, then the system is
deemed to be intelligent.
?

5. Systems that act like humans
• The Turing Test approach
• a human questioner cannot tell if
• there is a computer or a human answering his question, via
teletype (remote communication)
• The computer must behave intelligently
• Intelligent behavior
• to achieve human-level performance in all cognitive
tasks

6. Systems that act like humans
• These cognitive tasks include:
• Natural language processing
• for communication with human
• Knowledge representation
• to store information effectively & efficiently
• Automated reasoning
• to retrieve & answer questions using the stored
information
• Machine learning
• to adapt to new circumstances

7. The total Turing Test
• Includes two more issues:
• Computer vision
• to perceive objects (seeing)
• Robotics
• to move objects (acting)

8. What is Artificial Intelligence ?
Systems that act
rationally
Systems that think
like humans
Systems that think
rationally
Systems that act
like humans
THOUGHT
BEHAVIOUR
HUMAN RATIONAL

9. Systems that think like humans:
cognitive modeling
• Humans as observed from ‘inside’
• How do we know how humans think?
• Introspection vs. psychological experiments
• Cognitive Science
• “The exciting new effort to make computers
think … machines with minds in the full and
literal sense” (Haugeland)
• “[The automation of] activities that we associate
with human thinking, activities such as decision-
making, problem solving, learning …” (Bellman)

10. What is Artificial Intelligence ?
Systems that act
rationally
Systems that think
like humans
Systems that think
rationally
Systems that act
like humans
THOUGHT
BEHAVIOUR
HUMAN RATIONAL

11. Systems that think ‘rationally’
"laws of thought"
• Humans are not always ‘rational’
• Rational - defined in terms of logic?
• Logic can’t express everything (e.g. uncertainty)
• Logical approach is often not feasible in terms of
computation time (needs ‘guidance’)
• “The study of mental facilities through the use of
computational models” (Charniak and
McDermott)
• “The study of the computations that make it
possible to perceive, reason, and act” (Winston)

12. What is Artificial Intelligence ?
Systems that act
rationally
Systems that think
like humans
Systems that think
rationally
Systems that act
like humans
THOUGHT
BEHAVIOUR
HUMAN RATIONAL

13. Systems that act rationally:
“Rational agent”
• Rational behavior: doing the right thing
• The right thing: that which is expected to
maximize goal achievement, given the available
information
• Giving answers to questions is ‘acting’.
• I don't care whether a system:
• replicates human thought processes
• makes the same decisions as humans
• uses purely logical reasoning

14. Systems that act rationally
• Logic  only part of a rational agent, not all of rationality
• Sometimes logic cannot reason a correct conclusion
• At that time, some specific (in domain) human knowledge or information is
used
• Thus, it covers more generally different situations of problems
• Compensate the incorrectly reasoned conclusion

15. Systems that act rationally
• Study AI as rational agent –
2 advantages:
• It is more general than using logic only
• Because: LOGIC + Domain knowledge
• It allows extension of the approach with more scientific methodologies

16. Rational agents
 An agent is an entity that perceives and acts
 This course is about designing rational agents
 Abstractly, an agent is a function from percept histories
to actions:

[f: P*  A]
 For any given class of environments and tasks, we seek
the agent (or class of agents) with the best performance
 Caveat: computational limitations make perfect
rationality unachievable
  design best program for given machine resources


17. • Artificial
• Produced by human art or effort, rather than
originating naturally.
• Intelligence
• is the ability to acquire knowledge and use it"
[Pigford and Baur]
• So AI was defined as:
• AI is the study of ideas that enable computers to be
intelligent.
• AI is the part of computer science concerned with
design of computer systems that exhibit human
intelligence(From the Concise Oxford Dictionary)

18. From the above two definitions, we can see that AI has two major
roles:
• Study the intelligent part concerned with humans.
• Represent those actions using computers.

19. What is AI?
• The definitions on top
are concerned with
thought processes and
reasoning, whereas
the ones on the bottom
address behavior.
• The definitions on the
left measure success
in terms of fidelity to
human performance,
whereas the ones on
the right measure
against an ideal
performance measure,
called rationality.
• A system is rational if
it does the “right thing,”
given what it knows.

20. Acting humanly: The Turing Test approach
• The Turing Test, proposed by Alan Turing (1950), was designed to provide a
satisfactory operational definition of intelligence.
• A computer passes the test if a human interrogator, after posing some written
questions, cannot tell whether the written responses come from a person or
from a computer.
• Programming a computer to pass a rigorously applied test provides plenty to
work on. The computer would need to possess the following capabilities:
• Natural language processing to enable it to 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.

21. Thinking humanly: The cognitive modeling
approach
• If we are going to say that a given program thinks like a human, we
must have some way of determining how humans think.
• We need to get inside the actual workings of human minds.
• There are three ways to do this:
• through introspection—trying to catch our own thoughts as they go by;
• through psychological experiments—observing a person in action; and
• Through brain imaging—observing the brain in action.

22. Thinking humanly: The cognitive modeling
approach
• Once we have a sufficiently precise theory of the mind, it becomes
possible to express the theory as a computer program.
• If the program’s input–output behavior matches corresponding
human behavior, that is evidence that some of the program’s
mechanisms could also be operating in humans.
• The interdisciplinary field of cognitive science brings together
computer models from AI and experimental techniques from
psychology to construct precise and testable theories of the
human mind.

23. Thinking rationally: The “laws of thought”
approach
• The Greek philosopher Aristotle was one of the first to attempt to
codify “right thinking,” that is, irrefutable reasoning processes.
• His syllogisms provided patterns for argument structures that always
yielded correct conclusions when given correct premises.
• for example, “Socrates is a man; all men are mortal; therefore, Socrates is
mortal.”
• These laws of thought were supposed to govern the operation of the
mind; their study initiated the field called logic.

24. Thinking rationally: The “laws of thought”
approach
• Logicians in the 19th century developed a precise notation for
statements about all kinds of objects in the world and the relations
among them. (Contrast this with ordinary arithmetic notation,
which provides only for statements about numbers.)
• By 1965, programs existed that could, in principle, solve any
solvable problem described in logical notation. (Although if no
solution exists, the program might loop forever.)
• The so-called logicist tradition within artificial intelligence hopes
to build on such programs to create intelligent systems.

25. Thinking rationally: The “laws of thought”
approach
• There are two main obstacles to this approach.
• First, 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.
• Second, there is a big difference between solving a problem “in
principle” and solving it in practice.
• Even problems with just a few hundred facts can exhaust the
computational resources of any computer unless it has some
guidance as to which reasoning steps to try first.

26. Acting rationally: The rational agent
approach
• An agent is just something that acts (agent comes from the Latin agere, to do).
• Of course, all computer programs do something, but computer
agents are expected to do more: operate autonomously, perceive
their environment, persist over a prolonged time period, adapt to
change, and create and pursue goals.
• A rational agent is one that acts so as to achieve the best outcome
or, when there is uncertainty, the best expected outcome.

27. Acting rationally: The rational agent
approach
• In the “laws of thought” approach to AI, the emphasis was on correct
inferences.
• Making correct inferences is sometimes part of being a rational agent,
because one way to act rationally is to reason logically to the conclusion
that a given action will achieve one’s goals and then to act on that
conclusion.
• On the other hand, correct inference is not all of rationality; in some
situations, there is no provably correct thing to do, but something must
still be done.
• There are also ways of acting rationally that cannot be said to involve
inference.
• For example, recoiling from a hot stove is a reflex action that is usually more
successful than a slower action taken after careful deliberation.

28. Acting rationally: The rational agent
approach
• All the skills needed for the Turing Test also allow an agent to act
rationally.
• Knowledge representation and reasoning enable agents to reach
good decisions.
• We need to be able to generate comprehensible sentences in
natural language to get by in a complex society.

29. Acting rationally: The rational agent
approach
• The rational-agent approach has two advantages over the other
approaches.
• First, it is more general than the “laws of thought” approach
because correct inference is just one of several possible
mechanisms for achieving rationality.
• Second, it is more amenable to scientific development than are
approaches based on human behavior or human thought.

30. 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
• 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?
• Mathematics
• What are the formal rules to draw valid conclusions?
• What can be computed?
• How do we reason with uncertain information?

31. 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?
• Neuroscience
• How do brains process information?
• Psychology
• How do humans and animals think and act?
• Computer engineering
• How can we build an efficient computer?

32. THE FOUNDATIONS OF ARTIFICIAL
INTELLIGENCE
• Control theory and cybernetics
• How can artifacts operate under their own control?
• Linguistics
• How does language relate to thought?

33. THE HISTORY OF ARTIFICIAL INTELLIGENCE
The gestation of artificial intelligence (1943–1955)
• 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.
• Year 1950: Two undergraduate students at Harvard, Marvin Minsky and Dean Edmonds, built the first neural
network computer in 1950. The SNARC, as it was called, used 3000 vacuum tubes and a surplus automatic
pilot mechanism from a B-24 bomber to simulate a network of 40 neurons.

34. 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.
• At that time high-level computer languages such as FORTRAN, LISP, or COBOL were invented. And the
enthusiasm for AI was very high at that time.

35. THE HISTORY OF ARTIFICIAL INTELLIGENCE
The golden years-Early enthusiasm (1956-1974)
• Year 1958: In MIT AI Lab Memo No. 1,McCarthy defined the high-level language Lisp, which was to
become LISP the dominant AI programming language for the next 30 years.
• 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.

36. 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.

37. 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.

38. 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.

39. 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.

40. 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.

41. THE HISTORY OF ARTIFICIAL INTELLIGENCE
• Now AI has developed to a remarkable level.
• The concept of Deep learning, big data, and data science are now
trending like a boom.
• Nowadays companies like Google, Facebook, IBM, and Amazon are
working with AI and creating amazing devices.
• The future of Artificial Intelligence is inspiring and will come with
high intelligence.

42. Applications of AI in Pattern Recognition
Pattern recognition is one of the most common applications of artificial intelligence
(AI). Here are some examples of how AI is used in pattern recognition:
• Image recognition: AI is used to recognize patterns in images. This is used in facial
recognition, fingerprint recognition, object recognition, and even in medical imaging
to identify different types of tissues.
• Speech recognition: AI is used to recognize patterns in speech. This is used in
virtual assistants like Siri and Alexa, as well as in language translation software.
• Natural language processing: AI is used to recognize patterns in text. This is used
in text-to-speech systems, machine translation, and sentiment analysis.

43. Applications of AI in Pattern Recognition
• Fraud detection: AI is used to recognize patterns in financial transactions to
identify fraudulent behavior.
• Anomaly detection: AI is used to recognize patterns that are outside of the normal
range. This is used in manufacturing to detect defects in products and in
cybersecurity to detect unusual network activity.
• Recommender systems: AI is used to recognize patterns in user behavior to make
recommendations for products, services, and content.
• Predictive analytics: AI is used to recognize patterns in data to make predictions
about future events, such as sales forecasting and demand planning.
Overall, pattern recognition is an important application of AI that has many practical
applications in various industries.

44. Applications of AI in Autonomous planning
and scheduling
Autonomous planning and scheduling involve using artificial intelligence (AI) to create
plans and schedules without the need for human intervention. Here are some
applications of AI in autonomous planning and scheduling:
• Manufacturing: AI is used to plan and schedule production processes, including the
allocation of resources, scheduling of tasks, and optimization of production lines.
• Transportation: AI is used to plan and optimize transportation routes, schedules,
and logistics, including traffic management, vehicle routing, and fleet management.
• Supply chain management: AI is used to plan and optimize supply chain
operations, including inventory management, order processing, and logistics.

45. Applications of AI in Autonomous planning
and scheduling
• Agriculture: AI is used to plan and schedule agricultural operations, including
planting, harvesting, and irrigation, to optimize yields and reduce costs.
• Healthcare: AI is used to plan and schedule patient care, including appointment
scheduling, resource allocation, and patient flow management.
• Construction: AI is used to plan and schedule construction projects, including
resource allocation, task scheduling, and project management.
Overall, AI is a powerful tool for autonomous planning and scheduling, allowing
businesses and organizations to optimize their operations and achieve greater
efficiency and productivity. By automating these processes, AI can help reduce costs,
improve quality, and increase competitiveness.

46. Applications of AI in Game playing
Artificial intelligence (AI) has been used extensively in game playing. Here are some of the
applications of AI in game playing:
• Chess and Go: AI has been used to develop algorithms that can play chess and Go at a
superhuman level. For example, Deep Blue, developed by IBM, defeated the world champion
Garry Kasparov in a chess match in 1997. More recently, Google's AlphaGo program defeated
the world champion Lee Sedol in a Go match in 2016.
• Video games: AI is used in many video games to control the behavior of non-player
characters (NPCs) and to create more challenging opponents for players. AI can also be used
to generate game content and to adapt the game difficulty to the player's skill level.
• Poker: AI has been used to develop algorithms that can play poker at a professional level. In
2017, an AI program developed by Carnegie Mellon University defeated several top poker
players in a tournament.

47. Applications of AI in Game playing
• Board games: AI is used to develop computer opponents for many popular
board games, such as Scrabble, Monopoly, and Risk. AI can also be used to
generate new game variants and to analyze game strategies.
• Multiplayer games: AI is used to match players of similar skill levels in
multiplayer games, to detect cheating and other forms of rule violations, and
to moderate player behavior.
Overall, AI has been used to enhance the gaming experience by creating more
challenging opponents, generating new game content, and adapting the game
difficulty to the player's skill level. AI has also been used to advance our
understanding of game strategy and to develop new gaming technologies.

48. Applications of AI in Spam Filtering
AI is commonly used in spam filtering to detect and filter out unwanted or unsolicited email
messages. Here are some applications of AI in spam filtering:
• Content-based filtering: AI algorithms analyze the content of email messages and compare
them to a database of known spam messages to identify and filter out potential spam
messages. This includes analyzing keywords, phrases, and patterns that are commonly
associated with spam.
• Behavioral filtering: AI algorithms analyze the behavior of email users, such as their history
of opening and responding to messages, to identify and filter out potential spam messages.
• Machine learning: AI algorithms use machine learning techniques to learn from past
examples of spam and non-spam messages, enabling the algorithm to improve its accuracy
over time.

49. Applications of AI in Spam Filtering
• Natural language processing: AI algorithms use natural language processing
techniques to analyze the language used in email messages to identify and filter out
potential spam messages.
• Sender reputation analysis: AI algorithms analyze the reputation of email senders
based on factors such as their past behavior, IP address, and domain reputation to
identify and filter out potential spam messages.
Overall, AI is an essential tool for spam filtering, enabling email providers to protect
users from unwanted and potentially harmful email messages. By using AI, spam
filtering can be automated, efficient, and highly accurate, allowing users to focus on
important emails and reducing the risk of security breaches and other email-related
issues.

50. Applications of AI in Logistics Planning
Artificial intelligence (AI) has numerous applications in logistics planning, which is the
process of organizing, managing, and optimizing the flow of goods and materials from
their origin to their destination. Here are some applications of AI in logistics planning:
• Route optimization: AI can be used to optimize delivery routes by analyzing factors
such as traffic patterns, weather conditions, and road closures, to minimize travel
time and reduce fuel consumption.
• Inventory management: AI can be used to optimize inventory levels by analyzing
demand patterns and adjusting supply accordingly, to reduce waste and avoid
stockouts.
• Predictive maintenance: AI can be used to predict equipment failures and
maintenance needs, to reduce downtime and improve reliability.

51. Applications of AI in Logistics Planning
• Real-time tracking: AI can be used to track shipments in real-time, to improve
visibility and enable timely interventions in case of delays or other issues.
• Risk management: AI can be used to analyze and predict risks, such as theft or
damage to goods, and to develop strategies to mitigate those risks.
• Customer service: AI can be used to improve customer service by providing real-
time information about shipment status and delivery times, and by automating
customer support tasks such as tracking inquiries and return management.
Overall, AI can help logistics companies optimize their operations, reduce costs, and
improve customer satisfaction. By automating and optimizing logistics planning tasks,
AI can help companies to achieve greater efficiency and productivity, while also
reducing environmental impact and improving sustainability.

52. Applications of AI in Machine Translation
Artificial intelligence (AI) has revolutionized machine translation by enabling more accurate
and natural-sounding translations between different languages. Here are some applications of
AI in machine translation:
• Neural machine translation: AI algorithms based on neural networks can be trained on
large datasets of parallel texts to generate more accurate and natural-sounding translations.
• Real-time translation: AI can be used to provide real-time translation services, such as
speech-to-text and text-to-speech translation, enabling people to communicate across
language barriers in real-time.
• Language identification: AI algorithms can be used to identify the language of a text or
speech input, enabling automated translation services to select the appropriate translation
model.

53. Applications of AI in Machine Translation
• Domain-specific translation: AI algorithms can be trained on specialized datasets to
provide more accurate translations for specific domains, such as legal or medical texts.
• Quality control: AI can be used to evaluate the quality of machine translations and identify
errors or inaccuracies, enabling human translators to correct and refine the translations.
• Post-editing automation: AI can be used to automate post-editing tasks, such as
proofreading and formatting, to reduce the time and effort required by human translators.
Overall, AI has transformed machine translation by enabling more accurate and natural-
sounding translations, and by making translation services more accessible and efficient. By
using AI-powered machine translation, individuals and organizations can overcome language
barriers, expand their global reach, and facilitate cross-cultural communication.