The document provides an introduction to artificial intelligence (AI), including definitions, concepts, and types of AI. It defines AI as the ability of computers to learn and think like humans. The key concepts discussed are machine learning, deep learning, and neural networks. It describes narrow/weak AI as able to perform specific tasks, general AI as able to perform any intellectual task, and super AI as able to surpass human intelligence. The document also outlines components of AI like learning, reasoning, problem-solving, perception, and language understanding. It presents a three-dimensional model of AI and discusses types based on functionality like reactive machines and those with limited memory.

1. Unit-1
Artificial Intelligence

2. 1.1 Introduction of AI
• A branch of Computer Science named Artificial
Intelligence (AI)pursues creating the computers
/ machines as intelligent as human beings.
• John McCarthy the father of Artificial
Intelligence described AI as, “The science and
engineering of making intelligent machines,
especially intelligent computer programs”.

3. • There are other possible definitions “like AI is
a collection of hard problems which can be
solved by humans and other living things, but
for which we don’t have good algorithms for
solving.”
• e. g., understanding spoken natural language,
medical diagnosis, circuit design, learning,
self-adaptation, reasoning, chess playing,
proving math theories, etc.

5. • Data: Data is defined as symbols that represent
properties of objects events and their environment.
• Information: Information is a message that contains
relevant meaning, or input for decision and/or
action.
• Knowledge: It is the
(1) cognition or recognition (know-what),
(2) capacity to act(know-how), and
(3) understanding (know-why)that resides or is
contained within the mind or in the brain.

6. • Intelligence: It requires ability to sense the
environment, to make decisions, and to
control action.

8. Ai - Concept:
• Artificial Intelligence is the ability of a
computer program to learn and think.
• Artificial intelligence (AI) is an area of computer
science that emphasizes the creation of
intelligent machines that work and reacts like
humans.
• AI is built on these three important concepts :
• Machine learning
• Deep learning
• Neural networks

9. • Machine learning: When you command your
smartphone to call someone, or when you chat
with a customer service chatbot, you are
interacting with software that runs on AI.
• But this type of software actually is limited to
what it has been programmed to do.
• However, we expect to soon have systems that
can learn new tasks without humans having to
guide them.
• The idea is to give them a large amount of
examples for any given chore (task, job), and they
should be able to process each one and learn how
to do it by the end of the activity.

10. • Deep learning: The machine learning examples
provided above is limited by the fact that
humans still need to direct the AI’s development.
• In deep learning, the goal is for the software to
use what it has learned in one area to solve
problems in other areas.
• For example, a program that has learned how to
distinguish images in a photograph might be able
to use this learning to seek out patterns in
complex graphs.

11. • Neural networks: These consist of computer
programs that mimic the way the human brain
processes information.
• They specialize in clustering information and
recognizing complex patterns, giving computers
the ability to use more sophisticated processes
to analyze data.

12. Scope of AI:
• There is scope in developing machines in
robotics, computer vision, language detection
machine, game playing, expert systems,
speech recognition machine and much more.
• The following factors characterize a career in
artificial intelligence:
• Automation
• Robotics
• The use of sophisticated computer software

13. AI Approach:
• The difference between machine and human
intelligence is that the human think / act
rationally compare to machine.
• Historically, all four approaches to AI have
been followed, each by different people with
different methods.

15. Rational agent
• A rational agent is an artificial intelligence (AI)
component. It applies AI to different real-world
problems. As such, it chooses an action from a set
of distinct options. It has models that allow it to
deal with unexpected variables and always selects
the best possible outcome from all the available
options.
• The term “rational agent,” however, is not only
applied to a system. It can also refer to a person, a
company, or an application, practically anything or
anyone that makes rational decisions.

16. • Think Well:
• Develop formal models of knowledge
representation, reasoning, learning, memory,
problem solving that can be rendered in
algorithms.
• There is often an emphasis on a systems that
are provably correct, and guarantee finding an
optimal solution.

17. Ex: Vacuum cleaner: Amount of time
taken, Amount noise generated,
Amount electricity consumed and
Amount of DIRT CLEANED.

18. • Act Well:
• For a given set of inputs, generate an appropriate output
that is not necessarily correct but gets the job done.
• Heuristic : shortcuts to solutions
• A heuristic (heuristic rule, heuristic method) is a rule of
thumb, strategy, trick, simplification, or any other kind of
device which drastically limits search for solutions in large
problem spaces.
• Heuristics do not guarantee optimal solutions; in fact, they
do not guarantee any solution at all:
• All that can be said for a useful heuristic is that it offers
solutions which are good enough most of the time.
• Ex : “traveling salesman problem”
• https://study.com/academy/lesson/heuristic-methods-in-ai-definition-uses-
examples.html#:~:text=One%20example%20of%20a%20heuristic,efficient%20trip%20between%20many%20cities.

19. • Think like humans:
• Cognitive (ज्ञानाने आकलनीय)science approach. Focus
not just on behavior and I/O but also look at
reasoning process.
• The Computational model should reflect “how”
results were obtained.
• Provide a new language for expressing cognitive
theories and new mechanisms for evaluating them.
• GPS (General Problem Solver): Goal not just to
produce humanlike behavior (like ELIZA), but to
produce a sequence of steps of the reasoning process
that was similar to the steps followed by a person in
solving the same task.

20. • Act like humans:
• Behaviorist approach-Not interested in how you
get results, just the similarity to what human
results are.
• Example: ELIZA, A program that simulated a
psychotherapist interacting with a patient.

21. Components of AI
a. Learning
Similar to human, computer programs also learn in
different manners.
learning for AI includes the trial-and-error method.
The solution keeps on solving problems until it comes
across the right results.
This way, the program keeps a note of all the moves
that gave positive results and stores it in its database
to use the next time.
The learning component of AI includes memorizing
individual items like different solutions to problems,
vocabulary, foreign languages, etc.

22. b. Reasoning
• The art of reasoning was something that was only limited
to humans until five decades ago.
• The ability to differentiate makes Reasoning one of the
essential components of artificial intelligence.
• To reason is to allow the platform to draw conclusions that
fit with the provided situation.
• Further, these inferences (a conclusion reached on the basis
of evidence and reasoning.)are also categorized as either
inductive or deductive. The difference is that in an
inferential case, the solution of a problem provides
guarantees of conclusion.
• In contrast, in the inductive case, the accident is always a
result of instrument failure.
• The use of deductive interferences by programming
computers has provided them with considerable success.

23. Reasoning
• For example: I have a Sony television, a Sony
stereo, a Sony car radio, a Sony video system,
and they all work well. It is clear that Sony
produces superior electronic products.

24. Problem Solving in Artificial Intelligence
• The reflex agent of AI directly maps states into action.
Whenever these agents fail to operate in an environment
where the state of mapping is too large and not easily
performed by the agent, then the stated problem
dissolves and sent to a problem-solving domain which
breaks the large stored problem into the smaller storage
area and resolves one by one. The final integrated action
will be the desired outcomes.
• On the basis of the problem and their working domain,
different types of problem-solving agent defined and use
at an atomic level without any internal state visible with a
problem-solving algorithm. The problem-solving agent
performs precisely by defining problems and several
solutions. So we can say that problem solving is a part of
artificial intelligence that encompasses a number of
techniques.

25. There are basically three types of problem in artificial
intelligence:
• 1. Ignorable: In which solution steps can be
ignored.
• 2. Recoverable: In which solution steps can be
undone.
• 3. Irrecoverable: Solution steps cannot be
undo.

26. • Steps problem-solving in AI: The problem of AI is
directly associated with the nature of humans and
their activities. So we need a number of finite
steps to solve a problem which makes human easy
works.
• These are the following steps which require to
solve a problem :
• Problem definition: Detailed specification of
inputs and acceptable system solutions.
• Problem analysis: Analyse the problem
thoroughly.
• Knowledge Representation: collect detailed
information about the problem and define all
possible techniques.
• Problem-solving: Selection of best techniques.

27. c. Problem-solving
• In its general form, the AI’s problem-solving ability
comprises data, where the solution needs to find x. AI
witnesses a considerable variety of problems being
addressed in the platform. The different methods of
‘Problem-solving’ count for essential artificial intelligence
components that divide the queries into special and
general purposes.
• In the situation of a special-purpose method, the solution
to a given problem is tailor-made, often exploiting some
of the specific features provided in the case where a
suggested problem is embedded.
• On the other hand, a general-purpose method implies a
wide variety of vivid issues. Further, the problem-solving
component in AI allows the programs to include step-by-
step reduction of difference, given between any goal state
and current state.

28. d. Perception
• In using the ‘perception’ component of Artificial
Intelligence, the element scans any given environment by
using different sense-organs, either artificial or real.
• Further, the processes are maintained internally and allow
the perceiver to analyze other scenes in suggested objects
and understand their relationship and features.
• This analysis is often complicated as one, and similar items
might pose considerable amounts of different
appearances over different occasions, depending on the
view of the suggested angle.
• At its current state, perception is one of
those components of artificial intelligence that can propel
self-driving cars at moderate speeds.
• FREDDY was one of the robots at its earliest stage to use
perception to recognize different objects and assemble
different artifacts.

29. Perception
• Perception in AI implies the ability of
machines to use input data from sensors
(e.g., cameras, LiDAR, RADAR, microphones,
wireless signals, tactile sensors, etc.) to learn
about many facets of the world. For example,
machine perception is when it can tell the
object's position or movement trajectory in
the scene.

30. e. Language-understanding
• In simpler terms, language can be defined as a set of
different system signs that justify their means using
convention.
• Occurring as one of the widely used artificial intelligence
components, language understanding uses distinctive
types of language over different forms of natural meaning,
exemplified overstatements.
• One of the essential characteristics of languages is
humans’ English, allowing us to differentiate between
different objects.
• Similarly, AI is developed in a manner that it can easily
understand the most commonly used human language,
English. This way, the platform allows the computers to
understand the different computer programs executed
over them easily.

31. • https://www.geeksforgeeks.org/problem-
solving-in-artificial-intelligence/
Problem Solving in Artificial Intelligence

32. Three dimensional model of AI

33. The First Dimension (Core):
• The x-y plane is the foundation of AI.
• The z-direction consists of correlated systems of
physical origin such as language, vision and
perception as shown in Figure.
• Philosophy from its very start of origin covered all
the facts, directions and dimensions of human
thinking output.
• Views of philosophers regarding LOGIC :
• 1. Knowledge-based on logic.
• 2. Power of computation (calculation) logic.
• 3. Logic with mathematics.
• 4. Integrate reasoning with knowledge

34. • Cognition is a term referring to the mental processes
involved in gaining knowledge and understanding.
• These cognitive processes include thinking, knowing,
remembering, judging, and problem-solving.
• Information processing paradigm (IPP) of human
beings: sensing organs as input, mechanical
movement organs as output and the central nervous
system (CNS) in brain as control and computing
devices, short-term and long-term memory were not
distinguished by computer scientists but, as a whole, it
was in conjunction, termed memory.

35. • The interaction of stimuli (Example: An animal is cold so it
moves into the sun.) with the stored information to
produce new information requires the process of learning,
adaptation and self-organization.
• These functionalities in the information processing at a
certain level of abstraction of brain activities demonstrate
a state of mind which exhibits certain specific behavior to
qualify as intelligence.
• Computational models were developed and incorporated
in machines which mimicked the functionalities of human
origin.
• The creation of such traits of human beings in the
computing devices and processes originated the concept of
intelligence in machine as virtual mechanism.
• These virtual machines were termed in due course of time
artificial intelligent machines.

36. Computation
• The action of mathematical calculation.
• The use of computers, especially as a subject of
research or study.

37. The Second Dimension
• The second dimension contains knowledge,
reasoning and interface which are the
components of knowledge-based system (KBS).
• Knowledge can be logical, it may be processed as
information which is subject to further
computation.
• This means that any item on the y-axis is
correlated with any item on the x-axis to make
the foundation of any item on the z-axis.

38. Example:
• Interface is a means of communication between one
domain to another.
• Here, it means a different concept then the user's
interface.
• Between two different domains of different permittivity is
termed interface.
• For example, in the industrial domain, the robot is an
interface. A robot exhibits all traits of human intelligence in
its course of action to perform mechanical work.
• Example of the interface between computing machine and
the user.
• Human-to-machine is program and machine-to-machine is
hardware.
• These interfaces are in the context of computation and AI
methodology.

39. The Third Dimension
• It is basically the application domain. Here, if the
entities are near the origin, more and more
concepts are required from the x-y plane.
• For example, consider information and
automation, these are far away from entities on
z-direction, but contain some of the concepts of
cognition and computation model respectively on
x-direction and concepts of knowledge (data),
reasoning and interface on the y-direction.
• In general, any quantity in any dimension is
correlated with some entities on the other
dimension.

40. Types of AI

41. 1. Weak AI or Narrow AI:
• Narrow AI is a type of AI which is able to perform a
dedicated task with intelligence.
• The most common and currently available AI
• Narrow AI cannot perform beyond its field or limitations, as
it is only trained for one specific task. Hence it is also
termed as weak AI.
• Narrow AI can fail in unpredictable ways if it goes beyond
its limits.
• Apple Siri : is an easy way to make calls, send texts, use
apps, and get things done with just your voice. And Siri
is the most private intelligent assistant. It is good example
of Narrow AI, but it operates with a limited pre-defined
range of functions.
• Examples of Narrow AI are:
• Playing chess, Purchasing suggestions on ecommerce site,
Self-driving cars, Speech recognition, and image
recognition.

43. General AI:
• General AI is a type of intelligence which could
perform any intellectual task with efficiency like a
human.
• The idea behind the general AI to make such a
system which could be smarter and think like a
human by its own.
• Currently, there is no such system exist which
could come under general AI and can perform
any task as perfect as a human.
• The worldwide researchers are now focused on
developing machines with General AI.

44. Super AI:
• Super AI is a level of Intelligence of Systems at
which machines could surpass human
intelligence, and can perform any task better
than human with cognitive properties.
• Some key characteristics of strong AI :
ability to think, to reason, solve the puzzle,
make judgments, plan, learn, and communicate
by its own.

45. Artificial Intelligence type-2: Based on functionality
1. Reactive Machines
• Purely reactive machines are the most basic types of
Artificial Intelligence.
• Such AI systems do not store memories or past
experiences for future actions.
• These machines only focus on current scenarios and
react on it as per possible best action.
• IBM's Deep Blue (chess-playing expert system)
system is an example of reactive machines.
• Google's AlphaGo is also an example of reactive
machines.
https://www.youtube.com/watch?v=8dMFJpEGNLQ
Google's AI AlphaGo Is Beating Humanity At Its Own Games

47. 2. Limited Memory
• Limited memory machines can store past
experiences or some data for a short period of
time.
• These machines can use stored data for a limited
time period only.
• Self-driving cars are one of the best examples of
Limited Memory systems.
• These cars can store recent speed of nearby cars,
the distance of other cars, speed limit, and other
information to navigate the road.

48. 3. Theory of Mind
• Theory of Mind AI should understand the
human emotions, people beliefs, and be able
to interact socially like humans.
• This type of AI machines are still not
developed, research is going on it.
• https://www.youtube.com/watch?v=6SniaiSbx
7o

49. 4. Self-Awareness
• Self-awareness AI is the future of Artificial
Intelligence.
• These machines will be super intelligent, and
will have their own consciousness, sentiments,
and self-awareness.
• These machines will be smarter than human
mind.
• Self-Awareness AI does not exist in reality still
and it is a hypothetical concept.

50. Application of AI
• Gaming
• Natural Language Processing
• Vision Systems
• Speech Recognition
• Handwriting Recognition
• Intelligent Robots.