The document provides an introduction to artificial intelligence (AI), defining it as a branch of computer science focused on creating intelligent systems that can learn and reason. It categorizes AI into systems that think and act humanly and rationally, discusses the Turing test as a measure of machine intelligence, and explores the historical development of AI. Additionally, it highlights real-world applications of AI, including virtual assistants, gaming, fraud detection, online customer support, security surveillance, and recommendation systems.

1. Chapter 1
Introduction to AI
By - Suraj Awal

2. Definition of AI:
Intelligence:
- Ability to create, understand, learn, plan and reason.
- Examples of Intelligent Behaviour : recognition, interpretation, playing logical
games, proving mathematical theorems, analysis and so on.

3. Definition of AI:
What Actually is Artificial Intelligence:
- Branch of Computer science that deals with design of intelligent systems.
- Science of creating a system that could acquire knowledge and use that
base for reasoning.

4. Categories of AI:
System that thinks humanly
- Cognitive modeling approach
- Associated with the way
human thinks
- Requires scientific theory on
internal activities of brain
- General Problem Solver
(GPS) : compare the steps of
problem solving with
reasoning.
System that acts humanly
- Behaviorist approach
- Machine that do things that
require intelligence
- Associated with what
humans do; not what human
thinks
- System that pass turing test
- Turing test is not
reproducible and
constructive

5. Categories of AI:
System that thinks rationally
- System that can think,
perceive and reason; uses
computational models
- Emphasis on correct
inference
- Also termed as law of
thoughts
- Every intelligent behaviour
can not be formalized
System that acts rationally
- System that acts to achieve
the best possible outcome
- Emphasis on minimal
solution
- Not necessarily involve
thinking, but if involved
should be in service of
rational action

6. Turing Test:
When a machine pass?
A machine is said to have passed Turing Test
if it exhibits following factors:
1. Natural Language Processing
2. Knowledge Representation
3. Automated Reasoning
4. Machine Learning
Why?
- To determine the operational accuracy
of intelligent behaviour shown by the
machine or system.

7. Turing Test:
How it is performed?
- The human interrogator communicates
with two sources: human and machine
- Within a time duration, he must decide
which source is human and which
source is machine for each
interrogation.
- The decision of the interrogation is
recorded.
How result is determined?
- If the interrogator is wrong half the time
to determine which source is human and
which source is machine, then the
machine is said to be intelligent.

8. Importances of AI
Long Lasting
Process
Able to create a never
ending process of
thoughts that could
solve the human
problems.
Fast
If AI can solve the
problem, then the
solution will be faster
than that of the human
effort.
Efficient
If AI can solve the
problem, then the
solution will be more
efficient as machine has
less error rate than
human
Big Data
Analysis
AI could contribute in
analysis of large
volumes of data to
generate important
information and patterns

9. AI and Related Fields
- Logic, reasoning and learning
- Building fast computers
- Knowlege representation
- Neurons as info processor
- Biomedical tools
- Formal repr. And proofs
- Utility and Decision theory

10. History of AI:
- McCulloch and Pitts (1943) proposed a boolean circuit of
brain.
- Turing (1950) proposed computing machinery and
intelligence
- In 1956, Dartmouth conference was held and AI was
officially adopted.
- In 1950’s, early AI programs were devised such as
Samuel’s checker; Newell and Simon’s Logic Theorist

11. History of AI:
- Between 1955 - 1965:
# General Problem Solver (Newell and Simon)
# Geometry Theorem Prover (Gelertner)
# Invention of LISP (McCarthy)
- Between 1966 - 1973, AI support funds were cancelled
as no any significant results are achieved
- Between 1969 - 1985, knowledge based system and rule
based expert system (DENDRAL, MYCIN) are devised.
But, they did not scale well in practice

12. History of AI:
- In 1986, machine learning was implemented
- In 1995, AI was implemented as a science used in vision,
language, learning, reasoning and knowledge
representation.

13. Real World Applications of AI:
1. Virtual Personal Assistants
- Help to find useful informations when you instructs
- Learns about the user
- Develop ability to anticipate user’s needs
- Eg: SIRI, Google Now, Cortana
- A new groundbreaking development at Google I/O 2018
(Duplex)

14. Real World Applications of AI:
2. Gaming
- Games with characters that learn your behaviour
- Respond to stimuli
- React in unpredictable ways
- Eg: Chess, Call of Duty

15. Real World Applications of AI:
3. Fraud Detection
- System that monitors frauds
- By providing large samples of fraudulent and non-
fraudulent activities and then making it learn about the
activity based on signs and indications
- Eg: Confirmation mail sent by bank on purchase

16. Real World Applications of AI:
4. Online Customer Support
- Real time chat system provided by websites to their
users to interact as a customer support
- Mostly, AI is deployed at the backend as a automated
responder

17. Real World Applications of AI:
5. Security Surveillance
- Security algorithms take input from security cameras and
determine it as a threat or not
- Supervised training is used in general
- May respond to threat by itself with some automatic
threat control system or by alerting to the human security
officers

18. Real World Applications of AI:
6. Recommendation System
- Generate recommended list based on interests made in
the past or interests shared by similar users
- Eg: Video recommendation by Youtube, Group or Event
recommendation by Facebook, Item recommendation by
e-commerce sites

19. End of Chapter 1
Query Section for
Introduction to Artificial Intelligence
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