This document provides an overview of artificial intelligence including: - It defines four approaches to AI: acting humanly through the Turing test, thinking humanly through cognitive modeling, thinking rationally through symbolic logic, and acting rationally as intelligent agents. - It then summarizes the history of AI from its origins in the 1940s through developments in knowledge-based systems, connectionism, and the emergence of intelligent agents using large datasets. - Finally, it briefly discusses recent applications of AI such as algorithms used by Facebook, TikTok, and noise cancellation in Zoom calls.

1. Presented By
Er. Bikash Acharya
Artificial Intelligence
Chapter 1:
INTRODUCTION
BE 6th Semester(TU)

2. Course Contents
1. Definition of Artificial Intelligence
2. Importance of Artificial Intelligence
3. AI and related fields
4. Brief history of Artificial Intelligence
5. Applications of Artificial Intelligence
6. Definition and importance of Knowledge, and learning.

3. Knowledge and Wisdom

4. Expectations
VS
Reality

5. ARTIFICIAL INTELLIGENCE

6. ARTIFICIAL INTELLIGENCE

7. ARTIFICIAL INTELLIGENCE
• Act Humanly: Turing Test Approach
• Think Humanly: Cognitive Modelling Approach
• Think Rationally: The Laws of Thought Approach
• Act Rationally: The Rational Agent Approach

8. ARTIFICIAL INTELLIGENCE
One is concerned with thought processes and reasoning,
where as other addresses the behavior.

9. acting humanly: Searle’s Chinese Room
• Another famous test is called the “Chinese Room” which was proposed by John Searle in
a paper published in 1980.
• Suppose you have a computer in a room that reads Chinese characters as input, follows a
program and outputs (other) Chinese characters.
Suppose this computer does this so well that it passes the Turing Test (convinces a human
Chinese speaker that it is talking to another human Chinese speaker). Does the computer
understand Chinese?
• Suppose Searle is in the room, and he uses a dictionary to translate the input characters
from Chinese to English; he then constructs his answer to the question, translates that back
into Chinese and delivers the output—does Searle understand Chinese?
• Of course not
. • This is Searle’s argument: the computer doesn’t understand it either, because all it is
doing is translating words (symbols) from one language (representation) to another.

10. thinking humanly: Cognitive Modeling
Thinking humanly means trying to understand and model how the human mind works.
-- There are (at least) two possible routes that humans use to find the answer to a question:
– We reason about it to find the answer. This is called “introspection”.
– We conduct experiments to find the answer, drawing upon scientific techniques to
conduct controlled experiments and measure change.
--The field of Cognitive Science focuses on modeling how people think.

11. thinking humanly: Cognitive Modeling

12. thinking humanly: Cognitive Modeling

13. thinking rationally
• Trying to understand how we actually think is one route to AI. • But another approach is
to model how how we should think.
• The “thinking rationally” approach to AI uses symbolic logic to capture the laws of
rational thought as symbols that can be manipulated.
• Reasoning involves manipulating the symbols according to well-defined rules, kind of like
algebra.
• The result is an idealized model of human reasoning. This approach is attractive to
theoretists, i.e., modeling how humans should think and reason in an ideal world.

14. acting rationally: Rational Agents
• Acting rationally means acting to achieve one’s goals, given one’s beliefs or understanding
about the world. An agent is a system that perceives an environment and acts within that
environment. An intelligent agent is one that acts rationally with respect to its goals. For
example, an agent that is designed to play a game should make moves that increase its
chances of winning the game. • When constructing an intelligent agent, emphasis shifts
from designing the theoretically best decision-making procedure to designing the best
decision-making procedure possible within the circumstances in which the agent is acting. •
Logical approaches may be used to help find the best action, but there are also other
approaches. • Achieving so-called “perfect rationality”, making the best decision
theoretically possible, is not usually possible due to limited resources in a real environment
(e.g., time, memory, computational power, uncertainty, etc.). • The trick is to do the best
with the information and resources you have. This represents a shift in the field of AI from
optimizing (early AI) to satisfying (more recent AI).

15. Foundations of AI
Philosophy:
Logic, reasoning, mind as a physical system, foundations of learning, language and rationality.
1. Where does knowledge come from?
 How does knowledge lead to action?
 How does mental mind arise from physical brain?
2. Can formal rules be used to draw valid conclusions?
Mathematics:
Formal representation and proof algorithms, computation, undecidability, intractability, probability.
1. What are the formal rules to draw the valid conclusions?
 What can be computed?
 How do we reason with uncertain information?
Psychology:
Adaptation, phenomena of perception and motor control.
1. How humans and animals think and act?

16. Foundations of AI
Economics:
Formal theory of rational decisions, game theory, operation research.
 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 future?
Linguistics:
Knowledge representation, grammar
 How does language relate to thought? Neuroscience:
Physical substrate for mental activities
 How do brains process information?
Computer Engineering:
 How do we build efficient computers?
Control Theory:
Homeostatic systems (devices containing appropriate feedbacks to achieve stable adaptive behaviour), stability,
optimal agent design
 How can artifacts operate under their own control?

17. BRIEF HISTORY OF AI
The gestation of of AI (1943-
1955):
–1943: Warren Mc Culloch and
Walter Pitts: a model of artificial
boolean neurons to perform
computations.
– First steps toward connectionist
computation and learning (Hebbian
learning).

18. BRIEF HISTORY OF AI
The Birth of an Idea (1950)
Alan Turing’s “Computing Machinery and Intelligence”
First complete vision of AI.
Alan Turing published a landmark paper in 1950,
speculating about creating machines that “think.”
The premise of the Turing Test was that a computer
was truly intelligent if a normal human could not
design questions that could differentiate between a
computer response and a human one.

20. The Birth of AI (1956)
• Dartmouth Workshop bringing together top minds on automata theory, neural nets and the study of intelligence.
• Allen Newell and Herbert Simon: The logic theorist (first nonnumeric thinking program used for theorem proving)
– For the next 20 years the field was dominated by these participants.

21. Great expectations (1952-1969):
• Newell and Simon introduced the General Problem Solver. – Imitation of human problem-solving
• Arthur Samuel (1952-) investigated game playing (checkers ) with great success. –John McCarthy(1958-) :
– Inventor of Lisp (second-oldest high-level language)
– Logic oriented, Advice Taker (separation between knowledge and reasoning)
• Marvin Minsky (1958 )
• – Introduction of microworlds that appear to require intelligence to solve: e.g. blocks- world.
• – Anti-logic orientation, society of the mind.

22. A dose of reality (1966 - 1973):
• – Progress was slower than expected.
• – Unrealistic predictions.
• – programs knew nothing of their subject matter; they succeeded by means of simple syntactic
manipulations
• – Some systems lacked scalability.
• – Combinatorial explosion in search.
• – Most of the early AI programs solved problems by trying out different combinations of steps until the
solution was found. This strategy worked initially because microworlds contained
• very few objects and hence very few possible actions and very short solution sequences.

23. AI revival through knowledge-based systems (1969-1979):
• General-purpose vs. domain specific
- E.g. the DENDRAL project (Buchanan et al. 1969)
• First successful knowledge intensive system. The problem of inferring molecular
structure from the information provided by a mass spectrometer.
• – Expert systems
- MYCIN to diagnose blood infections (Feigenbaum et al.) Included introduction of
uncertainty in reasoning.
• Increase in knowledge representation research. - Logic, frames, semantic nets,
AI becomes an industry (1980 - present):
• – R1: Commercial Expert system
• – Fifth generation project in Japan (1981) to build intelligent computers
running prolog.
• – American response Puts an end to the AI winter, where many companies failed to
deliver on extravagant promises.

24. Connectionist revival (1986 - present): (Return of Neural Network):
• – Parallel distributed processing (RumelHart and McClelland, 1986);
• – Connectionist approach and backpropagation.
AI becomes a science (1987 - present):
• – In speech recognition: hidden markov models
• – In neural networks
• – In uncertain reasoning and expert systems: Bayesian network formalism
• – Data Mining
The emergence of intelligent agents (1995 - present):
• – The whole agent problem:
“How does an agent act/behave embedded in real environments with continuous
• sensory inputs:
• AI systems have become so common in Web-based applications that the “-bot” suffix has
entered everyday language
• – Human Level AI: machines that think, learn and create. Eg: Driving Cars, recognizing
speech etc.
• – Artificial General Intelligence: universal algorithm for learning and acting in any
environments

25. Availability of Very Large Datasets (2001 - present):
• – Throughout the 60-year history of computer science, the emphasis has been on the
algorithm as the main subject of study. But some recent work in AI suggests that for
many problems, it makes more sense to worry about the data and be less picky about
what algorithm to apply.
• – Suppose you use Photoshop to mask out an ex-(girl/boy) friend from a
group photo, but now you need to fill in the masked area with something that
matches the background.
• – Use an algorithm that searches through a collection of photos to find something
that will match. The algorithm may work poor when used with a collection of only ten
thousand photos, but may cross a threshold into excellent performance when grew the
collection to two million photos
• – “AI Winter” may be yielding to a “New Spring”

27. Applications of AI

28. Explore Recent:
• - TikTok Algorithm
• - https://ai.facebook.com/
• - PUBG finish placement prediction
• - AI in Zoom (Zoom introduced virtual backgrounds for mobile, laptop, and desktop devices. They have
introduced advanced AI background noise detection to automatically reduce environmental noises, such as
fans, dogs barking, and vacuum cleaners. )