Jimma University

Jimma Institute of Technology

Faculty of Computing and Informatics

Chapter One

By Bekan K
 Topics we will cover
2

• Introduction to AI
• What is AI?
• Objectives/Goals of AI
• Approaches to AI – making computer:
• Think like a human (Thinking humanly)
• Act like a human (Acting humanly)
• Think rationally (Thinking rationally)
• Act rationally (Acting rationally)
• The Foundations of AI
• Bits of History and the State of the Art
 What is Artificial Intelligence?
3

• Artificial Intelligence is composed of two words: Artificial and Intelligence.

⁃ Artificial - defines "man-made," and
⁃ Intelligence - defines "thinking power", or “the ability to learn and solve
problems”.
• Hence, Artificial Intelligence means a man-made thinking power.
• It is an area of computer science that emphasizes the creation of intelligent
machines that work and act like humans.
• It is the simulation of human intelligence processes by machines, especially
computer systems.
• These processes include learning (acquisition of information and rules),
reasoning (using rules to reach approximate or definite conclusions), and self-
correction.
 Goals of Artificial Intelligence
4

• The followings are the main goals of Artificial Intelligence:

1. Replicate human intelligence: AI systems are designed to emulate human
intelligence, such as decision-making, problem-solving, and reasoning.
2. Solve Knowledge-intensive tasks: AI systems to perform tasks that require a vast
amount of information, understanding complex concepts.
3. An intelligent connection of perception and action: AI systems not only perceive
their environment but also act intelligently based on that perception.
⁃ perception (such as vision, sound, or touch) and
⁃ action (deciding what to do in response to stimuli).
 Goals of Artificial Intelligence
5

4. Building a machine which can perform tasks that requires human

intelligence(Proving a theorem, Playing chess, Driving a car...)
5. Creating some system which can exhibit intelligent behavior, learn new things by
itself, demonstrate, and can advise to its user.
 Approaches to AI...
6
Approaches to AI – making computer
• AI research focuses on different approaches to replicate human-like
abilities in machines. These approaches can be broken down as
follows:

The course textbook (Russell/Norvig) advocates "acting rationally"

 Acting humanly: The Turing Test approach
7

• Making a computer act like a human being.

• Called the Turing Test approach and was proposed by Alan Turing (Turing
1950).
• Turing Test: Operational test for intelligent behavior.
⁃ Turing defined it as the ability to achieve human-level performance in all
cognitive tasks.
• It includes programming a computer to pass the test and also requires the
following capabilities.
• Natural Language Processing to enable it to communicate successfully in
English (or some other human language)
 Acting humanly (Cont.…)
8

• Automated Reasoning to use the stored information to answer questions and

draw new conclusions,
• Machine Learning to adapt to new circumstances and to detect and extrapolate
patterns.
 Thinking humanly: cognitive modeling
9
• Making a computer think like a human being.
• The goal of this approach is to model the cognitive processes that occur in the
human brain.
• The focus is on cognitive science and neuroscience to understand how human
thought works and then replicate these processes in AI systems.
• The challenge lies in simulating the complex mental processes, like perception,
reasoning, and memory, that humans engage in.
• There are two ways to do this:
 Through introspection: catching our thoughts, emotion and mental process,
 Through psychological experiments: observing person or brain in action.
 Thinking humanly: (Cont...)
10
• While introspection can provide insight into one's own internal activity, it is not
a reliable method for understanding the internal activity of the brain.
⁃ This is because introspection relies on subjective experiences, which can
vary greatly between individuals and can be influenced by a variety of
factors such as mood, memory, and attention.
• Example: If you’re trying to decide whether to buy a new phone, you might
reflect on how you feel about your current phone, how much money you
have, and what features you need. AI systems can attempt to simulate this
kind of reasoning.
 Thinking humanly: (Cont...)
11
• To understand the internal activity of the brain, neuroscientists use
psychological experiments such as functional magnetic resonance imaging
(fMRI), electroencephalography (EEG), and magnetoencephalography (MEG),
which allow them to measure changes in brain activity associated with specific
cognitive processes.
⁃ These methods provide more objective and reliable measures of brain
activity compared to introspection.
• Example: In an experiment, a person might be asked to recognize images of
animals. The researchers observe how quickly they can identify different
animals. AI researchers use this data to create image-recognition systems
that can "think" like humans when recognizing pictures.
 Thinking rationally: laws of thought
12
• Making a computer think rationally
• Use the laws of logic to determine computer’s reasoning.
• A system is rational if it thinks the right thing through correct reasoning.
• Aristotle: provided the correct arguments/ thought structures that always gave
correct conclusions given correct premises.
• Abebe is a man; all men are mortal; therefore Abebe is mortal.
• These Laws of thought governed the operation of the mind and initiated
the field of Logic.
• Problem:
• We human beings are not 100% certain and sometimes take informal
knowledge which is difficult for machines.
 Acting rationally: rational agent
13

• Making a computer act rationally.

• This approach is about to do the right thing so as to achieve one’s goal, given
one’s beliefs.
• Rational behavior: doing “ the right thing ”.
⁃ The right thing is the one that is expected to maximize goal achievement,
given the available information.
 The Foundations of AI
14

• In this section, we provide a brief history of the disciplines that

contributed ideas, viewpoints, and techniques to AI.
• Philosophy (428 B.C. - present)
• Can formal rules be used to draw valid conclusions?
• How does the mental mind arise from a physical brain?
• Where does knowledge come from?
• How does knowledge lead to action?
 Cont...
15

• Mathematics (800 - present)

• What are the formal rules to draw valid conclusions?
• What can be computed?
• How do we reason with uncertain information?
• Economics (1776 - present)
• 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?
 Cont...
16

• Neuroscience (1861-present)
• How do brains process information?
• Psychology (1879-present)
• How do humans and animals think and act?
• Computer engineering (1940-present)
• How can we build an efficient computer?
• Control theory and Cybernetics (1948-present)
• How can artifacts operate under their own control?
• Linguistics (1957-present)
• How does language relate to thought?
 The History of Artificial Intelligence
17

1. Early Foundations (Pre-1950s)

• Before AI as we know it today, the idea of intelligent machines existed for
centuries in mythology, philosophy, and literature. Some early inspirations for
AI included:
• Philosophy: Thinkers like Aristotle explored the nature of human thought
and logic.
⁃ These ideas helped later scientists develop ways to reason logically.
• Computing Machines: Early computers (like the abacus and mechanical
calculators) paved the way for more complex machines later on.
 Cont’d…
18

2. The Birth of AI (1950s)

• AI began to emerge as a field of study after the invention of computers. Key
moments include:
• Alan Turing (1950): proposed Turing Test, a way to measure if a
machine can exhibit human-like intelligence. "Can machines think?"
• John von Neumann: He created the architecture of modern computers
and influenced early AI research.
• 1956 – (Dartmouth Conference): The term AI was coined during the
conference led by John McCarthy, Marvin Minsky, Nathaniel
Rochester, and Claude Shannon.
⁃ This is where AI became recognized as a formal field of study.
 Cont’d…
19

3. Early AI Progress (1950s-1960s)

• During this time, AI researchers made significant progress in creating
programs that could simulate certain aspects of human intelligence:
• Logic Theorist (1955): Developed by Allen Newell and Herbert A.
Simon, this was the first AI program that could prove mathematical
theorems using logical reasoning.
• General Problem Solver (1957): Another important program by Newell
and Simon, aimed at solving a wide range of problems by breaking them
down into simpler steps, much like how humans solve problems.
• LISP (1958): The programming language LISP was developed by John
McCarthy and became widely used in AI research due to its ability to
handle symbolic computation, which was important for early AI systems.
 Cont’d…
20

4. AI Winter and Challenges (1970s-1980s)

• Despite early successes, the field of AI faced major setbacks during the AI
Winter, a period where progress slowed down due to limited computational
power and high expectations that weren’t met. Some key events:
• Lack of Progress: AI systems at the time couldn’t live up to the
ambitious promises made in the 1950s and 1960s.
• Funding Cuts: As a result of unfulfilled expectations, funding for AI
research was cut, leading to reduced interest in the field.
• Despite these challenges, some breakthroughs continued:
• Expert Systems (1980s): AI applications called Expert Systems became
popular. These systems used a database of expert knowledge and rules to
make decisions or solve specific problems (e.g., medical diagnosis).
 Cont’d…
21

5. The Revival and Growth of AI (1990s)

• In the 1990s, AI saw a resurgence due to advances in machine learning, data
analysis, and computing power:
• Deep Blue (1997): IBM's Deep Blue defeated world chess champion
Garry Kasparov, showing the power of AI in specialized tasks like
chess.
• Machine Learning: AI began focusing more on machine learning, the
idea that machines can learn from data rather than relying only on
programmed rules.
• Data Mining and Natural Language Processing (NLP): AI systems
began using data mining techniques to analyze large datasets and improve
decision-making. NLP (like chatbots) emerged, allowing computers to
understand and generate human language.
 Cont’d…
22

6. The Modern Era of AI (2000s-Present)

• In the 21st century, AI made huge leaps forward due to advances in computing
power, big data, and new algorithms. Key highlights:
• Deep Learning: a subset of machine learning, became prominent. It
uses neural networks with many layers to process large amounts of data.
This method has been particularly successful in areas like computer
vision and speech recognition.
• Self-Driving Cars: Companies like Tesla, Waymo, and others are
developing self-driving cars powered by AI, which use sensors, machine
learning, and deep learning to navigate roads safely.
• AI in Healthcare: AI is making breakthroughs in medical fields, from
diagnosing diseases (like using AI to analyze X-rays) to personalized
treatments based on patient data.
 Cont’d…
23

• Voice Assistants (Siri, Alexa, Google Assistant): AI-driven virtual assistants

use NLP to interact with users and complete tasks like setting reminders,
playing music's and answering questions.
• Generative AI (2010s-Present): AI systems like GPT-3 (from OpenAI) can
generate text, images, and even music, demonstrating creativity powered by
AI.
 Types of Artificial Intelligence
24

• Artificial Intelligence can be divided into various types, there are mainly
two types of the main categorization:
1) based on capabilities of AI and
2) based on functionally of AI.
 A. Based on Capabilities
25

1. Weak AI or Narrow AI:

• Is able to perform a dedicated task with intelligence.
• Is the most common and currently available AI in world of AI.
• can't perform beyond its field or limitations(it's only trained for one specific
task)
• It can fail in unpredictable ways if it goes beyond its limits.
• Examples: Apple Siri operates with a limited pre-defined range of functions.
Google translate, playing chess, purchasing suggestions on e-commerce sites,
self-driving cars, speech recognition, and image recognition.
 Cont…..
26

2. General AI:
• is a type of intelligence that could perform any intellectual task with
efficiency like a human. The idea behind the general AI is to make such a
system that could be smarter and think like a human on its own.
• Currently, there is no such system exists which can perform any task as
perfect as a human. It may arrive within the next 20 or so years.
3. Super/Strong AI:
• is a level of Intelligence of Systems at which machines could surpass human
intelligence, and can perform any task better than a human with cognitive
properties.
 B. Based on the functionality
27
1. Reactive Machines
• 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. Examples: - Google's AlphaGo.

2. 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.
• Example:- Self-driving cars: these cars can store the recent speed of nearby
cars, the distance of other cars, speed limits, and other information to navigate
the road.
 Cont…..
28
3. Theory of Mind
• This type of AI should understand human emotions, people, beliefs, and be
able to interact socially like humans. But it is still not developed.
4. Self-awareness
• These machines will be super intelligent and will have their own consciousness,
sentiments, and self-awareness. These machines will be smarter than the human
mind.
• does not exist in reality still and it is a hypothetical concept.
 The State of the Art
29

• What can AI do today?

1. AI in agriculture: Now a days agriculture is applying AI as agriculture
robotics, solid and crop monitoring, predictive analysis.
 Cont...
30

2. AI in Healthcare: Healthcare Industries are applying AI to make a better and

faster diagnosis than humans.
3. AI in education: AI Chabot can communicate with students as a teaching
assistant.
4. AI in Finance and E-commerce: The finance industry is implementing
automation, Chabot, adaptive intelligence, algorithm trading, and machine
learning into financial processes.
5. AI in Gaming: The AI machines can play strategic games like chess, where the
machine needs to think of a large number of possible places.
 Cont...
31

6. AI in Data Security: AI can be used to make your data more safe and secure.
Some examples such as AEG bot, AI2 Platform, are used to determine software
bugs and cyber-attacks in a better way.
7. AI in Social Media: Social Media sites such as Facebook, Twitter, and Snapchat
contain billions of user profiles, which need to be stored and managed in a very
efficient way.
8. AI in Travel &Transport: AI is capable of doing various travel related works
such as from making travel arrangements to suggesting the hotels, flights, and best
routes to the customers.
Any Question?