Chapter Three
Artificial Intelligence (AI)
20/05/2025 By:- Abinet A. (MSc) 1
What is artificial intelligence(introduction)
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.“
So, we can define Artificial Intelligence (AI) as the branch of
computer science by which we can create intelligent machines that
behave like a human, think like humans, and able to make decisions.
20/05/2025 By:- Abinet A. (MSc) 2
Introduction …
Intelligence is composed of
Reasoning
Learning
Problem Solving
Perception
Linguistic Intelligence
20/05/2025 By:- Abinet A. (MSc) 3
Introduction …
An AI system is composed of an agent and its environment.
An agent (e.g., human or robot) is anything that can perceive its
environment through sensors and acts upon that environment through
effectors.
Machine perception is the ability to use input from sensors (such
as cameras, microphones, etc.) to deduce aspects of the world. e.g.,
Computer Vision.
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Introduction …
High-profile examples of AI include
Autonomous vehicles (such as drones and self-driving cars)
Medical diagnosis
Creating art (such as poetry)
Proving mathematical theorems
Playing games (such as Chess or Go)
Search engines (such as Google search)
Online assistants (such as Siri)
Image recognition in photographs
Spam filtering
Prediction of judicial decisions
Online advertisements 5
Artificial intelligence, Machine Learning and
Deep Learning
Artificial intelligence is a technology which
enables a machine to simulate human behavior.
Machine Learning is a subfield of artificial
intelligence, which enables machines to learn
from past data or experiences without being
explicitly programmed.
Deep learning is the field of neural
networks with several hidden layers.
Figure 3.1 Artificial Intelligence (AI), Machine
Learning (ML) and Deep Learning (DL)
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Need for Artificial Intelligence
To create expert systems that exhibit intelligent behavior with the
capability to learn, demonstrate, explain and advice its users.
Helping machines to find solutions to complex problems like humans
do and applying them as algorithms in a computer-friendly manner.
Goals of Artificial Intelligence
Replicate human intelligence
Solve Knowledge-intensive tasks
An intelligent connection of perception and action
Building a machine which can perform tasks that requires human intelligence
such as: Proving a theorem, Playing chess, Plan some surgical operation, Driving
a car in traffic
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Goals of Artificial Intelligence
Creating some system which can exhibit intelligent behavior, learn new things by
itself, demonstrate, explain, and can advise to its user.
To create the AI-first we should know that how intelligence is composed,
so Intelligence is an intangible part of our brain which is a combination of
Reasoning, learning, problem-solving, perception, language understanding,
etc.
To achieve that AI requires the following disciplines:
Mathematics
Biology
Psychology
Sociology
Computer Science
Neurons Study
Statistics
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Disciplines of AI
Figure 3.2 Artificial Intelligence is multidisciplinary
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Advantages of Artificial Intelligence
High Accuracy with fewer errors
High-Speed
High reliability
Useful for risky areas
Digital Assistant
Useful as a public utility
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Disadvantages of Artificial Intelligence
High Cost
Can't think out of the box
No feelings and emotions
Increase dependence on machines
No Original Creativity
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History of AI
A. Maturation of Artificial Intelligence (1943-1952)
The 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.
The year 1949: Donald Hebb demonstrated an updating rule for
modifying the connection strength between neurons. His rule is now
called Hebbian learning.
The year 1950: The Alan Turing who was an English
mathematician and pioneered Machine learning in 1950.
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History of AI…
B. The birth of Artificial Intelligence (1952-1956)
The year 1955: An Allen Newell and Herbert A. Simon created
the "first artificial intelligence program" Which was named "Logic
Theorist".
The year 1956: The word "Artificial Intelligence" first adopted
by American Computer scientist John McCarthy at the Dartmouth
Conference.
C. The golden years-Early enthusiasm (1956-1974)
The year 1966: Joseph Weizenbaum created the first chatbot,
which was named as ELIZA.
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History of AI…
The year 1972: The first intelligent humanoid robot was built in Japan
which was named WABOT-1.
D. The first AI winter (1974-1980)
The duration between the years 1974 to 1980 was the first AI
winter duration.
AI winter refers to the time period where computer scientists dealt with a
severe shortage of funding from the government for AI researches.
E. A boom of AI (1980-1987)
The 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.
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History of AI…
F. The second AI winter (1987-1993)
Again, Investors and government stopped in funding for AI
research due to high cost but not efficient results.
G. The emergence of intelligent agents (1993-2011)
The 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.
The year 2006: AI came into the Business world until the year
2006. Companies like Facebook, Twitter, and Netflix also started
using AI.
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History of AI…
H. Deep learning, big data and artificial general intelligence (2011-
present)
The year 2011: In the year 2011, IBM's Watson won jeopardy, a
quiz show, where it had to solve complex questions as well as
riddles.
The year 2012: Google has launched an Android app feature
"Google now", which was able to provide information to the user as a
prediction.
The year 2015: Amazon alexa or Amazon Echo created by
Amazon company
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Levels of AI
Stage 1 – Rule-Based Systems
Are functioning based on pre defined rules and guidelines.
Stage 2 – Context Awareness and Retention
Those systems can understand and react contextually with human.
They trained from a given context and they do have the capability to
map on another context.
Example: chatbot.
Stage 3 – Domain-Specific Expertise
Going beyond the capability of humans, these systems build
up expertise in a specific context taking in massive volumes of
information which they can use for decision making.
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Levels of AI…
Stage 4 – Reasoning Machines
These systems can reason out about certain event.
They can debate, negotiate with humans or another machines.
They can understand how their internal systems works.
Those machines are not produced yet.
Stage 5 – Self Aware Systems / Artificial General Intelligence
(AGI)
These systems have human-like intelligence. They can do what
ever human beings can do.
Stage 6 – Artificial Superintelligence (ASI)
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Levels of AI…
AI algorithms can outsmart even the most intelligent humans in
every domain.
They can solve big problems that couldn’t solved yet.
Those machines expected to produced around 2029
Stage 7 – Singularity and Transcendence
Those AI systems are beyond human imaginations.
When machines are own this stage, it will be the end of human
race (Stephen hawking).
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Levels of AI…
Figure 3.4 The seven layers of AI maturity
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Types of AI
We can be categorize AI based on capabilities and based on
functionality
Figure 3.5 types of Artificial Intelligence (AI)
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Types of AI…
A. Based on Capabilities
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 is Narrow AI in the
world of Artificial Intelligence.
Example: Google translate, playing chess, self-driving cars, speech
recognition, and image recognition.
2. General AI:
General AI is a type of intelligence that could perform any intellectual
task with efficiency like a human.
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Types of AI…
The idea behind the general AI to make such a system that could be
smarter and think like a human on its own.
As systems with general AI are still under research, and it will take lots
of effort and time to develop such systems.
3. 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 a
human with cognitive properties.
Super AI is still a hypothetical concept of Artificial Intelligence.
The development of such systems in real is still a world-changing task.
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Types of AI…
B. Based on the functionality
1. Reactive Machines
This 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 system is an example of reactive machines.
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Types of AI…
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.
• Example: Self-driving cars
3. Theory of Mind
Theory of Mind AI should understand human emotions, people,
beliefs, and be able to interact socially like humans.
This type of AI machines is still not developed, but researchers are
making lots of efforts and improvement for developing such AI
machines.
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Types of AI…
4. Self-Awareness
Self-awareness AI is the future of Artificial Intelligence
These machines will be smarter than the human mind.
Self-Awareness AI does not exist in reality still and it is a hypothetical
concept.
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How humans think?
How does a human being think? Intelligence or the cognitive
process is composed of three main stages:
Observe and input the information or data.
Interpret and evaluate the input that is received from the surrounding
environment.
Make decisions as a reaction towards what you received as input and
interpreted and evaluated.
Mapping human thinking to artificial intelligence components
It is possible to map the human thinking stages to the layers or
components of AI systems
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Mapping human thinking to AI components
In the first stage, humans acquire information from their surrounding
environments through human organs, such as eyes, ears, hands and
other sensing organs.
In AI models, this stage is represented by the sensing layer, which
perceives information from the surrounding environment.
The second stage is related to interpreting and evaluating the input
data.
In AI, this stage is represented by the interpretation layer, that is,
reasoning and thinking about the gathered input that is acquired by the
sensing layer.
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Mapping human thinking to AI components…
• The third stage is related to taking action or making decisions.
Influencers of artificial intelligence
The following influencers of AI are described in this section:
Big data: Structured data versus unstructured data
Advancements in computer processing speed and new chip architecture
Cloud computing and APIs
The emergence of data science
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Influencers of artificial intelligence
Advancements in computer processing speed, new chip
architectures, and big data file systems
Significant advancements in computer processing and memory speeds
enable us to make sense of the information that is generated by big
data more quickly.
Cloud computing and application programming interfaces
Companies worldwide offer their services to customers over cloud
platforms.
These services might be data analysis, social media, video storage, e-
commerce, and AI capabilities that are available through the internet
and supported by cloud computing.
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Influencers of artificial intelligence…
Application programming interfaces (APIs) expose capabilities and
services.
APIs enable software components to communicate with each other
easily.
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Applications of AI
The following are some applications of AI in some sector:
1. AI in agriculture
Now a day's agriculture is becoming digital, and AI is emerging in
this field.
Agriculture is applying AI as agriculture robotics, solid and crop
monitoring, predictive analysis. This can be very helpful for farmers.
2. AI in Healthcare
Healthcare Industries are applying AI to make a better and
faster diagnosis than humans.
AI can help doctors with diagnoses and can inform when patients
are worsening so that medical help can reach the patient before
hospitalization.
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Applications of AI…
3. AI in education
AI can automate grading so that the tutor can have more time to teach.
AI chatbot can communicate with students as a teaching assistant.
In the future it can be work as a personal virtual tutor for
students, which will be accessible easily at any time and any
place.
4. AI in Finance and E-commerce
AI in e-commerce offers personalized and interactive buying experiences
Predictive marketing and sales.
Automated responders and online customer support.
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Applications of AI…
5. AI in Data Security
The security of data is crucial for every company and cyber-
attacks are growing very rapidly in the digital world.
AI can be used to make your data more safe and secure.
Example: spam filtering
6. 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.
AI can organize and manage massive amounts of data.
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Applications of AI…
7. 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.
8. AI in Robotics:
Artificial Intelligence has a remarkable role in Robotics.
Humanoid Robots are the best examples for AI in robotics,
recently the intelligent Humanoid robot named Erica and Sophia
has been developed which can talk and behave like humans.
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Applications of AI…
9. AI in Gaming
AI can be used for gaming purposes.
The AI machines can play strategic games like chess, PlayStation
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AI tools and platforms
AI platforms are defined as some sort of hardware architecture or
software framework (including application frameworks), that allows
the software to run.
It involves the use of machines to perform the tasks that are
performed by human beings.
The platform simulates the cognitive function that human
minds perform such as problem-solving, learning, reasoning,
social intelligence as well as general intelligence.
AI platforms provide users a tool kit to build intelligent applications.
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