Introduction to Artificial Intelligence ( AI)

Unit 1: Artificial Intelligence
C. P. Divate

What is Artificial Intelligence ?
 making computers that think?
 the automation of activities we associate with human
thinking, like decision making, learning ... ?
 the art of creating machines that perform functions that
require intelligence when performed by people ?
 the study of mental faculties through the use of
computational models ?
 a branch of computer science that is concerned with the
automation of intelligent behaviour ?

What is Artificial Intelligence ?
 According to the father of Artificial Intelligence, John
McCarthy, it is “The science and engineering of making
intelligent machines, especially intelligent computer
programs”.
 Artificial Intelligence is a way of making a computer, a
computer-controlled robot, or a software think
intelligently, in the similar manner the intelligent humans think.
• AI is accomplished by studying how human
brain thinks, and how humans learn, decide,
and work while trying to solve a problem, and
then using the outcomes of this study as a basis
of developing intelligent software and systems.

Processes in Artificial Intelligence

• Data: data is raw. It simply exists and has no significance
beyond its existence (in and of itself).
• It can exist in any form, usable or not.
• It does not have meaning of itself.
• In computer parlance, a spreadsheet generally starts out by
holding data.
• Information: information is data that has been given meaning by
way of relational connection.
• This "meaning" can be useful, but does not have to be.
• In computer parlance, a relational database makes
information from the data stored within it.

• Knowledge... knowledge is the appropriate collection of information,
such that it's intent is to be useful.
• Knowledge is a deterministic process.
• Knowledge has useful meaning to them, but it does not provide for,
in and of itself, an integration such as would infer further
knowledge.
• For example, elementary school children memorize, or amass
knowledge of, the "times table". They can tell you that "2 x 2 = 4"
because they have amassed that knowledge (it being included in
the times table). But when asked what is "1267 x 300", they can
not respond correctly because that entry is not in their times table.
To correctly answer such a question requires a true cognitive and
analytical ability that is only encompassed in the next level...
understanding.
• In computer parlance, most of the applications we use (modeling,
simulation, etc.) exercise some type of stored knowledge.

• Wisdom: wisdom is an predictive and non-deterministic, non-
probabilistic process.
• It calls upon all the previous levels of consciousness, and
specifically upon special types of human programming
(moral, ethical codes, etc.).
• It beckons to give us understanding about which there has
previously been no understanding, and in doing so, goes far
beyond understanding itself.
• Wisdom is therefore, the process by which we also
distinguish, or judge, between right and wrong, good and
bad.
• Wisdom is a uniquely human state, or as I see it, wisdom
requires one to have a soul, for it resides as much in the heart
as in the mind. And a soul is something machines will never
possess.

Goals of AI
• To Create Expert Systems − The systems which exhibit
intelligent behavior, learn, demonstrate, explain, and advice
its users.
• To Implement Human Intelligence in Machines −
Creating systems that understand, think, learn, and behave
like humans.

Programming Without and With AI
Programming Without AI Programming With AI
A computer program without
AI can answer
the specific questions it is
meant to solve.
A computer program with AI can
answer the generic questions it
is meant to solve.
Modification in the program
leads to change in its
structure.
AI programs can absorb new
modifications by putting highly
independent pieces of
information together. Hence you
can modify even a minute piece
of information of program without
affecting its structure.
Modification is not quick and
easy. It may lead to affecting
the program adversely.
Quick and Easy program
modification.

Advantages of Artificial
Intelligence
1. High Accuracy with less errors: AI machines or systems are prone to less
errors and high accuracy as it takes decisions as per pre-experience or
information.
2. High-Speed: AI systems can be of very high-speed and fast-decision making,
because of that AI systems can beat a chess champion in the Chess game.
3. High reliability: AI machines are highly reliable and can perform the same
action multiple times with high accuracy.
4. Useful for risky areas: AI machines can be helpful in situations such as
defusing a bomb, exploring the ocean floor, where to employ a human can be
risky.
5. Digital Assistant: AI can be very useful to provide digital assistant to the
users such as AI technology is currently used by various E-commerce websites
to show the products as per customer requirement.
6. Useful as a public utility: AI can be very useful for public utilities such as a
self-driving car which can make our journey safer and hassle-free, facial
recognition for security purpose, Natural language processing to communicate
with the human in human-language, etc.

Disadvantages of Artificial Intelligence
• High Cost: The hardware and software requirement of AI is very costly as it
requires lots of maintenance to meet current world requirements.
• Can't think out of the box: Even we are making smarter machines with AI,
but still they cannot work out of the box, as the robot will only do that work for
which they are trained, or programmed.
• No feelings and emotions: AI machines can be an outstanding performer, but
still it does not have the feeling so it cannot make any kind of emotional
attachment with human, and may sometime be harmful for users if the proper
care is not taken.
• Increase dependency on machines: With the increment of technology, people
are getting more dependent on devices and hence they are losing their mental
capabilities.
• No Original Creativity: As humans are so creative and can imagine some new
ideas but still AI machines cannot beat this power of human intelligence and
cannot be creative and imaginative.

Concepts in Artificial Intelligence
Machine learning:

Machine learning: Machine learning is a growing technology which enables
computers to learn automatically from past data.
• Machine learning uses various algorithms for building mathematical models
and making predictions using historical data or information.

Machine learning: Machine learning is a growing technology which enables
computers to learn automatically from past data.
• Currently, it is being used for various tasks such as image recognition, speech
recognition, email filtering, Facebook auto-tagging, recommender system,
and many more.
• This machine learning tutorial gives you an introduction to machine learning
along with the wide range of machine learning techniques such
as Supervised, Unsupervised, and Reinforcement learning.
• You will learn about regression and classification models, clustering methods,
hidden Markov models, and various sequential models.

Machine learning
Features of Machine Learning:
• Machine learning uses data to detect various patterns in a given
dataset.
• It can learn from past data and improve automatically.
• It is a data-driven technology.
• Machine learning is much similar to data mining as it also deals with
the huge amount of the data.
Classification of Machine Learning
1. Supervised learning
2. Unsupervised learning
3. Reinforcement learning

Supervised learning Machine learning
Supervised learning, as the name indicates, has the presence of a supervisor
as a teacher.
Basically supervised learning is when we teach or train the machine using
data that is well labeled. Which means some data is already tagged with the
correct answer.
After that, the machine is provided with a new set of examples(data) so that
the supervised learning algorithm analyses the training data (set of training
examples) and produces a correct outcome from labeled data.
For instance, suppose you are given a basket filled with different kinds of
fruits. Now the first step is to train the machine with all different fruits one
by one like this:
• If the shape of the object is rounded and has a
depression at the top, is red in color, then it
will be labeled as –Apple.
• If the shape of the object is a long curving
cylinder having Green-Yellow color, then it
will be labeled as –Banana

Unsupervised learning Machine learning
Unsupervised can be further classifieds into two categories of algorithms:
• Clustering
• Association

Supervised learning Machine learning
Supervised learning classified into two categories of algorithms:
• Classification: A classification problem is when the output variable is a
category, such as “Red” or “blue” or “disease” and “no disease”.
• Regression: A regression problem is when the output variable is a real
value, such as “dollars” or “weight”.
Advantages Supervised learning Machine learning:-
• Supervised learning allows collecting data and produces data output from
previous experiences.
• Helps to optimize performance criteria with the help of experience.
• Supervised machine learning helps to solve various types of real-world
computation problems.
Disadvantages:-
Classifying big data can be challenging.
Training for supervised learning needs a lot of computation time. So, it requires a
lot of time.

Unsupervised learning is classified into two categories of algorithms:
Clustering: A clustering problem is where you want to discover the inherent
groupings in the data, such as grouping customers by purchasing behavior.
Association: An association rule learning problem is where you want to
discover rules that describe large portions of your data, such as people that buy
X also tend to buy Y.

Unsupervised learning is the training of a machine using information that is
neither classified nor labeled and allowing the algorithm to act on that
information without guidance.
Here the task of the machine is to group unsorted information according to
similarities, patterns, and differences without any prior training of data.
Unlike supervised learning, no teacher is provided that means no training
will be given to the machine. Therefore the machine is restricted to find the
hidden structure in unlabeled data by itself.
For instance, suppose it is given an image having both dogs and cats which
it has never seen.
• Thus the machine has no idea about the
features of dogs and cats so we can’t
categorize it as ‘dogs and cats‘.
• But it can categorize them according to
their similarities, patterns, and
differences, i.e., we can easily
categorize the above picture into two
parts.

Reinforcement learning Machine learning
Unsupervised can be further classifieds into two categories of algorithms:
• Clustering
• Association

Reinforcement Machine learning
• Reinforcement Learning is a feedback-based Machine learning technique in
which an agent learns to behave in an environment by performing the
actions and seeing the results of actions.
• For each good action, the agent gets positive feedback, and for each bad
action, the agent gets negative feedback or penalty.
• In Reinforcement Learning, the agent learns automatically using feedbacks
without any labeled data, unlike supervised learning.
• Since there is no labeled data, so the agent is bound to learn by its
experience only.
• RL solves a specific type of problem where decision making is sequential,
and the goal is long-term, such as game-playing, robotics, etc.
• The agent interacts with the environment and explores it by itself. The
primary goal of an agent in reinforcement learning is to improve the
performance by getting the maximum positive rewards.

Neural networks in Artificial Intelligence
Neurons in human brain and pathways

How do ANNs work?
 An artificial neural network (ANN) is either a hardware
implementation or a computer program which strives to
simulate the information processing capabilities of its biological
exemplar. ANNs are typically composed of a great number of
interconnected artificial neurons. The artificial neurons are
simplified models of their biological counterparts.
 ANN is a technique for solving problems by constructing software
that works like our brains.

How do our brains work?
 The Brain is A massively parallel information processing system.
 Our brains are a huge network of processing elements. A typical brain
contains a network of 10 billion neurons.

 A processing element
Dendrites: Input
Cell body: Processor
Synaptic: Link
Axon: Output

A neuron is connected to other neurons through about 10,000
synapses

A neuron receives input from other neurons. Inputs are combined.

Once input exceeds a critical level, the neuron discharges a spike ‐
an electrical pulse that travels from the body, down the axon, to
the next neuron(s)

The axon endings almost touch the dendrites or cell body of the
next neuron.

Transmission of an electrical signal from one neuron to the next is
effected by neurotransmitters.

Neurotransmitters are chemicals which are released from the first
neuron and which bind to the
Second.

This link is called a synapse. The strength of the signal that
reaches the next neuron depends on factors such as the amount of
neurotransmitter available.

How do ANNs work?
An artificial neuron is an imitation of a human neuron

How do ANNs work?
• Now, let us have a look at the model of an artificial neuron.

How do ANNs work?
Output
x1
x2
xm
∑
y
Processing
Input
∑= X1+X2 + ….+Xm =y
. . . . . . .
. . . . .

How do ANNs work?
Not all inputs are equal
Output
x1
x2
xm
∑
y
Processing
Input
∑= X1w1+X2w2 + ….+Xmwm
=y
w1
w2
wm
weights
. . . . . . .
. . . . .
. . .
. .

How do ANNs work?
The signal is not passed down to the
next neuron verbatim
Transfer Function
(Activation Function)
Output
x1
x2
xm
∑
y
Processing
Input
w1
w2
wm
weights
. . . . . . .
. . . . .
f(vk)
. . .
. .

The output is a function of the input, that is
affected by the weights, and the transfer
functions

Artificial Neural Networks
 An ANN can:
1. compute any computable function, by the appropriate
selection of the network topology and weights values.
2. learn from experience!
 Specifically, by trial‐and‐error

Neural networks in Artificial Intelligence
The history of Neural Networks

Deep Learning in Neural Networks

Scope of Artificial Intelligence

The main topics in AI
Artificial intelligence can be considered under a number
of headings:
 Search (includes Game Playing).
 Representing Knowledge and Reasoning with it.
 Planning.
 Learning.
 Natural language processing.
 Expert Systems.
 Interacting with the Environment
(e.g. Vision, Speech recognition, Robotics)
We won’t have time in this course to consider all of these.

 more powerful and more useful computers
 new and improved interfaces
 solving new problems
 better handling of information
 relieves information overload
 conversion of information into knowledge
Some Advantages of Artificial
Intelligence

Artificial Intelligence Techniques

Artificial Intelligence Approach
Systems that act
rationally
Systems that think
like humans
Systems that think
rationally
Systems that act
like humans
THOUGHT
BEHAVIOUR
Like HUMAN RATIONAL

Artificial Intelligence Approach

Components of Artificial Intelligence

Three dimensional model of artificial intelligence

Types of artificial intelligence

AI type-1: 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.
• 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 Siriis a good example of Narrow AI, but it operates with a
limited pre-defined range of functions.
• IBM's Watson supercomputer also comes under Narrow AI, as it
uses an Expert system approach combined with Machine
learning and natural language processing.
• Some Examples of Narrow AI are playing chess, purchasing
suggestions on e-commerce site, self-driving cars, speech
recognition, and image recognition.

1. Weak AI or Narrow AI:

2. 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.
• As systems with general AI are still under research, and it will
take lots of efforts 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 human with
cognitive properties.
• It is an outcome of general AI.
• Some key characteristics of strong AI include
capability include the ability to think, to reason,
solve the puzzle, make judgments, plan, learn, and
communicate by its own.
• Super AI is still a hypothetical concept of Artificial
Intelligence. Development of such systems in real is
still world changing task.

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 system is an example of
reactive machines.
• Google's AlphaGo is also an example of reactive
machines.
Artificial Intelligence type-2: Based on functionality

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.

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, but
researchers are making lots of efforts and
improvement for developing such AI machines.

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.

Applications of artificial intelligence

1. AI in Astronomy
• Artificial Intelligence can be very useful to solve complex universe problems.
AI technology can be helpful for understanding the universe such as how it
works, origin, etc.
2. AI in Healthcare
• In the last, five to ten years, AI becoming more advantageous for the healthcare
industry and going to have a significant impact on this industry.
• 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 to the patient before hospitalization.
3. AI in Gaming
• AI can be used for gaming purpose. The AI machines can play strategic games
like chess, where the machine needs to think of a large number of possible
places.

4. AI in Finance
• AI and finance industries are the best matches for each other. The finance
industry is implementing automation, chatbot, adaptive intelligence, algorithm
trading, and machine learning into financial processes.
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. Some examples such as AEG bot, AI2 Platform,are used to
determine software bug and cyber-attacks in a better way.
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. AI can analyze lots of data to
identify the latest trends, hashtag, and requirement of different users.

7. AI in Travel & Transport
• AI is becoming highly demanding for travel industries. AI is capable of doing
various travel related works such as from making travel arrangement to suggesting
the hotels, flights, and best routes to the customers.
• Travel industries are using AI-powered chatbots which can make human-like
interaction with customers for better and fast response.
8. AI in Automotive Industry
• Some Automotive industries are using AI to provide virtual assistant to their user
for better performance. Such as Tesla has introduced TeslaBot, an intelligent virtual
assistant.
• Various Industries are currently working for developing self-driven cars which can
make your journey more safe and secure.
9. AI in Robotics:
• Artificial Intelligence has a remarkable role in Robotics. Usually, general robots are
programmed such that they can perform some repetitive task, but with the help of AI,
we can create intelligent robots which can perform tasks with their own experiences
without pre-programmed.
• Humanoid Robots are best examples for AI in robotics, recently the intelligent
Humanoid robot named as Erica and Sophia has been developed which can talk and
behave like humans.

10. AI in Entertainment
• We are currently using some AI based applications in our daily life with some
entertainment services such as Netflix or Amazon. With the help of ML/AI algorithms,
these services show the recommendations for programs or shows.
11. AI in Agriculture
• Agriculture is an area which requires various resources, labor, money, and time for best
result. 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. AI in agriculture can be very helpful for farmers.
12. AI in E-commerce
• AI is providing a competitive edge to the e-commerce industry, and it is becoming
more demanding in the e-commerce business. AI is helping shoppers to discover
associated products with recommended size, color, or even brand.
13. 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.
• AI in the future can be work as a personal virtual tutor for students, which will be
accessible easily at any time and any place.

AI Applications
 Autonomous
Planning &
Scheduling:
 Autonomous rovers.

AI Applications
 Autonomous Planning & Scheduling:
 Telescope scheduling

AI Applications
 Autonomous Planning & Scheduling:
 Analysis of data:

AI Applications
 Medicine:
 Image guided surgery

AI Applications
 Medicine:
 Image analysis and enhancement

AI Applications
 Transportation:
 Autonomous
vehicle control:

AI Applications
 Transportation:
 Pedestrian detection:

AI Applications
 Robotic toys:

Introduction to Artificial Intelligence ( AI)

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