As per the latest revised Skill Education Curriculum prescribed

by the CBSE, New Delhi for academic year 2024-25

SUPPLEMENT TO
A Textbook of

Artificial Intelligence
For Class 9
Subject Code : 417
By
Hema Dhingra

PART B : SUBJECT SPECIFIC SKILLS

Unit 1 : AI Reflection, Project Cycle and Ethics 2–38
1. AI Reflection .... .... .... 2
2. Applications of AI .... .... .... 17
3. AI Project Cycle .... .... .... 27
4. Ethics .... .... .... 29

Unit 2 : Data Literacy 39-73

5. Basics of Data Literacy .... .... .... 39
6. Acquiring Data, Processing and Interpreting Data 49
7. Project Interactive Data Dashboard and Presentation 62

Unit 3 : Math for AI (Statistics & Probability) 74-104

8. Importance of Math for AI .... .... .... 74
9. Statistics .... .... .... 83
10. Probability .... .... .... 94

Unit 4 : Introduction to Generative AI 105-120

11. Generative AI .... .... .... 105

For revised syllabus scan this QR Code.

 Unit 1 : AI Reflection, Project Cycle and Ethics

1 AI Reflection

Learning Objectives
After studying this chapter, students will be able to:
• Know more about the various types of Intelligence
• To identify and appreciate Artificial Intelligence
• Know more about the various types of Artificial Intelligence
• To recognize , engage and relate with Data, Computer Vision and Natural Language Processing

Before we look into what Artificial Intelligence is, perhaps a better starting point would be to ask,
“What is intelligence?” This is a complex question with no well-defined answer that has puzzled
biologists, psychologists, and philosophers for centuries. One could certainly define intelligence by the
properties it exhibits: an ability to deal with new situations; the ability to solve problems, to answer
questions, to devise plans, and so on.
Intelligence is the act or quality of being clever. Using intelligence, you can see patterns, solve
problems, understand a text, understand a concept, learn languages, and much more. Following are some
definitions of intelligence:
• Intelligence lets us solve problems, understand concepts and act purposefully.
• Intelligence allows us to imagine and use our experiences in life to solve problems.
• Intelligence allows us to be creative and create objects and concepts of beauty and originality.
• Someone’s intelligence is their ability to understand and learn things.
• Intelligence is the ability to think and understand instead of doing things by instinct or automatically.
So we can define intelligence as ‘the ability to learn and understand, to solve problems and to make
decisions’.

FEATURES OF INTELLIGENCE
• Handling incomplete data: Interpreting complete information from incomplete data.
• Handling contradictory data: Making sense from data having contradiction and ambiguity.
• Handling uncertain data: Making sense from fuzzy or uncertain data.
• Handling heuristics: An intelligent system should be able to search or think using some rules to
guide the search in the most probable direction.
• Ability to learn: An intelligent system has the ability to learn and slowly grow the knowledge
base of the system.

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It is due to intelligence that humans have emerged to be the dominant species on Earth. Humans are
not the strongest or fastest or generally do not have great athletic abilities. Many animals like tigers,
lions, hyenas, crocodiles, elephants, alligators, etc. are much stronger than the strongest humans. Deers,
tigers, cheetahs, etc. run much faster than humans. Still, the appropriate use of an intelligent mind can
conquer the strongest or fastest easily.

STRONGEST ANIMAL IN THE WORLD FASTEST ANIMAL IN THE WORLD

Humans are said to be an intelligent species, so what is it that makes us intelligent?

Human intelligence, a mental quality that consists of the abilities to learn from experience, adapt to new
situations, understand and handle abstract concepts, and use knowledge to manipulate one’s environment.
Intelligence includes the ability to benefit from past experience, act purposefully, solve problems, and
adapt to new situations.
You might be under the mistaken impression that only humans have intelligence. This is NOT true.
You must have seen dogs (especially in YouTube videos) that add numbers or the fact that parrots can
exactly mimic the words said by a person. Chimps have also been shown to be better at some memory
tasks than humans. Elephants are good at teamwork. Dolphins are considered to be able to identify
themselves in the mirror.
Naturalistic
Linguistic–Verbal Visual–Spatial
TYPES OF INTELLIGENCE Loves animals,
plants and nature Excels in words, Excels in shapes,
In order to capture the full range of abilities & world BIOLOGIST, languages, poetry
POETS, WRITERS
designs, graphics and
visualization
CONSERVATIONIST
and talents that people possess, Gardner DESIGNER, ENGINEER

theorizes that people do not have just an Musical

Bodily–Kinesthetic
intellectual capacity, but have many kinds of Excels in performing
Excels in performing
intelligence, including musical, interpersonal, sports, physical activities
MULTIPLE and composing
INTELLIGENCES musical pieces SINGER,
spatial-visual, and linguistic intelligences. & body movements
MUSICAL COMPOSER
ACTORS, ATHLETES
While a person might be particularly
Intrapersonal
strong in a specific area, such as musical Interpersonal
Logical–Mathematical Ability to understand
intelligence, he or she most likely possesses one's inner feelings &
Ability to organize people,
Excels in Math group activities and social
a range of abilities. For example, an and logical
have self realization
relationship LEADERS,
and to know about
individual might be strong in verbal, thinking BANKERS,
ACCOUNTANTS
one self PHILOSOPHER,
SOCIAL WORKERS

musical, and naturalistic intelligence. CLERGY

ACTIVITY Experiential Learning

You would have always seen that you are different from your friends in your likes, dislikes, what
a person is good at or not good at.
Lets tabulate the names of your 5 friends and the things that they are good at (Example Music,
Art, Dancing, Maths, Poetry etc. They can have multiple skills or talents also)

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CAN MACHINES BE MADE INTELLIGENT?
These are questions that humans have tried to answer in the positive, ever since the computers showed
up, with the effort of many brilliant and hard working scientists and engineers. The desire for intelligent
machines was just an elusive dream until the first computer was developed.
The earliest computers were just computing devices. They mimicked the human ability to manipulate
symbols in order to perform basic math tasks, such as addition. Logical reasoning later added the
capability to perform mathematical reasoning through comparisons (such as determining whether one
value is greater than another value). However, humans still needed to define the algorithm used to
perform the computation, provide the required data in the right format, and then interpret the result.
The early computers could manipulate large data bases effectively by following prescribed algorithms,
but could not reason about the data and information provided. This gave rise to the question of whether
computers could ever think. Alan Turing defined the intelligent behaviour of a computer as the ability
to achieve human-level performance in a cognitive task.
The goal of artificial intelligence (AI) as a science is to make machines do things that would
require intelligence if done by humans.
What comes to your mind when you hear the word “Artificial Intelligence”
What do you want to learn about AI?
Artificial Intelligence has always been a term which intrigues people all over the world. Artificial
intelligence (abbreviated: AI or A.I.) is how Google ranks pages, Amazon knows what we like, chatbots
like Siri chat, and computers play Chess and Go. There have been at least 120 movies and web series
about artificial intelligence released nationally and internationally as of today. Terminator, Her, Black Mirror,
and Enthiran are just a few names that are super popular. The media industry is going gaga over AI.You
can hardly avoid encountering mentions of AI today. You see AI in the movies, in books, in the news, and
online. AI is part of robots, self-driving cars, drones, medical systems, online shopping sites, and all sorts
of other technologies that affect your daily life in so many ways. Much of the hype about AI originates
from the excessive and unrealistic expectations of scientists, entrepreneurs, and businesspersons.

NOW THE QUESTION IS “WHAT IS ARTIFICIAL INTELLIGENCE”

Computers can store data but they do not have their own intelligence to use it. To use that data, they
need instructions from humans. Computers cannot decide and handle a situation on their own. Brain is
far superior to a computer. So now, computers are trained to learn and exhibit intelligence, which is
called Artificial intelligence.
Artificial intelligence is the study of systems that act in a way that to any observer would appear to be
intelligent. Artificial Intelligence involves using methods based on the intelligent behavior of humans
and other animals to solve complex problems.
When a machine possesses the ability to mimic human traits, i.e., make decisions, predict the future,
learn and improve on its own, it is said to have artificial intelligence. In other words, you can say that
a machine is artificially intelligent when it can accomplish tasks by itself - collect data, understand it,
analyse it, learn from it, and improve it.
AI is the ability of digital computers or computer
controlled robots to solve problems that are normally
associated with the higher intellectual processing
capabilities of humans. Artificial Intelligence is the
study of how to make computers do things at which,
at the moment, people are better.

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Narrow AI : Narrow AI is designed to perform tasks that are more specific. It is also called Weak
AI. This implies that Narrow AI are intelligent systems that are programmed to perform specific tasks
without the need for intrinsic coding. Such machines are deployed to perform some repeated tasks.
Examples
• Email spam filters • Netflix’s recommendations
• Self-driven vehicles • Voice interface-based assistants such as Alexa and Siri
• Performing content search (Google Search)
Key features of narrow AI
1. Perform a dedicated assigned task
2. Limited to a particular field of application
3. Has a predefined set of functions
General AI : Artificial general intelligence, unlike Narrow AI, includes the capability of understanding
a vast scope of activities. It is also called Strong AI. This AI is looked upon as a form of human
intelligence as is shown in many popular movies like Ex Machina, The Terminator, and 2001: A Space
Odyssey.
Examples
• Chatbot that understands customer’s needs and suggests solutions by its learned intelligence
• A training system that functions without the help of a trainer (Car, Airplane)
Key features of general AI
1. Capable of applying retained information to solve new problems
2. Think and respond(Smart) like humans do
3. Performing variety of tasks in changing contexts
Here are some examples of generative AI in action:
• Text Generation:
○ Creating realistic news articles or scripts based on a chosen style or topic.
○ Generating different creative text formats like poems, code, musical pieces, or even emails.
• Image Generation:
○ Creating new, realistic images of people, landscapes, or objects that don’t actually exist.
○ Editing existing images, like adding or removing elements, or changing styles (e.g., turning
a photo into a painting).
• Music Generation:
○ Composing new music pieces in different genres or styles based on existing samples.
○ Creating sound effects or background music for video games or movies.
The applications of generative AI are constantly expanding, with potential uses in various fields.
Generative AI will be discussed later in this book in detail.
TYPES OF AI ON FUNCTIONALITY BASIS
Reactive Machines
They show the most basic type of AI. This AI has no memory power of its own so they really can’t
work on using previous data for better decision-making capabilities. They replicate a human’s ability to
react to different types of stimuli. Example Chess game between Deep Blue (IBM) and Gary Kasparov.

5
Limited Memory
These are the machines with some form of memory capabilities but to a limited extent. With such AI
devices, you are able to conclude decisions based on past data that is used for better informed decisions
for the future. Chatbots and Self driven cars are based on limited memory functionality.
Theory of mind
These are the types of robots that are able to use their common sense in interpreting data and coming
up with decisions in real-time. These are capable of interacting socially by exhibiting understanding of
emotions and gestures.
Self-awareness
Self-aware AI is able to even interpret human emotions and base decisions that are not just logical but
also influenced by feelings. These are truly intelligent machines. Example life like bots in movies. Such
machines do not exist as of now but the work towards it is ongoing.

TASKS WHERE COMPUTERS OUTPERFORM HUMANS

Following are the tasks at which computers outperform humans.
• If a computer is programmed to do a task, the computer executes the task much much faster than
a human.
• A computer never needs rest, break and need to get entertained as humans need. This leads to
less delay in tasks than humans.
• A computer can store much much more data than a human can ever store. This leads to computers
being used for storage and retrieval tasks.
TASKS WHERE HUMANS OUTPERFORM COMPUTERS
Following are the tasks at which humans outperform computer.
• Although computers have some better qualities than humans, humans can take decisions and think
creatively to solve a problem. When a computer hits a roadblock in its solution to a problem, the
human can lead the computer to reach a solution.
• Tasks that require emotional intelligence, i.e. fine understanding of human emotions, like news
reporting or writing self help books, humans excel more than the AI systems.
ADVANTAGES OF AI OVER HUMAN INTELLIGENCE
• Human intelligence is not failure proof. In fact, human intelligence has a high error rate compared
to an AI system. In the case of AI systems, there can be only hardware failure, which is rare.
• AI can be easily fed large amounts of information and assess the input parameters based on the
input. Humans cannot be fed such large amounts of information.
• AI can work in hostile environments. So, AI can be relied upon to do its work correctly even in
otherwise difficult circumstances.
DISADVANTAGES OF ARTIFICIAL INTELLIGENCE
• Humans can become too dependent on AI and lose their mental abilities as can already be seen
with smartphones and video games.
• Robots can cause severe unemployment, by replacing humans at their jobs.
• Can cost a lot of money and time to build, rebuild and repair. Robotic repair can be used to
reduce time and humans needing to fix it, but that will cost more money and resources.
• No emotions are present in an AI system.
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WHY WE STILL NEED HUMANS
While scientists and researchers are developing new techniques for automating daily tasks, artificial
intelligence (AI) still requires human oversight to function well. Driverless operations are required by
cutting-edge technologies like flying robots, drones, and self-driving autos. Enterprise AI, however, depends
on people for further operational guidance and requires outside assistance to accomplish tasks effectively.
The best use of AI involves human monitoring and enhancement. When that occurs, people advance
along the skill spectrum and take on more difficult tasks as AI continues to develop, learn, and restrain
its potentially negative consequences. Humans are able to master complicated cognitive activities thanks
to their thinking capacity as well as emotions like self-awareness, enthusiasm, and aspiration.
An AI can free people from mundane work, so that they can do what people do best - INVENT. An AI
application will stick with what it knows, but humans experiment. An AI application is based on logic,
math and facts, but it lacks intuition.
Teaching Children : An AI can help a teacher with
• Grading papers • Providing help with homework
• Putting study material, revision sheets online • Tutoring online etc
An AI application can’t provide help to children with different levels of attention simultaneously because
all the children in a class are not of the same level. Similarly, it isn’t possible for an AI application to
deal with a stubborn child or a child with different needs, as the data for dealing with such cases is
not fed into the application. People with special needs require a human touch. Someone with a special
need might be fully functional in all but one way- it takes creativity and imagination to discover the
means to getting over the hurdle. An AI might be able to help someone perform their physical therapy
but will not be in a position to decide when to stop or start the therapy.
THE FATHER OF ARTIFICIAL INTELLIGENCE – JOHN McCARTHY
John McCarthy (1927-2011), an American computer scientist and cognitive
scientist, coined the term ‘Artificial Intelligence.’ He was one of the
founders of the discipline of AI. John McCarthy is one of the “founding
fathers” of artificial intelligence, together with Alan Turing, Marvin Minsky,
Allen Newell, and Herbert A. Simon. McCarthy coined the term “artificial
intelligence” in 1955, and organized the famous Dartmouth conference in
Summer 1956. This conference started AI as a field.

TURING TEST
Alan Turing, a young British polymath who explored the Is it a person or a machine? Machine
mathematical possibility of artificial intelligence and he Person A
was a part of the team which proposed the “Imitation
game”. He suggested that like humans take decisions on
the basis of available information, similarly machines
Person B
could also take decisions and solve problems.
This introduces the Turing Test to determine if a
computer can demonstrate the same intelligence as a
human. This test includes three players in which one
player is a human, another player is a computer, and

7
the third player is a human judge. The judge is isolated from the other two players and his responsibility
is to find which player is a machine among the two of them.
DECISION MAKING
Just a few years ago human judgment was the central processor of business decision-making.
Professionals relied on their highly-tuned intuitions, developed from years of experience (and a relatively
tiny bit of data) in their domain, to, say, pick the right creative for an ad campaign, determine the right
inventory levels to stock, or approve the right financial investments.
One-way door decisions are decisions that you can easily reverse. These decisions need to be done
carefully. Two-way door decisions can be reversed. You can walk through the door, see if you like it,
and if not go back. These decisions can be made fast or even automated.
“There’s an immense opportunity to use AI in all kinds of decision making”
Today’s AI systems start from zero and feed on a regular diet of big data. This is augmented intelligence in
action, which eventually provides executives with sophisticated models as the basis for their decision-making.
Example. There are many complexities to each marketing decision. One has to know and understand
customer needs and desires, and align products to these needs and desires. Likewise, having a good
grasp of changing consumer behavior is crucial to making the best marketing decisions, in the short- and
long-run. AI has been able to hasten processes and provide decision-makers with reliable insight. You
get invaluable insight on your customers, which can help you enhance your interactions with them.
When decision-makers and business executives have reliable data analyses, recommendations and follow-
ups through artificial intelligence systems, they can make better choices for their business and employees.
You don’t just enhance the wsork of individual team members. AI also improves the competitive
standing of the business.

DOMAINS OF ARTIFICIAL INTELLIGENCE

1. Data
While Artificial Intelligence (AI) has captured the imagination with its potential to revolutionise various
aspects of our lives, it’s crucial to recognize the hidden hero behind this potential: data. Data isn’t
simply a domain of AI in the traditional sense, but rather the very foundation upon which AI is built.
Firstly, data acts as the fuel that propels AI to perform its tasks. Just as a car needs petrol to run, AI
algorithms require vast amounts of data to learn and improve.
Secondly, data acts as the sculptor that shapes the capabilities of AI. The type and quality of data used
for training significantly influence the resulting AI model.
Finally, data acts as the compass that guides the development and application of AI. By analysing data
patterns and trends, researchers can identify areas where AI can offer the most significant impact. For
example, analysing healthcare data may reveal patterns that could be used to develop AI-powered tools
for early disease detection. Similarly, studying social media data can help identify potential societal
issues that AI can be used to address.

ACTIVITY Experiential Learning

Activity: The Treasure Hunt: Uncovering the Secrets of Data for AI

Understanding AI and its reliance on data and how data acts like fuel for AI models. Explaining
the use of many different types of data (text, images, audio etc.). AI models need data to
learn and improve. The type of data affects what the AI can learn and do.

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 Materials:
● Index cards ● Markers ● Pencils ● Small prizes (optional)

Preparation:
1. Data Cards: Write down different types of data on index cards. Eg – animal image.
2. Treasure Cards: Prepare a set of “treasure cards” with clues about the data cards.
These clues can relate to the format of the data (text, image, etc.)
3. Hiding Spots: Hide the data cards around the classroom or designated area.
Instructions:
1. Treasure Hunt Begins: Divide students into small groups and distribute pencils to each group.
2. Find the Data: Explain that each group needs to find the hidden data cards around the
classroom.
3. Decipher the Clues: Once a group finds a data card, they need to find the corresponding
treasure card with clues. The clues will help them understand the type of data and its
potential use for AI.

Modern day scholars have coined the phrase ‘Data is the new oil’. What is this data?
Data can be defined as a representation of facts or instructions about some entity (students, school,
sports, business, animals etc.) that can be processed or communicated by humans or machines. Data
is a collection of facts, such as numbers, words, pictures, audio clips, videos, maps, measurements,
observations or even just descriptions of things. Data may be represented with the help of characters
such as alphabets (A-Z, a-z), digits (0-9) or special characters (+, -, /, *, <,>, = etc.)
Data can be broadly categorised into two main types: qualitative and quantitative.
1. Qualitative Data:
• Description: This type of data describes qualities or characteristics and is non-numerical.
• Examples:
○ Colors (red, blue, green) ○ Customer reviews (positive, negative)
○ Social media comments (opinions, emotions) ○ Survey responses (open ended)
2. Quantitative Data:
• Description: This type of data is numerical and represents measurable quantities.
• Examples:
○ Temperature (in degrees) ○ Height (in centimeters)
○ Number of website visitors (count) ○ Sales figures (amount)

ACTIVITY Experiential Learning

Let’s play a fun game of Rock-Paper-Scissors with an AI twist! This is a paper scissors rock
game created using artificial intelligence. This game can read the players’ patterns to determine
the next steps for ‘AI’ will take in order to win. This version will keep track of your throws
over time to make predictions for future rounds. Here’s how it will work:
1. Throw Selection: You’ll choose between Rock, Paper, and Scissors.
2. AI Prediction: Based on your past throws, the AI will predict your next move and choose
its counter accordingly. (Remember, the AI is still under development and might be fooled
by your unpredictable throws!)

9
 3. Outcome Display: We’ll reveal
both throws and declare the winner
based on the classic Rock-Paper-
Scissors rules.
4. Learning and Adaptation: The AI
will learn from your choices and
update its prediction model for
future rounds.
Are you ready to play?

Visit https://www.afiniti.com/corporate/rock-paper-scissors
Write three things you learnt from the game.

Quick Quiz
The Indian Government banned a few apps stating – “servers in the hostile nation are receiving
and using the acquired data improperly”. Which terminology suits best for this action?
(a) AI Ethics (b) Data Privacy (c) AI Bias (d) AI Access

2. Natural Language Processing

NLP is the ability of a computer program to understand human language as it is spoken. NLP is a
component of artificial intelligence (AI).
NLP bridges the gap between human communication and what computers can understand. This allows
for a variety of applications, such as:
• Machine translation: Breaking down language barriers by translating text from one language to
another.
• Chatbots and virtual assistants: Enabling machines to have conversations with us, like Siri or
Alexa.

Know More

This digital version of the classic game allows

kids to fill in the blanks with different parts
of speech, creating funny stories. The app
recognizes the different parts of speech chosen
by the child (nouns, verbs, adjectives etc.) and
incorporates them into a pre-written story template. The app showcases how NLP can categorize words based
on their grammatical function (parts of speech) and use them to fill in the blanks within a structured text format.
Visit https://www.madlibs.com/

Daily Life NLP examples

There are many common and practical applications of NLP in our everyday lives. Tasks include speech
recognition, language translation, sentiment analysis, and chatbot development. Beyond conversing with
virtual assistants like Alexa or Siri, here are a few more examples:

10
 • Smarter Search Results: Search engines like Google use NLP to understand the intent behind
your search queries. Not just the keywords themselves, but also the context of your search. This
helps them surface more relevant results even if you don’t use perfect keywords.
• Enhanced Chatbots & Virtual Assistants: NLP powers chatbots you encounter on websites or
through messaging apps. They can understand your questions and requests better, allowing for
more natural and helpful conversations.
• Grammar Assist and Autocorrect: When you type on your phone or computer, NLP is often
behind the scenes suggesting corrections and completing your sentences. This helps ensure clear
and error-free communication.

ACTIVITY Creativity

Interactive Story Speaker

Story Speaker lets anyone create talking, interactive stories with no coding required. Just
write your story in a Google Doc, push a button, and every Google Home device linked to your
account can play it, instantly. There is a lot a Story Speaker can do, including respond to what
players say, give random responses, and remember what the player said. You can even export
your story and so anyone with a Google Home can hear it. (Experimental!). Try creating your
first interactive story by clicking on
LINK: https://experiments.withgoogle.com/story-speaker

Visit https://experiments.withgoogle.com/semantris
Semantris is a word association game
created by Google AI as a fun way to
demonstrate how AI can understand
and process language.
• Goal: Guess the word the AI
is thinking of by providing clues
through word associations.
• Gameplay: There are two modes:
Arcade (fast-paced) and Blocks
(turn-based).
• AI behind the scenes: The AI
is trained on a massive dataset
of text and conversations,
allowing it to make connections
between words and phrases.
Write three things you learnt from the game.

COMPUTER VISION
As humans we can see things, analyse it and then do the required action on the basis of what we see.
But can machines do the same? Can machines have the eyes that humans have? If you answered Yes,
then you are absolutely right.
Imagine you have a super cool superpower that lets you see the world through the eyes of a computer!
That’s kind of what computer vision is. It’s a field of artificial intelligence (AI) that allows computers
to interpret and understand the visual world around them, just like we do with our eyes and brains.

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Computer vision is a field of artificial intelligence that trains computers to interpret and understand the
visual world. It is like imparting human intelligence and instincts to a computer.The Computer Vision
domain of Artificial Intelligence, enables machines to see through images or visual data, process and
analyse them on the basis of algorithms and methods in order to analyse actual phenomena with images.
• Seeing the World: Computers use cameras to capture images and videos, similar to how our eyes
take in visual information.
• Understanding the Picture: Special software analyzes these images, breaking them down into smaller
parts like shapes, colors, and patterns. It’s like figuring out the building blocks of what you see.
• Making Sense of it All: The software uses these building blocks to recognize objects, people,
and even actions happening in the image or video. It’s like putting those building blocks together
to understand the whole picture.
Think of it like this:
• Your eyes see a dog in the park.
• Computer vision sees an image with specific shapes, colors, and fur textures.
• By comparing these details to its stored information (like what a dog typically looks like), the
computer vision system recognizes it as a dog!
Computer Vision, abbreviated as CV, is a domain of AI that depicts the capability of a machine to
get and analyse visual information and afterwards predict some decisions about it. The entire process
involves image acquiring, screening, analysing, identifying and extracting information. This extensive
processing helps computers to understand any visual content and act on it accordingly. In computer
vision, Input to machines can be photographs, videos and pictures from thermal or infrared sensors,
indicators and different sources.
APPLICATIONS OF COMPUTER VISION
Helping Robots See: Self-driving cars
use computer vision to navigate roads,
recognize traffic signs, and avoid obstacles.
It’s like giving them super sight to drive
safely!
Unlocking Phones with Your Face: Facial
recognition technology uses computer vision
to identify you based on your unique facial
features. It’s like your phone recognizing
your face as a “key” to unlock it. Airports
and other secure areas use facial recognition
systems to compare faces against databases
for identification and security purposes.
Image Description for Visually Impaired
Users: Computer vision can be used
to analyze images and generate audio
descriptions for visually impaired users,
helping them understand the content of
pictures on websites or social media.

12
Sign Language Recognition: Some applications use computer vision to recognize sign language gestures
and translate them into text or spoken language, promoting better communication for deaf and hard-of-
hearing individuals.
Smart Emojis and Stickers: Many messaging platforms use computer vision to recognize facial
features in selfies and suggest corresponding emoji or sticker overlays for a more expressive way of
communication.
Augmented Reality Filters: Social media filters that add virtual elements to your face or surroundings
often rely on computer vision to track your movements and position these elements realistically. Many
camera apps use computer vision to recognize objects in your frame and suggest filters or effects that
complement the scene.
Self-Checkout Lanes: Computer vision allows self-checkout systems to identify and scan items you
place in the designated area, streamlining the checkout process like Amazon Go stores.
Virtual Try-On Apps: Some clothing retailers offer apps that use computer vision to virtually place
clothes on your avatar, allowing you to try on different styles without physically putting them on.
Smart Home Security Cameras: These cameras can detect motion and even identify objects or people
using computer vision, sending alerts when unusual activity is observed.
Autofocus and Auto Exposure: Your phone camera uses computer vision to adjust focus and exposure
automatically, ensuring clear and well-lit pictures even for non-photography enthusiasts.

Know More

Quick, Draw! is an online guessing game developed and published

by Google that challenges players to draw a picture of an object or
idea and then uses a neural network artificial intelligence to guess
what the drawings represent. The AI learns from each drawing,
improving its ability to guess correctly in the future.
Visit https://quickdraw.withgoogle.com/

It’s time for you to create a game!

Games are an integral part of our culture. People across the world participate in different kinds of
games as a form of social interaction, competition, and enjoyment. The basic principle of every game
is rule-setting and following the rules.
Create a simple game which can be played without a computer.
Write down three rules in the given spaces you would set before playing any game.

13
 RECAP
l Intelligence can be defined as a general mental ability for reasoning, problem-solving, and learning.
l John McCarthy (1927-2011), coined the term ‘Artificial Intelligence.’ Artificial intelligence is a
theory and development of computer systems that can perform tasks that normally require
human intelligence.
l NLP focuses on enabling computers to understand and process human language.Tasks include
speech recognition, language translation, sentiment analysis, and chatbot development.

KEY TERMS
● Intelligence is defined as mental capability that involves the ability to reason, to plan, to solve
problems, to think abstractly, to comprehend complex ideas, to learn quickly and to learn from
experience.
● Artificial Intelligence is a technique which enables computers to mimic human behaviour
● Narrow AI are intelligent systems that are programmed to perform specific tasks without the
need for intrinsic coding.
● General AI is an AI system with generalized human cognitive abilities. When presented with an
unfamiliar task, a strong AI system is able to find a solution, without human intervention.

AI EXERCISES
A. Multiple choice questions.
1. Which of the following is NOT related to intelligence?
(a) It lets us solve problems, understand concepts and act purposefully
(b) It allows us to imagine and use our experience in life to solve problems
(c) It allows us to be creative and create objects and concepts of beauty and originality
(d) It helps us to act violently and wage wars
2. Which of the following technologies relies on computer vision?
(a) Voice assistants like Siri or Alexa (b) Self-driving cars that navigate roads
(c) Text-to-speech software (d) Anti-virus programs
3. What is the main purpose of collecting data for AI applications?
(a) To entertain users (b) To understand human sbehavior
(c) To train and improve AI models (d) To connect to the internet
4. Which of the following is NOT a task typically performed by NLP?
(a) Summarizing a long text document
(b) Identifying the genre of a book based on its writing style
(c) Recognizing handwritten text in an image
(d) Translating a spoken language into another language
14
B. Decide whether AI or Not
(a) Google Maps for finding the fastest route from your home to your friend’s house. Yes No Maybe
(b) Image editing softwares that allows you to edit the brightness, colour and contrast Yes No Maybe
in a picture.
(c) A Google Sheet that determines batting averages based on a given data set. Yes No Maybe

C. Fill in the blanks

1. __________ are considered to be able to identify themselves in the mirror.
2. The goal of artificial intelligence (AI) as a science is to make machines do things that would require intelligence
if done by _________.
3. Chess game between Deep Blue (IBM) and Gary Kasparov is an example of _________ machines type of AI.
4. Chatbots and __________ cars are based on limited memory functionality.
5. The “Imitation game” was devised by the computer scientist _____________.
D. Assertion/Reason Type
Assertion (A): Data is the new oil.

Reason (R): AI applications/models cannot typically work without consuming some to a lot of data. The more

complex the learning system, more the data input requirement.
(a) Both A and R are correct and R is the correct reason for statement A
(b) A is true but R is false
(c) A is false and R is true
(d) Both A and R are true but R is NOT the correct explanation for A
E. Competency Based Questions
1. Shyna’s brother is good at understanding and interacting with other people. He is good at assessing the
emotions, motivations, desires and intentions of other people around him. What sort of intelligence does
Shyna’s brother possess?
2. Ramesh was reading about narrow AI. Related to this, he also read about General AI. Unlike narrow AI, it
includes the capability of understanding a vast scope of activities. What is the other name given to general AI?
F. Short Answer Questions
1. What domain of AI does the spam filtering technology for email inboxes use?
2. Describe Computer Vision.
3. Which technology bridges the gap between human communication and what computers can understand? Is
sentiment analysis based on this domain?
4. Based on the structure of data, what type of data does customer information in a table fall under?
5. How can computers understand what we say and write? (NLP)
6. Differentiate between Narrow AI and General AI.
G. Long Answer Type Questions
1. Your grandmother watches you use AI applications. She wants to understand more about it. Help her understand
the term artificial intelligence by giving the right definition and explain to her with an example how machines
become artificially intelligent.
2. Why is it important to have a lot of data for training AI?

15
H. Subject Enrichment
Multiple Intelligence Test Analogy

Read each statement. If it expresses some characteristic of yours and sounds true for the most part, jot down a
“T.” If it doesn’t, mark an “F.” If the statement is sometimes true, sometimes false, leave it blank.
1. _____ I’d rather draw a map than give someone verbal directions.
2. _____ I can play (or used to play) a musical instrument.
3. _____ I can associate music with my moods.
4. _____ I can add or multiply in my head.
5. _____ I like to work with calculators and computers.
6. _____ I pick up new dance steps fast.
7. _____ It’s easy for me to say what I think in an argument or debate.
8. _____ I enjoy a good lecture, speech or sermon.
9. _____ I always know north from south no matter where I am.
10. _____ Life seems empty without music.

I. Multiple Intelligence Scoring Sheet

1. Cartoonify turns your photo into a cartoon drawing with the help of a neural network. Visit
https://experiments.withgoogle.com/cartoonify Creativity

2. Visit https://experiments.withgoogle.com/scroobly Communication

With Scroobly, you are using artificial intelligence as a creative tool to become a digital
animator, even if you’ve never written code or taken a design class.
J. Multiple Assessment
1. Watch the film and then discuss it in class.
The Imitation Game is a 2014 American historical
drama thriller film loosely based on the biography
Alan Turing: The Enigma by Andrew Hodg
 Communication

2.  lan Turing, a British mathematician, joins the

A
cryptography team to decipher the German enigma
code. With the help of his fellow mathematicians, he
builds a machine to crack the codes.
 atch the movie, understand ‘The Imitation Game’
W
and present it in class. Critical Thinking

K. Knowledge Hub Subject Enrichment

https://erwinwidiyatmoko.files.wordpress.com/2012/08/multiple-intelligencies-in-the-classroom.pdf

L. Experiential Learning Technology Literacy

https://youtu.be/XFZ-rQ8eeR8
https://youtu.be/3wLqsRLvV-c
uuu
16
 Unit 1 : AI Reflection, Project Cycle and Ethics

2 Applications of AI

Learning Objectives
After studying this chapter, students will be able to:
• Knowing about the various applications of Artificial Intelligence
• Understanding how AI can help in daily life

SOME AI APPLICATIONS
There are many examples of the use of AI around us, especially in the online
world.
1. Netflix: Netflix is the world’s most popular subscription-based video streaming
offered worldwide! But hey, how could Netflix possibly know which genre best
fits the tastes of the user? When you watch Netflix films or TV shows from the
Internet, the recommender system suggests movies and TV shows based on past
choices of the user. Netflix uses Artificial Intelligence.
Personalisation of Movie Recommendations — Users who watch A are likely
to watch B. This is perhaps the most well known feature of Netflix. Netflix
uses the watching history of other users with similar tastes to recommend what
you may be most interested in watching next so that you stay engaged and
continue your monthly subscription for more. Despite having two individuals log-in Netflix at the same
time, both would be offered different program recommendations. The algorithm learns as data gets
collected. Therefore, the
more time you spend
on Netflix, the more
relevant programs will
be recommended.
Auto-Generation and
Personalisation of
Thumbnails / Artwork
— Using thousands of
video frames from an
existing movie or show
as a starting point for

17
thumbnail generation. These calculations are based on what others who are similar to you have clicked
on. One finding could be that users who like certain actors / movie genres are more likely to click
thumbnails with certain actors/image attributes. Most of the users tend to choose movies or series based
on the thumbnail to determine whether it is worth watching the movie or not.
Optimal streaming quality: With over 225 million subscribers worldwide, it gets challenging for Netflix
to offer the best streaming quality to its viewers. However, with the help of AI and machine learning,
Netflix can now predict the future demands and position assets at strategic server locations way ahead
of time. By pre-positioning the video assets closer to the subscribers, viewers can stream high-quality
video even during peak hours without any interruption.
2. Facebook Photo Recognisation: The photo albums on smartphones
are able to recognise photos by people. So, your photos will be saved in
a folder and your father or mother’s photos will be saved in a different
folder. When you recognise photos on Facebook, the FB software is able
to recognise the face of the person(s) in other photos and automatically
tags the person. This is possible due to improvements in face recognition
technology in recent times.

DID YOU KNOW ?

https://tagthatphoto.com/
Tag That Photo face recognition wizard can help you organise your photos so you can reconnect with
cherished memories quickly. TagThatPhoto starts by scanning each photo and uses visual landmarks - (eyes,
nose, and mouth) to find every face. The secure algorithm analyzes all the pixels and landmark measurements
and calculates a unique code for every face. Once you tag a name to that face code, the software quickly and
accurately matches it to other photos of the same person. It uses the face recognition technique.
3. Chatbots: A chatbot is a software application used
to conduct an on-line chat conversation via text or text-
to-speech, in lieu of providing direct contact with a live
human agent. Chatbots also known as “conversational
agents” – are software applications that mimic written
or spoken human speech for the purposes of simulating
a conversation or interaction with a real person. There
are two primary ways chatbots are offered to visitors: via web-based applications or standalone apps.
Companies like Amazon, various banks and other websites often deal with customer queries about
products or services by letting the users chat with bots. The user types in the queries and the bot
provides a solution to the queries, or asks about the specifics. This goes on for a while until the
problem is solved or a human is arranged to replace the bot.

ACTIVITY Experiential Learning

Try the following to understand this concept.

Chatting with a Machine
● Eliza is one of the first chatbots ever created (1964). It is somewhat limited; however, it is still
fun to chat with it. It can be accessed here https://tinyurl.com/AIEx-Eliza
● Mitsuku is one of the most advanced chatbots currently in existence. It won various prizes and
can talk about most topics. It can be located here https://tinyurl.com/AIEx-Mitsuku

18
 ACTIVITY Experiential Learning

Visit this website and play a quiz on Artificial Intelligence

https://wordwall.net/resource/18022003/artificial-intelligence

4. Web search: The process to find results after searching for something in
a search engine is incredibly complex and uses machine learning. How does
Google know that all the thousands of results listed are related to a search
inquiry? No one is manually categorising everything on the internet—it’s all
a very advanced form of AI and machine learning that decides it.
5. Smart Cars/Driverless Cars:
Artificial Intelligence can evaluate the
driving environment and driver condition based on information
obtained from different external and internal sensors.
For example, a smart car is able to make an observation and detect
an object, and can then identify that object using machine learning.
Since there are so many different objects in the world, it would be
nearly impossible to explicitly code in what every object is or could
be into the car’s framework. However, if you teach the car to identify objects through machine learning,
it can make those decisions itself.
6. Just Walk Out Shopping experience: All you
need is an Amazon account, the free Amazon Go
app, and a recent-generation iPhone or Android phone.
When you arrive, scan the QR code from the app
at the gate to enter the store, then feel free to put
your phone away—you don’t need it to shop. Then
just browse and shop like you would at any other
store. See something you want? Grab it off the shelf.
Change your mind? Put it back, no problem. Once you’re done shopping, you’re on your way.
The checkout-free shopping experience is made possible by the same types of technologies used in self-
driving cars: computer vision, sensor fusion, and deep learning. Just Walk Out Technology automatically
detects when products are taken from or returned to the shelves and keeps track of them in a virtual
cart. When you’re done shopping, you can just leave the store. The receipt is sent as soon as you leave
the store and your Amazon account is charged. Watch the following videos to understand
https://www.youtube.com/watch?v=NrmMk1Myrxc
https://www.youtube.com/watch?v=lTzPpAbjasA

Know More

IBM Watson Assistant Playground (Web)

This platform by IBM allows you to experiment with building chatbots powered by AI. You can create a
simple chatbot and interact with it to understand how natural language processing works.
Visit: https://cloud.ibm.com/docs/assistant?topic=assistant-index

19
7. Entertainment: The media and entertainment industry
is at the cusp of rapid transformation with digital media
taking center stage across all sub-sectors - TV, print, films,
advertising, animation & VFX, gaming, radio and music. AI
can help in automated editorials, reducing manual interventions
and optimising cost of the content creation.
Artificial intelligence in entertainment is a powerful engine for
far-reaching changes reshaping the landscape of the industry.
The global media and entertainment industry is witnessing
a rapid transformation in the way content is distributed. AI-
based automation can help entertainers and content creators spend more time on their craft and deliver
engaging content. AI will also help production houses make informed decisions about marketing and
advertising by analysing critical data.
Visuals included in the live broadcast should help build an engaging narrative in a match. AI systems
can be trained to identify objects and actions in sports events. AI can also use footage captured by
drones to deliver broadcasts that offer engaging content as well as deeper sports insights. For example,
in a football match, live footage developed by AI can capture adrenaline-fueled action as well as players’
and fans’ reactions after every goal. Therefore, the utilisation of AI in media and entertainment can help
to deliver action-packed sports broadcasting.
In video games, Artificial Intelligence (AI) is used to generate responsive, adaptive or intelligent
behaviors primarily in Non-player Characters (NPCs) similar to human-like intelligence. In modern
games, AI can analyze a player’s actions to predict their future moves. AI models can also identify
changes in a gamer’s behavior in specific scenarios. A majority of the video games, whether they’re
featuring racing-car games, shooting games, or strategy games – they all have different components that
are powered by AI or related applications. For example, the enemy bots or those neutral characters. The
main objective of utilising AI in gaming is to deliver a realistic gaming experience for players to battle
against each other on a virtual platform. In addition, AI in gaming also helps to increase the player’s
interest and satisfaction over a long period of time. Hence, AI has made video games more interesting
and challenging.
AI systems can be trained using large volumes of data such as images, videos, and text to identify and
analyse objects and language. With the help of acquired data, AI can help design posters and videos to
promote a movie. For instance, IBM’s Watson had created a six-minute long trailer for a horror movie
called ‘Morgan.’ To generate this trailer, the AI system was trained using visuals and audio from 100
different horror movies. Using this data, Watson created a trailer in just twenty-four hours. Hence, AI
can help production companies in their marketing campaigns or create content independently.
International media publishing companies need to
make their content fit for consumption by audiences
belonging to multiple regions. To do so, they need
to provide accurate multilingual subtitles for their
video content. Manually writing subtitles for multiple
shows and movies in dozens of languages may take
hundreds of hours for human translators apart from
being prone to errors too. Media companies are
using AI-based technologies like natural language
processing. For example, YouTube’s AI allows its

20
publishers to automatically generate closed captions for videos uploaded on the platform, making their
content easily accessible.
With countless pieces of content being created every minute, classifying these items and making them
easy to search for viewers becomes a herculean task for media company employees. Media creators are
using AI-based video intelligence tools to analyse the contents of videos frame by frame and identify
objects to add appropriate tags. As a result, regardless of its volume, all the content owned by media
companies becomes easily discoverable.
AI is also helping media companies to make strategic decisions. For instance, leading media and
broadcasting companies are using machine learning and natural language generation to create channel
performance reports from raw analytics data shared by BARC. The weekly data that is usually received
from the Broadcast Audience Research Council of India (BARC) is generally in the form of
voluminous Excel sheets. Analyzing these sheets on a weekly basis to derive and implement meaningful
learnings helps to create performance reports with easy-to-understand language commentaries, providing
them accurate insights to make informed data-driven decisions.
Channel Performance Report
l Rishtey Cineplex climbs to 5th
Rank of Top 10 channels of this week by GRP
because its GRP increased from 72
to 87 Channel LW CW
l Start Utsav Movies drops to 6th rank Max 1 1
even when its GRP increased from Star Gold 2 2
84 to 85 Z Cinema 3 3
l &Pictures climbs to 8th because its Sony Wah 4 4
GRP increased from 62 to 62 Rishtey Cineplex 5 5
l Zee Anmol Cinema drops to 9th rank Star Utsav Movies 6 6
as its GRP dropped from 64 to 62 Movies OK 7 7
l Max, Star Gold and Z Cinema Pictures& 8 8
maintanined the top 3 positions ZEE Anmol Cinema 9 9
from last week. Max 2 10 10

8. Education: With this generation having grown up with the

privilege of having access to technology at their fingertips,
the arena of education has massively revolutionised and
overturned in this digitally driven world. While the Academic
sector is still assumed to be a largely human sector, there are
still a multitude of ways that teachers and educational staff
can gain from employing this technology.

Artificial Intelligence tools and devices have been aiding in making

global classrooms accessible to all irrespective of their language or
disabilities. The technology has overturned the world of learning as
educational materials become more accessible to all with the use of
smart devices and computers while also automating all complicated
administrative tasks, allowing faculties to invest more time in
focusing on their students. Teachers can break down their lessons
into smaller study guides, smart notes or flashcards in order to help
21
the student in comprehending. With AI assisting in generating digital content, learning is proposed to
become more digital and less reliant on hard copies.
Various AI-Powered apps and systems help the students in accessing instant and customised responses
as well as in getting their doubts cleared from their teachers. AI is also playing a role in augmenting
tutoring and designing personal conversational, education assistants who can offer them aid in education
or assignment tasks. These education assistants are also attempting to improve their feature of adaptive
learning so that all the students are allowed to learn at their own pace and at their own suitable time.
For instance Presentation Translator is a free PowerPoint plug-in that develops subtitles in real-time
of what the teacher is saying. This also helps aid the sick absentees as well as students requiring a
different pace or level when it comes to learning or even in case they wish to understand a particular
subject that is unavailable in their own school.
Yet another AI component being fruitfully employed by educators in learning is voice assistants. These
include Amazon’s Alexa, Apple Siri, Microsoft Copilot, etc. These voice assistants allow the students
to converse with educational materials without the involvement of the teacher. Traditional methods
of learning are slowly being discarded in the case of higher education environments, with various
universities and colleges offering students voice assistants rather than the traditionally printed student
handbooks or complicated websites for assistance with their campus-related informational needs.
9. Space Exploration: NASA is already
using AI to look for life on other
planets. In the project “Mars 2020”,
the red planet will be explored more
thoroughly. The devices that will be
sent, known also as rovers, will be
able to explore Mars’ terrain and then
put with more certainty whether life
is possible on Mars. The present day
rovers that roam the face of Mars are
already using this decision-making form
of intelligence to act.
It takes radio waves up to 22 minutes
to reach from Earth to Mars. So, these robots must make some decisions without the help from humans
in mission control. Also, mission controllers on Earth are capable of sending and receiving data only
with the proper antennas during their allotted time. To make decisions, the rovers use an AI controlled
system to detect obstacles facing them and find out the best route to travel. For example, the NASA
Curiosity rover can image its surroundings to plan a path to a particular feature (such as a rock) up to
50 metres away, while avoiding obstacles on the way.
Artificial Intelligence is being considered as a way to find life on new planets. AI networks can find
patterns that humans may not be able to spot themselves. AI can better explore the planets on which
the chance of life to exist is there. Furthermore, it is possible that in the future space missions, AI will
more decide the behaviour of things that go to space than humans from Earth. As space missions go
deeper into space, the time taken by the mission control to contact the space mission may be significant,
so that the AI enabled mission has to make decisions in real time. This means the spacecraft will need
to be smart enough to learn and eventually decide when and how to return the data they have collected.

22
 As space missions along with astronauts go further and deeper
into space, the AI system in the spacecraft will have to make
its own decisions regarding emergency situations or medical
emergencies. For example, if the mission is deep into space,
then immediate human assistance in the form of a doctor cannot
be managed easily. So, in that case, the AI system must learn
and engage in the operation on the human itself.
10. Agriculture: AI holds the key to driving an agricultural
revolution at a time
when we want to
produce more food crops using less resources. Companies
are creating autonomous robots for the laborious tasks related
to agriculture, like harvesting the crop at a large volume.
Companies are also leveraging computer and deep learning
technologies to process data captured by drones and/or software
–based technologies to assess crop and soil health. Machine
learning models are able to assess the effects of various aspects
like weather change to crop and soil health. AI can be useful
in understanding timely planting, getting predictions, using fertilisers, harvesting and the climate.
Crop Yield prediction & Agriculture Robots
Price forecasts Using Autonomous
Identify the output yield of robots for harvesting
crops and forecast prices huge volumes of crop
for the next few weeks will at a higher volume
help the farmer to obtain and faster pace
maximum profit
Crop and soil monitoring
Intelligent spraying
Using ML/AI, we can
Al sensors can detect weed Artificial monitor the crop health
affected areas and can Intelligence in for diagnosing pests/
precisely spray herbicides in
the right region reducing the Agriculture soil defects, nutrients
.deficiencies in soil, etc
usage of herbicides
Predictive Insights Disease Diagnosis
Insights on "Right time to Prior information and
sow the seeds" for maximum classification of plant
productivity. Insights on diseases help farmers
the impacts created by the control the disease through
weather conditions .proper strategy

Before the crop cycle, drones can be arranged to produce a 3-D map of detailed terrain, drainage, soil
viability and irrigation. Nitrogen –level management can also be done by drone solutions. Aerial spraying
of seeds with pods along with soil nutrients can be done using drones to provide necessary supplements
for plants. Drone can also be used to spray liquids onto crops by adjusting the distance from the ground
based on the terrain of the soil.

11. Smart Homes : A smart home is a dwelling equipped with technology that allows you to automate,
monitor, and control various aspects of your home remotely using your smartphone, voice assistants, or
even pre-programmed schedules. It’s like having a house that can adapt to your needs and preferences!
Smart devices: These are electronic devices with built-in Wi-Fi or Bluetooth connectivity that allow for
remote control and automation. Examples include:

23
 l Smart lights: Control brightness, color temperature, and turn them on/off remotely.
l Smart thermostats: Adjust heating and cooling based on your preferences and schedule.
l Smart locks: Lock and unlock doors remotely, receive notifications when someone enters, and
even grant temporary access to guests.
l Smart plugs: Control power to any appliance plugged in, creating schedules or voice-activated
control.
l Smart security cameras: Monitor your home remotely, receive alerts for suspicious activity, and
even offer two-way communication.
Benefits:
l Convenience: Control your home from anywhere, automate repetitive tasks, and enjoy a more

hands-free living experience.

l Security: Monitor your home remotely, receive alerts for potential security breaches, and deter

burglars with smart lighting routines.

l Energy savings: Smart thermostats and lighting can optimise energy use by automatically

adjusting settings based on occupancy and preferences.

l Comfort: Create a comfortable living environment by pre-programming desired temperatures and

lighting moods.
l Accessibility: Smart home features can be helpful for people with disabilities, allowing them to

control their environment with voice commands or through smartphone apps.

RECAP
Artificial intelligence in entertainment is a powerful engine for far-reaching changes reshaping the
landscape of the industry. AI-based automation can help entertainers and content creators spend
more time on their craft and deliver engaging content.
NASA is already using AI to look for life on other planets. AI holds the key to driving an agricultural
revolution at a time when we want to produce more food crops using less resources. To reduce
costs and improve efficiency, restaurants and bars around the world are increasingly turning to
artificial intelligence and robotics technology. We can profit from Artificial Intelligence for increased
comprehension of reading preferences, interfacing multi-kind books with readers, foreseeing top-
rated books, creating data-driven works, and editing manuscripts with devices like ProWritingAid.
Several music software programs have been developed that use AI to produce music.

KEY TERMS
BARC: Broadcast Audience Research Council India (BARC) is a joint-industry body founded by
stakeholder bodies that represent Broadcasters, Advertisers and Advertising & Media Agencies. It is
also the world’s largest television measurement science industry-body.
Chat Bot: A chatbot is a software application used to conduct an on-line chat conversation via text
or text-to-speech, in lieu of providing direct contact with a live human agent.

24
 AI EXERCISES
A. Multiple Choice Questions
1. Smart thermostats and lighting can lead to:
(a) Security (b) comfort (c) energy savings (d) None of these
2. What is now considered as a technology to find life in new planets?
(a) Artificial Intelligence (b) rail roads (c) laser (d) Airlines
3. What is the term for electronic devices with built-in Wi-Fi or Bluetooth connectivity that allows for remote
control and automation?
(a) IoT (b) GAN (c) LaMDA (d) Smart devices
4. Which one of the following is an application of AI? CBSE Handbook

(a) Remote controlled Drone (b) Self-driving car

(c) Self-Service Kiosk (d) Self-watering plant system

B. Fill in the blanks

1. ___________ is the world’s most popular subscription-based video streaming offered worldwide
2. ____________ was a chatbot created in 1964 and one of the first chatbots to be created.
3. A ____________ is a software application used to conduct an online chat conversation via text or text-to-
speech, in lieu of providing direct contact with a live human agent.
4. Just Walk Out Shopping experience is available at _____________ stores.
5. BARC stands for ______________________.

C. Assertion/Reason Type Questions

1. Assertion (A): Robots in Mars missions must make some decisions without the help from humans in mission
control.
Reason (R): It takes radio waves up to 22 minutes to reach from Earth to Mars.
(a) Both A and R are true and R is the correct explanation for A
(b) A is false and R is true
(c) A is true and R is false
(d) Both A and R is true but R is not the correct explanation for A
2. Assertion (A): Academic sector is still assumed to be a largely human sector.
Reason (R): There are still a multitude of ways that teachers and educational staff can gain from employing AI
technology.
(a) Both A and R are true and R is the correct explanation for A
(b) A is false and R is true
(c) A is true and R is false
(d) Both A and R is true but R is not the correct explanation for A
D. Competency Based Questions
1. Summarization is a task that Omar has to often do. Since the computers are involved, he thinks AI can do better,
i.e. summarise horror movies using trailers. Give Omar an account of this thing that IBM’s Watson did.
2. As AI is entering every field of human endevour, Pritha was wondering if there is AI usage in video games also.
Give her the idea about AI usage in video games.
25
 3. Aseem was wondering whether there has been any AI usage in Starbucks, being a famous world level chain.
Based on your knowledge of AI, enlighten Aseem.
E. Short Answer Questions
1. What are the advantages of letting AI do the work of humans in space missions?
2. Give any two examples of usage of AI in education sector.
3. What is the technology involved in making driverless cars?
4. Draw out the difference between the there domains of AI with respect to the types of data they use.
 CBSE Handbook
F. Long Answer Questions
1. Give examples of how smart devices are getting used in Smart homes.
2. Search for an online game that recognises the image drawn by you. Write down the observations including
the AI domain used by it. CBSE Handbook

G. Subject Enrichment
Visit: https://mysteryanimal.withgoogle.com/ Experiential Learning
Mystery Animal is a new spin on the classic 20-questions game. The computer
pretends to be an animal, and you have to guess what it is using your voice.
Ask any yes-or-no question you want, like “Do you have feathers?” or “Do
you sleep at night?” Play it on a Google Home by saying “Hey Google, talk to
Mystery Animal,” or try it on the site.
H. Multiple Assessment
1. Her is a 2013 American romantic science-fiction drama. The
film follows Theodore Twombly (Joaquin Phoenix), a man who
develops a relationship with Samantha (Scarlett Johansson),
an intelligent computer operating system personified through
a female voice.
Watch the movie ‘Her’ and then discuss in class the Artificial
Intelligence involved in it. Communication

2. Conduct your own orchestra in the browser by moving your

arms by visiting Creativity
Visit : https://experiments.withgoogle.com/semi-conductor
J. Knowledge Hub
https://intellipaat.com/blog/applications-of-artificial-intelligence/
https://data-flair.training/blogs/applications-of-artificial-intelligence/
K. Experiential Learning
https://youtu.be/YhSeTEumjVA
https://youtu.be/GgTfYXB3_Cs
https://youtu.be/Sw3TquOQ4v8

uuu

26
 Unit 1 : AI Reflection, Project Cycle and Ethics

3 AI Project Cycle

Deployment – It is the process of taking a trained model and solving real world problems with it.

Problem Data
Exploration Evaluation
Scoping

Data
Modelling
Acquisition Deployment

Deployment
In the context of an AI project cycle, deployment refers to the process of integrating your trained
AI model into a real-world environment where it can be used to make predictions or automate tasks.
Essentially, deployment is the bridge between the development phase and the practical application of
your AI model.

ACTIVITY Creativity

Activity(Preventable Blindness): Implementation of AI Project cycle to develop an AI model for

personalized education
This AI project aims to develop a model for personalized education to raise awareness and
promote preventative measures for blindness. Here’s a breakdown of the AI project cycle for
this specific scenario:
1. Problem Definition and Data Collection:
l 
Identify the challenges in current education systems (e.g., one-size-fits-all approach, lack
of individual student focus).
l Identify the target audience (e.g., students, educators, communities at risk).

l 
Define specific aspects of preventable blindness education (e.g., eye health practices, early
detection of symptoms).
l Gather data on:

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 r Demographics, learning styles of the target audience.
r Existing educational materials on preventable blindness.
r Information on common causes and risk factors for blindness in the target region.
r Ensure data quality and address any privacy concerns (parental consent for student data).
3. Data Exploration & Preprocessing:
l Analyze the collected data to identify patterns and trends.

l 
Clean and prepare the data for model training (e.g., handle missing values, format data
consistently).
4. Model Design & Training:
l 
Choose an appropriate AI model type based on the data and desired outcome (e.g., decision
tree for risk factor identification)
l 
Train the model on the prepared data, allowing it to learn from past student interactions
and performance.
l 
Fine-tune the model parameters to optimize its performance for personalized education tasks.
5. Evaluation & Validation:
l 
Evaluate the model’s performance on a separate dataset to assess its generalizability.
l 
Validate the model’s effectiveness through A/B testing or pilot programs in real classrooms.
6. Deployment & Monitoring:
l 
Integrate the trained model into the educational platform (e.g., Learning Management System).
l 
Gather new data as users interact with the educational platform. Continuously monitor the
model’s performance and gather user feedback.
l 
Retrain and update the model over time with new data to maintain its effectiveness.
l 
Refine the educational content and delivery methods based on user feedback and program
evaluation.

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 Unit 1 : AI Reflection, Project Cycle and Ethics

4 Ethics

Learning Outcomes
• Understanding and reflecting on the ethical issues around AI
• Gaining awareness around AI Bias and AI access.
• To let the students analyse the advantages and disadvantages of AI

Ethics in AI is a multifaceted field concerned with ensuring that artificial intelligence systems are
developed, deployed, and used in ways that are fair, transparent, accountable, and aligned with human
values and societal norms. Ethics is based on well-founded standards of right and wrong that prescribe
what humans ought to do, usually in terms of rights, obligations, benefits to society, fairness, or specific
virtues. Ethics consists of the standards of behavior our society accepts. Ethics is the branch of philosophy
that deals with morality. It is concerned with distinguishing between good and evil in the world, between
right and wrong human actions, and between virtuous and non virtuous characteristics of people.
The ethics of Artificial intelligence lies in the ethical quality of its prediction, the ethical quality of
the end outcomes drawn out of that and the ethical quality of the impact it has on humans. Ethical
AI isn’t just a discussion of far-off possibilities, like superhuman intelligence or a robot uprising. Experts
like Elon Musk, Bill Gates, and Stephen Hawking have expressed clear concerns about the need for AI
ethics and risk assessment. AI is, ultimately, an advanced tool for computation and analysis.

HOW AI IS DEVELOPED AND USED WILL HAVE A SIGNIFICANT IMPACT ON SOCIETY FOR
MANY YEARS TO COME,” GOOGLE CEO-SUNDAR PICHAI
The ethical obligations placed upon a technology and its creators demand that they work towards
mitigating all harms. Incorrectly representing or failing to represent an individual’s identity in an AI
system is a harm. A “harm” is caused when a prediction or end outcome negatively impacts an
individual’s ability to establish their rightful identity. Hence the need for ethics of AI.

NEED OF ETHICS RELATED TO AI

1. Fairness and Bias: Ensuring that AI systems do not discriminate against individuals or groups
based on characteristics such as race, gender, ethnicity, or religion. This involves mitigating
biases in data, algorithms, and decision-making processes. AI ethics provide frameworks to ensure
fairness and reduce bias in AI development and use.
2. Transparency: Many AI systems, especially complex ones, can be like black boxes - their

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 decision-making process is opaque. This lack of transparency makes it hard to identify and address
bias, and can erode trust in AI. AI ethics promote transparency, where it’s possible to understand
how AI systems arrive at their decisions. This includes providing explanations for AI-generated
decisions and making the inner workings of AI algorithms accessible to relevant stakeholders.
3. Accountability: As AI becomes more powerful, ensuring its safe and responsible use is paramount.
Individuals and organisations responsible for the development and deployment of AI systems
should be held accountable for their actions and the impacts of their technologies. This involves
establishing mechanisms for oversight, redress, and recourse in cases of harm or misuse. This
helps mitigate risks and ensures AI is used for good.
4. Privacy and Safety: AI systems often rely on vast amounts of data, raising privacy concerns.
AI ethics address these concerns by establishing guidelines for data collection, storage, and
use, protecting user privacy and security. Protecting the privacy and data rights of individuals
whose data is used by AI systems. This includes obtaining informed consent, minimizing data
collection and retention, and implementing robust security measures to safeguard sensitive
information.
What happens to TRUST in a world where machines generate human-like output and make human-like
decisions? Can I trust an autonomous vehicle to have seen me, so that it does not knock me out? Can
I trust the algorithm processing my housing loan to be fair?
“With great power comes great responsibility.” - Popularised by Spider-Man comics, often applied
to technology and innovation.
“Ethics is knowing the difference between what you have a right to do and what is right to do.” -
Potter Stewart. This underscores the importance of ethical decision-making in AI development and
deployment.

ACTIVITY Experiential Learning

Ask the students to visit the following website for different case studies on ethics and then
discuss the learnings from this in class.
https://en.unesco.org/artificial-intelligence/ethics/cases

The point is that artificial intelligence is a human creation, prone to flaws and biases. It was found that the
facial recognition software of tech giants IBM, Microsoft and Google struggled in detecting the faces of
black women. When biased data sets are fed into machine-learning algorithms, they perpetuate inequalities
in various pipelines where artificial intelligence is being used, from job recruitment to prison sentences.
Technology products often trigger second and third order consequences that are not always obvious at
first. This is especially so when products outgrow their original intent and audience and reach a scale
where a one-size-fits-all model fails miserably.
Examples of AI gone wrong
1. Tay is an artificial intelligence chatter bot and is named “Tay” after the acronym “thinking about
you”. It was originally released by Microsoft Corporation via Twitter on March 23, 2016; it caused
subsequent controversy when the bot began to post inflammatory and offensive tweets through its
Twitter account, forcing Microsoft to shut down the service only 16 hours after its launch.
2. In June 2005, a surgical robot at a hospital in Philadelphia malfunctioned during prostate surgery,
injuring the patient. In June 2015, a worker at a Volkswagen plant in Germany was crushed to
death by a robot on the production line.
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 3. In June 2016, a Tesla car operating in autopilot mode collided with a large truck, killing the car’s
passenger (Yadron and Tynan, 2016).
4. In 2019, a study published in Science revealed that a healthcare prediction algorithm, used by
hospitals and insurance companies throughout the US to identify patients in need of “high-risk
care management” programs, was far less likely to flag Black patients.
A balloon debate is a fun and interactive activity often used in educational settings to encourage
critical thinking, persuasive communication, and teamwork. It can be a creative and engaging way for
students to analyse the advantages and disadvantages of AI.
1. Character Assignment: Assign each student a role as a character representing a particular
viewpoint or perspective on AI. Characters could include:
• “Advantages Advocate”: Argues for the benefits and positive impacts of AI technology.
• “Disadvantages Defender”: Argues against the risks and negative consequences of AI
technology.
• “Ethical Ethicist”: Focuses on the ethical considerations and moral implications of AI.
• “Regulatory Expert”: Advocates for the need for regulation and oversight of AI development
and deployment.
• “Futurist”: Speculates on the potential long-term implications and societal transformations
resulting from AI advancement.
2. Debate Rounds: Conduct multiple rounds of debate, with each student delivering a persuasive
speech representing their assigned character’s viewpoint on AI. Encourage students to draw on
evidence, examples, and research to support their arguments.
3. Challenges and Rebuttals: After each participant has made their initial speech, allow time
for challenges and rebuttals. Students can challenge the arguments made by others, offer
counterpoints, or respond to criticisms of their own character’s viewpoint.
4. Audience Participation: Invite the rest of the class to participate as the audience, encouraging
them to listen actively, ask questions, and engage in the debate by offering feedback or raising
additional points for consideration.
5. Judging and Reflection: At the end of the debate, facilitate a discussion or reflection session to
debrief the activity. Encourage students to reflect on the arguments presented, consider different
perspectives, and evaluate the strengths and weaknesses of each viewpoint on AI.
6. Conclusion: Conclude the activity by summarising key takeaways and insights gained from
the debate. Encourage students to think critically about the complex and multifaceted nature
of AI technology, as well as the importance of considering diverse viewpoints and ethical
considerations in discussions about its impact on society.
AI encompasses so many different applications that it could raise a really wide variety of different
ethical questions.
For instance, what’s going to happen to the workforce if Artificial intelligence makes lots of people
redundant? That raises ethical issues because it affects people’s wellbeing and employment. Stories of
factory workers being replaced by robots and Artificial intelligence creating millions of jobs, all paint
conflicting pictures of the future of work in the age of Artificial intelligence.
But there are also ethical questions about AI in medicine, especially psychological treatments. For
instance, it seems more likely that people may sometimes open up more freely online. Now there are
going to be ethical issues in how you’re going to program the system to respond to someone saying
that they’re going to kill themselves.

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Examples of Ethical issues in an Artificial Intelligence application:
Weapons targeting Humans:
Russian weapons maker Mikhail Kalashnikov announced that it’s developing an artificial intelligence
machine capable of targeting and firing on humans. The company calls the weapon a “Combat Module”
and it’s equipped with a 7.62-millimeter machine gun that pairs with an onboard camera and computer
(because those never fail).
Uber self-driving car kills a pedestrian
In the first known autonomous vehicle-related pedestrian death on a public road, an Uber self-driving
SUV struck and killed a female pedestrian in Tempe, Arizona.

ACTIVITY Experiential Learning

An AI music software has composed a song which has become a worldwide hit. Who will own
the rights to this song? The team who developed the AI software or the music company?

The digital revolution of the late 20th century brought us information at our fingertips, allowing us to
make quick decisions, while the agency to make decisions, fundamentally, rested with us. AI is changing
that by automating the decision-making process, promising better qualitative results and improved
efficiency. As AI makes decisions for us, transparency and predictability of decision-making may become
a thing of the past.
Humans do not make decisions in a vacuum. Our actions are determined not just by external triggers,
but also by our intentions, norms, values and biases. What we consider safe also changes with time
and context. Example: Would you consider speeding your car to rush someone to the hospital. While
AI is increasingly finding use in enabling cyber-security by detecting and preventing intrusions, it is
itself susceptible to malicious use. In a data-driven, highly networked, always online world, the risks
are significant. In addition to classical threats, AI systems can be gamed by poisoning the input data or
modifying the objective function to cause harm.
Automation is already sparking fears about everything from job losses to our safety to the prospect
of AI becoming self-aware and threatening humanity. AI has spurred anxiety about unemployment, as
autonomous systems threaten to replace millions of truck drivers, and make Lyft and Uber obsolete.
Today, digital information technology has redefined how people interact with each other. Redefined
relationships between consumers, producers and suppliers, industrialists and laborers, service providers
and clients, friends and partners are already creating an upheaval in society.
For example In 2014, Amazon developed a recruiting tool for identifying software engineers it might
want to hire; the system swiftly began discriminating against women, and the company abandoned it in
2017. In 2016, ProPublica analysed a commercially developed system that predicts the likelihood that
criminals will re-offend, created to help judges make better sentencing decisions, and found that it was
biased against blacks.
Deep Fake Videos
AI systems are getting really good at creating fake images, videos, conversations, and all manner of
content. We already have trouble believing everything we hear, see, and read. What happens when you
can no longer tell if an image is real or AI-generated or if you’re talking to a bot or a real person?
Machine-generated videos have a massive potential to harm society by contributing to the spread of
misinformation and cybercrime attacks. Government agencies, researchers, and social media outlets are
scrambling to develop Deep fake detection technologies. Currently, very few provisions under the Indian

32
Penal Code (IPC) and the Information Technology Act, 2000 can be potentially invoked to deal with
the malicious use of deepfakes.
Deepfake videos are synthetic media that are created using artificial intelligence techniques, particularly
deep learning algorithms, to manipulate or superimpose existing images or videos onto other images or
videos. The term “deepfake” is a portmanteau of “deep learning” and “fake.” These algorithms analyse
and learn from large datasets of images or videos to create realistic-looking synthetic content.
1. Face Swapping: One common application of deepfake technology is face swapping, where the
face of a person in an existing video is replaced with the face of another person. The algorithm
learns the facial features and expressions of both individuals and seamlessly blends them together
to create a convincing result.
2. Voice Synthesis: In addition to visual manipulation, deepfake technology can also be used to
synthesise realistic-sounding speech. By training on audio recordings of a person’s voice, a
deep learning algorithm can generate new audio clips of that person saying things they never
actually said.
Examples:
• Celebrity Impersonations: Deepfake videos have been used to create videos where celebrities
appear to say or do things they never actually did. For example, a deepfake video might show a
famous actor delivering a political speech or endorsing a product they have no association with.
• Political Manipulation: Deepfake technology has raised concerns about its potential use in
spreading misinformation or disinformation. For instance, a deepfake video could depict a political
figure making inflammatory or incriminating statements, leading to confusion or social unrest.
• Entertainment and Satire: Some deepfake videos are created for entertainment purposes or
satire, such as putting famous faces into scenes from movies or television shows where they don’t
belong.
An AI-generated impersonation of a CEO’s voice successfully duped an employee into completing a
$243 million fraudulent wire transfer. It may not be particularly difficult to create a deep fake video or
audio file. Recently, a tech journalist created a deep fake video of Mark Zuckerberg in less than two
weeks with a widely-available smartphone app and less than $600 in cloud services.
Overall, while deepfake technology has the potential for creative and benign uses, it also presents
significant ethical and societal challenges, particularly regarding privacy, misinformation, and
manipulation. As such, there is ongoing research and debate around the regulation and ethical use of
deepfake technology.
AI Bias
AI bias, also referred to as machine learning bias or algorithmic bias, is a serious issue that arises when
AI systems produce results that are prejudiced or unfair. This can happen for a couple reasons:
• Biased training data: AI systems are trained on massive datasets. If this data contains inherent
biases, like historical prejudices or societal stereotypes, the AI system will learn and amplify
those biases. For instance, an AI hiring tool trained on resumes that were mostly from men might
undervalue resumes that use words more commonly used by women.
• Algorithmic bias: The algorithms themselves can introduce bias, even if the training data is unbiased.
This can happen if the programmers make assumptions about how the algorithm should weigh certain
factors, or if the algorithm is designed in a way that inherently favors certain outcomes.
AI can propel troubling gender, race, and age biases. Biased AI can reinforce harmful stereotypes and

33
put women, minorities, and other disadvantaged social groups at risk. Biased AI could lead to negative
outcomes in numerous settings, including healthcare, education, and hiring practice. Amazon abandoned
a machine learning algorithm designed to source talent after discovering a clear bias against female
applicants. The algorithm proposed candidates based on historical data, which caused a preference for
male applicants since past hires were predominantly men.
When we talk about a machine, we know that it is artificial and cannot think on its own. It can have
intelligence but we cannot expect a machine to have any biases of its own. But any bias can transfer
from the developer to the machine while the algorithm is being developed.

ACTIVITY Experiential Learning

Let us imagine that we are in 2030. Self-Driving cars which are just a concept in today’s time
are now on roads. Now, let us assume one day, your father is going to the office in his self-
driving car. He is sitting in the back seat as the car is driving itself. Suddenly, a small boy
comes in front of this car. The incident was so sudden that the car is only able to make either
of the two choices:
1. Go straight and hit the boy who has come in front of the car and injure him severely.
2. Take a sharp right turn to save the boy and smash the car into a metal pole thus damaging
the car as well as injuring the person sitting in it.
With the help of this scenario, we need to understand that the developer of the car goes
through all such dilemmas while developing the car’s algorithm. Thus, here the morality of the
developer gets transferred into the machine as what according to him/her is right would have
a higher priority and hence would be the selection made by the machine.
If you were in the place of this developer and if there was no other alternative to the situation,
which one of the two would you prioritise and why?

Addressing biases in data collection requires careful planning, rigorous methodology, and ongoing
evaluation throughout the research process. Researchers should strive to use diverse and representative
samples, employ multiple data collection methods, and implement measures to minimize response and
measurement biases. Additionally, transparency and documentation of data collection procedures are
essential for ensuring the validity and reliability of research findings.

ACTIVITY Art Integration

Visit https://ai-art.tokyo/en/
The AI artist named “AI Gahaku” generates a masterpiece from your photo.
Exploring Moralmachine.net can help users understand the complexities of ethical decision-
making in the context of autonomous vehicles and the broader implications of AI and technology
on society.
Activity: Visit Moralmachine.net to understand the impact of ethical concerns
1. Go to moralmachine.net to access the platform.
2. Scenario Selection: The platform presents users with various scenarios in which an
autonomous vehicle must make a decision that could result in harm to different groups of
individuals. Users can choose from different scenarios, each presenting a unique ethical
dilemma.

34
 3. Decision-Making: In each scenario, users are asked to make a decision about what the
autonomous vehicle should do in a given situation. For example, users may be asked
to choose between prioritizing the safety of passengers in the vehicle or pedestrians
crossing the street.
4. Consideration of Factors: The platform prompts users to consider various factors when
making their decision, such as the number of lives at stake, the age and health status
of individuals involved, and legal considerations.
5. Reflection and Discussion: After making a decision, users can see how their choices
compare to those of other participants and explore the reasoning behind different
decisions. The platform also provides resources for further reflection and discussion on
the ethical implications of autonomous driving technology.

IMPLICATIONS OF AI TECHNOLOGY
The implications of AI technology are vast and multifaceted, impacting various aspects of society,
economy, and individual lives.
1. Automation of Jobs: AI technology has the potential to automate a wide range of tasks across
different industries, leading to shifts in the labor market. While automation can increase efficiency
and productivity, it may also result in job displacement and require workers to adapt to new skill
requirements.
2. Ethical Concerns: The development and deployment of AI raise ethical considerations regarding
privacy, bias, transparency, accountability, and the potential misuse of technology. Addressing these
concerns is crucial to ensuring that AI technologies are developed and used in ways that are fair,
transparent, and aligned with societal values.

35
 3. Economic Impact: AI has the potential to drive economic growth and innovation by enabling new
products, services, and business models. However, it may also exacerbate income inequality if the
benefits of AI are not distributed equitably or if certain groups are disproportionately affected by
job displacement.
4. Social Impact: AI technologies can impact various aspects of society, including education,
healthcare, transportation, and public safety. For example, AI-powered educational platforms can
personalize learning experiences for students, while AI-driven healthcare systems can improve
diagnosis and treatment outcomes. However, there may be concerns about accessibility, equity,
and unintended consequences of AI deployment in these areas.

CASE STUDY: AI-POWERED RECRUITMENT TOOL AND GENDER BIAS

This case study explores the ethical concerns surrounding an AI-powered recruitment tool and potential
gender bias.
Scenario:
A large tech company, ABC Corp., implements a new AI recruitment tool to streamline the hiring
process for software engineering positions. The tool analyzes resumes and scores candidates based on
keywords, experience, and educational background. Initially, ABC Corp. is impressed by the tool’s
efficiency and reports a significant reduction in hiring time.
Ethical Issues:
• Potential for Gender Bias: The training data for the AI tool might have consisted primarily of
resumes from past male hires. This could lead to the tool prioritizing keywords and experiences
more commonly found on male resumes, undervaluing resumes with skills often used by women.
• Lack of Transparency: ABC Corp. might not fully understand how the AI tool arrives at its
candidate scores. This lack of transparency makes it difficult to identify and address potential
bias within the system.
• Fairness and Representation: The AI tool’s bias could lead to qualified female candidates being
overlooked, hindering diversity and equal opportunity within ABC Corp.’s workforce.
Consequences:
• Unequal Hiring Practices: The AI tool could perpetuate gender bias in hiring, leading to a less
diverse workforce and potentially talented women being excluded from opportunities.
• Loss of Public Trust: If the public becomes aware of the AI tool’s bias, it could damage Acme
Corp.’s reputation and erode trust in its hiring practices.
• Legal Issues: Depending on the jurisdiction, ABC Corp. could face legal challenges if the AI
tool is found to be discriminatory in its hiring practices.
Discussion Points:
• How can ABC Corp. ensure the fairness and lack of bias in its AI recruitment tool?
• What steps can be taken to promote transparency in the AI’s decision-making process?
• What alternative methods could ABC Corp. use alongside the AI tool to ensure a diverse and
qualified candidate pool?

RECAP
AI is changing that by automating the decision-making process, promising better qualitative results
and improved efficiency.
It is our responsibility to steer the use of AI in such a way to foster human flourishing and well-being
and mitigate the risks that this technology brings about.

36
KEY TERMS
● Ethics in AI is a multifaceted field concerned with ensuring that artificial intelligence systems are
developed, deployed, and used in ways that are fair, transparent, accountable, and aligned with
human values and societal norms.
• Deepfake videos are synthetic media that are created using artificial intelligence techniques,
particularly deep learning algorithms, to manipulate or superimpose existing images or videos onto
other images or videos.

AI EXERCISES
A. Multiple Choice Questions
1. systems are getting really good at creating fake images, videos, conversations and all manner
of content.
(a) AI (b) Mechanical (c) Fluid (d) All
2. Full form of ‘TAY’, the artificial intelligent chatbot is
(a) Trends About You (b) Thinking About You (c) Talking About You (d) Time And You
3. Artificially intelligent machines
(a) Do not have human touch (b) Can’t do repetitive tasks
(c) Brings precision and accuracy to processes (d) Improve productivity
4. A is caused when a prediction or end outcome negatively impacts an individual’s ability to
establish their rightful identity .
(a) Fairness (b) Harm (c) Job loss (d) Accuracy loss
B. Fill in the blanks
1. In June 2005, a at a hospital in Philadelphia malfunctioned during prostate surgery.
2. Tay is an artificial intelligence chatter bot and is named “Tay” after the acronym .
3. An Uber self-driving SUV struck and killed a in Tempe, Arizona.
C. Assertion/Reason Type
Assertion (A): Measurement bias is possible with data collection.
Reason (R): Measurement bias arises when the methods used to collect data systematically overestimate or
underestimate certain attributes or variables.
(a) Both A and R are correct and R is the explanation for A
(b) A is true and R is false
(c) A is false and R is true
(d) Both A and R are correct but R is NOT the explanation
D. Competency Based Questions
1. Raja was reading in a book that: “As AI becomes more powerful, ensuring its safe and responsible use is
paramount.” What one word related to use of power comes to mind with this sentence?

37
 2. In a book on AI, Antara read the following two lines. “Many AI systems, especially complex ones, can be like black
boxes – their decision making process is opaque. This lack of ___________ makes it hard to identify and address
bias, and can erode trust in AI.” Fill in the missing word.
E. Short Answer Questions
1. A hospital shares the details of the patients with an insurance company. Do you think it is ethical? Why/Why not?
2. Give some examples where AI is being used for good.
3. Why did the AI chat bot by Microsoft close down after some hours of operation?
4. Which are the basic requirements that an AI program should fulfill? Explain any two along with an example.
F. Long Answer Questions
1. Your younger sibling has spent a lot of time in preparing a 3D model of India Gate which is 3 ft tall and kept it on
ground for safety concerns. Your mother started the autonomous robot to clean the floor and it tries to clean
the area near the model but it falls and breaks.
i. Who can be held liable for damages caused by autonomous systems?
ii. List two AI Ethics.
2. “AI is interdisciplinary in nature and its foundations are in various fields.” Justify the statement with valid points..
G. Subject Enrichment Life Skill & Values
The project called “Cough Against Covid”, is funded by the Bill and Melinda Gates Foundation. They are working
on a self-screening tool for the public that will combine an analysis of solicited cough sounds along with self-reported
symptoms and contextual information such as location to identify the most probable potential Covid-19 cases and
carry on wider testing. Find out more about this initiative and discuss it in class.
H. Multiple Assessment
1. Divide the class into two groups and discuss what defines Ethics according to you in context of Artificial Intelligent
machines. The discussion should include points such as Communication

• Trustworthiness, Respect, Responsibility, Fairness, Caring and Honesty etc.

• Can we build AI without losing control over it ?
• What happens when computers get smarter than we are?
2. Visit https://thing-translator.appspot.com/
Take the picture of any object using a webcam and check how the AI algorithm identifies that object.
 Creativity

3. Since a computer can’t “see” a photo the way a human does, how can it extract meaningful data from one photo
and compare it with data from another? In this activity, students simulate a facial recognition algorithm by
breaking down images of Disney characters into a list of features they can then use to identify a mystery character.
Discuss the ethical issues involved in it. Critical Thinking

I. Knowledge Hub Critical Thinking

https://www.unesco.org/en/artificial-intelligence/recommendation-ethics
https://www.scu.edu/ethics/all-about-ethics/artificial-intelligence-and-ethics-sixteen-challenges-and-
opportunities/
https://www.thinkautomation.com/bots-and-ai/yes-positive-deepfake-examples-exist/
https://restofworld.org/2021/creating-personalized-deepfakes-for-corporations/

J. Experiential Learning
https://youtu.be/I9FOswjTSGg
https://youtu.be/gLoI9hAX9dw
uuu
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 Unit 2 : Data Literacy

5 Basics of Data Literacy

Learning Objectives
After studying this chapter, students will be able to:
• Understanding Data
• Learning about different types of data
• Understanding the difference between Data and Information.
• Understanding that data is pivotal to Artificial Intelligence
• Apply the Data literacy Process Framework
• Understanding the difference between Data Privacy and Data security

WHAT IS DATA
The concept of data isn’t limited to the digital world. Even before computers, information was collected,
analysed, and used for various purposes. For example, historical data might refer to records, documents,
or artifacts that provide information about the past. The word “data” is plural in its Latin origin,
meaning “things given” or “information that is presented.” However, in modern English usage, it’s
treated as both singular and plural. The first recorded use of “data” in English goes back to 1646. It
was primarily used in scientific and academic contexts to refer to factual information used as a basis
for reasoning or calculation.“Datum” is the singular form of “data” and is still used in some contexts,
particularly scientific ones. For example, we might refer to a specific “datum point” in a scientific
experiment.
With the rise of computers and information technology, Quantities

the meaning of “data” has broadened significantly. It now Information

Numbers
encompasses not just factual information but also all sorts of
digital information, including text, numbers, images, audio, What is
and video. While “data” is widely used in English, other Facts Data?
Graphs
languages have adopted their own variations of the word.
For example, the French use “donnée” and the Spanish use Observations Measurement
“datos”. The explosion of the digital age has led to the
coining of many new data-related terms in recent decades.
Examples include “metadata” (data about data), “big data” (massive datasets), and “data science” (the
field of studying and analysing data).

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Data refers to any collection of information that has been organised in a structured or unstructured
way. Data refers to any collection of facts, figures, or information that can be processed, analysed, and
interpreted to derive insights, knowledge, and understanding. It can be in the form of numbers, text,
images, audio, video, or any other digital format that can be captured, stored and analysed. Data can
be used to provide insights, support decision-making, or to create new knowledge. It can be collected
through various methods, such as surveys, experiments, observations, or sensors, and can be stored and
processed using computer systems. Data is often analysed using statistical and computational techniques
to extract meaningful information and to uncover patterns and relationships. Nearly every action that we
take in our daily lives generates data.
This makes us think:
Where do we collect data from?
Why do we need to collect data?
• Data helps to identify trends and patterns: By analysing data, organisations can identify
patterns, trends, and relationships that help them understand their customers’ needs, preferences,
and behaviors, understand past events and predict future outcomes. This information helps
businesses to improve their products and services, develop marketing strategies, and increase
customer satisfaction.
● Data enables better decision-making: Data can provide insights that help decision-makers make
better decisions based on evidence rather than intuition or opinion.
● Data drives innovation: By analysing data, we can identify new opportunities and develop
innovative solutions to complex problems.
● Data improves efficiency: By using data to measure and monitor processes and systems, we can
identify areas of inefficiency and optimise performance.
● Data enhances accountability: By collecting and analysing data, we can hold individuals and
organisations accountable for their actions and outcomes.
● Data is also essential in scientific research and innovation. Researchers use data to test
hypotheses, validate theories, and develop new ideas. They collect, analyse, and interpret data to
gain new knowledge and insights that can help solve complex problems and advance scientific
understanding.
Real world examples of data
There are many real-world examples of data that we encounter on a daily basis. Let’s see a few here
1. Sales Data: Sales data is a common type of data that businesses collect to monitor and optimise
their sales performance. This data may include information such as sales revenue, number of units
sold, average order value, customer demographics, and product performance. Businesses can use
this data to identify trends, optimise pricing strategies, and improve customer satisfaction.
2. Health Data: Health data includes information such as medical records, health insurance claims,
and personal health tracking data. This data can be used to track patient health outcomes, monitor
disease trends, and identify risk factors for specific conditions. Health data can also be used to
develop personalised treatment plans and to improve the overall quality of healthcare.
3. Social Media Data: Social media data includes information such as user profiles, posts, comments,
and engagement metrics. This data can be used to understand consumer behavior, identify trends,
and develop targeted marketing campaigns. Social media data can also be used to monitor public
opinion, track sentiment, and identify emerging issues or crises.
4. Financial Data: Financial data includes information such as stock prices, economic indicators, and

40
 financial statements. This data can be used to make investment decisions, monitor market trends,
and identify opportunities for growth.
5. Transportation Data: Transportation data includes information such as traffic patterns, public
transit schedules, and GPS tracking data. This data can be used to optimise transportation systems,
reduce congestion, and improve safety.
DATA Vs INFORMATION
Data and information are related terms, but they have different meanings and purposes. Here’s the
difference:
• Data: Data refers to raw facts, figures, and statistics that are collected and stored in a structured
or unstructured format. Data can be in the form of numbers, text, images, audio, or video. Data
has no meaning on its own and needs to be processed and organised to make it useful.
For example, if we have a list of numbers such as 1, 2, 3, 4, and 5, this is data. It is simply a
set of numbers with no context or meaning.
• Information: Information is data
that has been processed and
organised in a way that gives it
meaning and context. Information
is useful and relevant to a specific
purpose or decision. Information
provides knowledge, insight, and
understanding to its users.
For example, if we take the set
of numbers 1, 2, 3, 4, and 5 and
calculate their average, which is
3, this is information. It provides
meaningful insight into the data set, telling us the average value of the numbers.
Some other examples are:
l A list of dates, when coupled with the information that they are holidays.

l The number of COVID 19 patients per state when analysed indicates whether the count is rising

or not?
So, now we may ask the following question.
S.No. Data Information
Data is anything which we can read, write, Information is a type of data which is processed,
1
speak and observe. structured , organised and has a specific meaning.
Information has a meaning which is defined by
2. It does not have any specific meaning.
analysing and refining data.
Data comes in the form of numbers,
3. Information is a collection of ideas and inferences.
characters and letters.
The data is the plural form of Latin word
Information has French origin and its meaning is
4. “Datum”, whose meaning is “to give
“act of informing”.
something”.
Example : A list of dates when coupled with the
5. Example : A list of dates is data.
number of holidays becomes information.
41
There are several types of data, and each type has its own characteristics, properties, and uses. Data
comes in different types. Some of the common types of data include:
l Text l Image l Video l Numbers l Spreadsheets l Sound

ACTIVITY Experiential Learning

Download the Aarogya Setu App for information regarding COVID-19 statistics. Note down the
number of active cases, recovered, deceased and confirmed cases for seven consecutive days.
On the basis of records, comment on the scenario of disease in the country.

INTRODUCTION TO DATA LITERACY

Have you ever felt overwhelmed by all the information online? Charts,
graphs, statistics – it can be confusing! That’s where data literacy
comes in. Data literacy is the ability to read, understand, work with,
and communicate data. It’s like learning a new language, but instead
of words and grammar, you’re learning how to understand information
presented in charts, graphs, tables, and even text.
This includes:
• Being able to interpret different ways data is presented, like
understanding the information in a graph or a chart.
l Knowing what the data means, not just what the numbers are.

l Being able to organise and analyse data to answer questions or solve problems.

l Explaining what you’ve learned from data to others in a clear and concise way.

Data literacy is an important skill because data is everywhere! It helps you make informed decisions,
whether it’s choosing a movie to watch based on ratings or understanding weather forecasts.

ACTIVITY Art Integration

Interpret the image in three different ways

1.
_______________________________________
_______________________________________
_______________________________________
2.
_______________________________________
_______________________________________
_______________________________________
3.
_______________________________________
_______________________________________
_______________________________________
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Impact of Data literacy
From Smarter Decisions to a Brighter Future READ DATA ANALYSE DATA UTILISE DATA
Data is all around us, and it’s only going to grow!
Data literacy isn’t just a fancy term; it’s a critical
skill that empowers people in today’s information age.
Being data literate empowers you to:
l Make smart informed choices: Data literacy
equips you to make smarter decisions in all aspects of life.
l Critical Thinking: Data surrounds us, but not all of it is created equal. Data literacy helps you
analyse information critically, identify biases, and draw your own conclusions.
l Be a savvy citizen: News articles and social media are full of data. Data literacy helps you
understand the information you see and form your own opinions.
l Prepare for the future: Many jobs today involve working with data. Whether it’s analysing
marketing trends or interpreting scientific research, Data literacy can give you a head start in a
data-driven world!
l Enhanced Communication: Once you understand data, you can explain it to others clearly and
concisely.
l Reduced Misinformation: Data literacy empowers individuals to identify misleading information
and resist manipulation.

How to become Data Literate

1. Embrace Curiosity: Ask questions! Don’t just accept information at face value. Be curious about
where data comes from, how it’s collected, and who collected it. Be skeptical of data presented
without context or clear sources. Look for potential biases and limitations in the data.
2. Learn data basics: Understand the difference between quantitative and qualitative data, and
common data visualization techniques (graphs, charts, tables).
3. Find data in your daily life: News articles, weather reports, social media – data is everywhere!
Analyse the information you encounter and try to understand the story it tells.
4. Play with data sets: Many websites offer free, downloadable data sets on various topics. Explore
these datasets and practice organising, analysing, and visualizing the data.
5. Consider multiple perspectives: Data can be interpreted in different ways. Try to see the
information from various angles to gain a more comprehensive understanding.
6. Learn to visualize data: Charts, graphs, and other visuals can effectively communicate complex
data insights to others.
The Data Literacy Process Framework
The Data Literacy Process Framework provides a structured approach to understanding and working with
data effectively. Various key stages are as follow:
1. Data Identification: This stage involves recognizing and identifying the types of data available. It
includes understanding the sources of data, formats, and potential relevance to the organisation’s goals.
2. Data Collection: In this stage, data is gathered from various sources, which could include databases,
surveys, sensors, or external sources.
3. Data Processing and Preparation: This stage involves tasks such as filtering out irrelevant data,
handling missing values, and converting data into a usable format.

43
 4. Data Analysis: In this stage, data is analysed to derive insights, trends, or patterns. Analysis
techniques may include descriptive statistics, data visualization, predictive modeling, or machine
learning algorithms, depending on the goals of the analysis.
5. Interpretation and Communication: This stage involves translating technical insights into
actionable insights and presenting them in a clear and understandable manner.
6. Application and Decision Making: This stage often involves ongoing monitoring and iteration
based on feedback and new data.

DATA SECURITY
Data security refers to the protection of digital data
from unauthorised access, use, or disclosure. It
involves safeguarding personal information, such as
names, addresses, and passwords, from hackers and
cyber threats.
Here are key aspects of data security:
Confidentiality: Ensuring that data is accessible only to authorised individuals or systems.
Integrity: Guaranteeing the accuracy, completeness, and reliability of data throughout its lifecycle.
Authorisation: Authorisation mechanisms control what actions users can perform on specific data or
resources.
Encryption: Encoding data in such a way that it can only be decrypted and read by authorised parties
with the proper cryptographic keys.
IMPORTANCE OF DATA SECURITY
l Protects personal privacy: Ensures that sensitive information remains confidential and is not misused.
l Prevents identity theft: Safeguards against unauthorised access to personal accounts and financial
information.
l Maintains trust: Helps build trust and confidence in online transactions and communication.
l Preserves reputation: Avoids potential damage to reputation or embarrassment from leaked
personal information.
SECURITY MEASURES
1. Strong passwords: Use unique passwords that are difficult to guess and include a combination
of letters, numbers, and symbols. Avoid using easily guessable passwords like “123456” or
“password”. Never share passwords with anyone, including friends or classmates. Regularly update
passwords and avoid using the same password for multiple accounts.
2. Privacy settings: Adjust privacy settings on social media platforms and online accounts to control
who can see personal information.
3. Secure connections: Ensure that websites use HTTPS encryption for secure communication and
avoid connecting to unsecured Wi-Fi networks.
4. Antivirus software: Install reputable antivirus software to detect and remove malware from
computer systems.
5. Two-factor authentication (2FA): Enable 2FA for added security by requiring a second form of
verification, such as a text message or authentication app, in addition to a password.
6. Safe Browsing: Be cautious when clicking on links or downloading files from unknown or
suspicious websites.
7. Email Safety: Be wary of phishing emails that may try to trick you into revealing personal

44
 information or clicking on malicious links. Do not open email attachments from unknown senders,
as they may contain malware.
8. Data Backup: Regularly backup important files and schoolwork to an external hard drive, USB
flash drive, or cloud storage service like Google Drive or Dropbox. This ensures that even if your
device is lost, stolen, or damaged, you won’t lose your important data.
RESPONSIBLE ONLINE BEHAVIOR
l Think before sharing: Be cautious about sharing personal information online and avoid disclosing
sensitive details to strangers.
l Verify sources: Verify the credibility of websites and sources before trusting or sharing
information found online.
l Report suspicious activity: Report any suspicious emails, messages, or websites to trusted adults
or authorities.
DATA PRIVACY
Data privacy focuses on your right to control your personal information like Name, address, email,
social security number, browsing habits, online purchases, health records, location data , including who
collects it, how it’s used, and who has access to it. It’s about understanding and having a say in how
your online activities are tracked and used by others. Imagine you have a personal journal filled with
your thoughts, experiences, and maybe even secret dreams. Data privacy is about controlling who gets
to read that journal and deciding what information you share with others, especially online.

Know More
Think about having a special, secret box where you keep your favorite toys, drawings, or special notes. Data
privacy is like keeping that box safe and deciding who gets to look inside.
When you tell a friend a secret, that’s like sharing your data. It’s okay with some friends, but maybe not with
everyone. Websites and apps sometimes want to know your secrets, like your name, where you live, or even
what games you like to play.

The Importance of Data Privacy

Data privacy is not just about protecting personal details like names or addresses; it encompasses online
freedom, self-determination, and protection from harm. Individuals have the right to decide how their
information is collected, used, and shared. Unchecked data collection can lead to targeted advertising
and profiling, potentially influencing personal choices and decisions. Additionally, data breaches and
unauthorised access can have devastating consequences, from financial loss to identity theft.
You have the right to decide:
○ What information is collected about you? ○ Who has access to your information?
○ How your information is used?
Why is Data Privacy Important?
l Your online reputation: Everything you share online builds your virtual image. Schools,

employers, and even friends can look at your data to form an impression of you.
l Targeted advertising: Companies use your data to personalise the ads you see, which can

sometimes feel intrusive.

45
Laws and regulations to protect your data
Data privacy in India is governed by the Personal Data Protection Bill (PDPB), which was introduced in
2019 and is currently under review by the Indian government. The PDPB aims to establish a framework
for the protection of personal data and the rights of individuals in relation to their personal data. The
PDPB applies to both Indian and foreign entities that process personal data in India, and it defines
personal data broadly to include any information that can be used to identify an individual, such as
name, address, and biometric data. The PDPB also establishes a Data Protection Authority (DPA) to
oversee the implementation and enforcement of the bill’s provisions. The DPA will have the power to
investigate data breaches and impose penalties on entities that violate the bill’s provisions.
HOW IS DATA SECURITY AND PRIVACY RELATED TO AI
Data security and privacy are intricately linked to AI in a complex relationship.
l AI models learn and improve by analysing vast amounts of data. This data can include personal

information like browsing habits, social media posts, or even facial recognition data.
l As AI systems handle more sensitive data, the risk of breaches increases. Hackers targeting these
systems could steal personal information or disrupt critical AI applications.
l Malicious actors could intentionally feed AI systems with inaccurate or biased data, leading to
skewed results and unreliable outputs.
The relationship between data security, privacy, and AI is a complex balancing act. As AI continues
to evolve, finding ways to leverage its power while protecting data and privacy is essential. By
implementing strong security measures, developing transparent AI models, and empowering users, we
can ensure AI benefits society without compromising our privacy or security. By fostering collaboration
between AI developers, policymakers, and the public, we can strive to create a future where AI benefits
everyone without compromising security or privacy.

RECAP
Nearly every action that we take in our daily lives generates data. Be it in the physical world or
the digital world.
There are several types of data, and each type has its own characteristics, properties, and uses.
Data is a critical component of artificial intelligence (AI) as it is the foundation upon which AI
models are built.

KEY TERMS
● Data refers to any collection of facts, figures, or information that can be processed, analyzed,
and interpreted to derive insights, knowledge, and understanding.
● Information is data that has been processed and organised in a way that gives it meaning and
context.
● Data literacy is the ability to read, understand, work with, and communicate data.
● Data security refers to the protection of digital data from unauthorized access, use, or
disclosure.

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 AI EXERCISES
A. Multiple choice questions.
1. What is the purpose of transforming data into information?
(a) To make the data easier to store. (b) To make the data easier to analyse.
(c) To give the data meaning and context. (d) To make the data more valuable.
2. What is data privacy?
(a) The protection of personal information from unauthorised access or use.
(b) The sharing of personal information with others.
(c) The collection of personal information for marketing purposes.
(d) The sale of personal information to third parties.
3. Which of the following is a way to protect your data privacy?
(a) Using strong passwords. (b) Only sharing personal information with trusted sources.
(c) Using a virtual private network (VPN). (d) All of the above.
4. What does the term “data literacy” refer to?
(a) The ability to read and understand data (b) The ability to manipulate data using advanced software
(c) The ability to create data visualizations (d) The ability to code algorithms
5. Which of the following is NOT a component of data literacy?
(a) Data analysis (b) Data visualization (c) Data storage (d) Data interpretation
B. Fill in the Blanks.
1. ___________ is the singular form for data.
2. __________ is data about data.
3. _________ data includes information such as stock prices, economic indicators and financial statements.
4. ___________ is anything which we can read, write, speak and observe.
C. Assertion/Reason Type Questions
1. Assertion (A): Data does not depend on anything.
Reason (R): Information is what we get after processing data.
(a) Both A and R are correct and R is the correct reason for statement A
(b) A is true but R is false
(c) A is false and R is true
(d) Both A and R are true but R is NOT the correct explanation for A
2. Assertion (A): In Data Science, you become a detective of information, sifting through facts to uncover the
truth.
Reason (R): Data literacy helps you analyse information critically, identify biases and draw your own
conclusions.
(a) Both A and R are correct and R is the correct reason for statement A
(b) A is true but R is false
(c) A is false and R is true
(d) Both A and R are true but R is NOT the correct explanation for A

47
D. Competency Based Questions
1. Akshara does not understand how presence of data can help out researchers and entrepreneurs. As a person
knowledgeable about data literacy, explain how data literacy is very linked to innovation by scientists and
entrepreneurs.
2. Shyam wants to know how companies can manipulate the data privacy in order for targeted advertising. Explain
to Shyam how data privacy is related to targeted advertising by companies.
E. Short Answer Questions
1. What kinds of passwords should you set for your online accounts?
2. What are some sources that can be used for data collection?
3. What is encryption of data?
4. Give an example of data and its corresponding example of information, where the difference is clear.
F. Long Answer Questions
1. Explain the importance of data security and privacy in today’s digital landscape. Provide an examples of potential
consequences of data breaches or privacy violations.
2. What does it mean to be data literate? Describe the key skills and knowledge areas that contribute to data
literacy.
3. Differentiate between Data and Information.
G. Subject Enrichment
1. Students can be divided into small groups for the data breach role-play exercise, and each group will be given
a scenario involving a data breach. After that, the groups will need to act out the scenario in order to decide
what steps they would take to address the breach and lessen any potential damage it might cause. Students
will learn the value of protecting personal information and how to handle data breaches through this exercise.
 Creative Thinking

2. The task of analysing the privacy policies of different websites or applications can be given to students. They
can evaluate whether the policies are clear and simple for users to comprehend, identify any potential privacy
concerns, and compare and contrast the various privacy policies. Students will gain a better understanding of
privacy policies and the value of openness in the gathering and use of data from this exercise.
 Critical Thinking
H. Multiple Assessment
1. Students can construct a data backup plan for a fictitious company or organisation in this activity by working
in teams. They must decide how frequently backups should be made, what kinds of data need to be backed
up, and which backup techniques to use. Students will gain a better understanding of the value of data backup
plans and the different backup options available through this activity.  Experiential Learning

2. Divide the class into two groups and conduct a debate on the topic  Communication

● Data Privacy Concern - Ban on Chinese Apps in India.

● Is Technology making us less human?
● Big Data – future of tomorrow
● Data democratization: “Data of the people, by the people, for the people”
I. Knowledge Hub Life Skills & Values
https://www.nishithdesai.com/fileadmin/user_upload/pdfs/Research_Papers/Privacy_&_Data_in_India.pdf
J. Experiential Learning
https://www.youtube.com/watch?v=QcMzR1oFH20

uuu
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 Unit 2 : Data Literacy

6 Acquiring Data, Processing

and Interpreting Data

Learning Objectives
After studying this chapter, students will be able to:
• Determine the best methods to acquire data
• Classify different types of data and enlist different methodologies to acquire it.
• Define and describe data interpretation.
• Enlist and explain the different methods of data interpretation.

THE DATA JOURNEY: ACQUIRE, PROCESS, INTERPRET

Data is like raw ingredients. To turn it into something useful, we need to follow a three-step process:
1. Acquiring Data: This is where we gather the data we need. We can collect it ourselves through
surveys, experiments, or sensors. We can also get data from existing sources like databases or
public records.
2. Processing Data: Raw data is messy! It needs cleaning, organising, and formatting before it can
be analysed. This might involve removing errors, filling in missing information, and converting it
into a consistent format for analysis.
3. Interpreting Data: Now comes the fun part! We use various techniques like statistical analysis
or visualisation to understand what the data is telling us.

TYPES OF DATA
In the context of data analysis, data can be categorised into different types based on its nature, structure,
and characteristics. Understanding the types of data
is essential for data analysis, as it helps determine
the appropriate statistical methods, visualisation
techniques, and analytical tools to use based on the
nature and characteristics of the data. The main types
of data include:
1. Numerical Data: Numerical data consists of
numbers and can be further divided into two
subtypes:
• Discrete Data: Discrete data consists of

49
 distinct values that can be counted and are often whole numbers. Examples include the number
of students in a class or the number of cars in a parking lot.
• Continuous Data: Continuous data can take any value within a range and are typically
measured. Examples include height, weight, temperature, and time.
2. Categorical Data: Categorical data represents categories or groups and can be further divided
into two subtypes:
• Nominal Data: Nominal data consists of categories with no inherent order or ranking. Examples
include gender (male/female), colors (red, blue, green), or types of fruit (apple, banana, orange).
• Ordinal Data: Ordinal data consists of categories with a meaningful order or ranking. However,
the differences between categories may not be consistent or measurable. Examples include
ratings (poor, fair, good, excellent), educational levels (elementary, middle school, high school),
or income levels (low, medium, high).
3. Text Data: Text data consists of textual information such as documents, articles, emails, social
media posts, or website content. It is often analysed using natural language processing (NLP)
techniques to extract insights, sentiment, or patterns from the text.
4. Binary Data: Binary data consists of only two possible values, typically represented as 0 and 1. It
is commonly used in computer science, digital communications, and machine learning algorithms.
Examples include yes/no responses, true/false statements, or binary code in computers.
5. Univariate Data: This type of data involves only one variable. It describes a single characteristic
or measurement of an object or event. Examples include. A student’s test score, the daily
temperature in a city

DATA ACQUISITION
Data acquisition is the process of collecting, recording,
and gathering raw data from various sources for further
analysis, processing, or storage. It involves capturing
data in a format that can be easily manipulated,
analysed, and utilised to extract insights, make informed
decisions, or solve problems.
The data acquisition process typically involves the IX
following steps:
1. Identifying Data Sources: Determine the sources
from which data will be collected. These sources can include sensors, instruments, databases, files,
websites, APIs, or other data repositories.
2. Selecting Data Acquisition Methods: Choose the appropriate methods and techniques to collect
data from the identified sources. This may involve manual data entry, automated data retrieval,
sensor deployment, data logging, or real-time data streaming.
3. Designing Data Collection Systems: Design and set up data collection systems or platforms
to facilitate the acquisition process. This may involve developing custom software applications,
deploying hardware devices, or configuring data acquisition software and tools.
4. Capturing and Recording Data: Implement mechanisms to capture and record raw data from
the selected sources. This may involve reading sensor outputs, querying databases, scraping web
content, or extracting data from files.
50
 5. Data Transmission and Integration: If necessary, transmit or integrate the acquired data with
other systems, platforms, or applications for further processing, analysis, or sharing. This may
involve data integration techniques, data pipelines, or data streaming technologies.
Data acquisition is a crucial step in the data lifecycle and lays the foundation for subsequent data
analysis, modeling, visualisation, and decision-making activities.

ACTIVITY Experiential Learning

Collect the newspaper cuttings showing the weather information about your city for one week.
Form the tables for different weather parameters and arrange the data properly. What change
did you notice in the parameters‛ values?

DATA SOURCES
A data source is any location or
system that stores and manages data.
This data can be raw or processed,
static or dynamic.
It can come from a variety of formats,
including:
• Databases (structured data)
• Spreadsheets
• Flat files (text files)
• Sensor readings (real-time data)
• APIs (application programming
interfaces)
• Web scraping (extracted data
from websites)
Data sources are broadly categorised as
primary and secondary data.
PRIMARY DATA SOURCE
Primary data is the one which is
collected as the first hand information by a surveyor, investigator etc. This includes feedback forms,
interviews, online surveys, marketing campaigns etc.
Features of Primary Data source
• This is collected for the first time
• This kind of data has not been used for any kind of statistical analysis before.
• This is original and more reliable than secondary data sources.
Example: Record of number of people vaccinated against COVID-19 virus in a country
Methods of collecting from primary data source
• Direct personal investigation
• Indirect oral investigation

51
 • Telephonic interview
• The questionnaire filled by enumerators

SECONDARY DATA SOURCE

Secondary data is the one which has already been collected, analysed, published and has undergone
statistical treatment. It is considered as second hand information. It refers to the data that has already
been collected by some other person or agency and is used by us. This includes satellite data, IoT
sensor data, data from social media etc.
Features of Secondary Data Source
• This is extracted from the primary data source.
• This kind of data has gone through statistical analysis at least once.
• This is not original data.
Example: Information available on blogging websites by multiple bloggers

METHODS OF COLLECTING SECONDARY DATA SOURCE

Published sources
• Magazines, journals and periodicals published by various organisations
• Reports of various committees or commissions like pay commission report, finance commission
report etc.
• Reports of international agencies that are regularly published by agencies like UNO, WHO, etc.
Unpublished sources
• Research work done by scholar students or educational institutions
• Reports prepared by private investigation companies can also be used depending upon the need
• Data taken from social media tracking
ACTIVITY Experiential Learning

Note the number of glasses of water you drink in a day for one week along with maximum and
minimum temperature of the day. Draw a table for the recorded data for the whole week. How
does the change in temperature affect your water intake?

Data sources can also be broadly categorised into two main types:
• Machine Data Sources: This type encompasses data automatically generated by machines or
devices. They typically involve real-time or continuous data streams.
Examples:
○ Sensor data from industrial machinery.
○ Clickstream data from website interactions.
○ Network traffic logs.
• File Data Sources: These refer to data stored in structured or semi-structured formats within files
or databases.
Examples:
○ Sales data stored in a relational database.
○ Customer information in a CSV (comma-separated values) file.

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BIG DATA
Big Data is a collection
of data that is huge in
volume, yet growing
exponentially with time. It
is data with such large size
and complexity that none of
traditional data management
tools can store it or process
it efficiently. It is also data
but with a huge size. The
size and number of available
data sets has grown rapidly
as data is collected by
devices such as mobile devices, cheap and various information sensing devices, remote sensing, software
logs, cameras, microphones, Radio frequency identification (RFID) readers, wireless sensor networks etc.
The main characteristics of big data are commonly referred to as the four Vs - Volume, Velocity, Variety
and Veracity. These are the high level dimensions that data analysts, scientists and engineers use to break
everything down.
Volume: This defines the size of the data. If the data is greater than terabytes then the ordinary
data will fall under the category of big data. Thereafter it requires powerful analytical tools for
handling it.
Velocity: The data generation rate of big data is very fast. More than 90% of data has been
generated in the past 2 years.
Variety: Data scientists and analysts aren’t just limited to collecting data from just one source,
but many. The multiple sources’ data combines together to create even bigger data pool like data
of a particular user is combined from Facebook, Instagram, Gmail, etc. to fetch the interest areas
of the user.
Veracity: Veracity refers to the quality, accuracy and trustworthiness of the data that’s collected.
Low veracity or bad data affects the overall performance of the system.
1. Finance:
• Fraud Detection: Analysing vast transaction data helps identify suspicious patterns and prevent
fraudulent activities.
• Risk Management: Big data allows banks and financial institutions to assess creditworthiness and
manage risk more effectively.
2. Retail:
• Demand Forecasting: By analysing customer purchase history and social media trends, retailers
can predict future demand and optimise inventory management.
• Targeted Marketing: Big data helps personalise marketing campaigns and target customers based
on their preferences and purchase history.
3. Healthcare:
• Medical Research: Big data facilitates the analysis of medical records and genetic data,
accelerating research into new treatments and personalised medicine.
53
 • Disease Prediction and Prevention: Analysing trends and risk factors in vast datasets helps
predict disease outbreaks and identify individuals at higher risk.
4. Government:
• Public Safety: Big data analytics can be used to analyse crime patterns and predict potential
criminal activity.
• Urban Planning: By analysing traffic data and citizen demographics, governments can optimise
urban planning and infrastructure development.
BEST PRACTICES FOR DATA ACQUISITION
Data acquisition is a critical step in the data lifecycle, and implementing best practices ensures that the
collected data is accurate, reliable, and usable for analysis and decision-making. Here are some best
practices for data acquisition:
1. Define Clear Objectives: Clearly define the objectives and goals of the data acquisition process.
Understand what insights or information you aim to derive from the data and how it will be used
to support business objectives or decision-making.
2. Identify Relevant Data Sources: Identify and prioritize the data sources that contain the
information needed to achieve your objectives. Consider both internal and external sources such
as databases, sensors, APIs, third-party data providers, and IoT devices.
3. Ensure Data Quality: Implement measures to ensure the quality and integrity of the acquired
data. This includes data validation, error checking, data cleaning, and data preprocessing techniques
to identify and correct errors, inconsistencies, or missing values.
4. Standardise Data Formats: Standardise data formats and structures to facilitate data integration,
interoperability, and compatibility across different sources and systems. Use common data
standards, protocols, and schemas where applicable.
5. Implement Data Security Measures: Protect sensitive or confidential data by implementing
appropriate data security measures. This includes encryption, access controls etc.

DATA FEATURES
Data refers to raw facts, observations,
measurements, or information that are collected,
recorded, or stored for analysis, processing,
or use. The features of data describe various
characteristics or attributes that help classify,
organise, and understand the data. Here are some
common features of data:
1. Type: Data can be categorised into different types based on its nature and characteristics. The
main types of data include numerical data (discrete or continuous), categorical data (nominal or
ordinal), time series data, spatial data, text data, and binary data.
2. Format: Data format refers to the way data is represented or structured. Common data formats
include tabular data (e.g., spreadsheets), relational data (e.g., databases), hierarchical data (e.g.,
XML or JSON), text data (e.g., documents or files), image data (e.g., JPEG or PNG), and audio
data (e.g., MP3 or WAV).
3. Size: Data size refers to the volume or quantity of data. It can be measured in terms of the
number of records, rows, columns, variables, bytes, or storage space required to store and manage
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 the data. Data size can range from small datasets to big data, which may involve terabytes or
petabytes of data.
4. Granularity: Granularity refers to the level of detail or precision in the data. It describes the
extent to which data is disaggregated or aggregated.
5. Quality: Data quality refers to the accuracy, completeness, consistency, reliability, and relevance of
the data. High-quality data is free from errors, duplicates, outliers, and inconsistencies and meets
the requirements of its intended use.
6. Structure: Data structure refers to the organisation or arrangement of data elements within a
dataset. It defines the relationships, dependencies, and hierarchies among the data components.
Common data structures include flat files, tables, lists, trees, graphs, and arrays.

Imagine you’re planning a pizza party with your friends! To make sure everyone has a good time, you
need to gather some data. This data has different features, just like the information you might see in
charts or graphs. Here’s how your pizza party planning can be an example of data features:
• Number of People (Quantity): This is how many friends you’re inviting. It’s a numerical feature,
telling you the amount or quantity of something.
• Favorite Toppings (Category): This is a qualitative feature, describing something that falls into
different groups. Each friend’s favorite topping (pepperoni, veggie, etc.) is a category.
• Allergies (Yes/No): This is a binary feature, meaning it can only have two values - “Yes” if
someone has an allergy, or “No” if they don’t.
Here’s how these data features help you plan your pizza order:
• Quantity (Number of People): Knowing how many friends are coming (quantity) helps you
determine the size of the pizzas you need to order.
• Category (Favorite Toppings): Knowing your friends’ favorite toppings (categories) helps you
choose pizzas with a variety of toppings to please everyone.
• Binary Feature (Allergies): Knowing if anyone has allergies (binary feature) helps you avoid
ordering pizzas with those ingredients, ensuring everyone can enjoy the party safely.
By considering these data features, you can make informed decisions and plan a delicious and inclusive
pizza party for you and your friends!

DATA PREPROCESSING
Data preprocessing is a crucial step in the data analysis pipeline that
involves transforming raw data into a clean, organised, and structured
format suitable for analysis, modeling, and visualisation. It aims to
improve the quality, consistency, and usability of the data by addressing
issues such as missing values, outliers, noise, and inconsistencies.
Data preprocessing is an iterative process that requires careful
consideration of the data characteristics, analysis objectives, and
domain-specific knowledge.
Data preprocessing typically involves several steps, including:

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1. Data Cleaning:
• Identify and handle missing values: Determine how missing values are represented in the data
(e.g., NaN, NULL) and decide whether to impute missing values, remove observations with
missing values, or retain missing values based on the analysis context.
• Detecting and handling outliers: Identify outliers using statistical techniques (e.g., Z-score,
IQR) or domain knowledge and decide whether to remove, replace, or transform outlier values to
mitigate their impact on the analysis.
• Remove duplicate records: Identify and remove duplicate observations or records from the dataset
to ensure data integrity and avoid redundancy.
2. Data Transformation:
• Feature scaling: Standardise or normalise numerical features to ensure they have a similar scale
and distribution. Common scaling techniques include Min-Max scaling and Z-score normalisation.
• Feature encoding: Convert categorical variables into numerical representations using techniques such as
one-hot encoding, label encoding, or binary encoding to make them suitable for modeling algorithms.
• Feature engineering: Create new features or derive meaningful insights from existing features through
techniques such as binning, polynomial features, or interaction terms to improve model performance.
3. Data Reduction:
• Dimensionality reduction: Reduce the number of features or variables in the dataset while
preserving relevant information using techniques such as principal component analysis (PCA),
t-distributed stochastic neighbor embedding (t-SNE), or feature selection methods.
• Sampling: If the dataset is large, consider sampling techniques such as random sampling, stratified
sampling, or oversampling/undersampling to create a representative subset of data for analysis.
4. Data Integration:
• Integrate Data: Integrate data from multiple sources or datasets by combining, merging, or joining
them based on common identifiers or keys to create a unified dataset for analysis.
• Resolve inconsistencies: Resolve inconsistencies or discrepancies between integrated datasets, such
as differences in data formats, units of measurement, or data definitions.
5. Data Formatting and Standardisation:
• Formating into Structures: Format data into a consistent structure and format suitable for
analysis, visualisation, and modeling. This may involve converting data types, renaming variables,
reordering columns, or ensuring consistent units of measurement.
6. Data Splitting:
• Data Splitting: Split the preprocessed dataset into training, validation, and test sets for model
development, evaluation, and validation purposes using appropriate splitting ratios (e.g., 70-15-15
or 80-10-10).
DATA INTERPRETATION
Data interpretation is the process of reviewing data and using various analytical methods to arrive at
relevant conclusions. It’s the bridge between raw data and actionable insights.
1. Understanding the Data:
• Data Source: Identifying the origin of the data (e.g., surveys, databases, sensor readings) is crucial
for understanding its context and potential limitations.
• Data Types: Distinguishing between qualitative (descriptive) and quantitative (numerical) data
helps determine appropriate analysis methods.

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 • Data Quality: Assessing the accuracy, completeness, and consistency of the data ensures reliable
results.
2. Choosing the Right Tools:
• Data Visualisation: Charts, graphs, and maps can reveal trends, patterns, and relationships within
the data.
• Statistical Analysis: Descriptive statistics (e.g., mean, median) and inferential statistics
(e.g., hypothesis testing) provide deeper insights and allow for drawing conclusions beyond the
data itself.
3. Extracting Meaning:
• Identify Trends: Look for patterns or changes in the data over time or across different categories.
• Analyse Relationships: Explore how different variables within the data are connected or influence
each other.
4. Communication and Storytelling:
• Context Matters: Clearly explain the context and purpose of the data analysis to avoid
misinterpretations.
• Visual Appeal: Use clear and concise visualisations to effectively communicate insights to a wider
audience.
• Limitations and Biases: Acknowledge any limitations of the data or potential biases that might
influence the interpretation.
5. Actionable Insights:
• Decision Making: Use the insights gained from data interpretation to support informed decisions
and strategies.
• Problem-solving: Data analysis can help identify root causes of problems and develop solutions.

TYPES OF DATA INTERPRETATION

Data interpretation can be approached from various angles depending on the nature of the data and the
desired insights.
Some common types of data interpretation are as follows:
1. Qualitative Data Interpretation:
This method focuses on analysing non-numerical data, such as text, images, or audio recordings. It
involves understanding the meaning, themes, and patterns within the data. Here are some common
techniques:
• Thematic Analysis: Identifying recurring themes and concepts that emerge from the data.
• Content Analysis: Systematically analysing the content of text or media to understand underlying
messages and sentiment.
• Grounded Theory: Developing theories based on patterns and relationships discovered within the
data.
2. Quantitative Data Interpretation:
This method deals with numerical data and utilizes statistical analysis to uncover trends, patterns, and
relationships within the data. Here are some common approaches:

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 • Descriptive Statistics: Summarizing the data using measures like mean, median, mode, and
standard deviation to understand central tendency and variability.
• Regression Analysis: Examining the relationship between a dependent variable and one or more
independent variables to understand how they influence each other.
3. Visual Data Interpretation:
This method involves interpreting data presented in visual formats like charts, graphs, and maps. It
focuses on identifying trends, patterns, and relationships through visual cues:
• Identifying Trends: Looking for upward, downward, or cyclical patterns in lines or bars on charts
and graphs.
• Comparing Groups: Visually comparing data points or bars across different categories to identify
differences or similarities.
• Spatial Analysis: Analysing data presented on maps to understand geographical patterns or
relationships.
4. Text Mining:
This technique focuses on analysing large amounts of textual data (e.g., social media posts, online
reviews). It utilizes natural language processing (NLP) techniques to extract keywords, sentiment analysis,
and identify emerging topics.

ACTIVITY Experiential Learning

Create a database about the advertisements during television shows. Pick up any 5 popular
television shows of different genres (say kids show, daily soaps, animal shows, mythological serials
etc). Prepare the record about the advertisements in between these shows. Note the number
of ads in one show, the type of ads, product type, duration etc. Create the necessary charts to
display your observations and then present them in class.

IMPORTANCE OF DATA INTERPRETATION

In today’s data-driven world, information is
abundant, but true value lies in unlocking the
meaning behind that data. This is where data
interpretation shines. Raw data is like a pile
of puzzle pieces. Data interpretation helps you
assemble the picture and identify patterns and
trends. This empowers individuals and organisations
to make informed decisions based on evidence, not
just intuition or guesswork.
DATA VISUALISATION
Data visualisation isn’t just about making pretty charts; it’s a powerful tool used across many aspects
of our daily lives. Data visualisation is the art and science of representing information using visual
elements like charts, graphs, maps, and infographics. It translates complex data sets into a format that
is easier to understand and interpret.
Here are some real-life examples of how data visualisation is used:

58
1. Business Intelligence & Marketing:
• Sales Dashboards: Companies use interactive dashboards with
charts and graphs to track key performance indicators (KPIs)
like sales figures, website traffic, and customer engagement. This
allows them to monitor progress and make data-driven decisions
in real-time.
• Marketing Campaigns: Targeted advertising campaigns often
leverage data visualisation to understand customer demographics
and preferences. They might use heatmaps to see how website
visitors interact with content or create infographics to showcase
product benefits in an engaging way.
2. Science & Research:
• Scientific Discoveries: Researchers use data visualisation tools
to analyse complex datasets, identify trends and patterns, and
communicate their findings. For example, they might use scatter
plots to show correlations between variables or create 3D models
of molecules to understand their structure.
• Public Health Communication: Data visualisation plays a vital role in communicating public
health trends and risks. Line graphs can show the spread of diseases over time, while choropleth
maps can illustrate geographical variations in infection rates.

RECAP
Data acquisition is the process of collecting, recording, and gathering raw data from various sources
for further analysis, processing, or storage. A data source is any location or system that stores
and manages data. Primary data is the one which is collected as the first hand information by
a surveyor, investigator etc. This includes feedback forms, interviews, online surveys, marketing
campaigns etc. Secondary data is the one which has already been collected, analysed, published
and has undergone statistical treatment. This includes satellite data, IoT sensor data, data from
social media etc. Big Data is a collection of data that is huge in volume, yet growing exponentially
with time. The main characteristics of big data are commonly referred to as the four Vs - Volume,
Velocity, Variety and Veracity. The features of data describe various characteristics or attributes that
help classify, organise, and understand the data.
Data preprocessing is a crucial step in the data analysis pipeline that involves transforming raw data
into a clean, organised, and structured format suitable for analysis, modeling, and visualisation. It
aims to improve the quality, consistency, and usability of the data by addressing issues such as
missing values, outliers, noise, and inconsistencies.

KEY TERMS
● Data acquisition is the process of collecting, recording, and gathering raw data from various
sources for further analysis, processing, or storage.
● A data source is any location or system that stores and manages data.

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 ● The features of data describe various characteristics or attributes that help classify, organise,
and understand the data.
● Data interpretation is the process of reviewing data and using various analytical methods to
arrive at relevant conclusions. It’s the bridge between raw data and actionable insights.

AI EXERCISES
A. Multiple choice questions.
1. Gathering the data we need is known as ___________ acquisition.
(a) Make data interesting (b) Make data interactive
(c) Reduced coding (d) All of these
2. Surveys, databases and sensor readings are some examples of ___________ of the data or data source.
(a) FusionCharts (b) Datawrapper (c) Tableau Public (d) My Family Tree
3. NaN and NULL are some examples of ___________ values when doing data cleaning.
(a) Chart (b) Tables (c) Graphs (d) Text file
B. Fill in the Blanks.
1. Data ___________ is the art and science of representing information using visual elements like charts, graphs,
maps and inforgraphics.
2. Data __________ plays a vital role to communicating public health trends and risks.
3. __________ data in retail helps personalise marketing campaigns and target customers based on their
preferences and purchase history.
C. Assertion/Reason Type
1. Assertion (A): Variety is one of the Vs of big data.
Reason (R): Data scientists and analysts are not just limited to collecting data from just one source, but many.
(a) Both A and R are correct and R is the correct reason for statement A
(b) A is true but R is false (c) A is false and R is true
(d) Both A and R are true but R is NOT the correct explanation for A
2. Assertion (A): Big data finds use in retail.
Reason (R): Among the many uses, by analysing customer purchase history and social media trends, retailers
can predict future demand and optimise inventory management.
(a) Both A and R are correct and R is the correct reason for statement A
(b) A is true but R is false (c) A is false and R is true
(d) Both A and R are true but R is NOT the correct explanation for A
D. Competency Based Questions
1. Arvind was reading up about the common types of data interpretation of qualitative data. As you have knowledge
on this topic, explain the thematic and content analysis which lets you analyse this form of data.
2. Anushree was reading up about data interpretation. She read the definition of the term “Data interpretation”
but could not understand. As you have knowledge on this topic, explain her the meaning of data interpretation
in a way she can understand.
3. Pinky was reading up about some best practices for data acquisition. The heading of one of the topics is “Identify
Relevant Data Sources”. Explain to her why such a practice is needed.

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 4. Anil’s teacher was teaching about big data in class and observed that he was not paying attention. So she asked
him, how big data can help the government in matters related to Public Safety. What can Anil say at this point
to save himself?
E. Short Answer Questions
1. Where are KPIs needed?
2. Give any two methods of data interpretation for quantitative data.
3. Describe granularity as a feature of data.
4. What are the 4 V’s characteristics of big Data?
F. Long Answer Questions
1. Give some examples of how data impacts our daily lives.
2. Name a few sectors where big data analysis is used.
3. What is data interpretation? Discuss its significance.
4. Describe how big data is useful for the government related to:
(a) Urban Planning (b) Resource Management
G. Subject Enrichment Problem Solving

Fitness trackers collect various data points through sensors, including steps taken, distance traveled, calories burned,
and heart rate. The fitness tracker app processes this data and presents it in an easy-to-understand format. Users
can see graphs of their daily steps, analyse trends over time, and set goals for themselves. By interpreting the data,
users can gain insights into their activity levels, fitness progress, and overall health. They can see if they’re reaching
their step goals, identify periods of increased activity, and adjust their exercise routines accordingly. This data can
motivate users to stay active and make informed decisions about their health and fitness.
Take any one such fitness tracker and create a short report on it.
Communication
H. Multiple Assessment
Read the book “ Moneyball: The Art of Winning an Unfair Game” or watch Moneyball (2011) starring Brad Pitt
and Jonah Hill. Billy Beane, general manager of the Oakland Athletics baseball team, uses statistical data on players
instead of traditional scouting methods. In the movie, there’s no specific app shown, but it depicts the concept of
using statistical analysis software to evaluate baseball players. Imagine an app that analyses a player’s batting average,
on-base percentage, slugging percentage, and other metrics to generate a score indicating their potential value.
Billy Beane interprets the data to identify undervalued players with high on-base percentages, even if they weren’t
traditionally considered “stars.” This strategy allowed him to build a competitive team with a limited budget by
focusing on what the data showed about a player’s ability to get on base and score runs, which is crucial for winning
baseball games.
Create a presentation on the above case study and present in class.
I. Knowledge Hub Life Skills & Values
https://www.tableau.com/learn/articles/data-visualization
https://www.kaggle.com/learn/data-visualization
J. Experiential Learning
https://youtu.be/l7cAdp0f4X0
https://youtu.be/5Zg-C8AAIGg

uuu

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 Unit 2 : Data Literacy

7 Project Interactive Data

Dashboard and Presentation

Learning Objectives
After studying this chapter, students will be able to:
• Summarize the topics learned previously
• Recognize the importance of data visualization
• Discover different methods of data visualization

DATA VISUALIZATION
Data visualization is the art and science of representing information in a visual format like charts,
graphs, and maps. It helps us understand complex data sets, identify trends and patterns, and
communicate insights in a clear and concise way. It helps us in
○ Enhanced Understanding: Visual representations make it easier for the human brain to grasp
complex information compared to raw data tables.
○ Identification of Trends and Patterns: By visualizing data, we can easily spot trends, outliers,
and correlations that might be missed in plain text.
○ Effective Communication: Well-designed data visualizations can effectively communicate insights
to a wider audience, even those without a strong data background.
○ Data Storytelling: Data visualization allows you to tell a story with data, highlighting key points
and prompting further exploration.

WHAT IS TABLEAU?
Tableau is a software that allows users to visually
explore and analyze data. It offers an effective
platform for creating visually appealing charts,
graphs, and dashboards.
Tableau Public is a free platform designed for anyone to explore, create, and share data visualizations
publicly online. It offers a wealth of features that make data analysis and communication accessible to a
wide audience. Unlike the paid version of Tableau, Tableau Public is completely free to use. This makes
it an attractive option for individuals, students, and non-profit organizations who want to leverage data
visualization without a financial barrier.
Tableau Public boasts a vast repository of data visualizations on various topics created by a global
community. You can browse these visualizations to gain insights, discover trends, and find inspiration for

62
your own projects. The platform provides an intuitive interface for creating
interactive data visualizations. You can connect to various data sources, drag
and drop elements to build charts and graphs, and customize the visuals to
effectively communicate your message.
Tableau Public is a valuable tool for anyone interested in exploring, creating, and sharing data
visualizations. It empowers individuals to communicate insights effectively and contribute to a vibrant
online data community.
Tableau Installation
Ready to get started with Tableau?
Download Tableau public with the help of an adult
using this link -
https://public.tableau.com/en-us/s/download
Install the package via the install wizard.
1. Download Tableau Desktop from the official
website
2. Run the installer from the downloads
3. Begin with the installation and agree to the
terms and conditions
4. Follow the prompts while installing the software

ACTIVITY Experiential Learning

Data Visualization Using Excel and

Tableau
Your favorite songs
● Think about songs! Which songs do you
listen to? Which songs do you sing?
● Do you have a favorite song, artist,
album, or playlist?

63
 ● Let‛s start thinking about the different aspects of a song, like instruments and lyrics.
● Do your favorite songs have anything in common?
Maybe your favorite music falls within the same genre.
● A genre refers to the different styles of music.
● Common genres include hip-hop, pop, alternative, and rock.
● Classifying songs by genre, and other traits allows us to see trends in our favorite
music.
● All of this information
is valuable data that we
can count, summarize, and
present!

Instructions
● Draw a grid with 6
columns as shown.
● Title the first column Song Name, then write down the names of 5-10 of your favorite
songs
● For this activity, we‛re going to collect data about the Album, Artist, Genre, Year, and
Song Length.

LET’S VISUALIZE IN EXCEL

• Count the number of songs that fall into each genre.
• Make a bar chart to visualize the number of songs within
each genre using your counting. Color each bar a different
color.
• You will get a graph as shown in the image.
• Looking at the data visualization, can you tell which genre
has the most songs?
This is the Tableau Public screen

64
TABLEAU INTERFACE

Since Tableau Public is a tool for data discovery as well as data visualization, the interface is designed
to encourage discovery through the drag-and-drop features for data. The user interface for Tableau Public
is segmented into separate areas, namely data elements, cards, shelves, and the canvas. The data is also
divided into two general categories—dimensions and measures. By understanding how data interacts with
the user interface, you can design, configure, and polish chart objects that will be built into worksheets.
These worksheets can then be assembled into one or more dashboards.
• Workbook (1): This is the workbook title, the name given to the workbook when you save it
• Toolbar (2): This is where you can save your work, among other functions
• Cards and shelves (3): These are the areas where you can add fields or filters to the visualization
• The View, or the Visualization (4): This is the graph itself
• The ShowMe card (5): This prompts you to create visualization types based on the data selected
• Sheet tabs (6): This allows you to create, rename, or duplicate sheets and dashboards
• The Status bar (7): This shows the aggregated totals of the marks on your visualization
• Data Source (8): Links back to data sources
• The Sidebar (9): This contains both the Data window and the Analytics pane
• The Start button (10): This takes you back to the home screen
Connecting to Data Sources in Tableau
Tableau is capable of connecting with a wide range of data sources. It can connect to files present in
your system, such as Microsoft Excel, text files, JSON, PDF, etc. It can also work on data present on
a database server, such as Microsoft SQL Server, MySQL, Oracle, Teradata, etc. There are other saved
data sources that Tableau can connect with. It also can connect and fetch data from cloud sources, like
AWS, Azure SQL Data Warehouse, and Google Cloud SQL.

65
Now, we need to pull our data from Excel which we had saved earlier for using tableau for data
visualization. To pull in the data, click on Microsoft Excel in the top left corner.
Choose the Excel workbook called Excel 1

Then click on Sheet 1 on the Tableau interface in the left

bottom screen.

66
We open the following screen

Then Hover over the word “Genre”. You will notice a blue oval appear behind it.
Click and drag “Genre” up and to the right, releasing it next to the word Columns when a little orange
arrow appears.
Now drag “Sample (Count)” to Rows, following the same steps as above.
“Sample (Count)” represents the total number of songs in your table

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Tableau made us a bar graph!
What if you want to make each bar a different color?
Simply click and drag “Genre” out to where it says Color.

Tableau colored our genres for us!

Let’s explore another way of visualization

First, we’ll start by duplicating our current bar chart sheet. This will create an exact copy in a new
sheet.
You’ll do this by right clicking “Sheet 1” and selecting “Duplicate”.

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In the upper right corner, click “Show Me”.
We will see all of the different types of visualizations that Tableau can create using Genre and Sheet
Count 1.
Select “Packed Bubbles”.

Tableau quickly transformed our bar chart to a chart of bubbles.

Pop genre is the most popular because it is the biggest circle.
We can make the text a little more fun and easier to read.
To do that, click the label square.

This opens up a box that allows us to change the font and text size and see the result

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The result is the following chart

Lets try a pie chart with the same data. It looks like the below chart.

You may also use Ms Excel or Datawrapper (https://www.datawrapper.de/) for the data visualization
instead of Tableau.

70
 RECAP
Data visualization is the art and science of representing information in a visual format like charts,
graphs, and maps. Tableau is a software that allows users to visually explore and analyze data. It
offers an effective platform for creating visually appealing charts, graphs, and dashboards. These
visualizations can then be shared with others in the organization.

KEY TERMS
● Data visualization is the art and science of representing information in a visual format like charts,
graphs, and maps.
● Tableau Public is a free platform designed for anyone to explore, create, and share data
visualizations publicly online.

AI EXERCISES
A. Multiple choice questions.
1. What is the primary purpose of Tableau Public?
(a) To create interactive dashboards for internal business use.
(b) To analyze and visualize large datasets for advanced data science.
(c) To create and share data visualizations publicly for free.
(d) To collaborate on data analysis projects with a large team.
2. What are some limitations of Tableau Public compared to the paid version of Tableau?
(a) It has fewer data source connectors.
(b) It offers a limited selection of chart types.
(c) Published visualizations cannot be refreshed with updated data.
(d) All of the above.
3. What is an advantage of using Tableau Public for data visualization?
(a) It provides access to powerful statistical analysis tools.
(b) It offers features for hiding sensitive data in visualizations.
(c) It is a free and accessible platform for anyone to use.
(d) It allows for complex data modeling and calculations.
B. Fill in the Blanks.
1. By __________ data, we can easily spot trends, outliers and correlations that might be missed in plain text.
2. ___________ is a software that allows users to visually explore and analyse data.
3. The _________ part of the Tableau software links back to data sources.
4. The __________ part of the Tableau interface takes you back to the home screen.
5. In a bubble chart, the ________ of the bubble represents an attribute of the plotted point.
C. Assertion/Reason Type
1. Assertion (A): MS Excel or DataWrapper can be used instead of Tableau.
Reason (R): The above tools can also be used for data visualisation.

71
 (a) Both A and R are correct and R is the correct reason for statement A
(b) A is true but R is false
(c) A is false and R is true
(d) Both A and R are true but R is NOT the correct explanation for A
2. Assertion (A): It is not possible to make a bar graph of different colors in Tableau.
Reason (R): By dragging and dropping the coloring is possible.
(a) Both A and R are correct and R is the correct reason for statement A
(b) A is true but R is false
(c) A is false and R is true
(d) Both A and R are true but R is NOT the correct explanation for A
3. Assertion (A): You cannot drag and drop a column of data in Tableau.
Reason (R): You can hover the mouse over the column and after he oval appears, drag and drop it.
(a) Both A and R are correct and R is the correct reason for statement A
(b) A is true but R is false
(c) A is false and R is true
(d) Both A and R are true but R is NOT the correct explanation for A
D. Competency Based Questions
1. Raju wants to learn about the software by himself. Since you are expert in Tableau software, give Raju the link
for downloading the software.
2. Ravi was thinking of adding data sources to Tableau from cloud sources. Give Ravi names of three cloud sources
from which addition to Tableau is possible.
3. Rajeeb was thinking of downloading and checking out the DataWrapper software. Since you are technically
good, using your knowledge give Rajeeb the link to access Datawrapper.
4. Ravindra was studying the Tableau software. He was inspecting the interface. As you are his friend and technically
proficient, help Ravindra out with what part are the Cards and Shelves in the interface?
E. Short Answer Questions
1. What does Status Bar do in Tableau?
2. How can you duplicate a sheet using Tableau?
3. What is a difference between free and paid version of the Tableau software?
4. Which element of the Tableau interface contains the Data window and the Analytics pane?
F. Long Answer Questions
1. What is the importance of data visualization?
2. At which stage of the AI project cycle does Tableau software prove useful?
3. Name any five graphs that can be made using Tableau software
4. Describe a real-world scenario where Tableau Public could be a valuable tool.
G. Subject Enrichment

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 ● Is the Year qualitative or quantitative?
● Is Song Length discrete or continuous?
● Is the Genre discrete or continuous?
H. Multiple Assessment
Communication
Activity: Analyzing Movie Genres and Popularity
 ata Source: You can leverage a publicly available dataset of movies and their information, such as the TMDb
D
dataset on Kaggle. This dataset includes details like genre, release date, popularity score, etc.
Steps:
1. Connect to Data: In Tableau Public, connect to the downloaded movie dataset.
2. Explore the Data: Familiarize yourself with the available fields like genre, release year, popularity score, etc.
3. Identify Questions: Brainstorm questions you can answer with this data. Here are some examples:
○ Which movie genres are the most popular across different release years?
○ Is there a correlation between a movie’s budget and its popularity score?
○ How do popularity scores vary across different countries or regions?
4. Create Visualizations: Based on your chosen questions, create different visual elements in Tableau. Here are
some ideas:
○ Bar chart showing average popularity score by genre.
○ Scatter plot to visualize the relationship between budget and popularity score.
○ World map colored by average popularity score per region.
5. Customize your visualizations with appropriate colors, fonts, and titles. Add annotations to highlight key findings
or trends.
6. Share Your Work: Publish your visualizations on Tableau Public and share the link with others.
I. Knowledge Hub Life Skills & Values

https://intellipaat.com/blog/tutorial/tableau-tutorial/
https://www.simplilearn.com/tutorials/tableau-tutorial/what-is-tableau
J. Experiential Learning
https://www.youtube.com/watch?v=NLCzpPRCc7U
https://www.youtube.com/watch?v=_M8BnosAD78

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 Unit 3 : Math for AI (Statistics & Probability)

8 Importance of Math for AI

Learning Objectives
After studying this chapter, students will be able to:
• Understanding the use of Math in AI
• Analyzing the data in the form of numbers/images and finding the relation/pattern between
them.
• Understanding the number patterns and finding the missing number.
• To find connections between set of images and use that to solve problems.

Artificial Intelligence (AI) has emerged as a transformative technology, revolutionizing various aspects
of our lives. Behind the remarkable advancements and capabilities of AI lies the foundational role of
mathematics. Mathematics provides the framework that enables AI systems to learn, reason, and make
intelligent decisions. Mathematics serves as the backbone of AI algorithms and models, empowering
machines to process, analyze, and interpret vast amounts of data.
The application of mathematics in AI is fundamental to the development and success of intelligent
systems. Mathematics provides the tools and concepts necessary for AI algorithms to process data, learn
patterns, and make informed decisions.
• Imagine AI as a smart robot. Just like how our brains help us understand and solve problems,
math is like the brain of AI. It helps AI understand things, make decisions, and learn new stuff.
• Think about when you count how many friends you have. You’re using math! AI does something
similar. It counts things like how many pictures are of cats or dogs to help you find cute animal
pictures online.
• Imagine you’re a detective looking for clues. Math helps AI find clues in data to solve mysteries,
like spotting unusual activity in a game to catch cheaters or helping doctors find signs of sickness
in X-rays.
ROLE OF MATH IN AI
Mathematics plays a crucial role in the development and implementation of artificial intelligence (AI)
systems. Following are some reasons as to why math is important for AI:
1. Algorithms and Modeling: Mathematics provides the foundation for developing algorithms and
models that underpin AI systems. Concepts from calculus, linear algebra, probability theory, and
statistics are used to design and optimize algorithms for tasks such as machine learning, pattern
recognition, and optimization.

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 2. Machine Learning: Machine learning, a core component of AI, relies heavily on mathematical
principles. Linear algebra is used to represent and manipulate data in the form of matrices and
vectors, while calculus is used to optimize model parameters through techniques like gradient
descent. Probability theory is essential for understanding uncertainty and making probabilistic
predictions in machine learning models.
3. Data Analysis and Interpretation: AI systems analyze and interpret vast amounts of data to make
predictions, recommendations, and decisions. Mathematical techniques such as statistical analysis,
hypothesis testing, and regression analysis are used to extract insights from data, identify patterns,
and evaluate the performance of AI models.
4. Optimization and Control: Optimization techniques are used to improve the performance and
efficiency of AI systems. These techniques are applied in various contexts, such as parameter
tuning in machine learning models, resource allocation in optimization problems, and control
strategies in autonomous systems.
5. Simulation and Modeling: Mathematical modeling and simulation are essential tools for
understanding complex systems and predicting their behavior. AI researchers use mathematical
models to simulate the behavior of AI systems, analyze their performance under different
conditions, and validate their effectiveness before deployment.
A strong foundation in mathematics is essential for anyone working in the field of artificial intelligence,
enabling them to develop innovative solutions and advance the capabilities of AI technology.

Wolfram Alpha: Dubbed the “computational knowledge

engine,” Wolfram Alpha employs AI algorithms to
provide detailed answers to a wide range of mathematical
queries. Whether you need to solve an integral, find the
roots of an equation, or explore complex mathematical
concepts, this tool has got you covered. Visit https://www.
wolframalpha.com/

USES OF MATH
Mathematics serves as the backbone for several key fields, including statistics, linear algebra, probability
theory, and calculus. Mathematics plays a crucial role in statistics, linear algebra, probability theory, and
calculus, providing the theoretical foundation and analytical tools necessary for understanding and solving
a wide range of real-world problems in fields such as science, engineering, finance, and data analysis.
1. Statistics:
• Descriptive Statistics: Mathematics is used to summarize and describe data using measures such
as mean, median, mode, variance, and standard deviation.
• Inferential Statistics: Mathematical principles, such as probability distributions and hypothesis
testing, are applied to make inferences and draw conclusions about populations based on sample
data.
• Regression Analysis: Mathematical techniques are used to model and analyze the relationship
between variables in data sets.

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2. Linear Algebra:
• Matrix Operations: Linear algebra is used to perform operations such as matrix multiplication,
addition, and inversion, which are fundamental for solving systems of linear equations and
representing transformations in space.
• Vector Spaces: Linear algebra provides the framework for studying vector spaces, subspaces,
and linear transformations, which have applications in computer graphics, cryptography, and
optimization.
3. Probability Theory:
• Probability Distributions: Mathematics is used to define and analyze different types of probability
distributions, such as the normal distribution, binomial distribution, and Poisson distribution, which
describe the likelihood of different outcomes in random experiments.
• Bayesian Inference: Probability theory provides the foundation for Bayesian inference, a statistical
method for updating beliefs and making predictions based on prior knowledge and observed
evidence.
4. Calculus:
• Derivatives: Calculus is used to compute derivatives, which represent rates of change and are
essential for optimization, curve fitting, and modeling dynamic systems.
• Integrals: Integrals are used to calculate areas, volumes, and accumulated quantities, as well as
to solve differential equations that model physical phenomena.
FINDING PATTERNS IN NUMBERS
Finding patterns in numbers is like uncovering hidden rules or regularities within numerical sequences
or sets of numbers. Whether you’re analyzing the digits of pi, deciphering sequences in mathematics, or
exploring numerical patterns in real-world datasets, finding patterns in numbers is a fascinating journey
that combines observation, analysis, and creativity.
Here’s the process :
1. Observation: Start by looking at the numbers and trying to see if there are any obvious
repetitions, sequences, or relationships between them.
2. Identifying Regularities: Once you’ve observed the numbers, try to identify any consistent
patterns. These could be as simple as adding or subtracting the same number each time, or they
could be more complex, like following a specific mathematical formula.
3. Mathematical Analysis: Use math to describe and understand these patterns. This might involve
arithmetic operations (like addition, subtraction, multiplication, or division), algebraic equations,
geometric sequences, or even more advanced mathematical concepts.
4. Generalization: Once you’ve found a pattern in a set of numbers, see if you can generalize it to
predict future numbers or understand how the pattern might continue or change over time.
5. Verification: Test your hypothesis by applying the pattern to new sets of numbers or by checking
if it holds true for additional data points.
6. Application: Finally, consider how you can use the discovered patterns. This might involve
predicting future values, optimizing processes, solving problems, or gaining insights into the
underlying structure of the data.

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 ACTIVITY Creativity

Finding Patterns in the Fibonacci Sequence

Let‛s explore finding patterns in numbers with a fun activity using a simple sequence of numbers.
We‛ll use the Fibonacci sequence, which is a famous sequence of numbers found by adding the
two preceding numbers to form the next one. The sequence starts with 0 and 1, and then each
subsequent number is the sum of the two preceding ones. So, it goes like this: 0, 1, 1, 2, 3, 5,
8, 13, 21, and so on.
1. One student should write down the first few numbers of the Fibonacci sequence on the board
or a piece of paper: 0, 1, 1, 2, 3, 5, 8, 13, 21.
2. Pattern Recognition: Now observe the sequence and look for any patterns or regularities.
l Do you notice any relationships between the numbers?
l How is each number related to the previous ones?
lAre there any trends in how the numbers increase?
3. The students should now
l write down the next few numbers in the Fibonacci sequence.

l express the Fibonacci sequence using mathematical notation or formulas.

Discuss real-world examples where Fibonacci numbers or sequences appear, such as in the growth
patterns of plants or the arrangement of petals in flowers.
Encourage students to explore variations of the Fibonacci sequence, such as starting with different
initial numbers or using different rules for generating the sequence. This can foster creativity
and critical thinking.
Here are a few number pattern puzzles that you can use for your activity:
1. Arithmetic Sequence:
l 2, 5, 8, 11, ?, 17
Sol. The pattern increases by 3 each time. So, the missing number is 14.
2. Geometric Sequence:
l 3, 9, 27, ?, 243
Sol. Each number is multiplied by 3 to get the next number. So, the missing number is 81.
3. Prime Numbers:
l 2, 3, 5, 7, ?, ?

Sol. The missing numbers are the next two prime numbers, which are 11 and 13.
4. Squares:
l 1, 4, 9, 16, 25, ?, ?

Sol. Each number is a perfect square. The missing numbers are 36 and 49.

DID YOU KNOW ?

Designed for younger learners, DragonBox employs clever game
mechanics to introduce fundamental algebraic concepts without
overwhelming students. By gradually increasing the complexity
of puzzles and problems, this app builds a strong foundation in
algebra while keeping young minds entertained. Visit https://
dragonbox.com/

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FINDING PATTERNS IN IMAGES
Picture analogies are a fun and creative way to find connections between sets of images and use them
to solve problems or draw conclusions. Picture analogies offer a creative and engaging way to explore
connections between visual elements and apply them to problem-solving or decision-making tasks. They
encourage lateral thinking, pattern recognition, and associative reasoning skills.
Here’s how you can approach this:
1. Identify Sets of Images: Start by gathering sets of images that share common characteristics or
themes. For example, you might have sets of images representing different categories such as
animals, transportation, food, or geometric shapes.
2. Analyze the Relationships: Examine each set of images closely and look for patterns, similarities,
or relationships between them. Identify common attributes, features, or concepts that are shared
among the images in each set.
3. Establish Analogies: Once you’ve analyzed the sets of images, try to establish analogies or
connections between them based on the relationships you’ve identified. Look for associations or
similarities that suggest a logical connection between the images within each set.
4. Solve Problems or Draw Conclusions: Apply the analogies you’ve established to solve problems or
draw conclusions based on new sets of images or scenarios. Use the connections you’ve identified
to make predictions, generate hypotheses, or infer information about unfamiliar images.
ROLE OF MATH AND AI IN PICTURE ANALOGIES
When we look at an image, our brains are really good at recognizing patterns like shapes, colors, and
objects. But for computers, it’s not as easy. That’s where math and AI come in.
Imagine you have a bunch of pictures. Each picture is made up of tiny dots called pixels. These pixels
have different colors that form the image you see. Now, finding patterns in these images using math
and AI is like teaching a computer to recognize these patterns automatically.
First, we use math to describe how these pixels are arranged and what colors they have. We create
formulas and equations to understand these patterns.
Then comes AI, which stands for Artificial Intelligence. This is like giving the computer a brain to learn.
We feed the computer lots of pictures and tell it what patterns we want it to find. The computer then
looks for similarities and differences in the pictures based on the math we taught it.
So, math helps us understand the patterns, and AI helps us find them automatically.
1. Breaking Down the Image: First, we break down the image into smaller pieces called pixels.
Each pixel has a color value, like red, green, or blue.
2. Feature Extraction: We use math to describe these pixels and their relationships. For example, we
might look at how colors change from one pixel to the next, or how certain shapes are formed.
3. Pattern Recognition: Then, we use AI algorithms to analyze these descriptions and find patterns.
This could involve comparing the features of different images to see if they’re similar or looking
for specific shapes or colors that we’re interested in.
4. Training the AI: To do this effectively, we need to train the AI model with lots of examples.
We show it images with the patterns we want it to recognize, and it learns from these examples,
adjusting its calculations to become better at finding those patterns.
5. Application: Once the AI model is trained, we can use it to analyze new images and
automatically detect the patterns we’re interested in, whether that’s faces in photos, signs in
satellite images, or anything else we can think of!

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So, finding patterns in images is all about using math to describe the visual information and AI to
analyze it and identify the patterns we’re looking for. It’s a powerful combination that allows computers
to see and understand the world around us in a way that was once only possible for humans.

ACTIVITY Creativity

Activity: Creating Picture Analogies with Math

Objective: To create picture analogies using mathematical concepts and principles.
Materials Needed:
l Paper and drawing materials (markers, colored pencils, etc.)

l Math reference materials (such as geometric shapes, number charts, or equations)

Instructions:
1. Divide the class into small groups.
2. Provide each group with a set of math reference materials and drawing materials.
3. Encourage students to brainstorm ideas for their picture analogies based on mathematical
relationships, such as symmetry, geometric patterns, numerical sequences, or mathematical
operations.
4. Instruct students to sketch out their analogies on paper or digitally, incorporating mathematical
elements into their designs.
5. Each group should present their picture analogies to the class and explain the mathematical
concepts and relationships they used in their designs.
Facilitate a discussion about the different approaches and creative ideas presented by the groups.
Encourage students to reflect on how mathematical concepts can be applied creatively in visual
representations.
Here are some example of picture analogy puzzles for you to solve:
Puzzle 1:
Set 1: Circle, Triangle, Square
Set 2: Apple, Banana, Orange
Set 3: Sun, Moon, Star
What comes next in each set? Pentagon
Set 1: Circle, Triangle, Square -> Shapes with increasing number of sides
Puzzle 2:
Set 1: Cat, Dog, Rabbit
Set 2: Car, Bicycle, Motorcycle
Set 3: Winter, Spring, Summer
What comes next in each set?
Puzzle 3:
Set 1: Blue, Green, Red
Set 2: Circle, Square, Triangle
Set 3: Elephant, Lion, Giraffe
What comes next in each set?
Puzzle 4:
Set 1: Book, Pen, Notebook; Set 2: Chair, Table, Lamp; Set 3: Watermelon, Pineapple, Grape
What comes next in each set?

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In the past, struggling with difficult math problems often meant seeking help from human tutors or
spending hours poring over textbooks. However, with the advent of virtual math tutors powered by artificial
intelligence, personalized assistance is just a few clicks away. These virtual tutors use AI algorithms to
adapt to your learning pace and style, ensuring an effective and personalized learning experience.

RECAP
Mathematics serves as the backbone of AI algorithms and models, empowering machines to
process, analyze, and interpret vast amounts of data. Concepts from linear algebra, calculus,
probability theory, and statistics are essential for developing machine learning algorithms.
Finding patterns in numbers is like uncovering hidden rules or regularities within numerical
sequences or sets of numbers. Whether you’re analyzing the digits of pi, deciphering sequences in
mathematics, or exploring numerical patterns in real-world datasets.

KEY TERMS
● Finding patterns in numbers is like uncovering hidden rules or regularities within numerical
sequences or sets of numbers.
● Picture analogies are a fun and creative way to find connections between sets of images and use
them to solve problems or draw conclusions.

AI EXERCISES
A. Multiple choice questions.
1. Calculus is used to compute ___________, which represent rates of change.
(a) Regression Analysis (b) Linear Algebra (c) Set Theory (d) Calculus
2. Which of the following completes the analogy?
Tree : Leaves :: Flower : ?
(a) Stems (b) Petals (c) Roots (d) Branches
3. Which of the following completes the analogy?
If Shoe : Foot :: Glove : ?
(a) Hand (b) Finger (c) Wrist (d) Head
4. Find the missing number in the series: 1, 4, 9, ?, 25
(a) 12 (b) 13 (c) 16 (d) 15
B. Fill in the Blanks.
1. Fibonacci sequence is a sequence of _________ .
2. Linear Algebra is a branch of Mathematics that involves _________ operations and _______ spaces.
3. 11, 22, 33, 44, 55 – Can you find out the middle value from the given numbers?_______ CBSE Handbook

4. ____________ techniques are used to improve the performance and efficiency of AI systems.

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 5. Poisson distribution describes the likelihood of different outcomes in __________ experiments.
6. Probability theory provides the foundation for __________ inference, a statistical method for updating beliefs
and making predictions based on prior knowledge and observed evidence.
7. _____________ is dubbed the “computational knowledge engine”.
8. A has 2 plants, B has 3 plants, C has 1 plant, D has 7 plants. How many plants are there in total? _____________
CBSE Handbook
C. Assertion/Reason Type
1. Assertion (A): Between Apple, Banana, Orange and Strawberry, Banana is the odd one.
Reason (R): Banana is the odd one as it is yellow while the other fruits are red or orangish.
(a) Both are correct and R is the correct explanation (b) A is true and R is false
(c) A is false and R is true
(d) Both are true but R is not the correct reason for A
2. Assertion (A): The Fibonacci sequence is an increasing sequence of numbers.
Reason (R): The first few numbers are 0, 1, 1, 2, 3, which is the same for 1 and 1.
(a) Both are correct and R is the correct explanation (b) A is true and R is false
(c) A is false and R is true
(d) Both are true but R is not the correct reason for A
3. Assertion (A): Mathematics provides the framework for cryptography.
Reason (R): Linear Algebra is a branch of Mathematics which have applications in computer graphics and
cryptography.
(a) Both are correct and R is the correct explanation (b) A is true and R is false
(c) A is false and R is true
(d) Both are true but R is not the correct reason for A
4. Assertion (A): Mathematics is a field of study that can be used to describe data using measures.
Reason (R): Descriptive statistics is used to summarize and describe data using measures such as mean, median,
mode, variance and standard deviation.
(a) Both are correct and R is the correct explanation (b) A is true and R is false
(c) A is false and R is true
(d) Both are true but R is not the correct reason for A
D. Competency Based Questions
1. Amit wants to know who uses mathematical models to simulate the behavior of AI systems, analyse their
performance under different conditions, etc. Help Amit out.
2. Shaily wants to learn which subject or field of study is helpful in designing experiments and surveys to collect
data efficiently and draw valid statistical conclusions. Help Shaily out.
3. Krushna is trying to find the next shape out of:
Line, Angle, Triangle, Square, …..
What is the reasoning by which Krushna can find the answer to this question?
4. Avik is wondering what the normal, binomial and Poisson distributions are, collectively. What is the point of
going through these terms in Maths? Help Avik out.
E. Short Answer Questions
1. What is feature extraction?
2. What is pattern recognition?
3. Give any two examples of geometric sequences.
4. Using squares of numbers, what is the next term in the sequence: 2, 5, 10, 17, ….
5. How do you make out that a certain sequence is an arithmetic sequence?

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F. Long Answer Questions
1. How does mathematics serve as the foundation for developing algorithms and models in artificial intelligence
(AI)? Provide examples to support your answer.
2. Explain a few applications of Math in AI.
3. Choose the odd one out of:
(a) 2, 8, 10, 9 (b) 3, 6, 9, 27, 29 (c) -1, -5, -7, 21
G. Subject Enrichment Subject Enrichment
Present students with a fictional scenario where a group of AI scientists has encountered a mysterious problem in
their AI system. The AI, which was designed to predict weather patterns, suddenly started giving inaccurate forecasts.
The scientists suspect that there’s a mathematical error in the AI’s algorithms, but they need the students’ help to
solve the mystery.
Problem Statement: “The AI system receives data about temperature, humidity, and wind speed to predict weather
patterns. However, despite having accurate data inputs, the AI’s predictions have become unreliable. Your task is
to investigate possible mathematical errors in the AI’s algorithms and propose solutions to fix the problem.”
● Divide students into small groups and encourage them to brainstorm possible mathematical reasons for the
AI’s errors.
● Research relevant mathematical concepts and analyze how they could be applied to the AI’s algorithms. Discuss
potential sources of error and ways to mitigate them using mathematical techniques.
● Each group presents their solution proposals to the class, explaining their reasoning and addressing any
questions or challenges raised by their peers.
H. Multiple Assessment Communication
1. Discuss the following in class
● One popular movie that showcases the use of math for AI is “A Beautiful Mind” (2001), which is a biographical
drama about the mathematician John Nash. While the movie primarily focuses on Nash’s struggles with
schizophrenia and his contributions to game theory, it also touches upon his work in mathematics, which
laid the groundwork for various applications in AI and computer science.
● One movie that portrays the intersection of mathematics and AI is “Moneyball” (2011). While the primary
focus of the film is on baseball and statistics, it demonstrates how mathematical analysis and data-driven
decision-making revolutionized the sport. In the movie, the Oakland Athletics baseball team employs
statistical analysis, known as sabermetrics, to assemble a competitive team on a limited budget. This
application of mathematics in sports management parallels how AI algorithms utilize data analysis and
mathematical models to make informed decisions in various fields.
2. Discuss on any of the following in class
● The Impact of Math Education on AI Innovation
● The Role of Mathematics in AI Accountability
● The Potential of Math-Driven AI for Social Good

I. Knowledge Hub Life Skills & Values

https://www.smartpaperapp.com/post/ai-powered-personalized-learning-transforming-education-in-the-digital-
age
https://indiaai.gov.in/article/mathematics-and-its-essential-role-in-ai
https://builtin.com/articles/math-for-ai
J. Experiential Learning
https://youtu.be/pZBNdGMG1rQ
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 Unit 3 : Math for AI (Statistics & Probability)

9 Statistics

Learning Objectives
After studying this chapter, students will be able to:
• Understand the concept of Statistics in real life
• Knowing more about the application of Statistics in various real life scenarios

STATISTICS
Statistics is the branch of
mathematics that deals with
data collection, analysis,
interpretation, presentation, and
organization.
1. Descriptive Statistics:
l Descriptive statistics involve methods for summarizing and describing characteristics of a data

set. It focuses on organizing, summarizing, and presenting data in a meaningful way to provide
insights into its key features. Descriptive statistics are used to provide a snapshot of the data,
allowing researchers to understand its basic properties and characteristics without drawing
conclusions beyond the data itself.
l Examples of descriptive statistics include calculating the average income of a population,

summarizing the distribution of test scores in a classroom, or presenting the frequency of different
responses in a survey.
2. Inferential Statistics:
l Inferential statistics involve methods for making inferences and generalizations about a population

based on sample data. It focuses on drawing conclusions, making predictions, and testing
hypotheses about the population parameters using sample statistics.
l Inferential statistics allow researchers to generalize their findings from the sample to the larger

population and make predictions or draw conclusions about the population based on the observed
sample data.
l Examples of inferential statistics include testing whether a new drug treatment is effective based

on clinical trial data, estimating the mean income of a population from a sample survey, or
determining if there is a significant difference in test scores between two groups of students.

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Descriptive statistics are used to summarize and describe data, while inferential statistics are used to
draw conclusions and make predictions about populations based on sample data. Both branches of
statistics play essential roles in analyzing and interpreting data in various fields, including science,
business, social sciences, and beyond.
STATISTICAL METHODS IN ARTIFICIAL INTELLIGENCE
Regression analysis is a statistical method used in AI to identify the relationship between a dependent
variable and one or more independent variables. The method is used in AI to model and predict
outcomes based on a set of input variables.
Bayesian Statistics is a statistical method used in AI to estimate the probability of an event based
on prior knowledge and new data. The method is used in AI to classify data, make predictions, and
optimize decision making.
Machine learning algorithms are statistical methods used in AI to learn from data without being explicitly
programmed. The algorithms are used in AI to identify patterns, classify data, and make predictions.
Neural networks are a type of machine learning algorithm used in AI to mimic the structure and
function of the human brain. Neural networks are used in AI for image and speech recognition, natural
language processing, and robotics.
APPLICATIONS OF STATISTICS IN ARTIFICIAL INTELLIGENCE
Natural language processing (NLP) is a field of AI that deals with the interaction between computers
and humans using natural language. Statistics is used in NLP to understand and interpret the meaning
of natural language, classify text, and generate responses.
Computer vision is a field of AI that deals with the interpretation of visual data from the world.
Statistics is used in computer vision to classify images, recognize objects, and track movements.
Robotics is a field of AI that deals with the design, construction, operation, and use of robots. Statistics
is used in robotics to control robot movements and optimize robotic systems.
Data analytics is a field of AI that deals with the analysis of large and complex data sets. Statistics is
used in data analytics to identify patterns, trends, and relationships in the data.
GENERAL APPLICATIONS OF STATISTICS
Statistics is used in various fields such as science, social sciences,
business, economics, engineering, and medicine to:
l Describe and summarize data using measures such as mean,

median, mode, variance, and standard deviation.

l Make inferences about populations based on sample data

using techniques such as hypothesis testing and confidence

intervals.
l Model and analyze relationships between variables using

regression analysis and correlation.

l Understand uncertainty and variability in data using

probability distributions and statistical inference.

Statistics play a crucial role in various aspects of disaster management, sports, disease prediction,
and weather forecasting. Here are some applications in each domain:
1. Disaster Management:
l Risk Assessment: Statistics is used to assess the probability and potential impact of natural

disasters such as earthquakes, hurricanes, and floods. It helps in identifying high-risk areas and
developing mitigation strategies.
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 l Resource Allocation: Statistics informs decision-making regarding the allocation of resources such
as emergency personnel, supplies, and equipment during disaster response and recovery efforts.
l Damage Assessment: Statistical methods are employed to quantify and analyze the extent of
damage caused by disasters, aiding in prioritizing response efforts and allocating resources
effectively.
2. Disease Prediction:
l Epidemiological Studies: Statistics is used to analyze disease patterns, risk factors, and

transmission dynamics through epidemiological studies. It helps in identifying trends, clusters, and
predictors of disease occurrence.
l Surveillance Systems: Statistical methods are employed to monitor and analyze disease

surveillance data, including case counts, incidence rates, and spatial-temporal patterns, to detect
outbreaks and monitor disease trends over time.
l Predictive Modeling: Statistics is utilized to develop predictive models for disease forecasting,

estimating future disease burden, and assessing the impact of interventions such as vaccination
campaigns and public health policies.
l Forecasting Outbreaks: Statistical models are employed to forecast disease outbreaks, estimate

transmission rates, and assess the impact of interventions, enabling authorities to implement timely
control measures and allocate resources effectively.
3. Weather Forecasting:
l Data Analysis: Statistics is used to analyze

meteorological data collected from weather

stations, satellites, and other sources to
understand atmospheric patterns, trends, and
variability.
l Forecast Models: Statistical models, such

as numerical weather prediction (NWP)

models, are used to simulate atmospheric
processes and generate weather forecasts
for different time scales, ranging from
short-term (hours to days) to medium-term
(weeks to months).

Know More

Symbolab: Symbolab is another powerful

calculator that uses AI to solve and explain step-
by-step solutions for various math problems.
It covers topics from algebra and calculus
to statistics and trigonometry, making it a
comprehensive resource for students at all levels.
Visit https://www.symbolab.com/

85
 ACTIVITY Creativity

Activity: Statistical Analysis of Survey Data

Objective: To illustrate how statistics are used in real-life situations by analyzing and interpreting
survey data.
Materials Needed:
l Survey questionnaire (create your own or use a pre-made survey)

l Access to a survey platform (e.g., Google Forms, SurveyMonkey) for data collection

l Spreadsheet software (e.g., Microsoft Excel, Google Sheets) for data analysis

l Projector or whiteboard for presentation

Instructions:
l Prepare a survey questionnaire on a topic of interest that is relevant to the students’ daily

lives (e.g., social media usage, favorite foods, transportation preferences).

l Create the survey using a survey platform and distribute the survey link to the students to

collect responses.
l Allow time for students to respond to the survey and collect a sufficient number of responses

(at least 50 responses are recommended for meaningful analysis).

l Once data collection is complete, compile the survey responses into a spreadsheet software

for analysis.
l Guide students through the process of cleaning and organizing the data, including removing

any outliers or incomplete responses.

l Use descriptive statistics (e.g., frequencies, percentages, averages) to summarize the survey

data and identify key trends and patterns.

l Discuss the results with students, highlighting interesting findings and interpreting what the

data reveal about the topic of interest.

l Create visual representations of the survey data using charts and graphs (e.g., bar charts,

pie charts, histograms).

This activity provides students with hands-on experience in collecting, analyzing, and interpreting
real-life data, demonstrating the practical applications of statistics in everyday situations. It also
promotes critical thinking skills and encourages students to become informed consumers of data
and information.

SIMPLE STATISTICAL CONCEPTS

Data is the foundation of Artificial Intelligence and to understand and analyse data, statistics is the
key. This chapter only introduces you to statistics for the perspective of the Artificial Intelligence and
Machine Learning
It refers to techniques or methods relating to collection, classification, presentation analysis and
interpretation of quantitative data. It’s the science of data, which is in fact a collection of mathematical
techniques that helps to extract information from data.
CENTRAL TENDENCY
Usually, Statistics deals with large datasets (population of a country, country wise number of infected
people from CORONA virus and similar datasets). For the understanding and analysis purpose, we need
a data point, be it a number or set of numbers, which can represent the whole domain of data and this
data point is called the central tendency.

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 Central tendency is a descriptive summary of a
dataset through a single value that reflects the
center of the data distribution. A measure of
central tendency is a single value that attempts to
describe a set of data by identifying the central
position within that set of data. Central tendency
does not talk about individual values in the datasets but it gives a comprehensive summary of the whole
data domain.
MEASURES OF CENTRAL TENDENCY
The central tendency of the dataset can be found out using the three important measures namely mean,
median and mode.
Mean
Arithmetic mean represents a number that is obtained by dividing the sum of the elements of a set by
the number of values in the set. The mean represents the average value of the dataset. The formula to
calculate the mean value is given as:
If we have n values in a data set and they have values x1, x2, x3…, the sample mean,
M = (x1 + x2 + x3…xn)/n
And if we need to calculate the mean of a grouped data,
M= ∑fx/n
Where M = Mean
∑ = Sum total of the scores
F = Frequency of the distribution
x = Scores
n = Total number of cases
Example:
To calculate the mean of the runs scored by Virat Kohli in the last few innings, all you would have
to do is sum up his runs to obtain the total and then divide it by the number of innings. For example;
Innings 1 2 3 4 5 6 7 8 9 10
Runs 50 59 90 8 106 117 59 91 7 74

The mean of Virat Kohli’s batting scores also called his Batting Average is;
661
Sum of runs scored/Number of innings = = 66.1
10
CALCULATING THE MEAN FROM GROUPED DATA
1. In Tim’s school, there are 25 teachers. Each teacher travels to school every morning in his or her
own car. The distribution of the driving times (in minutes) from home to school for the teachers
is shown in the table below:
Driving Times (minutes) Number of Teachers
0 to less than 10 3

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 10 to less than 20 10
20 to less than 30 6
30 to less than 40 4
40 to less than 50 2

The driving times are given for all 25 teachers, so the data is for a population. Calculate the mean of
the driving times.
Answer:
To better represent the problem and its solution, a table can be drawn as follows:
Driving Times (minutes) Number of Teachers f Midpoint of Class m Product mf
0 to less than 10 3 5 15
10 to less than 20 10 15 150
20 to less than 30 6 25 150
30 to less than 40 4 35 140
40 to less than 50 2 45 90
∑mf
For the population, N = 25 and ∑mf = 545, so using the formula µ = , the mean would again be
N
545
µ = = 21.8.
25
Step 1: Determine the midpoint for each interval.
Step 2: Multiply each midpoint by the frequency for the class.
Step 3: Add the results from Step 2 and divide the sum by 25.
15 + 150 + 150 + 140 + 90 = 545
545
   µ = = 21.8.
25

Each teacher spends a mean time of 21.8 minutes driving from home to school each morning.
Note :
The arithmetic mean can be negative. The data can be distributed anywhere. So, the mean value can
be negative or positive or zero.

Median
It is the positional value of the variables which divides the group into two equal parts, one part
comprising all values greater than median and the other part smaller than median.
The “median” is the “middle” value in the list of numbers. To find the median, your numbers have to
be listed in numerical order from smallest to largest, so you may have to rewrite your list before you
can find the median.
In case of ungrouped data, the scores are arranged in order of size. Then the midpoint is found
out, which is the median. In this process two situations arise in computation of median, (a) N is
odd (b) N is even
(a) N is odd

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Step by Step Process for Finding the Median -
Step 1: Put the numbers in numerical order from smallest to largest.
Step 2: If there is an odd number of numbers, locate the middle number so that there is an equal number
of values to the left and to the right. If there is an even number of numbers locate the two middle
numbers so that there is an equal number of values to the left and to the right of these two numbers.
Step 3: If there is an odd number of numbers, this middle number is the median. If there is an even
number of numbers add the two middles and divide by 2. The result will be the median.
Find the median for the following list of values:
13, 18, 13, 14, 13, 16, 14, 21, 13
The median is the middle value, so first I’ll have to rewrite the list in numerical order:
13, 13, 13, 13, 14, 14, 16, 18, 21
There are nine numbers in the list, so the middle one will be the (9 + 1) ÷ 2 = 10 ÷ 2 = 5th number:
13, 13, 13, 13, 14, 14, 16, 18, 21 …… So the median is 14.
(b) N is even
In your class, 5 students scored following marks in the unit test mathematics, find median value: 11,
11, 14, 18, 20, 22
Solution:
They are already in order - 11, 11, 14, 18, 20, 22
Total count is in even number, so
(14 + 18)
Median is the average of the two-middle number = 16.
2
CALCULATING MEDIAN FROM GROUPED DATA
Calculation of a median in continuous series involves the following steps:
(i) The data arranged in ascending order of their class interval.
(ii) Frequencies are converted into commutative frequencies
(iii) Median class of the series is identified
(iv) Formula used to find actual median value And the formula is:
n
− c. f
And the formula is : Median = l1 + 2 ×i
f
l1 = Lower limit of median class
c.f = Cumulative frequency of the class preceding the median class
f = Frequency of the median class
i = Class size
Mode
The mode is the value that appears most frequently in a data set. To find the mode, or modal value,
it is best to put the numbers in order. Then count how many of each number. A number that appears
most often is the mode. On a histogram it represents the highest bar in a bar chart or histogram. A set
of data may have one mode, more than one mode, or no mode at all.
More Than One Mode
We can have more than one mode.
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Example: {1, 3, 3, 3, 4, 4, 6, 6, 6, 9}
3 appears three times, as does 6.
So there are two modes: at 3 and 6
Having two modes is called “bimodal”.
Having more than two modes is called “multimodal”.
To Calculate the mode, different methods are described below -
(i) Inspection Method :
Under this method, the value of mode or the modal class is determined by simple inspection of the
distribution. The value which is observed to have occurred for maximum times, or the value (or the
class of values) against which the maximum frequency stands is taken as the modal value , or the modal
class. When more than one value, or class appear with the same maximum frequency, all such values
or the classes are taken as the modal values or the modal classes. In such cases, the series is marked
as a bimodal, trimodal, or multimodal series in accordance with the number of modal values the series
possesses.
Example:
Age of 15 students of a class
Age (years) 22, 24, 17, 18, 17, 19, 18, 21, 20, 21, 20, 23, 22, 22, 22,22,21,24
We arrange this series in ascending order as 17,17,18,18,19,20,20,21,21,22,22,22,
An inspection of the series shows that 22 occurs most frequently
Mode = 22
(ii) Mode for Frequency Distribution
Here we have to find a modal class. The modal class is the one with the highest frequency value. The
class just before the modal class is called the pre-modal class. Whereas, the class just after the modal
class is known as the post-modal class. Lastly, the following formula is applied for calculation of mode:
Mode = l + h [(f1–f0)/(2f1–f0–f2]
Here, l = The lower limit of the modal class
f1 = Frequency corresponding to the modal class,
f2 = Frequency corresponding to the post-modal class,
and f0 = Frequency corresponding to the pre-modal class
Comparison :
Mean Median Mode
The mean is a good measure of the The median is a good measure of Mode is used when you need to find
central tendency when a data set the central value when the data the distribution peak and peak may
contains values that are relatively include exceptionally high or low be many.
evenly spread with no exceptionally values. The median is the most For example, it is important to print
high or low values. suitable measure of average for data more of the most popular books;
classified on an ordinal scale. because printing different books
in equal numbers would cause a
shortage of some books and an
oversupply of others.

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 ACTIVITY Creativity

Activity: Car Spotting and Data Analysis

Objective: To engage students in collecting, analyzing, and interpreting data related to car spotting
to understand the concepts of data collection, analysis, and interpretation.
Materials Needed:
l Paper or laptop for recording data
l Pens or pencils
l Access to a spreadsheet software (e.g., Microsoft Excel, Google Sheets) for data analysis
l Projector or whiteboard for presentation
Instructions:
1. Data Collection :
l Divide students into small groups and provide each group with a designated area for car
spotting (e.g., near the school, in a parking lot).
l Instruct students to observe and record data about the cars they spot, including make,
model, color, year, and any additional features or observations.
l Set a time limit for data collection (e.g., 30 minutes) and encourage students to collect
as much data as possible during that time.
2. Data Analysis:
l After the data collection phase, reconvene as a class and compile the data from all groups

into a spreadsheet software.

l Guide students through the process of cleaning and organizing the data, including removing

any inconsistencies or errors.

l Use descriptive statistics (e.g., frequencies, percentages) to summarize the data and

identify patterns and trends in car types, colors, and other characteristics.
l Create visual representations of the data using charts and graphs to help visualize the

findings.
3. Interpretation and Discussion:
l Lead a discussion with students to interpret the findings of the data analysis.

l Encourage students to discuss any notable patterns or trends they observed in the data

and speculate about possible reasons behind them.

l Prompt students to think critically about the limitations of the data and potential biases

that may have influenced the results.

RECAP
Statistics is the branch of mathematics that deals with data collection, analysis, interpretation,
presentation, and organization. Statistical methods are used to analyze and interpret data to make
inferences about populations from samples. AI relies heavily on statistical methods to learn from
data and make predictions. Statistical methods enable AI systems to detect patterns, identify
relationships, and infer conclusions from data. Statistics play a crucial role in various aspects of
disaster management, sports, disease prediction, and weather forecasting.

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KEY TERMS
● Statistics is the branch of mathematics that deals with data collection, analysis, interpretation,
presentation, and organization.
● Descriptive statistics involve methods for summarizing and describing characteristics of a data
set. It focuses on organizing, summarizing, and presenting data in a meaningful way to provide
insights into its key features.
● Arithmetic mean represents a number that is obtained by dividing the sum of the elements of
a set by the number of values in the set.
● Median is the positional value of the variables which divides the group into two equal parts, one
part comprising all values greater than median and the other part smaller than median.

AI EXERCISES
A. Multiple choice questions.
1. Statistics plays a crucial role in AI because it helps:
(a) Make computers think like humans. (b) Identify patterns and relationships within data.
(c) Increase the processing speed of AI algorithms. (d) Give AI systems emotions and feelings.
2. In the movie “Moneyball,” Billy Beane uses sabermetrics, a statistical approach to evaluate baseball players.
This is an example of how statistics can be used in AI for:
(a) Natural Language Processing (NLP) (b) Computer Vision
(c) Machine Learning (d) Robotics.
3. Which kind of analysis is a statistical method used in AI to identify the relationship between a dependent
variable and one or more independent variables?:
(a) Regression analysis (b) Naïve Bayes (c) Bayesian statistics (d) None of these
4. _____________ is a descriptive summary of a dataset through a single value that reflects the center of the
data distribution.
(a) Regression (b) Correlation (c) Central tendency (d) None of the above
5. The __________ is the value that appears most frequently in a data set.
(a) Mode (b) Median (c) Mean (d) None of the above
B. Fill in the blanks.
1. ___________ is used to assess the probability and potential impact of natural disasters such as earthquakes,
hurricanes and floods.
2. Statistics is utilized to develop __________ models for disease forecasting, estimating future disease burden
and assessing the impact of interventions such as vaccination campaigns and public health policies.
3. According to _________, “Statistics are numerical statements of facts in any department of enquiry placed in
relation to each other.”
4. __________ is particularly susceptible to the influence of outliers.
5. In the formula:
µ = ∑mf /N
m stands for the __________ of class m.

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C.
Assertion/Reason Type
1. Assertion (A): The median is the middle value in the list of numbers.
Reason (R): The formula for median of a grouped data is:
Median = l1 + (n/2 -c.f.)/f x i
(a) Both the statements are true and R is the reason behind A
(b) Statement A is true and R is false
(c) Statement A is false and R is true
(d) Both the statements are false
2. Assertion (A): The variance formula measures how far a set of numbers are spread out.
Reason (R): Variance is the mean of squares of differences between all numbers and means.
(a) Both the statements are true and R is the reason behind A
(b) Statement A is true and R is false
(c) Statement A is false and R is true
(d) Both the statements are false
D. Competency Based Questions
1. Avishek is not much of a book reader. He just saw the term “Central Tendency” in a document and wants to
know what it means. As a friend of Avishek and knowledgeable about the term, help Avishek out.
2. Abheet wants to learn the definition of Statistics without wanting to read the books. As a student of Statistics,
explain to Abheet what Statistics is defined as.
E. Short Answer Questions
1. What is the general application of statistics in sports related to fan engagement?
2. What is the general application of statistics in disaster management related to damage assessment?
3. What is central tendency?
F. Long Answer Questions
1. Give any three benefits of integrating statistics in Artificial Intelligence.
2. How is statistics used for weather forecasting?
3. Define Mean, Median and Mode
G. Subject Enrichment Critical Thinking
Can you please perform statistical research on “The time students spend on social media”? Work in a group of
5 students and each group needs to capture data from a minimum 10 students. Once you have data ready with
you, then do your statistical analysis (central deviation, variance and standard deviation)
H. Multiple Assessment Communication
Watch the following video and then divide the class into two groups and conduct a debate on “ Can statistics mislead
us”
https://youtu.be/sxYrzzy3cq8
I. Knowledge Hub Logical & Analytical Thinking
https://www.khanacademy.org/math/statistics-probability/summarizing-quantitative-data/mean-median-basics/a/
mean-median-and-mode-review
https://www.forbes.com/advisor/in/business/ai-statistics/
J. Experiential Learning
https://www.ted.com/talks/alan_smith_why_you_should_love_statistics?language=en
https://youtu.be/_JcN_b3euAM
uuu

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 Unit 3 : Math for AI (Statistics & Probability)

10 Probability

Learning Objectives
After studying this chapter, students will be able to:
• Understand the concept of Probability in real life
• Explore various types of Probable events
• Know more about the applications in various real life scenarios

PROBABILITY
Probability is the branch of mathematics concerned with
describing the likelihood of events occurring. It provides a way
to quantify uncertainty and make predictions based on available
information. In everyday language, probability is often used to
describe the likelihood of an event happening, ranging from
impossible (probability of 0) to certain (probability of 1).
It’s a way to quantify how probable or certain an event is. We
express probability as a numerical value between 0 and 1, where:
• 0 represents an impossible event (e.g., flipping a coin and
getting both heads and tails).
• 1 represents a certain event (e.g., flipping a coin and getting either heads or tails).
The higher the probability, the more likely the event is to happen. Here are some key concepts in
understanding probability:
• Sample Space: This refers to the collection of all possible outcomes in a given experiment or
situation. For example, the sample space when flipping a coin is {heads, tails}.
• Outcome: An outcome is a possible result of an experiment. For instance, when rolling a dice, the
outcomes are the numbers 1 through 6. Each outcome has an associated probability of occurring.
• Favorable Outcomes: An event that has produced the desired result or expected event is called
a favorable outcome. For example, when we roll two dice, the possible/favorable outcomes of
getting the sum of numbers on the two dice as 4 are (1,3), (2,2), and (3,1).
• Experiment: In probability theory, an experiment refers to any process that can produce a set of
outcomes. For example, rolling a dice, flipping a coin, or selecting a card from a deck are all
examples of experiments.

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 • Random Experiment: An experiment that has a well-defined set of outcomes is called a random
experiment. For example, when we toss a coin, we know that we would get ahead or tail, but
we are not sure which one will appear.
• Trial: A trial denotes doing a random experiment.
• Event: This is a subset of the sample space, representing a specific outcome or group of outcomes
you’re interested in. For example, the event “getting heads” when flipping a coin is {heads}.
The probability of an event (P(E)) is calculated by dividing the number of favorable outcomes by the
total number of outcomes in the sample space:
P(E) = Number of Favorable Outcomes / Total
Number of Outcomes
This formula allows us to calculate the probability of
various events and make predictions about the likelihood
of their occurrence. Probability plays a vital role in many
fields, including statistics, machine learning, finance,
games of chance, and even weather forecasting.
The team which wins the toss gets to make the decision
of batting or bowling first in a cricket match. This is
one of the most common applications of the coin toss
experiment. Why do you think this method is used? This
is because the probability of obtaining a Head in a coin
toss is as likely as obtaining a tail, that is, 50%.

Let’s consider a relatable example to explain the concept of probability: flipping a coin.
Sample Space: The sample space of this experiment consists of all possible outcomes, which are
“heads” and “tails.”
Outcomes: The possible outcomes are:
● Heads (H) ● Tails (T)
Event: Let’s define the event A as “getting heads” when flipping a coin.
Probability of Event A (P(A)): The probability of getting heads (event A) when flipping a fair coin can
be calculated as:
● P(A) = Number of favorable outcomes / Total number of possible outcomes
Calculation:
● Number of favorable outcomes (getting heads) = 1
● Total number of possible outcomes = 2 (heads or tails)
So, the probability of getting heads (event A) is: P(A) = 1/2 = 0.5
The probability of getting heads when flipping a fair coin is 0.5 or 50%. This means that if we were
to flip the coin many times, we would expect to get heads approximately half of the time.

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Case Study: Duckworth-Lewis Method (D/L Method) in Cricket
The Duckworth-Lewis Method (D/L Method) is a mathematical formula used in cricket to set a revised
target score for the team batting second when a match is interrupted by rain or other unforeseen
circumstances. This method relies heavily on probability to ensure a fair outcome for both teams
Scenario: Imagine a One-Day International (ODI) cricket match between India and Australia. India bats
first and scores 250 runs in their allotted 50 overs. However, with Australia needing 10 overs remaining
to chase down the target (251 runs), rain forced a stoppage in play.
Challenge: Since Australia haven’t completed their innings, a traditional run-rate comparison wouldn’t
be fair. How many runs should Australia be set to chase in their remaining overs to ensure an equal
contest considering the lost time?
Probability in Action: The D/L method takes into account several factors to determine a revised target:
● Overs bowled by the first team: This reflects the number of wickets lost and the runs scored
by the batting team.
● Overs remaining in the match: This considers the time lost due to the interruption and the
remaining opportunity for the second team to bat.
● Historical data: The method uses a database of past rain-affected matches to analyze scoring
patterns at different stages of an innings.

Probability Calculations: Complex mathematical formulas involving probability distributions and run-rate
predictions are used to calculate the number of runs Australia would likely have scored had they been
able to complete their innings under normal circumstances. This becomes their revised target score.
Benefits of Probability: The D/L method removes the element of guesswork from rain-affected
matches. By using probability and historical data, it strives to create a level playing field for both
teams despite the interruption. This ensures a fairer outcome and avoids situations where one team
might benefit disproportionately from the rain.
Limitations: The D/L method isn’t perfect. Factors like momentum swings, individual player form, and
pitch conditions can’t be fully accounted for in the calculations. However, it represents a significant
improvement over earlier methods and is widely considered the most statistically robust approach for
setting revised targets in rain-affected cricket matches.
The D/L method exemplifies the use of probability in cricket. This case study highlights how
probability plays a crucial role in maintaining the integrity and competitive spirit of cricket.

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Case Study: Packing an Umbrella
Imagine you’re getting ready for work and checking the weather forecast. It says there’s a 30% chance
of rain. Should you pack an umbrella? Let’s break down the decision using probability:
● Event: Rain or No Rain
● Outcomes:
○ Favorable Outcome (for you): It rains (you want to avoid getting wet).
○ Unfavorable Outcome: No rain (carrying an umbrella unnecessarily).
● Probability:
○ P(Rain) = 30% (based on the weather forecast).
○ P(No Rain) = 100% - 30% = 70% (since there are only two possibilities, rain or no rain).
Making the Decision:
Here’s where things get interesting. There’s a 30% chance you’ll encounter the “unpleasant” event
(getting rained on), but there’s also a 70% chance you’ll be carrying an umbrella for no reason.
● Risk vs. Reward: Consider how much you dislike getting caught in the rain (risk) compared to
the inconvenience of carrying an umbrella on a sunny day (reward).
● Personal Preference: Some people might prioritize staying dry even with a low chance of rain,
while others might find carrying an umbrella a bigger hassle.
Probability in Action:
● Packing the Umbrella (Risk-Averse Approach): If you strongly dislike getting wet, the 30% chance
of rain might be enough to convince you to pack the umbrella. You’re prioritizing avoiding the
negative outcome (getting caught in the rain) even if it means carrying it on a sunny day.
● Skipping the Umbrella (Risk-Tolerant Approach): If you don’t mind the occasional downpour or
have alternative ways to stay dry (like a raincoat), the 70% chance of no rain might make you
skip the umbrella. You’re prioritizing convenience over a lower chance of getting wet.

Case Study : Predicting Travel Time

Traffic Prediction with Historical Data:
● Both Uber and Google Maps use historical traffic data to analyze patterns and congestion levels
on different roads at various times of the day. This data provides a foundation for predicting
future traffic conditions.
● Probability Distributions: They employ statistical models that factor in probability distributions
to account for the variability in traffic flow. Traffic isn’t always consistent, and there can be
unexpected delays due to accidents, construction, or weather events. These models consider the
likelihood of these occurrences and how they might impact travel time.
Real-Time Updates:
● Uber and Google Maps incorporate real-time data feeds from various sources like GPS data from
other users and traffic sensors. This allows them to constantly update their traffic predictions
and adjust estimated travel times based on current conditions.

97
 ● Confidence Levels (Implicit Probability): While not explicitly stated, the travel time estimate can
be seen as an implicit representation of probability. A range of possible travel times might be
shown, indicating a less certain prediction compared to a single, definitive time.
Limitations:
● Unforeseen Events: Accidents, road closures, or extreme weather can significantly impact traffic
flow and cause delays beyond what can be predicted with absolute certainty.
● Individual Driving Behavior: The model’s estimates assume an average speed and driving
behavior. Aggressive driving or unusual traffic maneuvers can alter the actual travel time.
Overall, Uber and Google Maps leverage probability through historical data, statistical modeling, and
real-time updates to provide the most likely travel time estimate. However, it’s important to remember
that these are estimates, and unexpected events can always influence the actual travel time.

Here are some other daily situations where probability plays a role:
• Packing for a trip: Deciding what clothes to bring based on the weather forecast and planned
activities.
• Estimating how much of each item you’ll need based on your typical consumption and the number
of people in your household.
• Setting the alarm clock: Factoring in the time it takes you to get ready and potential delays (like
hitting snooze) to ensure you wake up on time.
• Weather Forecasting: Probability is used to predict weather conditions, such as the likelihood of
rain, snow, or sunshine. Meteorologists analyze historical weather data and current atmospheric
conditions to estimate the probability of different weather events occurring within a specific
time frame.Probability is used to assess the risk of extreme weather events such as hurricanes,
tornadoes, or heatwaves.
• Sports: Probability is used to predict the outcomes of sports events such as matches, games, or
races. Statistical models and historical data are analyzed to estimate the likelihood of different
teams or athletes winning.
• In-Game Decision Making: Coaches and managers use probability to make strategic decisions
during games, such as when to substitute players, call timeouts, or go for it on fourth down.
Probability models help assess the risk and reward of different game scenarios.
• Odds: These represent the bookmaker’s (the betting service’s) prediction of how likely an event
is to happen. They are usually shown in numbers (e.g., 2.50) or fractions (e.g., 5/2). Lower odds
indicate a favorite (more likely to win), while higher odds represent an underdog (less likely to
win).
• Health and Medicine: Probability is used in medical diagnostics to assess the likelihood of a
patient having a particular disease or condition based on symptoms, test results, and risk factors.
Medical researchers use probability models to study the effectiveness of treatments, estimate
disease prevalence, and predict patient outcomes.

98
 DID YOU KNOW ?

Prodigy combines role-

playing elements with
mathematics to create
an immersive gaming
experience. Students
engage in battles against
mythical creatures by
co r re c t l y a n swe r i n g
math questions. The
difficulty level adjusts
according to their
performance, ensuring
they are continuously
challenged.

As a student, you encounter probability in many ways throughout your daily routine. Here are some
specific examples:
1. Deciding How to Study:
• Scenario: You have an upcoming exam in two subjects: history and math. History is easier for
you and usually requires less studying, while math is more challenging. You have limited study
time.
• Probability in Action: You can estimate the probability of getting a good grade in each subject
based on past performance and difficulty level. You might assign a higher probability of success
in history (due to ease) and a lower probability in math (due to difficulty). This helps you allocate
your study time strategically, focusing more on math to improve your chances of a good grade.
2. Project Completion Probability:
• Scenario: You’re working on a group project with a deadline approaching. However, one team
member seems unreliable and hasn’t completed their assigned part yet.
• Probability in Action: You can assess the probability of the project being completed on time.
Consider the likelihood of your unreliable teammate finishing their part and the potential
consequences of a delay. This helps you make informed decisions like delegating tasks differently
or planning alternative solutions in case the project is incomplete.
3. Test Scores and Guessing:
• Scenario: You’re unsure about an answer on a multiple-choice test with four options. There’s no
penalty for guessing, so you’re considering taking a chance.
• Probability in Action: If you have no clue about the answer, each option has a 25% chance of
being correct (assuming all options are equally likely). However, if you can partially eliminate
some options based on your knowledge, the probability of guessing correctly increases.
Understanding probability helps you decide whether to stick with your initial answer or take an
educated guess.
By understanding and applying basic probability concepts, students can make better decisions, manage
their time efficiently, and ultimately improve their academic performance and overall well-being.
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 Know More

Photomath: This app takes advantage of AI technology by allowing users to take a photo of
a handwritten or printed math problem and instantly receive a step-by-step solution. It’s like
having a personal math tutor in your pocket! Visit https://photomath.com/

In probability theory, events are outcomes or sets of outcomes of an experiment. Events can be classified
into various types based on their characteristics and relationships with other events. Here are the main
types of events in probability:
1. Simple Event: A simple event is an event that consists of a single outcome. For example, when
rolling a fair six-sided die, the event “rolling a 3” is a simple event because it corresponds to
one specific outcome.
2. Compound Event: A compound event is an event that consists of two or more outcomes. It can
be composed of simple events or other compound events. For example, the event “rolling an even
number or rolling a number less than 3” is a compound event because it combines the simple
events “rolling an even number” and “rolling a number less than 3.”
3. Equally Likely Events: Events that have the same chances or probability of occurring are called
equally likely events. The outcome of one event is independent of the other. For example, when
we toss a coin, there are equal chances of getting a head or a tail.
4. Mutually Exclusive Events: Mutually exclusive events
are events that cannot occur simultaneously. If one event
happens, the other event cannot happen at the same time.
For example, when flipping a coin, the events “getting
a head” and “getting a tail” are mutually exclusive. the
climate can be either hot or cold. We cannot experience
the same weather simultaneously.
5. Independent Events: Independent events are events that
do not influence each other. The occurrence of one event does not affect the probability of the
other event happening. For example, when rolling a die twice, the outcomes of the first roll do
not influence the outcomes of the second roll.
6. Dependent Events: Dependent events are events where the outcome of one event depends on the
outcome of another event. The occurrence of one event affects the probability of the other event
happening. For example, drawing two cards from a deck without replacement is an example of
dependent events because the probability of drawing a certain card depends on what cards have
already been drawn.
7. Complementary Events: Complementary events are events that are mutually exclusive and
together cover all possible outcomes of an experiment. The complement of an event A, denoted
as “not A” or “A’,” consists of all outcomes that are not in event A. For example, if event A is
“rolling a 6 on a six-sided die,” then the complement of event A is “not rolling a 6.”
8. Exhaustive Events: Exhaustive events are events that cover all possible outcomes of an
experiment. Together, they encompass all possible outcomes with no overlap. For example, when
rolling a fair six-sided die, the events “rolling an even number” and “rolling an odd number” are
exhaustive events because every outcome falls into one of these two categories.
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Basic Probability Rules:
1. Addition Rule: The probability of the union of two mutually exclusive events (events that cannot
occur simultaneously) is the sum of their individual probabilities.
P(A or B) = P(A) + P(B)
Using the addition rule for mutually exclusive events like Flipping a coin
P(A or B) = P(A) + P(B)
Calculation:
P(H or T) = (1/2) + (1/2) = 1/2 + 1/2 = 1
Example : At a school, 40% of students are learning Spanish, 20% of the students are learning
German, and 8% of the students are learning both Spanish and German. What is the
probability that a randomly selected student is learning Spanish or German?
Solution : P(Spanish or German) = P(Spanish) + P(German) - P(Spanish and German)
= 0.4 + 0.2 – 0.08
= 0.52
2. Multiplication Rule: The probability of the intersection of two independent events (events that
do not influence each other) is the product of their individual probabilities.
P(A and B) = P(A) * P(B)
Scenario: Imagine you reach into a big bag of trail mix that contains peanuts (P) and chocolate
chips (C) along with other goodies. You’re interested in the probability of grabbing a handful that
contains both a peanut and a chocolate chip.
Independent Events: In this case, grabbing a peanut (event A) and grabbing a chocolate chip
(event B) are considered independent events. Why? Because picking a peanut doesn’t affect
whether you’ll also get a chocolate chip, and vice versa. You might pull out a handful with
multiple peanuts and no chocolate chips, or vice versa, or you could get both!
Individual Probabilities: Let’s say you know from experience (or peeking into the bag!) that
there’s a 20% chance of grabbing a peanut (P(A) = 0.2) and a 30% chance of grabbing a
chocolate chip (P(B) = 0.3) on any given grab.
Probability of Getting Both Peanut and Chocolate Chip:
We want to find the probability of getting event A (peanut) and event B (chocolate chip) together.
This is denoted by P(A and B).
Using the multiplication rule for independent events:
P(A and B) = P(A) * P(B)
Calculation:
P(Peanut and Chocolate Chip) = (0.2) * (0.3) = 0.06
Example : Suppose that we are going to roll two fair -sided dice. Find the probability that both
dice show a 3
Solution : Since the results of the dice are independent, we can multiply the probability of rolling
a 3 on each die.
P(Both 3) = P( 3 and 3) = 1/6 * 1/6 = 1/36
3. Complement Rule: The probability of the complement of an event (the event not occurring) is
one minus the probability of the event itself.
P(not A) = 1 - P(A)
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 Scenario: You roll a fair six-sided die. You’re curious about the probability of not rolling a 6.
Examples:
Example: A coin is tossed once. Find the probability of
a. Getting a head
b. Not getting a head
Solution : We know that the total number of possible outcomes is 2
a. P (Getting a head) = ½
b. P (not getting a head) = 1/2
USE OF PROBABILITY IN ARTIFICIAL INTELLIGENCE
In the realm of AI, uncertainty is a common phenomenon. Whether it’s predicting the stock market
or diagnosing a disease, there’s always a degree of uncertainty involved. Numerous problems in
AI (reasoning, planning, learning, perception, and robotics) necessitate that the agent operates with
incomplete or uncertain information.This is where probability comes into play. It provides a mathematical
framework to quantify uncertainty, making it an indispensable tool in the AI toolkit.
AI makes Use of Probabilistic Reasoning:
• When we are uncertain about the premises
• When the number of possible predicates becomes unmanageable
• When it is known that an experiment contains an error

RECAP
Probability is the branch of mathematics concerned with describing the likelihood of events
occurring. It provides a way to quantify uncertainty and make predictions based on available
information. In probability theory, events are outcomes or sets of outcomes of an experiment.
Events can be classified into various types based on their characteristics and relationships with
other events.
In the realm of AI, uncertainty is a common phenomenon. Whether it’s predicting the stock market
or diagnosing a disease, there’s always a degree of uncertainty involved.

KEY TERMS
● Simple Event: A simple event is an event that consists of a single outcome.
● Compound Event: A compound event is an event that consists of two or more outcomes.
● Mutually Exclusive Events: Mutually exclusive events are events that cannot occur simultaneously.
If one event happens, the other event cannot happen at the same time.
● Independent Events: Independent events are events that do not influence each other. The
occurrence of one event does not affect the probability of the other event happening.
● Complementary Events: Complementary events are events that are mutually exclusive and
together cover all possible outcomes of an experiment.
● Exhaustive Events: Exhaustive events are events that cover all possible outcomes of an
experiment.
● Probability: The branch of mathematics concerned with describing the likelihood of events
occurring.

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 AI EXERCISES

A. Multiple choice questions.

1. Which of the following best describes a simple event in probability?
(a) An event that consists of two or more outcomes
(b) An event that cannot occur simultaneously with another event
(c) An event that consists of a single outcome
(d) An event that is influenced by the outcome of another event
2. Mutually exclusive events in probability are events that:
(a) Depend on the outcome of another event (b) Cannot occur simultaneously
(c) Are independent of each other (d) Cover all possible outcomes of an experiment
3. Complementary events in probability are events that:
(a) Cannot occur simultaneously (b) Cover all possible outcomes of an experiment
(c) Are mutually exclusive (d) Are independent of each other
4. Which of the following is an equation? CBSE Handbook

(a) 2x + 5 (b) x + 2 = 4x (c) x2 + 2x (d) 5 + 5x + 5x2

5. The median of the data: 155, 160, 145, 149, 150, 147, 152, 144, 148 is: CBSE Handbook

(a) 149 (b) 150 (c) 147 (d) 144

B. Fill in the Blanks.
1. The probability of a certain event is _________ .
2. The probability of an impossible event is _________ .
3. If the probabilities of two independent events are 0.2 and 0.4 respectively, then the chance that the two events
happen simultaneously is _________ .
4. Two events are mutually _________ if the happening of one event makes it impossible for the other event to
take place and vice versa.
C. Assertion/Reason Type
1. Assertion (A): The higher the chances of an event, the lower the chances of its complementary event.
Reason (R): For the chance p of an event, the chances of the complementary event is 1- p.
(a) Both A and R are true and R is the correct explanation for A (b) A is true and R is false
(c) A is false and R is true
(d) Both A and R are true but R does not explain A
2. Assertion (A): In most cases, the chances of any number showing up on a die are equally likely.
Reason (R): The different numbers are mutually exclusive events with respect to die throw.
(a) Both A and R are true and R is the correct explanation for A (b) A is true and R is false
(c) A is false and R is true
(d) Both A and R are true but R does not explain A
D. Competency Based Questions
1. Asin is bad at Maths but rather enthusiastic about AI. She cannot understand the need for probability in making
AI models. Explain her how the model gives its answers based on probabilistic models. It answers questions
based on the highest chances of things happening.
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 2. Ajay has been asked by his teacher to calculate the probability of two thrown dice showing 3 and 4 (in some
order). Since you are good in AI, explain to Ajay how he might get around to answering this question.
3. Shreya was reading about the kind of events in probability theory. But there were not sufficient examples in the
book she was reading from. Give two examples of dependent events to Shreya so that she understands better.
E. Short Answer Questions
1. Describe sample space.
2. Give any three examples of events that are certain.
3. Give the formula for probability of an event.
F. Long Answer Questions
1. The probability of heads and tails in a biased coin is 0.4 and 0.6 respectively. Based on this:
(a) Are they mutually exclusive (b) Are they complementary events
(c) Are they independent
2. Explain the main types of events in probability.
3. Explain a few applications of probability in real life scenarios.
4. Describe the use of probability in artificial intelligence.
G. Subject Enrichment
1. The Monty Hall problem is a classic example of probability in action. The Monty Hall problem is named for
its similarity to the Let’ss Make a Deal television game show hosted by Monty Hall. The problem is stated as
follows. Assume that a room is equipped with three doors. Behind two are goats, and behind the third is a
shiny new car. You are asked to pick a door, and will win whatever is behind it. Students can participate in a
simulation of the problem to understand the counter-intuitive result and the role of probability in the problem.
Visit https://mathigon.org/course/probability/monty-hall (Experiential Learning)
2. Tower of Hanoi: Assign students to solve a Tower of Hanoi puzzle. This classic problem involves moving a stack
of disks from one peg to another while following specific rules. It requires strategic thinking and problem-
solving skills. (Creative Thinking)

H. Multiple Assessment Communication

Divide the class into four groups and ask them to watch one movie out of the following. The group needs to present
the next day about the use of probability in the movie to the entire class.
● The Big Short (2015): This financial drama depicts the 2008 financial crisis. Several characters, including Michael
Burry, use complex statistical models and probability analysis to predict the collapse of the housing market.
The movie highlights the role of probability in financial risk assessment and investment strategies.
● 21 (2008): This crime thriller follows a group of MIT students who use their knowledge of probability and card
counting to win at blackjack in casinos. While the movie portrays a fictional scenario, it demonstrates how
understanding probability can give someone an edge in games of chance.
● Ocean’s Eleven (2001): This heist film features a group of professionals planning a complex casino robbery. They
use various techniques, including probability calculations, to assess risks, plan escape routes, and increase their
chances of success. The movie showcases how probability can be applied to strategic planning and decision-
making in high-pressure situations.
Subject Enrichment
I. Knowledge Hub
https://www.mathsisfun.com/data/probability.html
https://pub.towardsai.net/why-is-probability-important-for-machine-learning-e424442f6e99
J. Experiential Learning Technology Literacy
https://youtu.be/0aZmCCnZI9E
https://youtu.be/DwzrTR1i6ps
https://youtu.be/uzkc-qNVoOk
uuu
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 Unit 4 : Introduction to Generative AI

11 Generative AI

Learning Objectives
After studying this chapter, students will be able to:
• Students will be able to define Generative AI & classify different kinds.
• Students will be able to explain how Generative AI works and recognize how it learns.
• Students will be able to apply Generative AI tools to create content.
• Students will understand the ethical considerations of using Generative AI

The field of artificial intelligence (AI) has come a long way since its inception, with generative AI and
prompt engineering playing crucial roles in its advancement.

INTRODUCTION TO GENERATIVE AI
Generative AI is a type of artificial intelligence technology that can produce various types of content,
including text, imagery, audio and synthetic data. Generative AI encompasses a range of models and
techniques designed to generate new data based on existing input data. These models have demonstrated
significant capabilities in natural language processing, image generation, and more.
Generative AI is used in everything from creative to academic writing and translation; composing,
dubbing, and sound editing; infographics, image editing, and architectural rendering; and in industries
from automotive to media/entertainment to healthcare and scientific research. ChatGPT, DALL-E, and
Bard(now Gemini) are examples of generative AI applications that produce text or images based on
user-given prompts or dialogue. Prompt engineering, on the other hand, deals with the art of crafting
effective prompts to guide AI models in generating desired outputs.
What do you understand about generative AI?
_______________________________________________________________________________________
Give a few examples of generative AI.
_______________________________________________________________________________________
What do you know about Deep Fake?
_______________________________________________________________________________________
Whereas traditional AI algorithms may be used to identify patterns within a training data set and make
predictions, generative AI uses machine learning algorithms to create outputs based on a training data
set. In contrast to other forms of AI, Generative AI is specially made
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to produce new and unique content rather than merely processing or categorising already-existing data.
Generative AI can produce outputs in the same medium in which it is prompted (e.g., text-to-text) or
in a different medium from the given prompt (e.g., text-to-image or image-to-video).
Watch the video: https://www.youtube.com/watch?v=26fJ_ADteHo
GENERATIVE AI VS. CONVENTIONAL AI
Conventional AI:
Imagine you have a big box full of toys. Here’s how traditional AI and generative AI play with those
toys differently:
• The Rule Follower: This AI is like a good friend who follows your instructions perfectly. You
show them how to build a tower with blocks, and they can build exactly the same tower again
and again. They’re great at following directions and solving problems in ways they’ve been shown
before.
• Think of Siri or Alexa: They answer your questions based on the information they’ve been
trained on, kind of like looking things up in a giant encyclopedia!
• Think of Traditional AI as a copy machine: Imagine you have a textbook with lots of facts
and solutions to problems. Traditional AI excels at learning from this data and using it to perform
specific tasks accurately. It can:
Generative AI:
• The Super Creative Friend: This AI is like a super imaginative friend who can come up with
brand new things! You show them your block tower, and they might use those blocks to build
a crazy car or a funny animal. They can take what they know and use it to create entirely new
and surprising things.
• Think of a magic paintbrush: You give it a starting point, like a few dots on a paper, and it
can use those to create a whole new picture, maybe a spaceship or a friendly monster! Generative
AI goes beyond copying existing information. It uses its knowledge to create entirely new things,
like a painter who uses their skills and creativity to make a unique artwork. Generative AI can:
The key difference:
• Traditional AI is good at following instructions and solving problems in familiar ways. Traditional
AI excels at tasks that require learning and applying existing knowledge.
• Generative AI is like a super creative mind that can take what it knows and use it to make
entirely new and surprising things! Generative AI focuses on creating entirely new and original
content, pushing the boundaries of creativity.

Traditional AI Generative AI
Analyses, processes, and classifies data. Creates entirely new and original content
Replicates or predicts based on learned data Generates never-before-seen content
Conventional AI produces more predictable output Generative AI output is fresh, innovative, and
based on existing data. often unexpected.
Conventional AI employs fewer libraries Generative AI models use vast libraries of
samples to train neural networks and other
complicated structures to produce new content
based on those patterns.

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Needs large datasets of existing content Requires large datasets of labeled data
Conventional AI is used in banking, healthcare, image Examples: Image generation, music
recognition, and language processing. Examples: Spam composition, creative writing, gaming
filters, recommendation systems, chatbots
Imagine you’re a really creative student who can write stories, draw pictures, and even compose music.
Generative AI is like a superpowered version of that creativity! It uses computers to create entirely new
things, like:
• Cool pictures: You give the AI a starting point, maybe a picture of a cat, and it can create a
new image based on that, like a cat wearing a hat on a sunny beach.
• Fun stories: You tell the AI a few sentences about a story idea, and it can continue the story
and come up with surprising twists and turns.
• Catchy music: You give the AI a melody or some lyrics, and it can create a whole new song
with its own rhythm and style.
HOW DOES IT WORK?
Specifically, generative AI models are fed vast quantities of existing content to train the models to
produce new content. They learn to identify underlying patterns in the data set based on a probability
distribution and, when given a prompt, create similar patterns (or outputs based on these patterns).
Generative AI uses a neural network that allows it to handle more complex patterns than traditional

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machine learning. Inspired by the human brain, neural networks do not necessarily require human
supervision or intervention to distinguish differences or patterns in the training data.
Generative AI is like a super good guesser. It looks at tons of existing examples of things (pictures,
stories, music) and learns the patterns behind them. Then, it uses those patterns to create something new
that’s similar but also unique.
Here’s the catch:
• Generative AI is still under development, so its creations aren’t always perfect. They might be a
bit strange or nonsensical at times.
• It needs a lot of data to work well, so the more information it has, the better the results will be.
The recent buzz around generative AI has been driven by the simplicity of new user interfaces for
creating high-quality text, graphics and videos in a matter of seconds.
TYPES OF GENERATIVE AI
Generative AI comes in a variety of forms, each with unique advantages and uses. Some of the most
typical varieties are listed below:
1. Generative Adversarial Networks (GANs):
GANs are neural networks that work together to generate new data consisting of two neural networks:
Discriminator and Generator Networks.The discriminator network evaluates and provides feedback on
the data, whereas the generator network generates the data. Imagine two AI models playing a game
against each other. One, the generator, tries to create realistic images, music, or text. The other, the
discriminator, tries to identify the fakes. As they compete in a loop, the generator gets better at creating
realistic outputs, while the discriminator hones its detection skills. This competition pushes both models
to improve, leading to impressive results. Examples include producing realistic videos, transforming
daytime photos into nighttime ones, generating pictures of fictional individuals, and more.

HANDS-ON ACTIVITY: GAN PAINT Experiential Learning

From customizing digital landscapes to revolutionizing design processes across various industries,
the scope of GANs is as vast as it is intricate. Imagine a tool that can extrapolate your
simplest sketches into vivid scenes or adapt renowned artistic styles to your snapshots,
effectively democratizing creativity and blurring the lines between novice and maestro.
● GAN Paint directly activates and deactivates neurons in a deep network trained to create
pictures.
● Each left button (“door”, “brick”, etc.) represents
20 neurons. The software shows that the network
learns about trees, doorways, and roofs by drawing.
● Switching neurons directly shows the network’s
visual world model.
To use GAN Paint, you will first need to select a base
image from the website‛s library. You can then use
the brush tool to add objects and textures to the
image. As you paint, the GAN network will learn to
generate more realistic images. ▪ You are encouraged
to experiment with GAN Paint and see what you can
create. Have fun! Link: https://ganpaint-v2.vizhub.ai/

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Try out
• Selective Feature Brushes: Choose from a variety of feature brushes such as tree, grass, door,
sky, cloud, brick, or dome.
• Adjustable Strength: Find just the right touch by adjusting the strength of the brush to your
liking.
2. Variational Autoencoders (VAEs)
Think of a VAE like a creative compression tool. In order to produce fresh data, VAEs learn the
distribution of the data and then sample from it. It takes data (images, text, etc.) and condenses it into
a smaller, more manageable form. This compressed version captures the essence of the data. Then, the
VAE can use this compressed form to generate entirely new data points that resemble the originals, but
with a twist of creativity. Examples- Generation of new images similar to a given training set, image
reconstruction, generating drafts for writers, generating new sounds and music composition etc.
3. Autoregressive Models:
These models are like storytellers. They analyse existing data, like a sequence of words in a sentence,
and predict the next element in the sequence. They do this step-by-step, building up a new creation
based on the patterns they learn. This allows them to generate realistic and coherent text formats, code,
or even music.
4. RNNs
RNNs are a special class of neural networks that excel at handling sequential data, like music or text.
They function by ingesting past inputs and applying that knowledge to anticipate future inputs. Example-
Generating novel text in the style of a specific author or genre, predicting the next character or word
in a sequence etc.
5. Auto encoders
These are Neural networks that have been trained to learn a compressed representation of data They
function by compressing data first, then decompressing that compressed data to restore it to its original
form. Auto encoders can be applied to denoising or picture compression applications. Examples- artistic
image creation, drug discovery. They generate highly realistic samples.
6. Diffusion Models
Imagine taking a clear picture and slowly adding noise to it until it becomes completely unrecognizable.
Diffusion models work in reverse. They start with noisy data and gradually remove the noise, revealing a
clear and coherent image or other kind of content underneath. This allows them to learn the underlying
structure of the data and create new variations based on that understanding.
7. Reinforcement Learning for Generative Tasks:
This approach involves training an AI model through a system of rewards and penalties. The model
tries different approaches to generate content, and it receives positive reinforcement for outputs that are
deemed successful based on pre-defined criteria. This allows the model to learn what works best and
continuously improve its generative abilities.
These are just a few of the main types of generative AI. Each has its strengths and weaknesses, and
the choice of which one to use depends on the specific task at hand. As the field continues to evolve,
we can expect even more innovative generative AI models to emerge in the future.
What Are Some Popular Examples of Generative AI?
Popular generative AI interfaces include ChatGPT, Bard, DALL-E, Midjourney, and DeepMind.

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What are Dall-E, ChatGPT and Bard(Gemini)?
ChatGPT, Dall-E and Bard(Now Gemini) are popular generative
AI interfaces.
Dall-E. Trained on a large data set of images and their associated
text descriptions, it is an example of a multimodal AI application
that identifies connections across multiple media, such as vision,
text and audio. In this case, it connects the meaning of words to
visual elements.DALL•E, DALL•E 2, and DALL•E 3 are text-to-image models developed by OpenAI
using deep learning methodologies to generate digital images from natural language descriptions known
as “prompts”. The first version of DALL-E was announced in January 2021. In the following year, its
successor DALL-E 2 was released. It enabled users to generate imagery in multiple styles driven by
user prompts. DALL•E 3 was released natively into ChatGPT for ChatGPT Plus and ChatGPT Enterprise
customers in October 2023,
Imagine you tell a very creative artist what you have in mind, and they can bring your idea to life with
an image. DALL-E is kind of like that, but with the power of artificial intelligence!
Here’s how it works:
• Tell DALL-E what you want: You provide a text description of the image you have in mind.
It can be as specific as “a cat riding a bicycle on Mars” or more general like “a peaceful
landscape.”
• DALL-E gets creative: DALL-E uses its knowledge of text and images to generate a unique
image based on your description. It might create different variations of the image, so you can
choose the one you like best.
Here are some cool things DALL-E can do:
• Create images in different styles, like a painting by Van Gogh or a photorealistic image.
• Generate images of objects or scenes that don’t exist in the real world.
• Help artists and designers come up with new ideas.
ChatGPT
The AI-powered chatbot that took the world by storm in November 2022
was built on OpenAI’s GPT-3.5 implementation. OpenAI has provided
a way to interact and fine-tune text responses via a chat interface with
interactive feedback. Earlier versions of GPT were only accessible via
an API. GPT-4 was released March 14, 2023. ChatGPT incorporates the
history of its conversation with a user into its results, simulating a real conversation. After the incredible
popularity of the new GPT interface, Microsoft announced a significant new investment into OpenAI
and integrated a version of GPT into its Bing search engine.
ChatGPT can create different creative text formats based on your instructions. You can ask it to write
poems, code snippets, scripts, musical pieces (in text format), email replies, letters, etc.
• Need help brainstorming ideas? ChatGPT can be a great tool. Give it a starting point, and it can
generate different creative text formats to spark your imagination.
• Need a summary of a complex topic? ChatGPT can analyse information and provide a concise
overview in a clear and understandable way.
Conversational: Unlike traditional AI that might just give you factual answers, ChatGPT can engage
in a back-and-forth conversation, answering your questions in a comprehensive way and even following
your lead on the conversation topic. It can adapt its conversation style based on your cues and

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preferences. ChatGPT excels at engaging in open-ended dialogues.
Informative: ChatGPT is trained on a massive amount of text data, allowing it to access and process
information from various sources. This enables it to provide informative and insightful responses to your
inquiries. While not a definitive source, ChatGPT can access and process vast amounts of information.
You can use it to learn about different topics, get summaries of factual concepts, or even translate
languages.

BARD
Google was another early leader in pioneering transformer AI
techniques for processing language, proteins and other types of content.
It open sourced some of these models for researchers. However, it
never released a public interface for these models. Microsoft’s decision
to implement GPT into Bing drove Google to rush to market a public-
facing chatbot, Google Bard, built on a lightweight version of its
LaMDA family of large language models. It was formerly known as
LaMDA (Language Model for Dialogue Applications), but in March
2023, it was introduced as Bard, a generative AI chatbot developed by
Google AI. Google suffered a significant loss in stock price following
Bard’s rushed debut after the language model incorrectly said the
Webb telescope was the first to discover a planet in
a foreign solar system. Meanwhile, Microsoft and
ChatGPT implementations also lost face in their early
outings due to inaccurate results and erratic behavior.
Both ChatGPT and Bard represent the exciting world
of generative AI, pushing the boundaries of human-
computer interaction. They can be helpful tools for
communication, creativity, and information access, but
it’s important to remember that they are still under
development. Always be critical of the information
they provide, and use them responsibly!
Hands-on Activity Chit-Chat with ChatGPT & Gemini
• Sign up & Login into both ChatGPT and Gemini.
• Chat with the ChatGPT and ask it to write a paragraph on How it Works? - ChatGPT
• Similarly, Chat with Bard and ask it to write a paragraph on How it Works? - Gemini
Here are 6 prompts that can be tried on ChatGPT and Gemini:
1. Write a summary of the history of the internet.
2. Explain how to code a simple website.
3. Write a blog post about the latest trends in artificial intelligence.
4. Create a presentation about the benefits of cloud computing.
5. Write a research paper about the future of technology.
6. Design an app that solves a real-world problem.

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Quick,
APPLICATIONS OF GENERATIVE AI
Content Generation
Algorithms used in generative AI can produce content that appears to have been created by humans.
These kinds of generative AI use cases are becoming more and more common. The most well-known
applications of generative AI in content creation involve training machine learning models on enormous
amounts of pre-existing text from publications, social media posts, and novels. Furthermore, training
data is necessary for generative AI models to understand the conventions and patterns found in natural
language. The generative AI models may produce fresh text with the same tone and style as the input
material once they have been trained. ChatGPT, Jasper Chat, and Google Bard are a few of the top
generative AI use cases in content creation. (Watch video: Video source: BBC News. (2023, January
15). What is ChatGPT, the AI software taking the internet by storm? - BBC News [Video]. YouTube.
https://www.youtube.com/watch?v=BWCCPy7Rg-s)
Music Creation
You may create creative music for a variety
of projects with the aid of generative AI. The
ability to compose music for commercials
is the most potential benefit of utilizing
generative AI to provide appropriate music
for a project. AIVA, Amper Music, and
Soundful are a few generative AI music-
making technologies. For example, AIVA is an
AI composer that can create original pieces of
music in various genres. (Watch video: Video
source: TED. (2018, October 1). How AI could
compose a personalised soundtrack to your life
| Pierre Barreau [Video]. YouTube. https://www.
youtube.com/watch?v=wYb3Wimn01s)
Video Creation and Editing
The possibilities of employing generative
AI for video creation and editing are further
indicated by the implementations of generative
AI in creative use cases. AI may be used to
produce both feature-length films and short films. To create visual elements and create a soundtrack,
generative AI uses picture generating algorithms and audio production technologies. A text generation
model also assists in writing the screenplay or storyboard for the movie.
Game Development
By utilizing AI algorithms, generative AI can assist game developers in creating many components of a
video game. Generative AI is mostly used in game production to create game areas, objects, characters,
and storylines for the full experience. It can assist developers in providing immersive gaming experiences
and interesting information. Another important application of artificial intelligence is the creation of
non-player characters, or NPCs, with distinctive personalities and actions. Consequently, the NPCs could
make the game more interactive. The gaming industry’s leading instances of generative AI use cases are
Charisma AI and Unity Machine Learning Agents.

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Art Creation
The goal of generative AI use cases in art is to produce novel and creative works of art without the
need for human involvement. For instance, generative AI makes it simpler to produce abstract paintings.
DALL-E 2 and Nightcafe are two instances of generative AI tools for these kinds of application cases.
For example, The Next Rembrandt project used data analysis and 3D printing to create a new painting
in the style of Rembrandt. (Watch video: Video source: The Next Rembrandt. (2016, April 5). The Next
Rembrandt [Video]. YouTube. https://www.youtube.com/watch?v=IuygOYZ1Ngo)
Voice Generation
One of the most well-known applications of generative AI is voice generation. Generic Adversarial
Networks can produce speech that sounds lifelike. These kinds of use cases have a variety of uses in
marketing, teaching, and advertising. Replica Studios, Lovo, and Synthesys are a few companies that
use generative AI for voice generation.
WHAT ARE SOME EXAMPLES OF GENERATIVE AI TOOLS?
Generative AI tools exist for various modalities, such as text, imagery, music, code and voices. Some
popular AI content generators to explore include the following:

• Text generation tools include GPT, Jasper, AI-Writer and Lex.

• Image generation tools include Dall-E 2, Midjourney and Stable Diffusion.
• Music generation tools include Amper, Dadabots and MuseNet.

BENEFITS OF USING GENERATIVE AI

Overall, generative AI offers a range of benefits, including increased creativity, efficiency, personalisation,
exploration, accessibility, and scalability. By leveraging these benefits, businesses and organisations can
improve their content creation processes and provide better experiences for their users.
Creativity: Creatives can push the envelope to improve the efficiency and customization of creative
processes with the help of generative AI. Generative AI can spark new ideas and concepts by analysing
existing data and generating unexpected variations. This can be very helpful in industries like music,
design, and the arts.
Accessibility: People with little finances or technological know-how may find it simpler to create
high-quality content because of generative AI’s ability to democratize access to content creation tools.
Generative AI can be used to develop tools that help people with disabilities, like creating audio
descriptions of images or generating alternative text formats for websites. This can make information
more accessible and inclusive for everyone.
Scalability: Generative AI is a scalable solution for companies and organisations that need to produce

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big amounts of information since it can be utilized to generate massive volumes of content fast and
efficiently.
Efficiency: When compared to manual operations, generative AI may automate content generation
processes, saving money and time.
Personalisation: Personalised news articles or product recommendations can be generated for each user
using generative AI, taking into account their interests and usage patterns.

LIMITATIONS OF GENERATIVE AI
Data Bias: Generative AI models rely heavily on the quality and quantity of data they are trained on.
Biased or incomplete data can lead to biased or nonsensical outputs. If generative AI is trained on
biased or incomplete data, the output may be similarly biased or flawed. This can lead to inaccurate or
problematic results in certain applications, such as in facial recognition or natural language processing.
Uncertainty: Since generative AI often works with massive datasets of text and code, there’s always a
chance the information it generates might be inaccurate or misleading. Double-checking facts, especially
for critical topics, is essential. Generative AI can produce unexpected and often unpredictable results,
which can be both a benefit and a drawback. Generative models can sometimes create realistic-looking
but entirely fictional content. This can be problematic for tasks that require factual accuracy, like
scientific research or legal documents.
Computational Demands : Generative AI requires significant computational resources to train and
generate its output, which can be expensive and time-consuming.
Job displacement: As generative AI automates tasks like content creation, there’s a potential for job
displacement in some sectors. However, it’s also likely to create new opportunities in areas like AI
development and management.
Deepfakes and Misinformation: Malicious actors can misuse generative AI to create deepfakes or
spread misinformation. It’s important to be critical of information encountered online and to check
sources carefully.
Deepfake videos are a type of manipulated media that uses generative AI to create videos where a
person appears to be saying or doing something they never did. Deepfakes are a prime example of how
generative AI can be used.
Imagine a super-realistic copy machine, but for videos! You feed it a video of someone saying
something, and the AI can manipulate that video to make it look like they’re saying something
completely different. It can change facial expressions, lip movements, and even voices to create a very
convincing illusion.
● Deepfakes can be misused to spread misinformation or create fake news. It’s important to be
critical of what you see online.
● Deepfake technology is still evolving, but experts are developing ways to detect them.
Here are some other potential uses of deepfake videos:
● Entertainment: Imagine seeing your favorite actor in a historical reenactment, even if they
weren’t alive at the time! Deepfakes could be used to create new scenes in movies or even bring
historical figures to life.
● Education: Deepfakes could be used to create realistic simulations for training purposes. For
example, doctors could practice surgery on patients created with deepfakes.

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Deepfake videos are a powerful tool with both positive and negative applications. As with any
technology, it’s important to use them responsibly and be aware of the potential risks.

ACTIVITY Experiential Learning

Guess the real Image Vs the AI generated Image

Hive AI-Generated Content Detection
Hive Moderation, a company that sells AI-directed content-moderation solutions, has an AI
detector into which you can upload or drag and drop
images. Visit https://hivemoderation.com/ai-generated-content-detection

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ETHICAL CONSIDERATIONS OF USING GENERATIVE AI
While Generative AI offers many benefits, there are also several ethical considerations that should be
considered when using this technology.
Ownership: The ownership of content produced by generative AI is a matter of debate. This is
especially important in artistic, literary, and musical domains because generative AI can produce unique
works that conflate human and machine authorship.
Human Agency: Concerns regarding human agency and control are brought up by generative AI. There
may be a loss of human autonomy and agency as a result of the inability to tell content created by
machines from that created by people as technology advances.
Bias: Biases in the data used to train the model can be amplified and replicated by generative AI.
This may result in negative or unfair effects, particularly if the produced material is applied to crucial
processes like loan approval, hiring, or criminal justice
THE POTENTIAL NEGATIVE IMPACT ON SOCIETY
• Deep fakes and fake news that propagate false information and sway public opinion can be
produced using generative artificial intelligence.
• Displace humans who previously carried out these duties from their jobs.
• Sensitive personal data, like social security numbers and medical records, may be produced by
generative AI and utilized maliciously.
RESPONSIBLE USE OF GENERATIVE AI
• Ensuring that the training data used are diverse and representative.
• The results are examined closely for prejudice and false information.
• Giving user privacy and consent first priority,
• Establishing precise policies about generating content ownership and attribution.
• Having open dialogues on the moral and social ramifications of this technology to make sure it
is created and applied in ways that benefit society.
MORE EXAMPLES
Creative Expression:
• AI Drawing Apps: Apps like AutoDraw (https://www.autodraw.com/) or Crayon Kids (https://
kidgeni.com/) allow children to start with a simple sketch or choose from prompts, and the AI
helps them complete or enhance their drawings with color, effects, or even suggest new elements.
• Imagen (web app): This AI tool allows you to type in a creative text prompt and see an image
generated based on your description. Want to see a “pizza playing basketball on Mars”? Imagen
can try to visualise that for you! It can be a fun way to spark creative writing or drawing ideas.

CREATIVE EXPLORATION:
● Image Generation:
○ Midjourney (paid): This platform uses text descriptions to generate stunning and artistic
images. You can describe anything from fantastical landscapes to photorealistic portraits.
○ NightCafe Creator (https://creator.nightcafe.studio/studio (freemium): This app offers a variety
of AI art styles and allows you to experiment with different filters and effects to create unique
visuals. This app allows users to turn text prompts into unique works of art. Imagine describing a
“cyberpunk city under a neon sky” and letting the AI generate a cool image based on your words.
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 ● Text Generation:
○ Rytr (freemium): This AI writing tool helps with creative writing prompts, blog post ideas, and
even marketing copy.
○ Jasper (paid): Jasper is a powerful tool for generating various creative text formats like scripts,
poems, song lyrics, and even different writing styles.

PRODUCTIVITY AND DESIGN:

● Copywriting and Marketing:
○ Copy AI (freemium): This AI helps craft engaging marketing copy, social media posts, and
website content.
○ Headlime (paid): Headlime uses AI to analyse large amounts of data and generate compelling
headlines and ad copy.
LEARNING AND EXPLORATION:
● AI Music Apps:
○ Incredibox (https://www.incredibox.com/) is a fun way for kids to experiment with music
creation. They can drag and drop icons to create different beats and melodies, with the AI
providing a catchy soundtrack.
○ BandLab (https://www.bandlab.com/) offers a more advanced music creation platform where kids
can use AI tools to generate song ideas, suggest harmonies, or create drum loops.
○ Amper Music (https://welcome.ai/solution/amper): (Free)This platform allows users to create
short music pieces with different genres and moods. Teenagers can experiment with different
styles and sounds to compose their own unique music.
● AI World Exploration Apps: Koa (https://lablab.ai/apps) is an interactive story app where kids
can explore different worlds and make choices that influence the narrative. The AI personalises
the story based on these choices, creating a unique experience each time.
AI GAME CREATION APPS:
● Apps: Kodable, ScratchJr
● What it does: These apps introduce kids to the basics of coding with a playful twist. They can
use drag-and-drop interfaces and AI suggestions to create their own simple games. This fosters
their problem-solving skills and introduces them to programming concepts in an engaging way.
PERSONAL PRODUCTIVITY AND ENTERTAINMENT:
● Longform AI: This AI tool helps with long-form writing tasks like blog posts or articles by
researching, outlining, and even suggesting content based on your topic.
● Resemble AI/Eleven Labs: These AI voice generators allow you to create realistic voiceovers
based on text input. You can use them for narration, creating custom audio greetings, or even
adding voiceovers to video projects.
● Artbreeder: This platform lets you explore the potential offspring of existing artwork by
combining styles from different pieces. It’s a fun way to discover new artistic possibilities. It is
a web-based tool that enables users to generate new images by combining different GAN models.
Users can select and combine different GAN models to create new and unique images.

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 RECAP
● Generative AI is a type of artificial intelligence technology that can produce various types of
content, including text, imagery, audio and synthetic data.
● AI is used in everything from creative to academic writing and translation; composing, dubbing,
and sound editing; infographics, image editing, and architectural rendering; and in industries
from automotive to media/entertainment to healthcare and scientific research.
● Generative AI comes in a variety of forms, each with unique advantages and uses.

KEY TERMS
● Generative AI is a type of artificial intelligence technology that can produce various types of
content, including text, imagery, audio and synthetic data.
● Traditional AI algorithms may be used to identify patterns within a training data set and make
predictions
● GANs are neural networks that work together to generate new data consisting of two neural
networks: Discriminator and Generator Networks
● Deepfake videos are a type of manipulated media that uses generative AI to create videos where
a person appears to be saying or doing something they never did.

AI EXERCISES
A. Multiple choice questions.
1. A __________ AI algorithms may be used to identify patterns within a training data set and make predictions
(a) Traditional (b) Generative (c) Overall (d) None of these
2. ____________ learn the distribution of the data and then sample from it.
(a) GAN (b) VAE (c) RNN (d) None of these
3. ______ are a type of manipulated media that uses generative AI to create videos where a person appears to
be saying or doing something they never did.
(a) Fabricated videos (b) Counterfeit videos (c) Deceptive videos (d) Deep Fake Videos
B. Fill in the Blanks.
1. ____________ AI is an AI tool that helps with long-form writing tasks like Blog posts or articles by researching,
outlining and even suggesting content based on your topic.
2. _________ is an AI tool that lets you explore the potential offspring of existing artwork by combining styles
from different pieces.
3. Quizlet Learn ___________ your study experience by analyzing your progress and creating custom quizzes
based on your strengths and weaknesses.
4. _________ and fake news that propagate false information and sway public opinion can be produced using
generative AI.

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C.
Assertion/Reason Type
1. Assertion (A): AI is capable of helping children out with their painting tasks.
Reason (R): Apps like AutoDraw or Crayon Kids allow children to start with a simple sketch or choose from prompts,
and the AI helps them complete or enhance their drawings with colors, effects, or even suggest new elements.
(a) Both A and R are correct and R is the correct reason for statement A
(b) A is true but R is false
(c) A is false and R is true
(d) Both A and R are true but R is NOT the correct explanation for A
2. Assertion (A): Text generation tools are there on the Internet.
Reason (R): GPT, Jasper, AI-Writer are some examples of text generation tools.
(a) Both A and R are correct and R is the correct reason for statement A
(b) A is true but R is false
(c) A is false and R is true
(d) Both A and R are true but R is NOT the correct explanation for A
3. Assertion (A): Natural Language Processing is a useful tool for generative AI–based chatbots and virtual
assistants to increase productivity.
Reason (R): Generative AI technologies include Siri and Google Assistant.
(a) Both A and R are correct and R is the correct reason for statement A
(b) A is true but R is false
(c) A is false and R is true
(d) Both A and R are true but R is NOT the correct explanation for A
4. Assertion (A): Bard is now renamed as Gemini.
Reason (R): Bard is a chatbot by Google.
(a) Both A and R are correct and R is the correct reason for statement A
(b) A is true but R is false
(c) A is false and R is true
(d) Both A and R are true but R is NOT the correct explanation for A
D. Competency Based Questions
1. Ravi is amazed at how a software can build or generate feature-length films and short films. Explain to Rajiv
how software can do this creation.
2. Omar found the term LLM in one of his brother’s AI books. He does not know what this LLM is. As someone
experienced in AI related questions, enlighten Omar in this regard.
3. Rashmi wants to know whether ChatGPT, Jasper Chat and Google Bard are capable of generative AI. Answer
Rashmi.
4. Nikki came to know that voice generation can be used at a number of places. But she cannot think of any
places where voice generation bots may come to be useful. Give her some examples of places where voice
generation is useful.
E. Short Answer Questions
1. Give name of three bots that can be used for code generation.
2. Give any two bots available for:
(a) Text generation (b) Image generation (c) Code generation
3. Give an idea of which software can be used for art creation.
4. Expand the following:
(a) GPT (b) LAMDA (c) GAN

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F. Long Answer Questions
1. What do you understand about Generative Artificial Intelligence? Give any two examples.
2. Write any two AI tools each for the following-
● Generative AI image generation tools ● Generative AI text generation tools
● Generative AI audio generation tools
3. Differentiate between
● GANs and VAEs ● Traditional and generative AI
4. How Generative AI can be helpful in following fields-
● Coding ● Music ● Content Creation
G. Subject Enrichment
Divide the class into two groups and conduct a debate on any of the following topics
● How do you think generative AI can revolutionize the creative industry, such as art and fashion, by enabling
the generation of unique and innovative designs?
● Considering the ethical challenges associated with generative AI, what are your thoughts on establishing
guidelines or regulations to ensure responsible use of these technologies? How can we balance the potential
benefits and risks?

H. Multiple Assessment Communication

Document the findings from above activity on ChatGPT vs Gemini vs Copilot based on the parameters below:
● Human-Like Response.
● Training Dataset and Underlying Technology.
● Authenticity of Response.
● Access to the Internet.
● User Friendliness and Interface.
● Text Processing: Summarization, Paragraph Writing, Etc.
● Charges and Price.
Subject Enrichment
I. Knowledge Hub
https://medium.com/@09gauravbisht/generative-ai-for-deepfake-and-synthetic-data-20ef8f6cdef7
https://www.techtarget.com/whatis/definition/deepfake
https://www.thehindu.com/news/national/regulating-deepfakes-generative-ai-in-india-explained/
article67591640.ece
https://www.ipic.ai/blogs/what-can-gan-technology-paint-for-you/
J. Experiential Learning Creativity & Communication

https://youtu.be/L0Hc3gSdfrw
https://youtu.be/neAojZL7JHI
https://youtu.be/_6R7Ym6Vy_I
https://youtu.be/M8n97cPKNQM

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