CBSE Textbook: Revisiting AI Project Cycle & Ethical Frameworks for AI | Artificial Intelligence for Class 10 PDF Download

 Page 1


 
1.1: AI Project Cycle 
Let’s revisit the concept of the AI Project Cycle. 
 
Introduction 
 
Let us assume that you have to make a greeting card for your mother as it is her birthday. You 
are very excited about it and have thought of many ideas to execute the same. Let us look at 
some of the steps which you might take to accomplish this task: 
 
1. Look for some cool greeting card ideas from different sources. You might go online and 
check out some videos or you may ask someone who knows about it. 
2. After finalising the design, you would make a list of things that are required to make this 
card. 
3. You will check if you have the material with you or not. If not, you could go and get all the 
items required, ready for use. 
4. Once you have everything with you, you will start making the card. 
5. If you make a mistake in the card somewhere which cannot be rectified, you will discard it 
and start remaking it. 
6. Once the greeting card is made, you will gift it to your mother. 
 
These steps show how we plan to execute the tasks around us. Consciously or subconsciously 
our mind makes up plans for every task which we have to accomplish which is why things 
become clearer in our mind. Similarly, if we have to develop an AI project, the AI Project Cycle 
provides us with an appropriate framework which can lead us towards the goal. The AI project 
cycle is the cyclical process followed to complete an AI project. The AI Project Cycle mainly has 
6 stages: 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Page 2


 
1.1: AI Project Cycle 
Let’s revisit the concept of the AI Project Cycle. 
 
Introduction 
 
Let us assume that you have to make a greeting card for your mother as it is her birthday. You 
are very excited about it and have thought of many ideas to execute the same. Let us look at 
some of the steps which you might take to accomplish this task: 
 
1. Look for some cool greeting card ideas from different sources. You might go online and 
check out some videos or you may ask someone who knows about it. 
2. After finalising the design, you would make a list of things that are required to make this 
card. 
3. You will check if you have the material with you or not. If not, you could go and get all the 
items required, ready for use. 
4. Once you have everything with you, you will start making the card. 
5. If you make a mistake in the card somewhere which cannot be rectified, you will discard it 
and start remaking it. 
6. Once the greeting card is made, you will gift it to your mother. 
 
These steps show how we plan to execute the tasks around us. Consciously or subconsciously 
our mind makes up plans for every task which we have to accomplish which is why things 
become clearer in our mind. Similarly, if we have to develop an AI project, the AI Project Cycle 
provides us with an appropriate framework which can lead us towards the goal. The AI project 
cycle is the cyclical process followed to complete an AI project. The AI Project Cycle mainly has 
6 stages: 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Starting with Problem Scoping, you set the goal for your AI project by stating the problem 
which you wish to solve with it. Under problem scoping, we look at various parameters which 
affect the problem we wish to solve so that the picture becomes clearer. 
To proceed, 
 
? You need to acquire data which will become the base of your project as it will help you 
understand what the parameters that are related to problem scoping are. 
 
? You go for data acquisition by collecting data from various reliable and authentic sources. 
Since the data you collect would be in large quantities, you can try to give it a visual image of 
different types of representations like graphs, databases, flow charts, maps, etc. This makes it 
easier for you to interpret the patterns which your acquired data follows. 
 
? After exploring the patterns, you can decide upon the type of model you would build to 
achieve the goal. For this, you can research online and select various models which give a 
suitable output. 
 
? You can test the selected models and figure out which is the most efficient one. 
 
? The most efficient model is now the base of your AI project and you can develop your 
algorithm around it. 
 
? Once the modelling is complete, you now need to test your model on some newly fetched 
data. The results will help you in evaluating your model and improving it. 
 
? Finally, after evaluation, the deployment stage is crucial for ensuring the successful 
integration and operation of AI solutions in real-world environments, enabling them to deliver 
value and impact to users and stakeholders. 
 
 
       1.2: Introduction to AI Domains 
 
Artificial Intelligence becomes intelligent according to the training it gets. For training, the 
machine is fed with datasets. According to the applications for which the AI algorithm is being 
developed, the data fed into it changes. With respect to the type of data fed in the AI model, AI 
models can be broadly categorized into three domains: 
 
 
Page 3


 
1.1: AI Project Cycle 
Let’s revisit the concept of the AI Project Cycle. 
 
Introduction 
 
Let us assume that you have to make a greeting card for your mother as it is her birthday. You 
are very excited about it and have thought of many ideas to execute the same. Let us look at 
some of the steps which you might take to accomplish this task: 
 
1. Look for some cool greeting card ideas from different sources. You might go online and 
check out some videos or you may ask someone who knows about it. 
2. After finalising the design, you would make a list of things that are required to make this 
card. 
3. You will check if you have the material with you or not. If not, you could go and get all the 
items required, ready for use. 
4. Once you have everything with you, you will start making the card. 
5. If you make a mistake in the card somewhere which cannot be rectified, you will discard it 
and start remaking it. 
6. Once the greeting card is made, you will gift it to your mother. 
 
These steps show how we plan to execute the tasks around us. Consciously or subconsciously 
our mind makes up plans for every task which we have to accomplish which is why things 
become clearer in our mind. Similarly, if we have to develop an AI project, the AI Project Cycle 
provides us with an appropriate framework which can lead us towards the goal. The AI project 
cycle is the cyclical process followed to complete an AI project. The AI Project Cycle mainly has 
6 stages: 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Starting with Problem Scoping, you set the goal for your AI project by stating the problem 
which you wish to solve with it. Under problem scoping, we look at various parameters which 
affect the problem we wish to solve so that the picture becomes clearer. 
To proceed, 
 
? You need to acquire data which will become the base of your project as it will help you 
understand what the parameters that are related to problem scoping are. 
 
? You go for data acquisition by collecting data from various reliable and authentic sources. 
Since the data you collect would be in large quantities, you can try to give it a visual image of 
different types of representations like graphs, databases, flow charts, maps, etc. This makes it 
easier for you to interpret the patterns which your acquired data follows. 
 
? After exploring the patterns, you can decide upon the type of model you would build to 
achieve the goal. For this, you can research online and select various models which give a 
suitable output. 
 
? You can test the selected models and figure out which is the most efficient one. 
 
? The most efficient model is now the base of your AI project and you can develop your 
algorithm around it. 
 
? Once the modelling is complete, you now need to test your model on some newly fetched 
data. The results will help you in evaluating your model and improving it. 
 
? Finally, after evaluation, the deployment stage is crucial for ensuring the successful 
integration and operation of AI solutions in real-world environments, enabling them to deliver 
value and impact to users and stakeholders. 
 
 
       1.2: Introduction to AI Domains 
 
Artificial Intelligence becomes intelligent according to the training it gets. For training, the 
machine is fed with datasets. According to the applications for which the AI algorithm is being 
developed, the data fed into it changes. With respect to the type of data fed in the AI model, AI 
models can be broadly categorized into three domains: 
 
 
 
 
Statistical Data 
 
Statistical Data is a domain of AI related to data systems and processes, in which the system 
collects numerous data, maintains data sets and derives meaning/sense out of them. 
The information extracted through statistical data can be used to make a decision about it. 
 
Example of Statistical Data 
 
Price Comparison Websites 
These websites are being driven by lots and lots of data. 
If you have ever used these websites, you would know, 
the convenience of comparing the price of a product 
from multiple vendors in one place. PriceGrabber, 
PriceRunner, Junglee, Shopzilla, DealTime are some 
examples of price comparison websites. Nowadays, price 
comparison websites can be found in almost every 
domain such as technology, hospitality, automobiles, 
durables, apparel, etc. 
 
 
Computer Vision 
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. 
 
Computer vision-related projects translate digital visual data into descriptions. This data is then 
turned into computer-readable language to aid the decision-making process. The main objective 
of this domain of AI is to teach machines to collect information from pixels. 
 
Examples of Computer Vision 
 
Agricultural Monitoring 
 
Computer vision is employed in agriculture for crop 
monitoring, pest detection, and yield estimation. Drones 
with cameras capture aerial images of farmland, which are 
then analysed to assess crop health and optimize farming 
practices. 
 
 
 
Page 4


 
1.1: AI Project Cycle 
Let’s revisit the concept of the AI Project Cycle. 
 
Introduction 
 
Let us assume that you have to make a greeting card for your mother as it is her birthday. You 
are very excited about it and have thought of many ideas to execute the same. Let us look at 
some of the steps which you might take to accomplish this task: 
 
1. Look for some cool greeting card ideas from different sources. You might go online and 
check out some videos or you may ask someone who knows about it. 
2. After finalising the design, you would make a list of things that are required to make this 
card. 
3. You will check if you have the material with you or not. If not, you could go and get all the 
items required, ready for use. 
4. Once you have everything with you, you will start making the card. 
5. If you make a mistake in the card somewhere which cannot be rectified, you will discard it 
and start remaking it. 
6. Once the greeting card is made, you will gift it to your mother. 
 
These steps show how we plan to execute the tasks around us. Consciously or subconsciously 
our mind makes up plans for every task which we have to accomplish which is why things 
become clearer in our mind. Similarly, if we have to develop an AI project, the AI Project Cycle 
provides us with an appropriate framework which can lead us towards the goal. The AI project 
cycle is the cyclical process followed to complete an AI project. The AI Project Cycle mainly has 
6 stages: 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Starting with Problem Scoping, you set the goal for your AI project by stating the problem 
which you wish to solve with it. Under problem scoping, we look at various parameters which 
affect the problem we wish to solve so that the picture becomes clearer. 
To proceed, 
 
? You need to acquire data which will become the base of your project as it will help you 
understand what the parameters that are related to problem scoping are. 
 
? You go for data acquisition by collecting data from various reliable and authentic sources. 
Since the data you collect would be in large quantities, you can try to give it a visual image of 
different types of representations like graphs, databases, flow charts, maps, etc. This makes it 
easier for you to interpret the patterns which your acquired data follows. 
 
? After exploring the patterns, you can decide upon the type of model you would build to 
achieve the goal. For this, you can research online and select various models which give a 
suitable output. 
 
? You can test the selected models and figure out which is the most efficient one. 
 
? The most efficient model is now the base of your AI project and you can develop your 
algorithm around it. 
 
? Once the modelling is complete, you now need to test your model on some newly fetched 
data. The results will help you in evaluating your model and improving it. 
 
? Finally, after evaluation, the deployment stage is crucial for ensuring the successful 
integration and operation of AI solutions in real-world environments, enabling them to deliver 
value and impact to users and stakeholders. 
 
 
       1.2: Introduction to AI Domains 
 
Artificial Intelligence becomes intelligent according to the training it gets. For training, the 
machine is fed with datasets. According to the applications for which the AI algorithm is being 
developed, the data fed into it changes. With respect to the type of data fed in the AI model, AI 
models can be broadly categorized into three domains: 
 
 
 
 
Statistical Data 
 
Statistical Data is a domain of AI related to data systems and processes, in which the system 
collects numerous data, maintains data sets and derives meaning/sense out of them. 
The information extracted through statistical data can be used to make a decision about it. 
 
Example of Statistical Data 
 
Price Comparison Websites 
These websites are being driven by lots and lots of data. 
If you have ever used these websites, you would know, 
the convenience of comparing the price of a product 
from multiple vendors in one place. PriceGrabber, 
PriceRunner, Junglee, Shopzilla, DealTime are some 
examples of price comparison websites. Nowadays, price 
comparison websites can be found in almost every 
domain such as technology, hospitality, automobiles, 
durables, apparel, etc. 
 
 
Computer Vision 
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. 
 
Computer vision-related projects translate digital visual data into descriptions. This data is then 
turned into computer-readable language to aid the decision-making process. The main objective 
of this domain of AI is to teach machines to collect information from pixels. 
 
Examples of Computer Vision 
 
Agricultural Monitoring 
 
Computer vision is employed in agriculture for crop 
monitoring, pest detection, and yield estimation. Drones 
with cameras capture aerial images of farmland, which are 
then analysed to assess crop health and optimize farming 
practices. 
 
 
 
 
 
Surveillance Systems 
 
Computer vision is used in surveillance systems to monitor 
public spaces, buildings, and borders. It can detect suspicious 
activities, track individuals or vehicles, and provide real-time 
alerts to security personnel. 
 
 
Natural Language Processing 
 
Natural Language Processing, abbreviated as NLP, is a branch of artificial intelligence that deals 
with the interaction between computers and humans using the natural language. Natural 
language refers to language that is spoken and written by people, and natural language 
processing (NLP) attempts to extract information from the spoken and written word using 
algorithms. 
The ultimate objective of NLP is to read, decipher, understand, and make sense of human 
languages in a valuable manner. 
 
 
   Examples of Natural Language Processing 
 
 
Email filters 
 
Email filters are one of the most basic and 
initial applications of NLP online. It started 
with spam filters, uncovering certain words or 
phrases that signal a spam message. 
 
 
 
Machine Translation 
 
NLP is used in machine translation systems like Google Translate 
and Microsoft Translator to automatically translate text from 
one language to another. These systems analyze the structure 
and semantics of sentences in the source language and generate 
equivalent translations in the target language. 
Page 5


 
1.1: AI Project Cycle 
Let’s revisit the concept of the AI Project Cycle. 
 
Introduction 
 
Let us assume that you have to make a greeting card for your mother as it is her birthday. You 
are very excited about it and have thought of many ideas to execute the same. Let us look at 
some of the steps which you might take to accomplish this task: 
 
1. Look for some cool greeting card ideas from different sources. You might go online and 
check out some videos or you may ask someone who knows about it. 
2. After finalising the design, you would make a list of things that are required to make this 
card. 
3. You will check if you have the material with you or not. If not, you could go and get all the 
items required, ready for use. 
4. Once you have everything with you, you will start making the card. 
5. If you make a mistake in the card somewhere which cannot be rectified, you will discard it 
and start remaking it. 
6. Once the greeting card is made, you will gift it to your mother. 
 
These steps show how we plan to execute the tasks around us. Consciously or subconsciously 
our mind makes up plans for every task which we have to accomplish which is why things 
become clearer in our mind. Similarly, if we have to develop an AI project, the AI Project Cycle 
provides us with an appropriate framework which can lead us towards the goal. The AI project 
cycle is the cyclical process followed to complete an AI project. The AI Project Cycle mainly has 
6 stages: 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Starting with Problem Scoping, you set the goal for your AI project by stating the problem 
which you wish to solve with it. Under problem scoping, we look at various parameters which 
affect the problem we wish to solve so that the picture becomes clearer. 
To proceed, 
 
? You need to acquire data which will become the base of your project as it will help you 
understand what the parameters that are related to problem scoping are. 
 
? You go for data acquisition by collecting data from various reliable and authentic sources. 
Since the data you collect would be in large quantities, you can try to give it a visual image of 
different types of representations like graphs, databases, flow charts, maps, etc. This makes it 
easier for you to interpret the patterns which your acquired data follows. 
 
? After exploring the patterns, you can decide upon the type of model you would build to 
achieve the goal. For this, you can research online and select various models which give a 
suitable output. 
 
? You can test the selected models and figure out which is the most efficient one. 
 
? The most efficient model is now the base of your AI project and you can develop your 
algorithm around it. 
 
? Once the modelling is complete, you now need to test your model on some newly fetched 
data. The results will help you in evaluating your model and improving it. 
 
? Finally, after evaluation, the deployment stage is crucial for ensuring the successful 
integration and operation of AI solutions in real-world environments, enabling them to deliver 
value and impact to users and stakeholders. 
 
 
       1.2: Introduction to AI Domains 
 
Artificial Intelligence becomes intelligent according to the training it gets. For training, the 
machine is fed with datasets. According to the applications for which the AI algorithm is being 
developed, the data fed into it changes. With respect to the type of data fed in the AI model, AI 
models can be broadly categorized into three domains: 
 
 
 
 
Statistical Data 
 
Statistical Data is a domain of AI related to data systems and processes, in which the system 
collects numerous data, maintains data sets and derives meaning/sense out of them. 
The information extracted through statistical data can be used to make a decision about it. 
 
Example of Statistical Data 
 
Price Comparison Websites 
These websites are being driven by lots and lots of data. 
If you have ever used these websites, you would know, 
the convenience of comparing the price of a product 
from multiple vendors in one place. PriceGrabber, 
PriceRunner, Junglee, Shopzilla, DealTime are some 
examples of price comparison websites. Nowadays, price 
comparison websites can be found in almost every 
domain such as technology, hospitality, automobiles, 
durables, apparel, etc. 
 
 
Computer Vision 
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. 
 
Computer vision-related projects translate digital visual data into descriptions. This data is then 
turned into computer-readable language to aid the decision-making process. The main objective 
of this domain of AI is to teach machines to collect information from pixels. 
 
Examples of Computer Vision 
 
Agricultural Monitoring 
 
Computer vision is employed in agriculture for crop 
monitoring, pest detection, and yield estimation. Drones 
with cameras capture aerial images of farmland, which are 
then analysed to assess crop health and optimize farming 
practices. 
 
 
 
 
 
Surveillance Systems 
 
Computer vision is used in surveillance systems to monitor 
public spaces, buildings, and borders. It can detect suspicious 
activities, track individuals or vehicles, and provide real-time 
alerts to security personnel. 
 
 
Natural Language Processing 
 
Natural Language Processing, abbreviated as NLP, is a branch of artificial intelligence that deals 
with the interaction between computers and humans using the natural language. Natural 
language refers to language that is spoken and written by people, and natural language 
processing (NLP) attempts to extract information from the spoken and written word using 
algorithms. 
The ultimate objective of NLP is to read, decipher, understand, and make sense of human 
languages in a valuable manner. 
 
 
   Examples of Natural Language Processing 
 
 
Email filters 
 
Email filters are one of the most basic and 
initial applications of NLP online. It started 
with spam filters, uncovering certain words or 
phrases that signal a spam message. 
 
 
 
Machine Translation 
 
NLP is used in machine translation systems like Google Translate 
and Microsoft Translator to automatically translate text from 
one language to another. These systems analyze the structure 
and semantics of sentences in the source language and generate 
equivalent translations in the target language. 
 
 
       1.3: Ethical Frameworks for AI 
 
Frameworks 
 
Frameworks are a set of steps that help us in solving problems. It provides a step-by-step guide 
for solving problems in an organized manner. Moreover, frameworks offer a structured 
approach to problem-solving, ensuring that all relevant factors and considerations are taken into 
account. Additionally, they serve as a common language for communication and collaboration, 
facilitating the sharing of best practices and promoting consistency in problem- solving 
methodologies. 
You may have used frameworks without knowing it! Can you think of one framework you have 
come across during your AI journey? 
 
 
Ethical Frameworks 
 
We know that ethics are a set of values or morals which help us 
separate right from wrong. Frameworks are step-by-step 
guidance on solving problems. 
 
Hence, Ethical frameworks are frameworks which help us ensure 
that the choices we make do not cause unintended harm. 
Furthermore, ethical frameworks provide a systematic approach 
to navigating complex moral dilemmas by considering various 
ethical principles and perspectives. By utilizing ethical 
frameworks, individuals and organizations can make well- 
informed decisions that align with their values and promote 
positive outcomes for all stakeholders involved.