_AI UNIT 1 CLASS 9.pdf

ARTIFICIAL INTELLIGENCE CURRICULUM Curated with support from Intel® Acknowledgements Patrons: Mr. Rahul Singh, IAS, Chairperson, Central Board of Secondary Education Guidance and Support: Dr. Biswajit Saha, Director (Skill Education & Training), Central Board of Secondary Education Ms. Shweta Khurana, Senior Director APJ, Government Partnerships and Initiatives, International Government Affairs Group, Intel Education Value adder, Curator and Coordinator: Sh. Ravinder Pal Singh, Joint Secretary, Department of Skill Education, Central Board of Secondary Education Ms Saloni Singhal, Program Manager APJ, Intel Digital Readiness Programs Ms. Sarita Manuja, Educational Consultant & Program Director, NHES Ms. Shatarupa Dasgupta, National Program Manager, Intel Digital Readiness Program Content Curation Team: Ms. Ambika Saxena, Intel AI for Youth Coach Ms. Prachi Chandra, Intel AI for Youth Coach Ms. Shilpa Sethi, DAV Public School, Sector-14, Gurugram Ms. Shipra Panigrahi, Indirapuram Public School, Ghaziabad Ms. Sonu Lohchab, D.A.V. Public School, Sector-49, Gurugram Ms. Ritu Debnath, Gurukul Global School Sec-13, Chandigarh Ms. Anshu Banerjee, Uttam School for Girls, Ghaziabad Ms. Yukti, Army Public School, Meerut Ms. A. Sayeesubbulakshmi, Delhi Public School, Bangalore (South), Bengaluru About the Book In the rapidly evolving landscape of the global digital economy, Artificial Intelligence (AI) stands as the cornerstone of future innovation and growth. Recognizing this, nations worldwide are strategically positioning themselves to harness the transformative potential of AI. India, in particular, views AI not just as a technological advancement but as an opportunity to foster inclusive economic growth and social development. At the forefront of this vision is the Central Board of Secondary Education (CBSE), which is on a mission to equip the next generation with the skills and mindset necessary to thrive in an AI- driven world. As part of this initiative, CBSE has collaborated with Intel India since 2019, to curate a comprehensive Facilitator Handbook and accompanying AI training resources. The resources aim to empower educators and students alike, fostering a deeper understanding of AI concepts and their practical applications. This edition of the ‘AI Facilitator Handbook’ is more than just a curriculum; it's a roadmap for students to navigate the complexities of AI with confidence and creativity. Enriched with updated AI tech and social concepts, real-life examples, and AI project development guides using no-code tools, this book is designed to inspire students to not only understand AI but also to leverage it to drive positive social change. Key features include: Enhanced Content: Concepts are presented with further elaboration and fresh examples to facilitate deeper engagement and comprehension. Real-Life Examples: Additional real-world scenarios are integrated to offer clearer explanations, making complex AI concepts accessible to students. AI enabled social impact solutions: Students are encouraged to develop AI solutions for social impact in a straightforward manner, fostering understanding and empowerment. Use Case Walkthroughs: Practical implementation of AI concepts is demonstrated across various domains, enabling students to grasp their real-world applications. CBSE Grade IX AI Curriculum 2024-25 Units/ Sessions Topics Hours Subunits 1.1 Understanding Define Artificial Intelligence (AI) 10 AI: Domains and The applications of AI in everyday life Applications The three domains of AI and their applications 1.2 AI The AI Project The importance of the AI project cycle. 30 Reflection, Cycle- II To structure the AI problem statement Project with the AI project cycle Cycle and Ethics 1.3 AI Ethics- II The difference between ethics and 15 morality. The ethical scenarios faced while building AI solutions AI bias and to identify bias in AI 2.1 Basics of Data Data Literacy and its impact 10 Literacy How to become Data literate? Data security and privacy Best practices for Cyber Security 2.2 Acquiring Data, Types of data 20 Processing, and Sources of data Data Interpreting Data Best Practices for acquiring data Literacy Features of data and Data Preprocessing Importance of Data Interpretation Tools used for Data Interpretation 2.3 Project Data visualization & its importance 20 Interactive Data Visualization of data with a No-Code tool Dashboard & Create a simple and interactive chart Presentation with a No-Code tool 3.1 Importance of The applications of Mathematics in AI 5 Math in AI The different mathematical concepts important for understanding AI Math for AI 3.2 (Statistics Statistics Use of statistics in different AI 10 & applications Probability) 3.3 Probability Use of probability in different AI 10 applications 4 Introduction to Definition and Overview 20 Generative AI Applications and Use cases Total 150 hours Unit 1 AI Reflection Unit 1.1 – Understanding AI Purpose: Introduction to the program. The Artificial Intelligence Curriculum hopes to inspire AI-Readiness in you. At the end of this program, we hope you will get a deep understanding of AI, access to AI-powered tools and the ability to create solutions with AI. Welcome to an introduction to Artificial Intelligence! What do you think Artificial Intelligence is? ______________________________________________________________________________________________________ ______________________________________________________________________________________________________ ______________________________________________________________________________________________________ What do you want to learn about AI? ______________________________________________________________________________________________________ ______________________________________________________________________________________________________ How do you think we should go about it? ______________________________________________________________________________________________________ ______________________________________________________________________________________________________ ______________________________________________________________________________________________________ What will you learn? ______________________________________________________________________________________________________ ______________________________________________________________________________________________________ ______________________________________________________________________________________________________ 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 a form of intelligence; a type of technology and a field of study. AI theory and development of computer systems (both machines and software) are able to perform tasks that normally require human intelligence. Artificial Intelligence covers a broad range of domains and applications and is expected to impact every field in the future. Overall, its core idea is to build machines and algorithms which are capable of performing computational tasks that would otherwise require human-like brain functions. What is Artificial Intelligence? When a machine… Mimics human intelligence Can solve real-world problems Improves on its own from past experiences Can predict and make decisions on its own …it can be termed as Artificially Intelligent! How to make machine intelligent? AI Data Algorithm Machine! How do you think Artificial Intelligence can help you as you go about your daily life? Fill in your ideas below. Activity: Game Time In this activity, you will visit a few online resources to play games and experience the power of AI. Resources: Game 1 (Rock, Paper and Scissors): Rules for playing Game 1: ✔ Type the link below to launch the tool ✔ Scroll down and check the box “I Agree”. Click on Let’ Go ✔ You may turn off the camera to select the moves directly from the screen ✔ Start the game by selecting "rock", "scissors" or "paper" ✔ Choose continuously until you create a pattern and check how AI tries to win. Visit https://next.rockpaperscissors.ai/ to play the game online. Game 2 (Semantris): Rules for playing Game 2: ✔ Type the link given and click on launch experiment option to start the game. ✔ Click on Play Arcade option to start playing the game. ✔ Each time AI gives you the highlighted clue, you are supposed to enter the most closely associated word to get more scores. ✔ Check how machine understands your words Visit https://research.google.com/semantris/ to experience the magic online. Game 3 (Quick, Draw): Rules for playing Game 3: ✔ Type the link and click on Let’s Draw option to start playing the game. ✔ An item will be named on the screen for you to draw in 20 seconds after you click on Got it! ✔ AI will guess whatever you draw on the white screen. ✔ Try drawing 6 objects correctly in a row to win the game! Launch the game at https://quickdraw.withgoogle.com/ It’s time for you to try them out! 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. Write down three rules in the given spaces you would set before playing any game. Rule 1 Rule 2 Rule 3 Purpose: Expose you to the 3 domains of AI (Natural Language Processing, Computer Vision, and Data for AI). Brief: You will go through three AI games in the form of a challenge. Game Descriptions: Rock, Paper & Scissors: A game based on Data for AI where the machine tries to predict the next move of the participant. It is a replica of a basic rock, paper and scissors game where the machine tries to win ahead by learning from the participant’s previous moves. Semantris: A game based on Natural Language Processing is a set of word association games powered by machine-learned, natural language understanding technology. Each time you enter a clue, the AI looks at all the words in play and chooses the ones it thinks are most related. Quick, Draw: A game based on Computer Vision developed 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. We are going to get serious now! You are challenged by an eccentric data scientist, to solve 3 challenges he designed. You have 60 mins before he inserts a virus into every electronic device in the world! We will work in groups of 4-5 students now. Whether you are ready or not, the countdown is going to start now! Grab a seat in front of the computer and start your challenge. Game 1: The AI Game Challenge Guess what……? ❖ Here are some visuals that will help you guess the games you are going to play. You have 10 seconds to guess and write the name of the games below: Guess the game. Have you tried it before? Guess the game. Have you tried it before? Guess the game. Have you tried it before? Pair Activity: List the different sources Team up with a partner and let the challenge begin! from where you can collect data. Game 1: Rock, Paper and Scissors (based on Data Science) Write three things you learnt from the game. ________________________________________________________ ________________________________________________________ _______________________________________________________ Game 2: Semantris What is Natural (based on Natural Language Processing - NLP) Language Processing? Mention three things you understood about the game. ________________________________________________________ ________________________________________________________ _______________________________________________________ What is Computer Vision? Game 3: Quick, Draw (based on Computer Vision – CV) Did you face any difficulty while playing this game? How did you overcome this? ________________________________________________________ ________________________________________________________ _______________________________________________________ Depending on the type of data, we can divide AI into different domains: Computer Vision, is an AI domain works with videos and images enabling machines to interpret and understand visual information. CV Natural Language Processing (NLP) is an AI domain focused on textual data enabling machines to comprehend, generate, and manipulate human language. NLP Statistical Data refers to statistical techniques to analyse, interpret and draw insights from numerical/tabular data. Statistical acquired data. Data Some AI Applications Face Lock in Smartphones Smartphones nowadays come with the feature of face locks in which the smartphone’s owner can set up his/her face as an unlocking mechanism for it. The front camera detects and captures the face and saves its features during initiation. Next time onwards, whenever the features match, the phone is unlocked. Smart assistants Smart assistants like Apple’s Siri and Amazon’s Alexa recognize patterns in speech, then infer meaning and provide a useful response. Fraud and Risk Detection Finance companies were fed with bad debts and losses every year. However, they had a lot of data which used to get collected during the initial paperwork while sanctioning loans. They decided to bring in data scientists to rescue them from losses. Over the years, banking companies learned to divide and conquer data via customer profiling, past expenditures, and other essential variables to analyse the probabilities of risk and default. Moreover, it also helped them to push their banking products based on customer’s purchasing power. Medical Imaging: For the last decades, computer supported medical imaging application that has been a trustworthy help for physicians. It doesn’t only create and analyse images, but also becomes an assistant and helps doctors with their interpretation. The application is used to read and convert 2D scan images into interactive 3D models that enable medical professionals to gain a detailed understanding of a patient’s health condition. Let’s Discuss Why should these three games be relevant for AI awareness? Group Activity: Reflect and Analyse Purpose: To understand how three AI domains are inter-related to each other. You will get to know that even if these three domains of AI – Natural Language Processing, Computer Vision and Data for AI are quite distinct from each other, they together constitute the concept of Artificial Intelligence. Take three different colour strands and work them into a braid. See how long your braid can become within 30 seconds!! Ready? Go!!! Let’s understand: To understand AI, we draw an analogy from the three strands in a braid. One is the Data strand, the second is the Natural Language Processing strand and the third strand is the Computer Vision. They all together constitute the concept called Artificial Intelligence. Revision Time Part A Quiz Time: AI Quiz 1. Which one of the following is an application of AI? a. Remote controlled Drone b. Self-Driving Car c. Self-Service Kiosk d. Self-Watering Plant System 2. This language is easy to learn and is one of the most popular languages for AI today: a. C++ b. Python c. Ruby d. Java 3. This field is enabling computers to identify and process images as humans do: a. Face Recognition b. Model-view-controller c. Computer Vision d. Eye-in-Hand System 4. What does NLP stand for in AI? a. Neutral Learning Projection b. Neuro-Linguistic Programming c. Natural Language Processing d. Neural Logic Presentation 5. Which of the following is not a domain of artificial intelligence? a. Data Management System b. Computer Vision c. Natural Language Processing d. Data Science 6. How excited are you about this AI curriculum? a. Very Excited! b. A bit excited c. Same as always d. Not excited at all Part B 1. How can AI be used as a tool to transform the world into a better place? 2. Can you list down a few applications in your smartphone that widely make use of computer vision? 3. Draw out the difference between the three domains of AI with respect to the types of data they use. 4. Identify the features and the domain of AI used in them: (a) (b) (c) 5. Separate the following areas based on the kinds of domains widely used in them: a. Crop productivity b. Traffic regulation c. Maps and navigation d. Text editors and autocorrect e. Identifying and predicting disease 6. After the pandemic, it’s been essential for everyone to wear a mask. However, you see many people not wearing masks when in public places. Which domain of AI can be used to build a system to detect people not wearing masks? 7. Search for an online game that recognizes the image drawn by you. Write down the observations including the AI domain used by it. Teamwork: Pair yourself up with your classmates to come up with the dialogues. One out of the two will act like a chatbot answering stress-related queries during exams and the other can ask the questions. For example, you can ask ways to remain optimistic during exams and your friend acting as the chatbot may respond with answers like meditating, strolling through a park, etc. 1.2 AI Project Cycle Lesson Title: AI Project Cycle Approach: Interactive Session Summary: Students will learn about the AI Project Cycle and get familiar with it. Learning Objectives: Students will know how they can get started on an AI project. Learning Outcomes: Describe the stages in the AI project cycle. Pre-requisites: Basic computer literacy Key-concepts: AI project cycle Let us think! Problem Scoping means __________________________________________________________________________________ __________________________________________________________________________________ Data Acquisition means __________________________________________________________________________________ __________________________________________________________________________________ Data Exploration means _________________________________________________________________________________ _________________________________________________________________________________ Modelling means _________________________________________________________________________________ _________________________________________________________________________________ Evaluation means _________________________________________________________________________________ __________________________________________________________________________________ Deployment means __________________________________________________________________________________ __________________________________________________________________________________ Let us understand! Let us go through the AI project cycle with the help of an example. Problem: Pest infestation damages crops The cotton industry in India consists of 6 million local farmers. Cotton crops frequently get infected with the Pink Bollworm. It is difficult to see these insects with the naked eye. Small farmers find it very difficult to get rid of these insects. They do not have advanced tools and techniques to protect their plants from Pink Bollworm. Can we solve this problem with AI? How? Watch the video at this link - https://www.youtube.com/watch?v=LP_A4jydmz4 Ask students about possible solutions to this problem before moving ahead. Invite them to think of non-AI solutions as well. Now that you are aware of AI concepts, plan to use them in accomplishing your task. Start with listing down all the factors which you need to consider to save the cotton crop. This system aims to: ___________________________________________________________________________________ ___________________________________________________________________________________ ___________________________________________________________________________________ While finalising the aim of this system, you scope the problem which you wish to solve with the help of your project. This is Problem Scoping. Now, as you interact with the farmers, you get to know different types of worms affecting the cotton crop. You will collect the following data Images of the pest Farmer names Village names Farm size Pesticide usage As you start collecting the images, names of villages, farmers and other details you actually acquire data. This data now becomes the base of your pest management system. Note that the data needs to be accurate and reliable as it ensures the efficiency of your system. This is known as Data Acquisition. After acquiring the required data, you realise that it is not uniform. Some images are small in size while others are big. Some images and other data are missing while you have multiple copies of others. So, we clean the data, try to make it uniform and fill in the missing data to make it more understandable. By exploring the data, researchers can identify patterns and trends related to Pink Bollworm infestations, pesticide usage, crop yields, and other relevant factors. At this stage, you try to interpret some useful information out of the data you have acquired. For this, you explore the data and try to put it uniformly for a better understanding. This is known as Data Exploration. After exploring the data, now you know that you need to develop an AI-enabled app using which the farmers will click the pictures of the collected pests using the phone camera. The AI app then decides whether the image is valid. Based on the number of pests recognized by the system and rules laid out by entomologists, recommendations are displayed To implement your idea, you now look at different AI-enabled algorithms which work on Computer Vision (since you are working on visual data). You go through several models and select the ones which match your requirements. After choosing the model, you implement it. This is known as the Modelling stage. Your pest management system is now complete! You test it by first emptying the trap of pests onto a blank sheet of paper and opening the app, then clicking pictures of pests. You notice that the results were 70% correct. After evaluating this model, you work on other shortlisted AI algorithms and work on them. You test the algorithms to ___________________________________________________________________________________ ___________________________________________________________________________________ As you move towards deploying your model in the real-world, you test it in as many ways as possible. The stage of testing the models is known as Evaluation. In this stage, we evaluate each and every model tried and choose the model which gives the most efficient and reliable results. After proper testing, you deploy your pest management app by getting it installed on farmer’s mobile phones. The last stage where you deploy your solution based on the model you’ve selected is known as Deployment. Let us look at the main features of CottonAce app- CottonAce app ▪ CottonAce is a mobile application that can help farmers protect their crops from pests. ▪ CottonAce uses AI to warn the farmers about a possible pest infestation. ▪ It aids farmers in – ▪ Determining the correct amount of pesticides ▪ Knowing the right time to spray pesticides ▪ Seeking professional help as needed. How does it work? ▪ A farmer sets up a trap to capture pests. ▪ Take a picture of the captured pests. ▪ Upload the picture on the app. ▪ The app detects the insect, level of infestation, and the required measures to cure it. You can add ‘Small farms that used the app saw jumps in profit margins of up to 26.5 percent. A drop-in pesticide costs of up to 38 percent was also observed’. What is AI project cycle mapping? Mapping the individual steps in an AI project to the steps in the AI project cycle. Let us map the steps of Pest Management project to the steps in the AI project cycle. Why do we need an AI Project Cycle? Conclusion: “Greater efficiency implies that the solution can be developed faster and in a more convenient way. Due to modularity, the complex problem of cotton diseases and the process of making a solution for it can be broken down into simpler steps”. AI Project Cycle – Defined! What you did just now was an example of AI Project Cycle. Starting with Problem Scoping, you set the goal for your AI project by stating the problem which you wish to solve with it. AI project cycle is the cyclical process followed to complete an AI project. AI project cycle takes us through different steps involved in a project. AI project cycle helps us: ▪ to create better AI projects easily ▪ to create AI projects faster ▪ to understand the process 1.2.1 Problem Scoping Let us start with the first step of AI Project cycle – Problem Scoping. Let us Recap What according you does Problem Scoping mean? Write in your words below: __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ It is a fact that we are surrounded by problems. They could be small or big, sometimes ignored or sometimes even critical. Many times, we become so used to a problem that it becomes a part of our life. Identifying such a problem and having a vision to solve it, is what Problem Scoping is about. Title: Problem Scoping Approach: Instructor-led Interactive Session + Activity Summary: Students will be introduced to the 4Ws problem Canvas and Problem Statement template. They will be able to set goal for their AI projects to solve problems around them. Learning Objectives: Students will know how they can get started on an AI project. To problem scope with the help of template/worksheet. Learning Outcomes: Apply the problem scoping framework. Frame a Goal for the project. Pre-requisites: Basic computer literacy Key-concepts: Problem scoping Session Preparation Logistics: For a class of 40 Students [Group activity – Groups of 4] Purpose: Understanding how to narrow down to a problem statement from a broad theme. Say: “Let us now start with the first stage of AI Project Cycle that is – Problem Scoping! As we have understood, Problem Scoping means selecting a problem which we night want to solve using our AI knowledge.” Brief: Students will be selecting a theme either out of those mentioned in the handbook or from anywhere outside. They will then look inside the theme and find out topics where problems exist. They need to understand the vastness of a theme because of which one needs to go deeper. After listing down the topics, they will then find out various problems which exist under them. These problems will now be very specific as they have been narrowed down from a broader perspective. Ask the students to select any one problem out of the ones they scoped and write it as the goal of their project. Doing this, gives them a clear vision as to what exactly are they looking forward to solve using their AI knowledge. Let us now start scoping a problem. Look around you and select a theme which interests you the most. Suggested themes are: You can either select any one out of these or you can think of one on your own. For more options, you can also refer to the 17 Sustainable Development Goals we discussed in the Purpose module. Your selected theme is: ___________________________________________________________________________________ Why did you select this theme? ___________________________________________________________________________________ ___________________________________________________________________________________ ___________________________________________________________________________________ ___________________________________________________________________________________ As we know, a theme is a broad term which covers all the aspects of relevance under it. For example: In Agriculture, there are pest issues, yield rates, sowing and harvesting patterns, etc. all being very different from each other but still a part of the Agriculture theme. Thus, to effectively understand the problem and elaborate it, we need to select one topic under the theme. Some examples are: Theme: Digital Literacy Topics: Online learning platforms, digital awareness, e-books, etc. Theme: Health Topics: Medicinal Aid, Mobile Medications, Spreading of diseases, etc. Theme: Entertainment Topics: Media, Virtual Gaming, Interactive AVs, Promotions etc. Our Sun is here to throw more light on this! Go back to your selected Theme, select various Topics related to your theme and fill them up in the rays of this sun. Choose one Topic out of the ones mentioned in the rays of the Sun above, and fill it in below: ___________________________________________________________________________________ Let us now list down the problems which come under our Topic. You can recall daily life scenarios where you may have witnessed problems related to the Topic of your choice. Also, you can go online and research around your chosen topic. Fill up the problems that you find under your topic below. Great! We now know that there exist lot of problems to be solved around us! Thus, to set up the GOAL of your project, select one problem out of the ones listed above which you want to solve using your AI knowledge. This Problem now becomes the target of your AI project and helps you getting a clear vision of what is to be achieved. Let us now frame the selected problem as a goal. For example, a goal can be stated as How might we help farmers determine the best times for seeding and for sowing their crops? It’s your turn now! Write the Goal of your project below: ___________________________________________________________________________________ ___________________________________________________________________________________ ___________________________________________________________________________________ ___________________________________________________________________________________ ___________________________________________________________________________________ ___________________________________________________________________________________ ___________________________________________________________________________________ ___________________________________________________________________________________ Since you have now determined the Goal of your project, let’s start working around it. 4Ws Problem Canvas Purpose: To understand step by step how problem scoping is done using the 4Ws framework. Say: “We are now going to go through the 4Ws Problem Canvas. This canvas helps us in identifying 4 crucial parameters we need to know for solving a problem. So, what are the 4Ws? It refers to Who, What, When and Why.” “Let’s start with who. In this stage, we are looking at the person who is having the problem, they are also known as the stakeholders of the problem.” “Next we have what. In this stage, you consider the nature of the problem. What is the problem and how do you know that it is a problem? Is there evidence to support that it is a problem?” “Next we will ask Where does the problem arise? In this we describe the context of the problem.” The 4Ws Problem canvas helps you in identifying the key elements related to the problem. Let us go through each of the blocks one by one. Who? The “Who” block helps you in analysing the people getting affected directly or indirectly due to it. Under this, you find out who the ‘Stakeholders’ to this problem are and what you know about them. Stakeholders are the people who face this problem and would be benefited with the solution. Let us fill the “Who” canvas! Who? Who are the Stakeholders? What do you know about them? What? Under the “What” block, you need to look into what you have on hand. At this stage, you need to determine the nature of the problem. What is the problem and how do you know that it is a problem? Under this block, you also gather evidence to prove that the problem you have selected actually exists. Newspaper articles, Media, announcements, etc. are some examples. Let us fill the “What” canvas! What? What is the problem? ____________________________________________________________________________________ _____________________________________________________________________________________ ______________________________________________________________________________________ How do you know that it is a problem? (Is there any evidence?) ______________________________________________________________________________________ __________________________________________________________________________ Where? Now that you know who is associated with the problem and what the problem actually is; you need to focus on the context/situation/location of the problem. This block will help you look into the situation in which the problem arises, the context of it, and the locations where it is prominent. Let us fill the “Where” canvas! Where? What is the context/ situation the stakeholders experience the problem? _______________________________________________________________________________________ _______________________________________________________________________________________ Where is the problem located? _______________________________________________________________________________________ _______________________________________________________________________________________ Why? You have finally listed down all the major elements that affect the problem directly. Now it is convenient to understand who the people that would be benefitted by the solution are; what is to be solved; and where will the solution be deployed. These three canvases now become the base of why you want to solve this problem. Thus, in the “Why” canvas, think about the benefits which the stakeholders would get from the solution and how would it benefit them as well as the society. Let us fill the “Why” canvas! Why? Why will this solution be of value to the stakeholders? _______________________________________________________________________________________ _______________________________________________________________________________________ How will the solution improve their situation? _______________________________________________________________________________________ _______________________________________________________________________________________ Problem Statement Template Purpose: To understand how to phrase a problem statement using the Problem Statement Template. Say: “This is a problem statement template. It is used to frame the 4ws into a paragraph to describe your problem, the stakeholders involved and how solving the problem would benefit them.” Ask the students to fill the problem statement template on the basis of how they have filled the 4Ws Problem canvas. In the end, they should be able to get a statement describing the problem which they wish to solve considering the stakeholders, context of the problem and benefit of its solution. After filling the 4Ws Problem canvas, you now need to summarise all the cards into one template. The Problem Statement Template helps us to summarise all the key points into one single Template so that in future, whenever there is a need to look back at the basis of the problem, we can take a look at the Problem Statement Template and understand the key elements of it. Problem Statement Template with space to fill details according to your Goal: Our [stakeholders] Who has a problem that [issue, problem, need] What when / while [context, situation]. Where An ideal solution would [benefit of solution for them] Why Now let us create a problem statement template for our Pest management case study 4W canvas for Pest Management Our Farmers Who has a problem that Cotton Crops got infected with pest -Pink Ballworm What when / while On the crops in the field Where An ideal solution would To create an AI-enabled app that aids farmers in – Why ▪ Determining the correct amount of pesticides ▪ Knowing the right time to spray pesticides ▪ Increase in Production ▪ Increase in the profit share of the farmers. Revision Time 1. What are the various stages of Al Project Cycle? Can you explain each with an example? 2. How is an Al project different from an IT project? 3. Explain the 4Ws problem canvas in problem scoping. 4. Why is there a need to use a Problem Statement Template during problem scoping? 5. What is Problem Scoping? What are the steps of Problem Scoping? 6. Who are the stakeholders in the problem scoping stage? 1.2.2 Data Acquisition Lesson Title: Data Acquisition Approach: Interactive Session + System Maps Summary: Students will learn how to acquire data from reliable and authentic sources and will understand how to analyse the data features which affect their problem scoped. Also, they will learn the concept of System Maps Learning Objectives: Students will learn various ways to acquire data. Students will learn about data features. Students will learn about System Maps. Learning Outcomes: Identify data required regarding a given problem. Draw System Maps. Pre-requisites: Basic computer literacy Key-concepts: Develop an understanding of reliable and authentic data sources. System Mapping In the previous module, we learnt how to scope a problem and set a Goal for the project. After setting the goal, we listed down all the necessary elements which are directly/indirectly related to our problem. This was done using the 4Ws problem canvas. 4Ws were: 1. Who? a. Who are the stakeholders? b. What do we know about them? 2. What? a. What is the problem? b. How do you that it is a problem? (is there an evidence?) 3. Where? a. What is the context/situation the stakeholders experience this problem? b. Where is the problem located? 4. Why? a. What would hold value for the stakeholders? b. How will the solution improve their situation? To summarise, we then go for the problem statement template where we put in all the details together at one place. Our [Stakeholders] has/have a problem that [issue, problem, need] when/while [context, situation]. An ideal situation would be [benefit of solution for them]. What is Data Acquisition? As we move ahead in the AI Project Cycle, we come across the second element which is: Data Acquisition. As the term clearly mentions, this stage is about acquiring data for the project. Let us first understand what is data. Data can be a piece of information or facts and statistics collected together for reference or analysis. Whenever we want an AI project to be able to predict an output, we need to train it first using data. For example, If you want to make an Artificially Intelligent system which can predict the salary of any employee based on his previous salaries, you would feed the data of his previous salaries into the machine. This is the data with which the machine can be trained. Now, once it is ready, it will predict his next salary efficiently. The previous salary data here is known as Training Data while the next salary prediction data set is known as the Testing Data. For better efficiency of an AI project, the Training data needs to be relevant and authentic. In the previous example, if the training data was not of the previous salaries but of his expenses, the machine would not have predicted his next salary correctly since the whole training went wrong. Similarly, if the previous salary data was not authentic, that is, it was not correct, then too the prediction could have gone wrong. Hence…. For any AI project to be efficient, the training data should be authentic and relevant to the problem statement scoped. Data Features Purpose: The purpose of this section is to learn what data features are and how to find them for the problem scoped. Say: “We’ve come to the stage of data acquisition, how do we know what data to get based on the problem statement? We need to visualise the factors which affect the problem statement. For this, we need to extract the Data Features for the problem scoped. Now try to find out what are the parameters which affect your problem statement directly or indirectly and list them down below.” Look at your problem statement once again and try to find the data features required to address this issue. Data features refer to the type of data you want to collect. In our previous example, data features would be salary amount, increment percentage, increment period, bonus, etc. Acquiring Data from reliable sources Purpose: The purpose of this section is to identify reliable and authentic data sources for its acquisition. Say: “After finding out the Data Features, we now need to acquire the same. There exist various sources from which the data can be acquired. Do all the sources have authentic data? What if we do not get appropriate data? Data plays an important part of the AI project as it creates the base on which the AI project is built. Therefore, the data acquired should be authentic, reliable and correct. Also, the acquisition methods shall be authentic so that our project does not create any sort of conflicts with anyone.” After mentioning the Data features, you get to know what sort of data is to be collected. Now, the question arises- From where can we get this data? There can be various ways in which you can collect data. Some of them are: Surveys Web Scraping Sensors Cameras Observations API (Application Program Interface) Sometimes, you use the internet and try to acquire data for your project from some random websites. Such data might not be authentic as its accuracy cannot be proved. Due to this, it becomes necessary to find a reliable source of data from where some authentic information can be taken. At the same time, we should keep in mind that the data which we collect is open-sourced and not someone’s property. Extracting private data can be an offense. One of the most reliable and authentic sources of information are the open-sourced websites hosted by the government. These government portals have general information collected in suitable format which can be downloaded and used wisely. Some of the open-sourced Govt. portals are: data.gov.in, india.gov.in List down ways of acquiring data for a project below: 1. _____________________________________________________________________________________ _____________________________________________________________________________________ 2. _____________________________________________________________________________________ _____________________________________________________________________________________ 3. _____________________________________________________________________________________ _____________________________________________________________________________________ System Maps Session Preparation Logistics: For a class of 40 students [Group Activity – Groups of 4] Materials Required: ITEM QUANTITY Computers 10 Chart Paper 10 Sketch-Pens 40 Resources: Link to make System maps Online using an Animated tool: https://ncase.me/loopy/ Purpose: The purpose of this section is to introduce the concepts System Maps and its elements, relationships and feedback loops. Say: “Now that we have listed all the Data features, let us look at the concept of System Maps. System Maps help us to find relationships between different elements of the problem which we have scoped. It helps us in strategizing the solution for achieving the goal of our project. Here is an example of a System very familiar to you – Water Cycle. The major elements of this system are mentioned here. Take a look at these elements and try to understand the System Map for this system. Also take a look at the relations between all the elements. After this, make your own system map for the data features which you have listed. You can also use the online animated tool for creating your System Maps.” Brief: We use system maps to understand complex issues with multiple factors that affect each other. In a system, every element is interconnected. In a system map, we try to represent that relationship through the use of arrows. Within a system map, we will identify loops. These loops are important because they represent a specific chain of causes and effects. A system typically has several chains of causes and effects. You may notice that some arrows are longer than others. A longer arrow represents a longer time for a change to happen. We also call this a time delay. To change the outcome of a system, as a change maker, we have two options - change the elements in a system or change the relationships between elements. It is usually more effective to change the relationship between elements in a system. You may also notice the use of ‘+’ signs and ‘-’ signs. These are an indicator of the nature of the relationship between elements. What we did was a very basic introduction to systems thinking, you can use Google to find more detailed information on how to make systems maps. A system map shows the components and boundaries of a system and the components of the environment at a specific point in time. With the help of System Maps, one can easily define a relationship amongst different elements which come under a system. Relating this concept to our module, the Goal of our project becomes a system whose elements are the data features mentioned above. Any change in these elements changes the system outcome too. For example, if a person received 200% increment in a month, then this change in his salary would affect the prediction of his future salary. The more the increment presently, the more salary in future is what the system would predict. Here is a sample System Map: The Water Cycle The concept of Water cycle is very simple to understand and is known to all. It explains how water completes its cycle transforming from one form to another. It also adds other elements which affect the water cycle in some way. The elements which define the Water cycle system are: Clouds Snow Underground Rivers Soil Oceans Trees Land Animals Let us draw the System Map for the Water Cycle now. In this System Map, all the elements of the Water cycle are put in circles. The map here shows cause & effect relationship of elements with each other with the help of arrows. The arrow- head depicts the direction of the effect and the sign (+ or -) shows their relationship. If the arrow goes from X to Y with a + sign, it means that both are directly related to each other. That is, If X increases, Y also increases and vice versa. On the other hand, If the arrow goes from X to Y with a – sign, it means that both the elements are inversely related to each other which means if X increases, Y would decrease and vice versa. Now, it’s your turn to build your own System Map! Considering the data features for your problem, draw a system map in the box provided. (Hint: You can also use this animated tool for drawing and understanding system maps: https://ncase.me/loopy/) Revision Time 1. How will you differentiate between Training Data and Testing Data? Elaborate with examples. 2. Name various methods for collecting data. For each method, can you name at least one project in which you may use that method of data collection? 3. What must you keep in mind while collecting data so it is useful? 4. Imagine you are responsible to enable farmers from a village to take their produce to the market for sale. Can you draw a system map that encompasses all the steps and factors involved? 5. Name a few government websites from where you can get open-source data. 1.2.3 Data Exploration Title: Data Exploration Approach: Activity Summary: Students will explore different types of graphs used in data visualization and will be able to find trends and patterns out of it. Learning Objectives: Students will explore various types of graphical representations. Students will learn how to visualize the data they have. Learning Outcomes: Recognize different types of graphs used in data visualization. Exploring various patterns and trends out of the data explored. Pre-requisites: Basic computer literacy Key-concepts: Data Visualization Let us Recap! Quiz Time! 1. Which one of the following is the second stage of AI project cycle? a. Data Exploration b. Data Acquisition c. Modelling d. Problem Scoping 2. Which of the following comes under Problem Scoping? a. System Mapping b. 4Ws Canvas c. Data Features d. Web scraping 3. Which of the following is not valid for Data Acquisition? a. Web scraping b. Surveys c. Sensors d. Announcements 4. If an arrow goes from X to Y with a – (minus) sign, it means that a. If X increases, Y decreases b. The direction of relation is opposite c. If X increases, Y increases d. It is a bi-directional relationship 5. Which of the following is not a part of the 4Ws Problem Canvas? a. Who? b. Why? c. What? d. Which? Let us explore: Session Preparation Logistics: For a class of 40 Students. [Group Activity – Groups of 4] Materials Required: ITEM QUANTITY Computers 10 Resources: Link to visualisation website: https://datavizcatalogue.com/ Purpose: To understand why we do data exploration before jumping straight into training an AI Model. Say: “Why do you think we need to explore and visualize data before jumping into the AI model? When we pick up a library book, we tend to look at the book cover, read the back cover and skim through the content of the book prior to choosing it as it helps us understand if this book is appropriate for our needs and interests. Similarly, when we get a set of data in our hands, spending time to explore it will help get a sense of the trends, relationships and patterns present in the data. It will also help us better decide on which model/models to use in the subsequent AI Project Cycle stage. We use visualization as a method because it is much easier to comprehend information quickly and communicate the story to others.” Brief: In this session, we will be exploring various types of Graphs using an online open- sourced website. Students will learn about various new ways to visualise the data. When to intervene? Ask the students to figure out which types of graphs would be suitable for the data features that they have listed for their problem. Let them take their time in going through each graph and its description and decide which one suits their needs the best. In the previous modules, you have set the goal of your project and have also found ways to acquire data. While acquiring data, you must have noticed that the data is a complex entity – it is full of numbers and if anyone wants to make some sense out of it, they have to work some patterns out of it. For example, if you go to the library and pick up a random book, you first try to go through its content quickly by turning pages and by reading the description before borrowing it for yourself, because it helps you in understanding if the book is appropriate to your needs and interests or not. Thus, to analyse the data, you need to visualise it in some user-friendly format so that you can: Quickly get a sense of the trends, relationships and patterns contained within the data. Define strategy for which model to use at a later stage. Communicate the same to others effectively. To visualise data, we can use various types of visual representations. Are you aware of visual representations of data? Fill them below: Bar Graphs Visual Representation s As of now, we have a limited knowledge of data visualisation techniques. To explore various data visualisation techniques, visit this link: https://datavizcatalogue.com/ On this website, you will find various types of graphical representations, flowcharts, hierarchies, process descriptors, etc. Go through the page and look at various new ways and identify the ones which interest you the most. Identify the icons of different graphs: stream graph ____________________________________________________________ bar chart ___________________________________________________________ bubble chart ______________________________________________________________ bar chart _____________________________________________________________ List down 5 new data visualisation techniques which you learnt from https://datavizcatalogue.com Data Visualisation Technique 1 Name of the Representation One-line Description How to draw it Suitable for which data type? Data Visualisation Technique 2 Name of the Representation One-line Description How to draw it Suitable for which data type? ANSWERS: Data Visualisation Technique 3 Name of the Representation One-line Description How to draw it Suitable for which data type? Data Visualisation Technique 4 Name of the Representation One-line Description How to draw it Suitable for which data type? Data Visualisation Technique 5 Name of the Representation One-line Description How to draw it Suitable for which data type? Sketchy Graphs Session Preparation Logistics: For a class of 40 Students. [Group Activity – Groups of 4] Materials Required: ITEM QUANTITY Chart Paper 10 Sketch-pens 10 Ruler 10 Basic Stationary 10 Sets Purpose: To know the different visualization techniques and to use the right graph to display the data. Say: “In this activity, we are going to sketch graphs! Now that you have explored various types of graphs and have already chosen the best ones to plot your data features, let us start drawing them out! Select any two data features and plot their graphs on the chart paper provided. Make sure that you are able to relate this graph to the goal of your project. At the end of this activity, you would have to present your representations to all of us and describe what trends or patterns have you witnessed in it. Your time starts now!” Let us now look at the scoped Problem statement and the data features identified for achieving the goal of your project. Try looking for the data required for your project from reliable and authentic resources. If you are not able to find data online, try using other methods of acquiring the data (as discussed in the Data Acquisition stage). Once you have acquired the data, you need to visualise it. Under the sketchy graphs activity, you will visualise your collected data in a graphical format for better understanding. For this, select one of the representations from the link or choose the ones which you already know. The basis of your selection should be the data feature which you want you to visualise in that particular representation. Do this for all the data features you have for the problem you have scoped. Let us answer the following questions for a better understanding: 1. Which data feature are you going to represent? _________________________________________________________________________________________________________________ _________________________________________________________________________________________________________________ _________________________________________________________________________________________________________________ _________________________________________________________________________________________________________________ 2. Which representation are you going to use for this data feature? Why? _________________________________________________________________________________________________________________ _________________________________________________________________________________________________________________ _________________________________________________________________________________________________________________ _________________________________________________________________________________________________________________ _________________________________________________________________________________________________________________ Now, let’s start drawing visual representations for all the Data features extracted, and try to find a pattern or a trend from it. For example, if the problem statement is: How can we predict whether a song makes it to the billboard top 10? We would require data features like: Current trends of music, genre of music, tempo of music, duration of song, popularity of a singer, etc. Now to analyse a pattern, we can say that the popularity of the singer would directly have an effect on the output of the system. Thus, we would plot a graph showing the popularity of various singers and the one who is most popular has the maximum chance of getting to the billboard. In this way, the graphical representation helps us understand the trends and patterns out of the data collected and to design a strategy around them for achieving the goal of the project. Do it yourself: Take a chart paper and start representing your data features in various types of graphs. After completing this exercise, present your work to your friends and explain to them the trends and patterns you have observed in it. List down the trends you might have observed in your representations below: 1. __________________________________________________________________________________ _____________________________________________________________________________________ _____________________________________________________________________________________ 2. ___________________________________________________________________________________ _____________________________________________________________________________________ _____________________________________________________________________________________ 3. __________________________________________________________________________________ _____________________________________________________________________________________ _____________________________________________________________________________________ 4. __________________________________________________________________________________ _____________________________________________________________________________________ _____________________________________________________________________________________ 5. __________________________________________________________________________________ _____________________________________________________________________________________ _____________________________________________________________________________________ 6. __________________________________________________________________________________ _____________________________________________________________________________________ _____________________________________________________________________________________ Revision Time 1. What is the significance of Data Exploration after you have acquired the data for the problem scoped? Explain with examples. 2. What do you think is the relevance of Data Visualization in Al? 3. List any five graphs used for data visualization. 4. How is Data Exploration different from Data Acquisition? 5. Use an example to explain at least one Data Visualization technique. ANSWERS: 1.2.4 Modelling Title: Modelling Approach: Session + Activity Summary: Students will be introduced to rule based and AI models and undertake activities to appreciate the distinction between each. They will receive an overview of the various types of regression, classification and clustering models. Learning Objectives: Students are introduced to common regression, classification and clustering models Students are introduced to the decision tree algorithm as an example of rule- based models Students are introduced to image classification models. Learning Outcomes: List common regression, classification and clustering models Explain how decision trees work Describe the process involved in image classification Pre-requisites: Nil Key-concepts: Learning AI process Rule based vs AI model Decision Trees Image Classification In the previous module of Data Exploration, you explored the data you had acquired at the Data Acquisition stage for the problem you scoped in the Problem Scoping stage. Now, you have visualised some trends and patterns out of the data which would help you develop a strategy for your project. To build an AI based project, we need to work around Artificially Intelligent models or algorithms. This could be done either by designing your own model or by using the pre-existing AI models. Before jumping into modelling let us clarify the definitions of Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). AI, ML & DL Purpose: To differentiate between Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). Say: “As we enter the world of modelling, it is a good time to clarify something many of you may be having doubts about. You may have heard the terms AI, ML and DL when research content online and during this course. They are of course related, but how? Artificial Intelligence, or AI for short, refers to any technique that enables computers to mimic human intelligence. An artificially intelligent machine works on algorithms and data fed to it and gives the desired output. Machine Learning, or ML for short, enables machines to improve at tasks with experience. The machine here learns from the new data fed to it while testing and uses it for the next iteration. It also takes into account the times when it went wrong and considers the exceptions too. Deep Learning, or DL for short, enables software to train itself to perform tasks with vast amounts of data. Since the system has got huge set of data, it is able to train itself with the help of multiple machine learning algorithms working altogether to perform a specific task. Artificial Intelligence is the umbrella term which holds both Deep Learning as well as Machine Learning. Deep Learning, on the other hand, is the very specific learning approach which is a subset of Machine Learning as it comprises of multiple Machine Learning algorithms.” As you have been progressing towards building AI readiness, you must have come across a very common dilemma between AI and ML. Many of the times, these terms are used interchangeably but are they the same? Is there no difference between Machine Learning and Artificial Intelligence? Is Deep Learning also Artificial Intelligence? What exactly is Deep Learning? Let us see… As you can see in the Venn Diagram, Artificial Artificial Intelligence is the umbrella terminology Intelligen which covers machine and deep learning under it and Deep Learning comes under Machine Learning. It is a funnel type approach Machine Learning where there are a lot of applications of AI out of which few are those which come under ML out of which very few go into DL. Deep Learning Defining the terms: 1. Artificial Intelligence, or AI, refers to any technique that enables computers to mimic human intelligence. The AI-enabled machines think algorithmically and execute what they have been asked for intelligently. 2. Machine Learning, or ML, enables machines to improve at tasks with experience. The machine learns from its mistakes and takes them into consideration in the next execution. It improvises itself using its own experiences. 3. Deep Learning, or DL, enables software to train itself to perform tasks with vast amounts of data. In deep learning, the machine is trained with huge amounts of data which helps it into training itself around the data. Such machines are intelligent enough to develop algorithms for themselves. Deep Learning is the most advanced form of Artificial Intelligence out of these three. Then comes Machine Learning which is intermediately intelligent and Artificial Intelligence covers all the concepts and algorithms which, in some way or the other mimic human intelligence. Modelling Purpose: Classification of Models into Rule-based approach and Learning approach. Say: “In general, there are two approaches taken by researchers when building AI models. They either take a rule-based approach or learning approach. A Rule based approach is generally based on the data and rules fed to the machine, where the machine reacts accordingly to deliver the desired output. Under learning approach, the machine is fed with data and the desired output to which the machine designs its own algorithm (or set of rules) to match the data to the desired output fed into the machine” AI Modelling refers to developing algorithms, also called models which can be trained to get intelligent outputs. That is, writing codes to make a machine artificially intelligent. Let us ponder Use your knowledge and thinking ability and answer the following questions: 1. What makes a machine intelligent? _____________________________________________________________________________________ _____________________________________________________________________________________ _____________________________________________________________________________________ _____________________________________________________________________________________ _____________________________________________________________________________________ 2. How can a machine be Artificially Intelligent? _____________________________________________________________________________________ _____________________________________________________________________________________ _____________________________________________________________________________________ _____________________________________________________________________________________ _____________________________________________________________________________________ _____________________________________________________________________________________ 3. Can Artificial Intelligence be a threat to Human Intelligence? How? _____________________________________________________________________________________ _____________________________________________________________________________________ _____________________________________________________________________________________ _____________________________________________________________________________________ _____________________________________________________________________________________ _____________________________________________________________________________________ _____________________________________________________________________________________ In the previous module of Data exploration, we have seen various types of graphical representations which can be used for representing different parameters of data. The graphical representation makes the data understandable for humans as we can discover trends and patterns out of it. But when it comes to machine accessing and analysing data, it needs the data in the most basic form of numbers (which is binary – 0s and 1s) and when it comes to discovering patterns and trends in data, the machine goes for mathematical representations of the same. The ability to mathematically describe the relationship between parameters is the heart of every AI model. Thus, whenever we talk about developing AI models, it is the mathematical approach towards analysing data which we refer to. Generally, AI models can be classified as follows: Machine Learning Learning Based AI Models Deep Learning Rule Based Rule Based Approach Refers to the Al modelling where the rules are defined by the developer. The machine follows the rules or instructions mentioned by the developer and performs its task accordingly. For example, we have a dataset which tells us about the conditions on the basis of which we can decide if child can go out to play golf or not. The parameters are: Outlook, Temperature, Humidity and Wind. Now, let's take various possibilities of these parameters and see in which case the children may play golf and in which case they cannot. After looking through all the cases, we feed this data into the machine along with the rules which tell the machine all the possibilities. The machine trains on this data and now is ready to be tested. While testing the machine, we tell the machine that Outlook Overcast; Temperature = Normal; Humidity = Normal and Wind = Weak. On the basis of this testing dataset, now the machine will be able to tell if the child can go out to play golf or not and will display the prediction to us. This is known as a rule-based approach because we fed the data along with rules to the machine and the machine after getting trained on them is now able to predict answers for the same. A drawback/feature for this approach is that the learning is static. The machine once trained, does not take into consideration any changes made in the original training dataset. Rule Based AI Model Learning Based Approach Refers to the Al modelling where the machine learns by itself. Under the Learning Based approach, the Al model gets trained on the data fed to it and then is able to design a model which is adaptive to the change in data. That is, if the model is trained with X type of data and the machine designs the algorithm around it, the model would modify itself according to the changes which occur in the data so that all the exceptions are handled in this case. For example, suppose you have a dataset comprising of 100 images of apples and bananas each. These images depict apples and bananas in various shapes and sizes. These images are then labelled as either apple or banana so that all apple images are labelled 'apple' and all the banana images have 'banana' as their label. Now, the Al model is trained with this dataset and the model is programmed in such a way that it can distinguish between an apple image and a banana image according to their features and can predict the label of any image which is fed to it as an apple or a banana. After training, the machine is now fed with testing data. Now, the testing data might not have similar images as the ones on which the model has been trained. So, the model adapts to the features on which it has been trained and accordingly predicts if the image is of an apple or banana. Learning Based AI Model Revision Time 1. What are the various stages of the Al Project Cycle? Explain each with examples. 2. What is Artificial Intelligence? Give an example where Al is used in day-to-day life. 3. How is Machine Learning related to Artificial Intelligence? 4. Compare and contrast Rule-based and Learning-based approach in Al modeling indicating clearly when each of these may be used. 5. Identify which of the following are examples of classification/regression/clustering. a. Making a diagnosis for a patient on the basis of their symptoms b. Price prediction for a house coming up on sale c. HR shortlisting applications for interview based on information provided in candidates' resume d. Credit Card Fraud prevention e. SPAM filters 1.2.5 Evaluation In Stage 5, we have Evaluation, the main objective of this stage is to test different models and choose the best model. Lesson Title: Evaluation Approach: Interactive Session + Activity Summary: In this module youth will be learn concept of evaluation in the AI project cycle. They will also learn that evaluation is essential for assessing the success of AI projects, identifying areas for improvement, and making data-driven decisions. Learning Objectives Students will be able to understand the importance of evaluation in the AI project cycle. Students will be able to apply evaluation techniques to assess the effectiveness of AI projects. Students will be able to identify areas for improvement in AI projects through evaluation. Learning Outcomes By the end of this lesson, students should be able to apply evaluation techniques in their own AI projects. Pre-requisites: Basic knowledge of Artificial Intelligence and problem solving Key-concepts Importance of Evaluation techniques. What is evaluation? Evaluation is the process of understanding the reliability of any AI model, based on outputs by feeding test dataset into the model and comparing with actual answers. There can be different Evaluation techniques, depending of the type and purpose of the model. Remember that It’s not recommended to use the data we used to build the model to evaluate it. This is because our model will simply remember the whole training set, and will therefore always predict the correct label for any point in the training set. This is known as overfitting. Once a model has been made and trained, it needs to go through proper testing so that one can calculate the efficiency and performance of the model. Hence, the model is tested with the help of Testing Data (which was separated out of the acquired dataset at Data Acquisition stage) and the efficiency of the model is calculated on the basis of the parameters mentioned below: Note: You will learn more about these techniques in grade X. ▪ We test our models to check their performance and improve our models for best performance. ▪ The model is tested with collected data. ▪ We also check if the model is solving the identified AI problem properly. Model Evaluation Terminologies There are various new terminologies which come into the picture when we work on evaluating our model. Let’s explore them with an example of the Forest fire scenario. The Scenario Imagine that you have come up with an AI based prediction model which has been deployed in a forest which is prone to forest fires. Now, the objective of the model is to predict whether a forest fire has broken out in the forest or not. Now, to understand the efficiency of this model, we need to check if the predictions which it makes are correct or not. Thus, there exist two conditions which we need to ponder upon: Prediction and Reality. The prediction is the output which is given by the machine and the reality is the real scenario in the forest when the prediction has been made. Now let us look at various combinations that we can have with these two conditions. Case 1: Is there a forest fire? Here, we can see in the picture that a forest fire has broken out in the forest. Here, we can see in the picture that a forest fire has broken out in the forest. The model predicts a Yes which means there is a forest fire. The Prediction matches with the Reality. Hence, this condition is termed as True Positive. Case 2: Is there a forest fire? Case 3: Is there a forest fire? Here the reality is that there is no forest fire. But the machine has incorrectly predicted that there is a forest fire. This case is termed as False Positive. Case 4: Is there a forest fire? Here, a forest fire has broken out in the forest because of which the Reality is Yes but the machine has incorrectly predicted it as a No which means the machine predicts that there is no Forest Fire. Therefore, this case becomes False Negative Evaluation – Exoplanet Use case ▪ At this particular stage, we may need to evaluate the model to find out which algorithm makes the best prediction. ▪ The figure shows the accuracy of 5 different algorithms as discussed in the Modeling stage. ▪ ROC is a metric used to find out the accuracy of a model. Chapter Review Q1. What is Evaluation? Q2. What are various Model evaluation techniques? Q3. Why is model evaluation important in AI projects? Q4. What do you understand by the terms True Positive and False Positive? 1.2.6 Deployment In Stage 6, we have Deployment, the main objective of this stage is to make our solution ready to be used. Lesson Title: Deployment Approach: Interactive Session + Activity Summary: In this module youth will learn about the term "deployment" in the context of AI projects and why it is an important step. They will Connect the concept of deployment to real-world examples such as deploying a chatbot on a website or a predictive model in a mobile app. Learning Objectives Students will be able to understand the concept of deployment in the AI project cycle and demonstrate their knowledge through hands-on activities. Learning Outcomes By the end of this lesson, students should be able to emphasize the importance of deployment in the AI project cycle. Challenge students to think about how they can apply their knowledge of deployment in future AI projects and encourage them to continue exploring different deployment methods. Pre-requisites: Basic knowledge of Artificial Intelligence and problem solving Key-concepts Importance of Deployment in Ai project cycle What is deployment? Deployment as the final stage in the AI project cycle where the AI model or solution is implemented in a real-world scenario. Key Steps in Deployment Process the key steps involved in the deployment process: a. Testing and validation of the AI model b. Integration of the model with existing systems c. Monitoring and maintenance of the deployed model. Some examples of successful AI projects that have been deployed in various industries, such as self- driving cars, medical diagnosis systems, and chatbots. ▪ AI can be used on Mobile Apps, Website Apps, etc. Mobile Application Website Application Revision Time Choose the correct answer! 1. Does modeling mean creating an AI model? a. YES b. NO 2. Can we use AI on mobile phones? a. YES b. NO 3. What is deployment in the context of an AI project cycle? 4. Why is deployment an important phase in the AI project cycle? 5. What are some common challenges in deploying AI models? Case Study: Preventable Blindness Problem: Prevent loss of vision, and delay in report generation Approximately 537 million adults (20-79 years) are living with diabetes. Diabetes can lead to Diabetic Retinopathy It damages the blood vessels of the retina and can lead to blurred vision and blindness. Lack of qualified doctors and delay in reports increase the risk of Diabetic Retinopathy One of the early symptoms of the defect is ‘Blurred vision’ as shown below: Normal Vision Blurred Vision How can we solve this problem with AI? Solution: Using AI to detect Diabetic Retinopathy in pictures of eyes AI solution at Aravind Eye Hospital, India An AI eye screening solution is developed in partnership with Google. AI models have achieved an accuracy of 98.6% in detecting diabetic retinopathy, on par with the performance of specialist eye doctors. Seventy-one vision centers in rural Tamil Nadu, India are using this solution. Trained technicians take pictures of patients’ eyes with cameras. The digital images are analyzed by AI for the presence of Diabetic Retinopathy. AI has made the detection of Diabetic Retinopathy quicker. Any technician can use this machine, even without a skilled doctor. More and more parents can be treated at an early stage. Hence, early detection using AI can significantly benefit rural populations Let us map this problem to AI project cycle How would you scope the problem? AI Project Cycle Mapping Template Data Data Modeling Evaluation Deployment Acquisition Exploration Collecting Validating all Creating an Test the model Using the model in tools that can data from the data to AI model to for accuracy be used in clinics in even the patients make sense correctly and then fine remote and rural parts of the from many out of it and diagnose tune the model country. clinics using come up Diabetic further to get retinal with a Retinopathy the desired out cameras. model. when given a -put. retinal image as input. Activity Time! Purpose: Implementation of AI project cycle to develop an AI Model for Personalized Education. Activity Introduction: ▪ In this activity, students use the AI project cycle to conceptualize a solution for the given problem. ▪ AI project cycle is a 6-step process which aids in problem solving using Artificial Intelligence Description: ▪ All individuals have different cognitive levels and personalities. ▪ Different people need attention towards different parts of their learning. ▪ A generalized education system does not provide that. Activity Guidelines: ▪ Understand the problem. ▪ Learn the various aspects and developments in the field. ▪ Fill the AI Project Cycle mapping template for the problem. ▪ The solution to the problem of personalized education is an AI algorithm that trains over the behavior and choices of a student. Thus, all the requirements specific to a student could be recognized and addressed to. AI Project Cycle mapping template for Preventable blindness: Fill the AI Project Cycle mapping template for the discussed problem of personalized education. [Hint: Take the reference of the above AI Project cycle mapping template] AI Project Cycle Mapping Template Problem Data Data Modelling Evaluation Deployment Solving Acquisition Exploration Revision Time: 1. Rearrange the steps of AI project cycle in correct order: a. Data Acquisition b. Problem Scoping c. Modelling d. Data Exploration e. Deployment f. Evaluation 2. The process of breaking down the big problem into a series of simple steps is known as: a. Efficiency b. Modularity c. Both a) and b) d. None of the above 3. The primary purpose of data exploration in AI project cycle is _____________ a. To make data more complicated b. To simplify complex data c. To discover patterns and insights in data d. To visualize data 4. Deployment is the final stage in the AI project cycle where the AI model or solution is implemented in a real-world scenario. (True/False) 5. Identify A, B and C in the following diagram (Hint: How AI, ML &DL related to each other)

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