AI Math PDF

▪ We have our complete bubble chart now! Useful Videos to watch ▪ https://www.youtube.com/watch?v=NLCzpPRCc7U ▪ https://www.youtube.com/watch?v=_M8BnosAD78 Note: You may also use Ms Excel or Datawrapper (https://www.datawrapper.de/) for the data visualization instead of Tableau. Revi...

▪ We have our complete bubble chart now! Useful Videos to watch ▪ https://www.youtube.com/watch?v=NLCzpPRCc7U ▪ https://www.youtube.com/watch?v=_M8BnosAD78 Note: You may also use Ms Excel or Datawrapper (https://www.datawrapper.de/) for the data visualization instead of Tableau. Revision Time: 1. At which stage of the AI project cycle does Tableau software prove useful? 2. Name any five graphs that can be made using Tableau software 3. In the below excel sheet- ▪ Is the Year qualitative or quantitative? ▪ Is Song Length discrete or continuous? ▪ Is the Genre discrete or continuous? 4. What is the importance of data visualization? Unit 3: Math for AI (Statistics & Probability)  BC 3.1 Importance of Math for AI Activity 1: Purpose: observing and analyzing the numbers & Find the pattern. Title:▪ Math Findfor theAImissing number in the following series: Approach: Interactive Session + Activity Learning2, 4, 6, 8, 10, 12, ? objectives: ▪ Discuss the22, 4, 10, 16, applications 28, ? of Mathematics in AI. ▪ To know the different mathematical concepts important for understanding AI? ▪ How 34, 31, are 28, 25, 22,and statistics ? probability used in different AI applications? ▪ If Year Summary: 1 Profit In this was INR chapter, 1000; are Students Yearintroduced 2 Profit was toINR the1500; Year 3 Profit mathematics was INR required for 2000; Year an designing 4 AI Profitwill project. They wasknow INR 2500, about can the you predict essential the profit forconcepts mathematical Year 5? required to understand an AI project from the basics. They will be introduced to mathematical concepts of linear algebra, calculus, statistics, and probability through easy activities and examples. Learners will also be able to identify the use of statistics and probability in everyday life. Learning Outcomes: ▪ Students will be able to understand the importance of mathematics in the field of AI. ▪ Students will be able to identify the essential mathematical concepts required for the understanding of A ▪ Students will be able to define statistics and probability and describe their applications in AI Pre-requisites: ▪ Basic mathematical knowledge and analytical ability ▪ Basic familiarity with AI Key- Concepts: ▪ Important mathematical concepts in AI ▪ Introduction to statistics and probability Ask the learners “How did you solve these puzzles?” “Was there any pattern that you recognized which could help you solve the puzzles?” How are Math and AI related? Math is the study of patterns ▪ To solve the puzzles, you identify an order/arrangement in the list of numbers or the images. ▪ This arrangement is called a pattern. ▪ These patterns exist all around us. ▪ We have patterns in numbers, images, and language. Ask learners if they can identify any patterns around themselves. AI is a way to recognize patterns AI can learn to recognize patterns, like human beings. AI can see patterns in different types of data - numbers, images, and speech and text.  These patterns help AI to solve puzzles – like identifying dogs and muffins, or predicting hurricanes! Say “Just like we can recognize patterns in numbers, words, pictures, etc., AI can also recognize similar patterns.” Hence, ▪ Math is the study of patterns ▪ AI is a way to recognize patterns in order to take decisions ▪ AI needs Math to study and recognize patterns in order to take decisions Can you identify any pattern in the image given below?  Activity 3: Purpose: To find connections between sets of images and using that to solve problems, think smartly, and grasp tricky ideas. Complete the sequence in the left column by identifying the correct missing piece in the right column out of A or B. Understanding math will help us to better understand AI and its way of working, but what kind of math is needed for AI? Let us take a look! Essential Mathematics for AI Let’s think and answer the following questions: ▪ 11, 22, 33, 44, 55 – Can you find out the middle value from the given numbers? ____________________________________________________________________________ ▪ In the given figure, which of the two lines is more slanted? Line 1 or Line 2?  ________________________________________ ▪ A has 2 plants, B has 3 plants, C has 1 plant, D has 7 plants. How many plants are there in total? _______________________________________ ▪ If the coin shown in the figure below is used for a toss, what can be the possible result? Just like us, AI can also solve 4 type of problems using Math. AI uses Math for: ▪ Statistics (Exploring data): Example – What is the middle value of the data? Which is the most common value in the data? ▪ Calculus (training and improving AI model): Example – which line is more slanted? Which figure covers more area? ▪ Linear Algebra (finding out unknown or missing values): Example – How many plants are there in total? How many cars are there in a city? ▪ Probability (predicting different events): Example – what will be the possible results of a coin toss? Will it rain tomorrow? 3.2 Statistics  Ask learners to answer some or all of these questions as an assignment. Meanwhile, take dummy numbers and walk the learners through the questions. Can you find out the total weight of your family members? Can you find out the total number of students in your school? Can you find out the maximum temperature in your city during the last month? Definition of Statistics:” Statistics is used for collecting, exploring, and analyzing the data. It also helps in drawing conclusions from data.” ▪ Data is collected from various sources. ▪ Data is explored and cleaned to be used. ▪ Analysis of data is done to understand it better. ▪ Conclusions and decisions can be made from the data. Applications of Statistics: ▪ Predict the performance of sports teams ▪ It can be used to find out specific things such as: o the reading level of students o the opinions of voters o the average weight of a city’s resident Activity 4: Purpose: Uses of Statistics in real life. Write any two applications of Statistics in real life. ___________________________________________________________________________________ ___________________________________________________________________________________ Some more applications of Statistics Disaster Management ▪ Authorities use statistics to alert the citizens residing in places that might be affected by a natural disaster in near future. ▪ The disaster management teams use statistics to know about the population, and about the services and infrastructure present in the affected area. Ask students to think about more ways in which statistics can be used for disaster management. Sports ▪ The Tokyo 2020 Olympics were postponed due to the developing global situation in light of the Covid-19 pandemic. ▪ Statistics revealed that COVID cases sharply increased in Japan during the planned period of Olympics. Ask learners to think of more ways in which statistics can be used in sports. Disease prediction ▪ US government uses statistics to understand which disease is affecting the population the most. ▪ This helps them in curing these diseases more effectively. ▪ Example - government can analyze the areas where COVID cases are increasing, or where the vaccination drive needs to be improved. Weather forecast ▪ Computers use statistics to forecast weather. ▪ They compare the weather conditions with the information about past seasons and conditions. Few more facts Kids watch around 1.5-3 hours of TV per day while being in childcare. 72% of teens often (or sometimes) check for messages or notifications as soon as they wake up, while roughly four-in-ten feel anxious when they do not have their cellphone with them. 77% of children don’t get enough physical exercise.  Almost a quarter (23%) of children aged five to 16 believe that playing a computer game with friends is a form of exercise. 69% of all children experience one or more sleep-related problems at least a few nights a week. Only 54% of US children aged 3 to 5 years attend full-day preschool programs. Activity 5: Car Spotting and Tabulating Purpose: To implement the concept of data collection, analysis and interpretation. Activity Introduction: In this activity, youth will engage in data collection and tabulation. Data collection plays a key role in Artificial Intelligence as it forms the basis of statistics and interpretation by AI. This activity will also require youth to answer a set of questions based on the recorded data. At least 264 million children worldwide (about 12%) don’t go to school. Activity Guidelines Data Collection Visit the following link: https://www.youtube.com/watch?v=4A5L3x3TVuc&ab_channel=CarvingCanyons Fill the table while watching the video using tally. Reference Tally Data Analysis How many cars are spotted in total? ________________________________________________________________ Which colour has been spotted the maximum amount of time? Data Interpretation What is the most common colour choice for the residents of this area? _______________________________________________________________ Answer hint: The colour observed the maximum number of times. _______________________________________________________________ 3.3 Probability Activity-6 Purpose: To understand the possibility of occurrence of an event. Introduction to probability Probability is a way to tell us how likely something is to happen. For example – When a coin is tossed, there are two possible results or outcomes: heads (H) or tails (T) The probability equation defines the likelihood of the happening of an event. It is the ratio of favorable outcomes to the total favorable outcomes. The probability formula can be expressed as, Probability of an Event = Number of Favorable Outcomes / Total Number of Possible Outcomes We say that the probability of the coin landing H is ½ and the probability of the coin landing T is ½ When we talk about probability, we use a few terms that help us understand the chances for something to happen. Probability can be expressed in the following ways: ▪ Certain events: An event will happen without a doubt ▪ Likely events: The probability of one event is higher than the probability of another event ▪ Unlikely events: One event is less likely to happen than another event ▪ Impossible events: There's no chance of an event happening ▪ Equal Probability events: Chances of each event happening is same The probability of an event occurring is somewhere between impossible and certain. If an event is certain or sure to happen, it will have a probability of 1. For example, the probability that it will rain in the state of Florida at least once in a specific year is 1. If an event will never happen or is impossible, it will have a probability of 0. For example, the probability that you can pick a red ball from a bag containing only blue balls is 0. Imagine you have a bag full of stars where 7 stars are and 3 stars are Try to fill in the blanks with – likely, unlikely, certainly, impossible, equal probability 1. If you pick a star from the bag without looking, it is __________ that you will pick. 2. If you pick a star from the bag without looking, it is __________ that you will pick a. 3. If you pick a star from the bag without looking, it is __________ that you will pick a. 4. If you remove 4 from the bag, and pick a star without looking, there is an __________ that you will pick either or. 5. If you pick an object from the bag without looking, you will __________ pick a star. Let’s try to understand the concept of Probability using a relatable example. Consider a relatable scenario! You want to go to your best friend's birthday party next Saturday. Your parents decide to make a deal with you. Scenario 1 Scenario 2 Scenario 3  Scenario 4 Hope the terms impossible, unlikely, even, likely and certain are clearer now! Moving on, take a look at some applications of Probability in Real Life! Probability - Applications Sports ▪ Probability can be used in estimating batting average in Cricket.  ▪ Batting average in Cricket represents how many runs a batsman would score before getting out. ▪ For instance, if a batsman had scored 45 runs out of 100 from only boundaries in the last match. Then, there is a chance that he will score 45% of his runs in the next match from boundaries. Weather Forecasting ▪ One of the most common real-life examples of using probability is weather forecasting. ▪ It is used by weather forecasters to assess how likely it is that there will be rain, snow, clouds, etc., on a given day in a certain area. ▪ Forecasters may say things like “there is a 70% chance of rain today between 4 PM and 6 PM” to indicate a medium to high likelihood of rain during certain hours. Traffic Estimation ▪ Regular people often use probability when they decide to drive to someplace. ▪ Based on the time of day, location in the city, weather conditions, etc. people tend to make probability predictions about how bad traffic will be during a certain time. ▪ For example, if you think there’s a 90% probability that traffic will be heavy from 6 PM to 7:30 PM in your vicinity then you may decide to wait during that time. Let’s discuss 1. Does math play a crucial role in AI life cycle? _____________________________________________________________________ 2. What is statistics? _____________________________________________________________________ 3. What is probability? _____________________________________________________________________ Key Takeaway: 1. Math is essential for understanding AI models in depth. 2. Different math concepts used for AI are Statistics, Probability, Linear Algebra and Calculus. 3. Applications of math can be found in everyday life. Reflection  ▪ Why is math necessary for designing an AI project? Revision Time Part A 1. Match the following: A B I) Probability a) exploring data ii) Calculus b) finding out unknown or missing values iii) Statistics c) predicting different events iv) Linear Algebra d) training and improving AI model. 2. If you are to throw an arrow to this pie chart, in which color is the arrow more likely to fall? a) Red b) Blue c) Yellow d) Green 3. If you select a balloon without looking, how likely is it that you will pick a blue one? a) Probable b) Certain c) Unlikely d) Impossible 4. With one throw of a 6-sided die, what's the probability of getting an even number? a) 1/5 b) 2/5 c) 5/6 d) 1/2 5. Which of the following is an equation? a) 2x + 5 b) x + 2 = 4x c) x^2 + 2x d) 5 + 5x + 5x^2 6. What is the value of x? 10x-8=6x a) 8 b) 4 c) 2 d) 6 7. Which two are examples of descriptive statistics? a) Median and correlation. b) Mean and standard deviation. c) Mode and regression analysis. d) Variance and Hypothesis testing. 8. What is the probability of getting head when you toss a coin once? a) 0.75 b) 1 c) 0 d) 0.5 9. Getting seven in die throwing is a possible event. (True/False). 10. The median of the data: 155, 160, 145, 149, 150, 147, 152, 144, 148 is a) 149 b)150 c)147  d)144 Answer the following question: 1. Explain the relationship between Mathematics and Artificial Intelligence, providing justification for their interconnection. 2. Aman is confused, how probability theory is utilized in artificial intelligence, help Aman by providing two examples to illustrate its importance. 3. Define Certain events and likely events with examples. 4. Write any two examples of Impossible and equal probability events. Age (in years) 10 12 14 15 16 Cases admitted (in a day) 5 7 9 22 11 5. Radhika collected the data of the age distribution of cases admitted during a day in a hospital. Find the average number of cases admitted in hospital. Also, draw a line graph to represent the data graphically. 6. Identify the likely, unlikely, impossible and equal probability events from the following a. Tossing a coin b. Rolling an 8 on a standard die c. Throwing ten 5’s in a row d. Drawing a card of any suite Unit 4 - Generative Artificial Intelligence Lesson Title: Introduction to Generative AI Approach: Interactive Session + Activity Summary: The lesson covers four main topics, including an introduction to Generative AI, how it works, how to use it, and the ethical considerations that come with its use. By the end of the lesson, students will have a basic understanding of Generative AI, how it can be used, and the potential ethical implications to consider. Learning Objectives To understand Generative AI and its types. To know examples and benefits of using Generative AI. To identify popular Generative AI tools and their applications. To sensitize the students about the ethical considerations of using Generative AI. To explain students about the potential negative impact of Generative AI on society. Learning Outcomes: Students will be able to define Generative AI & classify different kinds. Students will be able to explain how Generative AI works and recognize how it learns. Students will be able to apply Generative AI tools to create content. Students will understand the ethical considerations of using Generative AI. Pre-requisites: Knowledge of AI project cycle. Key-concepts: Generative AI Programs/Applications Used: MS PowerPoint MS Word Web browser (any) Activity: Guess the Real Image vs. the AI-Generated Image Purpose: To understand the difference between real and AI-Generated Images. Examine the images and determine whether either of the images is a real image or an AI- generated image. Also, give reasons for your answer. Image Source: Ingram, D., Goode, J., & Nair, A. (2022, December 30). You against the machine: Can you spot which image was created by A.I.? www.nbcnews.com. https://www.nbcnews.com/specials/ai-generated-art-photo-quiz/index.html Let's look at the concepts behind the generation of these images. Supervised Learning and Discriminative Modeling Image Source: Generative AI, Explained by Humans. (n.d.). https://lingarogroup.com/blog/generative-ai-explained-by-humans The classification of data elements into categories or labels was initially taught to the machine learning models by humans. Unsupervised Learning and Generative Modeling Image Source: Generative AI, Explained by Humans. (n.d.). https://lingarogroup.com/blog/generative-ai-explained-by-humans In unsupervised or self-supervised learning, the machine learning model takes unlabeled datasets and figures out patterns and inherent structures within them — without human intervention. What is Generative AI? ▪ Generative artificial intelligence (AI) refers to the algorithms that generate new data that resembles human-generated content, such as audio, code, images, text, simulations, and videos. ▪ This technology is trained with existing data and content, creating the potential for applications such as natural language processing, computer vision, the metaverse, and speech synthesis. Activity Watch the video: https://www.youtube.com/watch?v=26fJ_ADteHo and Share your views Let us have a look at timeline of Generative AI Source: https://www.desdevpro.com/blog/talk-rise-of-generative-ai Generative AI has evolved over several years to reach its current form. Over time, advancements in neural networks and deep learning techniques have significantly enhanced its capabilities. From early experiments in generative models to breakthroughs in natural language processing and image generation, the development of generative AI has been a continuous journey of innovation and refinement. Today, generative AI encompasses a wide range of applications, including text generation, image synthesis, and creative content creation, showcasing the culmination of years of research and development efforts. What do you understand about generative AI? _____________________________________________________________________________________ _____________________________________________________________________________________ Give a few examples of generative AI. _____________________________________________________________________________________ _____________________________________________________________________________________ What do you know about Deep Fake? _____________________________________________________________________________________ _____________________________________________________________________________________ Generative AI vs Conventional AI In contrast to other forms of AI, Generative AI is specially made to produce new and unique content rather than merely processing or categorizing already-existing data. Here are some significant variations:  Generative AI creates new content, whereas Goal conventional AI analyzes, processes, and classifies data. Generative AI models use vast libraries of samples to train neural networks and other Training complicated structures to produce new content based on those patterns. Conventional AI employs fewer complex algorithms and training methods. Generative AI output is fresh, innovative, and often unexpected. Output Conventional AI produces more predictable output based on existing data. Generative AI benefits art, music, literature, gaming, and design. Applications Conventional AI is used in banking, healthcare, image recognition, and language processing. Types of Generative AI Generative AI comes in a variety of forms, each with unique advantages and uses. Some of the most typical varieties are listed below: GANs (Generative Adversarial Networks) GANs are neural networks that collaborate to produce fresh data Made up of two neural networks: Generator Network & Discriminator Network The generator network produces the data, while the discriminator network analyses the data and provides feedback. Until the generator can generate data that is identical to real data, the two networks collaborate in a feedback loop. Examples-creating portraits of non-existing people, convert images from day to night, generate images based on textual description, generate realistic video etc. VAEs (Variational Auto encoders) Another class of generative models is VAEs. In order to produce fresh data, VAEs learn the distribution of the data and then sample from it. Examples- Generation of new images similar to given training set, image reconstruction, generating drafts for writer, generating new sounds and music composition etc.  RNNs are a special class of neural networks that excel Neural RNNs (Recurrent at handling sequential data, like music or text. Networks) They function by ingesting past inputs and applying that knowledge to anticipate future inputs. Example- Generating novel text in the style of a specific author or genre, predicting next character or word in a sequence etc. Auto encoder These are Neural networks that have been trained to learn a compressed representation of data They function by compressing data first, then decompressing that compressed data to restore it to its original form. Auto encoders can be applied to denoising or picture compression applications. Examples- artistic image creation, drug discovery. They generate highly realistic samples. Examples of Generative AI Generative AI has many applications, from art and music to language and natural language processing. Here are some examples of how generative AI is being used in various fields: ▪ Art: Generative AI is being used to create unique works of art. ▪ For example, The Next Rembrandt project used data analysis and 3D printing to create a new painting in the style of Rembrandt (Watch video: Video source: The Next Rembrandt. (2016, April 5). The Next Rembrandt [Video]. YouTube. https://www.youtube.com/watch?v=IuygOYZ1Ngo) ▪ Music: Generative AI is being used to create new music, either by composing original pieces or by remixing existing ones. ▪ For example, AIVA is an AI composer that can create original pieces of music in various genres. (Watch video: Video source: TED. (2018, October 1). How AI could compose a personalized soundtrack to your life | Pierre Barreau [Video]. YouTube. https://www.youtube.com/watch?v=wYb3Wimn01s) ▪ Language: Generative AI is being used to generate new language, such as chatbots that can hold conversations with users or natural language generation systems that can produce written content. (Watch video: Video source: BBC News. (2023, January 15). What is ChatGPT, the AI software taking the internet by storm? - BBC News [Video]. YouTube. https://www.youtube.com/watch?v=BWCCPy7Rg-s) Benefits of using Generative AI Overall, generative AI offers a range of benefits, including increased creativity, efficiency, personalization, exploration, accessibility, and scalability. By leveraging these benefits, businesses and organizations can improve their content creation processes and provide better experiences for their users.  Creativity: Generative AI can assist creatives in pushing the boundaries in making creative processes more efficient and personalized. This can be particularly valuable in fields such as art, design, and music. Efficiency: Generative AI can automate content creation processes, which can save time and reduce costs compared to traditional manual processes. Personalization: Generative AI can be used to create personalized content for individual users based on their preferences and behaviors, such as customized product recommendations or personalized news articles. Exploration: Generative AI can be used to explore new design spaces or optimize complex systems, such as designing new drugs or improving industrial processes. Accessibility: Generative AI can democratize access to content creation tools, making it easier for people with limited resources or technical expertise to produce high- quality content. Scalability: Generative AI can be used to generate large volumes of content quickly and efficiently, making it a scalable solution for businesses and organizations that need to produce large amounts of content. Limitations of Using Generative AI 01 Data Bias If generative AI is trained on biased or incomplete data, the output may be similarly biased or flawed. This can lead to inaccurate or problematic results in certain applications, such as in facial recognition or natural language processing. 02 Uncertainty Generative AI can produce unexpected and often unpredictable results, which can be both a benefit and a drawback. 03 Computational Demands Generative AI requires significant computational resources to train and generate its output, which can be expensive and time-consuming. Hands-on Activity: GAN Paint ▪ GAN Paint directly activates and deactivates neurons in a deep network trained to create pictures. ▪ Each left button ("door", "brick", etc.) represents 20 neurons. The software shows that the network learns about trees, doorways, and roofs by drawing. ▪ Switching neurons directly shows the network's visual world model. ▪ To use GAN Paint, you will first need to select a base image from the website's library. You can then use the brush tool to add objects and textures to the image. As you paint, the GAN network will learn to generate more realistic images. ▪ You are encouraged to experiment with GAN Paint and see what you can create. Have fun! Link: https://ganpaint-v2.vizhub.ai/ Generative AI tools There are many generative AI tools available today that enable users to create and experiment with generative models. Here are some popular tools: ▪ Artbreeder: Artbreeder is a web-based tool that enables users to generate new images by combining different GAN models. Users can select and combine different GAN models to create new and unique images. Hands-on Activity Generate Images with Text Prompt 1. Go to artbreeder.com 2. Select Create from menu bar and click on New Image under Prompter category. 3. Give cool text prompt and see how AI generates a picture from those prompts. ▪ Runway ML: Runway ML is a platform for creating, training, and deploying generative models. It provides a user-friendly interface for building and training various types of generative models, including GANs, VAEs, and image classifiers. (Watch video: Video source: https://www.youtube.com/watch?v=trXPfpV5iRQ) Explore AI Magic Tools Of Runway ML 1. Go to https://runwayml.com/ 2. Explore the AI Magic Tools 3. Take any tool of your choice and generate new content with it. ▪ ChatGPT Link: https://chat.openai.com/ I asked ChatGPT to introduce itself. And here is the response  ▪ Gemini Link: https://gemini.google.com/ I asked Gemini to introduce itself. And here is the response! Image source: Khare, Y. (2023, April 10). Google VS Microsoft: The Battle of AI Innovation. Analytics Vidhya. https://www.analyticsvidhya.com/blog/2023/04/google-vs-microsoft-the-battle-of-ai-innovation/ Hands-on Activity Chit-Chat with ChatGPT & Gemini ▪ Sign up & Login into both ChatGPT and Gemini. ▪ Chat with the ChatGPT and ask it to write a paragraph on How it Works? - ChatGPT ▪ Similarly, Chat with Bard and ask it to write a paragraph on How it Works? - Gemini Here are 6 prompts that can be tried on ChatGPT and Gemini: 1. Write a summary of the history of the internet. 2. Explain how to code a simple website. 3. Write a blog post about the latest trends in artificial intelligence. 4. Create a presentation about the benefits of cloud computing. 5. Write a research paper about the future of technology. 6. Design an app that solves a real-world problem. Document the findings from above activity on ChatGPT vs Gemini vs Copilot based on the parameters below: ▪ Parameter 1: Human-Like Response. ▪ Parameter 2: Training Dataset and Underlying Technology. ▪ Parameter 3: Authenticity of Response. ▪ Parameter 4: Access to the Internet. ▪ Parameter 5: User Friendliness and Interface. ▪ Parameter 6: Text Processing: Summarization, Paragraph Writing, Etc. ▪ Parameter 7: Charges and Price. How to Use Generative AI Tools in Real-world Scenarios The table shows popular Generative AI tools that can be used in various fields. Some more tools Ethical considerations of using Generative AI While Generative AI offers many benefits, there are also several ethical considerations that should be considered when using this technology. There are questions about who owns the content generated by generative AI. This is particularly relevant in creative fields such as Ownership music, literature, or art, where generative AI can create original works that blur the lines between human and machine authorship. Generative AI raises questions about human agency and control. As technology becomes more sophisticated, it may become Human Agency increasingly difficult to distinguish between content generated by humans and machines, which could lead to a loss of human autonomy and agency. Generative AI can replicate and amplify existing biases present in the data used to train the model. Bias This can lead to harmful or discriminatory outcomes, especially if the generated content is used in high-stakes applications such as hiring, loan approvals, or criminal justice. Generative AI can be used to create fake news or deepfakes, which can be used to spread misinformation and manipulate public opinion. Misinformation This can have serious consequences for democracy and trust in institutions. Generative AI can potentially be used to generate Privacy sensitive personal information, such as credit card numbers, social security numbers, or medical records. This could be used for malicious purposes. The Potential Negative Impact on Society Generative AI can be used to create fake news or deep fakes that can spread misinformation and manipulate public opinion. Lead to job displacement for humans who previously performed these tasks. Generative AI has the potential to generate sensitive personal information, such as social security numbers or medical records, which could be used for malicious purposes. Responsible Use of Generative AI Ensuring that the training data used are diverse and representative. The outputs are scrutinized for bias and misinformation. Prioritizing user privacy and consent, Having clear guidelines around ownership and attribution of generative content. Engaging in public discussions around the social and ethical implications of this technology to ensure that it is developed and used in ways that are beneficial to society. In short, responsible use of Generative AI is essential for ensuring that this technology is developed and used in ways that benefit society! By emphasizing ethics, creating trust, limiting negative repercussions, defining legislation, and encouraging innovation, we may maximize Generative AI’s potential to improve society! Revision Time What do you understand about Generative Artificial Intelligence? Give any two examples. Write any two AI tools each for the following- ▪ Generative AI image generation tools ▪ Generative AI text generation tools ▪ Generative AI audio generation tools Give full forms of the following- ▪ GANs ▪ VAEs ▪ RNNs How Generative AI can be helpful in following fields- ▪ Architecture ▪ Coding ▪ Music ▪ Content Creation Sakshi has been assigned a homework essay on the topic, “The Impact of Climate Change on Coral Reefs.” The essay requires Sakshi to research and explain various aspects of climate change, such as ocean acidification and rising sea temperatures, and their effects on coral reef ecosystems. His friend suggested using some text generation tool. List some guidelines for Sakshi to prevent misuse of Generative AI and use it constructively. How do you think generative AI can revolutionize the creative industry, such as art and fashion, by enabling the generation of unique and innovative designs? Considering the ethical challenges associated with generative AI, what are your thoughts on establishing guidelines or regulations to ensure responsible use of these technologies? How can we balance the potential benefits and risks? Answers to MCQ Unit 1 Subunit 1.1 1. b, 2. b 3. c 4. c 5. a 6. a Subunit 1.2.3 1. b 2. b 3. d 4. a 5. d Subunit 1.2.5 1. b,a,d,c,f,e 2. b 3. c 4. True 5. A-AI, B-ML, C-DL Subunit 1.2.6 1. a 2. a Subunit 1.3 1. Ethics 2. AI principles 3. No, it is not considered theft. It is an ethical concern. 4. Data Privacy 5. Bias 6. True 7. Bias 8. True Unit 2: Part A 1. i. c ii. d iii. a iv. b 2. b 3. b 4. d 5, d 6. b 7. c 8. b 9. d 10. a Part B 1. c 2. c UNIT 3 Subunit 3.1.5 1. b 2. a 3. b 4. c 5. a Subunit 3.2 1. c 2. d 3. a 4. c 5. b