Class10_AI_Handbook.pdf

CLASS 10 FACILITATOR HANDBOOK Artificial Intelligence Curriculum Class 10 Curated with support from Intel® Acknowledgements Patrons: Sh. Ramesh Pokhriyal 'Nishank', Minister of Human Resource Development, Government of India Sh. Dhotre Sanjay Shamrao, Minister of State for Human Resource Development, Government of India Human Resource Development, Government of India Advisory, Editorial and Creative Inputs: Ms. Anita Karwal, IAS, Chairperson, Central Board of Secondary Education Ms. Shweta Khurana, Director, Programs, Partnerships and Policy Group, Intel India Guidance and Support: Sh. Anurag Tripathi, IRPS, Secretary, Central Board of Secondary Education Dr. Joseph Emmanuel, Director (Academics), Central Board of Secondary Education Dr. Biswajit Saha, Director (Skill Education & Training), Central Board of Secondary Education Education Value adder, Curator and Coordinator: Sh. Ravinder Pal Singh, Joint Secretary, Department of Skill Education, Central Board of Secondary Education Content Curation Team: Ms. Sharon E. Kumar, Innovation and Education Consultant, Intel AI4Youth Program Ms. Ambika Saxena, Intel AI For Youth Coach Mr. Bhavik Khurana, Intel AI For Youth Coach Mr. Akshay Chawla, Intel AI For Youth Coach Mr. Shivam Agrawal, Intel AI For Youth Coach Feedback By: Ms. Neelam Roy, ITL Public School, Delhi Ms. Mehreen Shamim, TGT, DPS Bangalore East, Bengaluru Ms. Saswati Sarangi, PGT Computer Science, RCIS Kalyan Nagar, Bengaluru Ms. Aayushi Agrawal, Salwan Girls School, Delhi Ms. Isha, HOD Computer Science, Salwan Public School, Delhi Special Thanks To: Ms. Indu Khetrapal, Principal, Salwan Public School, Delhi Ms. Rekha Vinod, Principal, RCIS Kalyan Nagar, Bengaluru Ms. Manilla Carvalho, Principal, Delhi Public School – Bangalore East, Bengaluru Ms. Sudha Acharya, Principal, ITL Public School, Delhi Ms. Puneet Sardana, Vice-Principal, Salwan Girls School, Delhi About the book Artificial Intelligence (AI) is being widely recognised to be the power that will fuel the future global digital economy. AI in the past few years has gained geo-strategic importance and a large number of countries are striving hard to stay ahead with their policy initiatives to get their country ready. India’s own AI strategy identifies AI as a n opportunity and solution provider for inclusive economic growth and social development. The report also identifies the importance of skills-based education (as opposed to knowledge intensive education), and the value of project related work in order to “effectively harness the potential of AI in a sustainable manner” to make India’s next generation ‘AI ready’. As a beginning in this direction, CBSE introduced Artificial Intelligence as an optional subject at Class IX from the Session 2019-2020 onwards. Also, to enhance the multidisciplinary approach in teaching- learning so as to sensitize the new generation, it was decided that schools may start AI “Inspire Module” of 12 hours at class VIII itself. CBSE has extended this subject to class X as well from the Session 2020-2021. CBSE is already offering various skill subjects at secondary and senior secondary level to upgrade the skills and proficiency of the young generation and also to provide them awareness to explore various career options. Ai secondary level, a skill subject may be offered as additional sixth subject along with the existing five compulsory subjects. CBSE acknowledges the initiative by Intel India in curating this Facilitator Handbook, the AI training video and managing the subsequent trainings of trainers on the Artificial Intelligence Curriculum. The aim is to strive together to make our students future ready and help them work on incorporating Artificial Intelligence to improve their learning experience. Table of Contents Introduction to AI: Foundational Concepts........................................................................................ 9 What is Intelligence?....................................................................................................................... 9 Decision Making............................................................................................................................ 12 How do you make decisions?.................................................................................................... 12 Make Your Choices!.................................................................................................................. 12 What is Artificial Intelligence?...................................................................................................... 14 How do machines become Artificially Intelligent?....................................................................... 14 Applications of Artificial Intelligence around us........................................................................... 15 What is not AI?.............................................................................................................................. 16 Introduction to AI: Basics of AI......................................................................................................... 18 AI, ML & DL.................................................................................................................................... 20 Introduction to AI Domains........................................................................................................... 21 Data Sciences............................................................................................................................ 21 Computer Vision....................................................................................................................... 21 Natural Language Processing.................................................................................................... 22 AI Ethics......................................................................................................................................... 23 Moral Issues: Self-Driving Cars.................................................................................................. 23 Data Privacy.............................................................................................................................. 24 AI Bias........................................................................................................................................ 26 AI Access................................................................................................................................... 27 AI Project Cycle................................................................................................................................. 29 Introduction.................................................................................................................................. 29 Problem Scoping....................................................................................................................... 30 Data Acquisition........................................................................................................................ 34 Data Exploration....................................................................................................................... 35 Modelling.................................................................................................................................. 36 Learning Based Approach......................................................................................................... 37 Evaluation................................................................................................................................. 39 Neural Networks........................................................................................................................... 40 Advance Python................................................................................................................................ 42 Recap............................................................................................................................................. 42 Recap 1: Jupyter Notebook....................................................................................................... 42 Introduction to Virtual Environments....................................................................................... 43 Recap 2: Introduction to Python............................................................................................... 47 Applications of Python.............................................................................................................. 48 Recap 3: Python Basics.............................................................................................................. 48 Python Packages........................................................................................................................... 52 Data Sciences.................................................................................................................................... 54 Introduction.................................................................................................................................. 54 Applications of Data Sciences....................................................................................................... 55 Getting Started.............................................................................................................................. 57 Revisiting AI Project Cycle......................................................................................................... 57 Data Collection.......................................................................................................................... 62 Data Access............................................................................................................................... 63 Basic Statistics with Python.......................................................................................................... 66 Data Visualisation......................................................................................................................... 67 Data Sciences: Classification Model.............................................................................................. 71 Personality Prediction............................................................................................................... 71 K-Nearest Neighbour: Explained............................................................................................... 72 Computer Vision............................................................................................................................... 75 Introduction.................................................................................................................................. 75 Applications of Computer Vision.................................................................................................. 76 Computer Vision: Getting Started................................................................................................. 78 Computer Vision Tasks.................................................................................................................. 78 Classification............................................................................................................................. 78 Classification + Localisation...................................................................................................... 78 Object Detection....................................................................................................................... 78 Instance Segmentation............................................................................................................. 78 Basics of Images........................................................................................................................ 79 Basics of Pixels.......................................................................................................................... 79 Image Features.............................................................................................................................. 84 Introduction to OpenCV................................................................................................................ 85 Convolution................................................................................................................................... 86 Convolution : Explained............................................................................................................ 88 Convolution Neural Networks (CNN)............................................................................................ 91 Introduction.............................................................................................................................. 91 What is a Convolutional Neural Network ?............................................................................... 92 Convolution Layer..................................................................................................................... 93 Rectified Linear Unit Function.................................................................................................. 94 Pooling Layer............................................................................................................................. 95 Fully Connected Layer............................................................................................................... 96 Natural Language Processing............................................................................................................ 99 Introduction.................................................................................................................................. 99 Applications of Natural Language Processing............................................................................. 100 Natural Language Processing: Getting Started........................................................................... 101 Revisiting the AI Project Cycle................................................................................................. 101 Chatbots...................................................................................................................................... 104 Human Language VS Computer Language.................................................................................. 105 Arrangement of the words and meaning................................................................................ 106 Multiple Meanings of a word.................................................................................................. 107 Perfect Syntax, no Meaning.................................................................................................... 107 Data Processing........................................................................................................................... 108 Text Normalisation.................................................................................................................. 108 Bag of Words........................................................................................................................... 112 TFIDF: Term Frequency & Inverse Document Frequency....................................................... 114 Applications of TFIDF.................................................................................................................. 118 DIY – Do It Yourself!.................................................................................................................... 118 Evaluation....................................................................................................................................... 119 Introduction................................................................................................................................ 119 What is evaluation?.................................................................................................................... 119 Model Evaluation Terminologies................................................................................................ 119 The Scenario............................................................................................................................ 119 Confusion matrix......................................................................................................................... 122 Evaluation Methods.................................................................................................................... 123 Accuracy.................................................................................................................................. 123 Precision.................................................................................................................................. 124 Recall....................................................................................................................................... 125 Which Metric is Important?.................................................................................................... 126 F1 Score................................................................................................................................... 127 Introduction to AI: Foundational Concepts What is Intelligence? Humans have been developing machines which can make their lives easier. Machines are made with an intent of accomplishing tasks which are either too tedious for humans or are time consuming. Hence, machines help us by working for us, thereby sharing our load and making it easier for us to fulfil such goals. Life without machines today is unimaginable, and because of this, humans have been putting efforts into making them even more sophisticated and smart. As a result, we are surrounded by smart devices and gadgets like smartphones, smartwatches, smart TV, etc. But what makes them smart? For example, how is a smartphone today different from the telephones we had in the last century? * Images shown here are the property of individual organisations and are used here for reference purpose only. Today’s phones can do much more than just call-up people. They can help us in navigating, recommend which songs we should listen to or which movies we should watch according to our own likes and dislikes. Our phones can help us connect with like- minded people, make our selfies fun with face filters, help us maintain a record of our health and fitness and a lot more. These drastic technological advancements lead us to recognize one key concept: the concept of Artificial Intelligence. What is Artificial Intelligence anyway? Well, the answer lies in the term itself. If we break up this term up, we get the words “Artificial” and “Intelligence”. Artificial is something which is man-made, which does not occur naturally. But what about Intelligence, how do we define that? Humans are said to be an intelligent species, so what is it that makes us intelligent? According to researchers, intelligence is the ‘ability to perceive or infer information, and to retain it as knowledge to be applied towards adaptive behaviours within an environment or context.’ If we try to define intelligence with the help of its traits, these are the abilities that are involved in intelligence: * Images shown here are the property of individual organisations and are used here for reference purpose only. Let us define each term mentioned above to get a proper understanding: Mathematical A person's ability to regulate, measure, and understand numerical Logical Reasoning symbols, abstraction and logic. Linguistic Language processing skills both in terms of understanding or Intelligence implementation in writing or verbally. Spatial Visual It is defined as the ability to perceive the visual world and the Intelligence relationship of one object to another. Kineasthetic Ability that is related to how a person uses his limbs in a skilled Intelligence manilr. Musical As the name suggests, this intelligence is about a person's ability to Intelligence recognize and create sounds, rhythms, and sound patterns. Intrapersonal Describes how high the level of self-awareness someone has is. Intelligence Starting from realizing weakness, strength, to his own feelings. Existential An additional category of intelligence relating to religious and Intelligence spiritual awareness. Naturalist An additional category of intelligence relating to the Intelligence ability to process information on the environment around us. Interpersonal Interpersonal intelligence is the ability to communicate with others intelligence by understanding other people's feelings & influence of the person. But even though one is more skilled in intelligence than the other, it should be noted that in fact all humans have all 9 of these intelligences only at different levels. One might be an expert at painting, while the other might be an expert in mathematical calculations. One is a musician, the other is an expert dancer. In other words, we may define intelligence as: Ability to interact with the real world o To perceive, understand and act ▪ Example: Speech Recognition – Understanding and synthesis ▪ Example: Image Recognition ▪ Example: Ability to take action: to have an effect Reasoning and planning o Modelling the external world, given input ▪ Solving new problems, planning and making decisions ▪ Ability to deal with unexpected problems, uncertainties Learning and adaptation o Continuous learning and adapting graph ▪ Our internal models are always being updated ▪ Example: Baby learning to categorize and recognise animals For example, if someone starts talking to us, we know how to keep the conversation going. We can understand what people mean and can reply in the same way. When we are hungry, we can come up with various options on what to eat depending upon the food we have at our homes. When we read something, we are able to understand its meaning and answer anything regarding it. While understanding the term intelligence, it must be noticed that decision making comprises of a crucial part of intelligence. Let us delve deeper into it. Decision Making You’re trapped. All the doors seem to have started shrinking and only one of them leads you out. Which door would you pick? How do you make decisions? The basis of decision making depends upon the availability of information and how we experience and understand it. For the purposes of this article, ‘information’ includes our past experience, intuition, knowledge, and self-awareness. We can’t make “good” decisions without information because then we have to deal with unknown factors and face uncertainty, which leads us to make wild guesses, flipping coins, or rolling a dice. Having knowledge, experience, or insights given a certain situation, helps us visualize what the outcomes could be. and how we can achieve/avoid those outcomes. Make Your Choices! Scenario 1 You are locked inside a room with 3 doors to move out of the locked room and you need to find a safe door to get your way out. Behind the 1st door is a lake with a deadly shark. The 2nd door has a mad psychopath ready to kill with a weapon and the third one has a lion that has not eaten since the last 2 months. * Images shown here are the property of individual organisations and are used here for reference purpose only. Which door would you choose? and Why? __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ The answer is gate number 3. The reason being that since the lion has not eaten for 2 months, he wouldn't have survived till now and would already be dead. This makes going out from gate 3 the correct option. Scenario 2 Aarti invited four of her friends to her House.. They hadn't seen each other in a long time, so they chatted all night long and had a good time. In the morning, two of the friends Aarti had invited, died. The police arrived at the house and found that both the friends were poisoned and that the poison was in the strawberry pie. The three surviving friends told the police that they hadn't eaten the pie. The police asked," Why didn’t you eat the pie ?". Shiv said, " I am allergic to strawberries.". Seema said, " I am on a diet." And Aarti said, "I ate too many strawberries while cooking the pie, I just didn't want anymore." The policemen looked at the pictures of the party and immediately identified the murderer. * Images shown here are the property of individual organisations and are used here for reference purpose only. Look at the picture and identify who is the murderer? Also state why do you think this is the murderer? __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ The answer is Seema, can you guess how the police could tell? It’s because she said she is on a diet and in the picture, she is eating a burger and fries which means she lied. The above scenarios show that it’s the information which helps humans take good decisions. What is Artificial Intelligence? 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. You will get to know more about it in the next unit. But, what makes a machine intelligent? How do machines become Artificially Intelligent? Humans become more and more intelligent with time as they gain experiences during their lives. * Images shown here are the property of individual organisations and are used here for reference purpose only. For example, in elementary school, we learn about alphabets and eventually we move ahead to making words with them. As we grow, we become more and more fluent in the language as we keep learning new words and use them in our conversations. Another example is how we learn walking. Initially a baby struggles to walk. He takes help from others while learning how to walk and once he knows it, he keeps on upgrading it by learning how to run, jump, etc. Similarly, machines also become intelligent once they are trained with some information which helps them achieve their tasks. AI machines also keep updating their knowledge to optimise their output. Applications of Artificial Intelligence around us Whether we notice it or not, we are surrounded by machines that work on AI. They are becoming a crucial part of our everyday life and provide us with an ease of having even some of the most complicated and time-consuming tasks being done at the touch of a button or by the simple use of a sensor. Every now and then, we surf the internet for things on Google without realizing how efficiently Google always responds to us with accurate answers. Not only does it come up with results to our search in a matter of seconds, it also suggests and auto- corrects our typed sentences. We nowadays have pocket assistants that can do a lot of tasks at just one command. Alexa, Google Assistant, Cortana, Siri are some very common examples of the voice assistants which are a major part of our digital devices. To help us navigate to places, apps like UBER and Google Maps come in haman. Thus, one no longer needs to stop repeatedly to ask for directions. AI has completely enhanced the gaming experience for its users. A lot of games nowadays are backed up with AI which helps in enhancing the graphics, come up with new difficulty levels, encourage gamers, etc. * Images shown here are the property of individual organisations and are used here for reference purpose only. AI has not only made our lives easier but has also been taking care of our habits, likes, and dislikes. This is why platforms like Netflix, Amazon, Spotify, YouTube etc. show us recommendations on the basis of what we like. Well, the recommendations are not just limited to our preferences, they even cater to our needs of connecting with friends on social media platforms with apps like Facebook and Instagram. They also send us customized notifications about our online shopping details, auto-create playlists according to our requests and so on. Taking selfies was never this fun as Snapchat filters make them look so cool. This isn’t all. AI is also being used to monitor our health. A lot of chatbots and other health apps are available, which continuously monitor the physical and mental health of its users. These applications are not limited to smart devices but also vary to humanoids like Sophia, the very first humanoid robot sophisticated enough to get citizenship, biometric security systems like the face locks we have in our phones, real-time language translators, weather forecasts, and whatnot! This list is huge, and this module will go on forever if we keep tabulating them. So, take some time, discuss with a friend and identify more and more AI applications around you! What is not AI? Since we have a lot of different technologies which exist around us in today’s time, it is very common for us to misunderstand any other technology as AI. That is why, we need to have a clear distinction between what is AI and what is not. As we discussed earlier, any machine that has been trained with data and can make decisions/predictions on its own can be termed as AI. Here, the term ‘training’ is important. A fully automatic washing machine can work on its own, but it requires human intervention to select the parameters of washing and to do the necessary preparation for it to function correctly before each wash, which makes it an example of automation, not AI. * Images shown here are the property of individual organisations and are used here for reference purpose only. An air conditioner can be turned on and off remotely with the help of internet but still needs a human touch. This is an example of Internet of Things (IoT). Also, every now and then we get to know about robots which might follow a path or maybe can avoid obstacles but need to be primed accordingly each time. We also get to see a lot of projects which can automate our surroundings with the help of sensors. Here too, since the bot or the automation machine is not trained with any data, it does not count as AI. Also, it would be valid to say that not all the devices which are termed as "smart" are AI-enabled. For example, a TV does not become AI-enabled if it is a smart one, it gets the power of AI when it is able to think and process on its own. Just as humans learn how to walk and then improve this skill with the help of their experiences, an AI machine too gets trained first on the training data and then optimises itself according to its own experiences which makes AI different from any other technological device/machine. But well, surely these other technologies too can be integrated with AI to provide the users with a much better and immersive experience! Robotics and AI can definitely open the doors to humanoids and self-driving cars, AI when merged with Internet of things can give rise to cloud computing of data and remote access of AI tools, automation along with AI can help in achieving voice automated homes and so on. Such integrations can help us get the best of both worlds! * Images shown here are the property of individual organisations and are used here for reference purpose only. Introduction to AI: Basics of AI As discussed in the last chapter, Artificial Intelligence has always been a term which intrigues people all over the world. Various organisations have coined their own versions of defining Artificial Intelligence. Some of them are mentioned below: NITI Aayog: National Strategy for Artificial Intelligence AI refers to the ability of machines to perform cognitive tasks like thinking, perceiving, learning, problem solving and decision making. Initially conceived as a technology that could mimic human intelligence, AI has evolved in ways that far exceed its original conception. With incredible advances made in data collection, processing and computation power, intelligent systems can now be deployed to take over a variety of tasks, enable connectivity and enhance productivity. World Economic Forum Artificial intelligence (AI) is the software engine that drives the Fourth Industrial Revolution. Its impact can already be seen in homes, businesses and political processes. In its embodied form of robots, it will soon be driving cars, stocking warehouses and caring for the young and elderly. It holds the promise of solving some of the most pressing issues facing society, but also presents challenges such as inscrutable “black box” algorithms, unethical use of data and potential job displacement. As rapid advances in machine learning (ML) increase the scope and scale of AI’s deployment across all aspects of daily life, and as the technology itself can learn and change on its own, multi-stakeholder collaboration is required to optimize accountability, transparency, privacy and impartiality to create trust. European Artificial Intelligence (AI) leadership, the path for an integrated vision AI is not a well-defined technology and no universally agreed definition exists. It is rather a cover term for techniques associated with data analysis and pattern recognition. AI is not a new technology, having existed since the 1950s. While some markets, sectors and individual businesses are more advanced than others, AI is still at a relatively early stage of development, so that the range of potential applications, and the quality of most existing applications, have ample margins left for further development and improvement. Encyclopaedia Britannica Artificial intelligence (AI), is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. As you can see, Artificial Intelligence is a vast domain. Everyone looks at AI in a different way according to their mindset. Now, according to your knowledge of AI, start filling the KWLH chart: K What I Know? W What I Want to know? L What have I learned? H How I learnt this? What do you know about Artificial Intelligence (AI)? __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ What do you want to know about AI? __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ What have you learnt about AI? __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ How have you learnt this about AI? __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ In other words, AI can be defined as: 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) enables machines 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 building machines and algorithms which are capable of performing computational tasks that would otherwise require human like brain functions. AI, ML & DL As you have been progressing towards building AI readiness, you must have come across a very common dilemma between Artificial Intelligence (AI) and Machine Learning (ML). Many times, these terms are used interchangeably but are they the same? Is there no difference in Machine Learning and Artificial Intelligence? Is Deep Learning (DL) Also Artificial Intelligence? What exactly is Deep Learning? Let us see. Artificial Intelligence (AI) Refers to any technique that enables computers to mimic human intelligence. It gives the ability to machines to recognize a human’s face; to move and manipulate objects; to understand the voice commands by humans, and also do other tasks. The AI-enabled machines think algorithmically and execute what they have been asked for intelligently. Machine Learning (ML) It is a subset of Artificial Intelligence which enables machines to improve at tasks with experience (data). The intention of Machine Learning is to enable machines to learn by themselves using the provided data and make accurate Predictions/ Decisions. * Images shown here are the property of individual organisations and are used here for reference purpose only. Deep Learning (DL) It 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 in 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. There are a lot of applications of AI out of which few are those which come under ML out of which very few can be labelled as DL. Therefore, Machine Learning (ML) and Deep Learning (DL) are part of Artificial Intelligence (AI), but not everything that is Machine learning will be Deep learning. Introduction to AI Domains Artificial Intelligence becomes intelligent according to the training which it gets. For training, the machine is fed with datasets. According to the applications for which the AI algorithm is being developed, the data which is fed into it changes. With respect to the type of data fed in the AI model, AI models can be broadly categorised into three domains: Data Sciences Computer Vision Natural Language Processing Data Sciences Data sciences is a domain of AI related to data systems and processes, in which the system collects numerous data, maintains data sets and derives meaning/sense out of them. The information extracted through data science can be used to make a decision about it. Example of Data Science Price Comparison Websites These websites are being driven by lots and lots of data. If you have ever used these websites, you would know, the convenience of comparing the price of a product from multiple vendors at one place. PriceGrabber, PriceRunner, Junglee, Shopzilla, DealTime are some examples of price comparison websites. Now a days, price comparison website can be found in almost every domain such as technology, hospitality, automobiles, durables, apparels etc. Computer Vision Computer Vision, abbreviated as CV, is a domain of AI that depicts the capability of a machine to get and analyse visual information and afterwards predict some decisions about it. The entire process involves image acquiring, screening, analysing, identifying and extracting information. This extensive processing helps computers to understand any visual content and act on it accordingly. In computer vision, Input to machines can be photographs, videos and pictures from thermal or infrared sensors, indicators and different sources. * Images shown here are the property of individual organisations and are used here for reference purpose only. Computer vision related projects translate digital visual data into descriptions. This data is then turned into computer-readable language to aid the decision-making process. The main objective of this domain of AI is to teach machines to collect information from pixels. Examples of Computer Vision Self-Driving cars/ Automatic Cars CV systems scan live objects and analyse them, based on whether the car decides to keep running or to stop. 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. Natural Language Processing Natural Language Processing, abbreviated as NLP, is a branch of artificial intelligence that deals with the interaction between computers and humans using the natural language. Natural language refers to language that is spoken and written by people, and natural language processing (NLP) attempts to extract information from the spoken and written word using algorithms. The ultimate objective of NLP is to read, decipher, understand, and make sense of the human languages in a manilr that is valuable. Examples of Natural Language Processing Email filters Email filters are one of the most basic and initial applications of NLP online. It started out with spam filters, uncovering certain words or phrases that signal a spam message. * Images shown here are the property of individual organisations and are used here for reference purpose only. Smart assistants Smart assistants like Apple’s Siri and Amazon’s Alexa recognize patterns in speech, then infer meaning and provide a useful response. AI Ethics Nowadays, we are moving from the Information era to Artificial Intelligence era. Now we do not use data or information, but the intelligence collected from the data to build solutions. These solutions can even recommend the next TV show or movies you should watch on Netflix. We can proudly say that India is leading in the AI usage trends, so we need to keep aspects relating to ethical practices in mind while developing solutions using AI. Let us understand some of the ethical concerns in detail. Moral Issues: Self-Driving Cars Scenario 1: Let us imagine that we are in year 2030. Self-Driving cars which are just a concept in today’s time are now on roads. People like us are buying them for ease and are using it for our daily transits. Of-course because of all the features which this car has, it is expensive. Now, let us assume, one day your father is going to office in his self-driving car. He is sitting in the back seat as the car is driving itself. Suddenly, a small boy comes in front of this car. The incident was so sudden that the car is only able to make either of the two choices: 1. Go straight and hit the boy who has come in front of the car and injure him severely. 2. Take a sharp right turn to save the boy and smash the car into a metal pole thus damaging the car as well as injuring the person sitting in it. With the help of this scenario, we need to understand that the developer of the car goes through all such dilemmas while developing the car’s algorithm. Thus, here the morality of the developer gets transferred into the machine as what according to him/her is right would have a higher priority and hence would be the selection made by the machine. If you were in the place of this developer and if there was no other alternative to the situation, which one of the two would you prioritise and why? __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ * Images shown here are the property of individual organisations and are used here for reference purpose only. Scenario 2: Let us now assume that the car has hit the boy who came in front of it. Considering this as an accident, who should be held responsible for it? Why? 1. The person who bought this car 2. The Manufacturing Company 3. The developer who developed the car’s algorithm 4. The boy who came in front of the car and got severely injured __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ Here, the choices might differ from person to person and one must understand that nobody is wrong in this case. Every person has a different perspective and hence he/she takes decisions according to their moralities. Data Privacy The world of Artificial Intelligence revolves around Data. Every company whether small or big is mining data from as many sources as possible. More than 70% of the data collected till now has been collected in the last 3 years which shows how important data has become in recent times. It is not wrongly said that Data is the new gold. This makes us think: Where do we collect data from? __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ Why do we need to collect data? __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ One of the major sources of data for many major companies is the device which all of us have in our hands all the time: Smartphones. Smartphones have nowadays become an integral part of our lives. Most of us use smartphones more than we interact with people around us. Smartphones in today’s era provide us with a lot of facilities and features which have made our lives easier. Feeling hungry? Order food online. Want to shop but don’t have time to go out? Go shopping online. From booking tickets to watching our favourite shows, everything is available in this one small box loaded with technology. Another feature of smartphones nowadays is that they provide us with customised recommendations and notifications according to our choices. Let us understand this with the help of some examples: 1. When you are talking to your friend on a mobile network or on an app like WhatsApp. You tell your friend that you wish to buy new shoes and are looking for suggestions from him/her. You discuss about shoes and that is it. After some time, the online shopping websites start giving you notifications to buy shoes! They start recommending some of their products and urge you to you buy some. 2. If you search on Google for a trip to Kerala or any other destination, just after the search, all the apps on your phone which support advertisements, will start sending messages about packages that you can buy for the trip. 3. Even when you are not using your phone and talking to a person face-to-face about a book you’ve read recently while the phone is kept in a locked mode nearby, the phone will end up giving notifications about similar books or messages about the same book once you operate it. In all such examples, how does the smartphone get to know about the discussions and thoughts that you have? Remember whenever you download an app and install it, it asks you for several permissions to access your phone’s data in different ways. If you do not allow the app these permissions, you normally cannot access it. And to access the app and make use of it, we sometimes don’t even give it a thought and allow the app to get all the permissions that it wants. Hence every now and then, the app has the permission to access various sensors which are there in your smartphone and gather data about you and your surroundings. We forget that the smartphone which we use is a box full of sensors which are powered all the time while the phone is switched on. This leads us to a crucial question: Are we okay with sharing our data with the external world? __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ Why do these apps collect data? __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ We need to understand that the data which is collected by various applications is ethical as the smartphone users agree to it (by clicking on allow when it asks for permission and by agreeing to all the terms and conditions). But at the same time if one does not want to share his/her data with anyone, he/she can opt for alternative applications which are of similar usage and keep your data private. For example, an alternative to WhatsApp is the Telegram app which does not collect any data from us. But since WhatsApp is more popular and used by the crowd, people go for it without thinking twice. AI Bias Another aspect to AI Ethics is bias. Everyone has a bias of their own no matter how much one tries to be unbiased, we in some way or the other have our own biases even towards smaller things. Biases are not negative all the time. Sometimes, it is required to have a bias to control a situation and keep things working. When we talk about a machine, we know that it is artificial and cannot think on its own. It can have intelligence, but we cannot expect a machine to have any biases of its own. Any bias can transfer from the developer to the machine while the algorithm is being developed. Let us look at some of the examples: 1. Majorly, all the virtual assistants have a female voice. It is only now that some companies have understood this bias and have started giving options for male voices but since the virtual assistants came into practice, female voices are always preferred for them over any other voice. Can you think of some reasons for this? __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ 2. If you search on Google for salons, the first few searches are mostly for female salons. This is based on the assumption that if a person is searching fora salon, in all probability it would be a female. Do you think this is a bias? If yes, then is it a Negative bias or Positive one? __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ Various other biases are also found in various systems which are not thought up by the machine but have got transferred from the developer intentionally or unintentionally. AI Access Since Artificial Intelligence is still a budding technology, not everyone has the opportunity to access it. The people who can afford AI enabled devices make the most of it while others who cannot are left behind. Because of this, a gap has emerged between these two classes of people and it gets widened with the rapid advancement of technology. Let us understand this with the help of some examples: AI creates unemployment AI is making people’s lives easier. Most of the things nowadays are done in just a few clicks. In no time AI will manage to be able to do all the laborious tasks which we humans have been doing since long. Maybe in the coming years, AI enabled machines will replace all the people who work as labourers. This may start an era of mass unemployment where people having little or no skills may be left without jobs and others who keep up with their skills according to what is required, will flourish. This brings us to a crossroads. On one hand where AI is advancing and improving the lives of people by working for them and doing some of their tasks, the other hand points towards the lives of people who are dependent on laborious jobs and are not skilled to do anything else. Should AI replace laborious jobs? Is there an alternative for major unemployment? __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ Should AI not replace laborious jobs? Will the lives of people improve if they keep on being unskilled? __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ Here, we need to understand that to overcome such an issue, one needs to be open to changes. As technology is advancing with time, humans need to make sure that they are a step ahead and understand this technology with its pros and cons. AI for kids As we all can see, kids nowadays are smart enough to understand technology from a very early age. As their thinking capabilities increase, they start becoming techno-savvy and eventually they learn everything more easily than an adult. But should technology be given to children so young? Consider this: A young boy in class 3 has got some Maths homework to finish. He is sitting at a table which has the Google chat bot - Alexa on it, and he is struggling with his homework. Soon, he starts asking Alexa to answer all his questions. Alexa replies with answers and the boy simply writes them down in his notebook. While this scenario seems funny, it still has some concerns related to it. On one hand where it is good that the boy knows how to use technology effectively, on the other hand he uses it to complete his homework without really learning anything since he is not applying his brain to solve the Math problems. So, while he is smart, he might not be getting educated properly. Is it ethical to let the boy use technology to help in this manilr? __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ Conclusion Despite AI’s promises to bring forth new opportunities, there are certain associated risks that need to be mitigated appropriately and effectively. To give a better perspective, the ecosystem and the socio- technical environment in which the AI systems are embedded needs to be more trustworthy. AI Project Cycle In this chapter, we will revisit the concept of AI Project Cycle. Introduction Let us assume that you have to make a greeting card for your mother as it is her birthday. You are very excited about it and have thought of many ideas to execute the same. Let us look at some of the steps which you might take to accomplish this task: 1. Look for some cool greeting card ideas from different sources. You might go online and checkout some videos or you may ask someone who has knowledge about it. 2. After finalising the design, you would make a list of things that are required to make this card. 3. You will check if you have the material with you or not. If not, you could go and get all the items required, ready for use. 4. Once you have everything with you, you would start making the card. 5. If you make a mistake in the card somewhere which cannot be rectified, you will discard it and start remaking it. 6. Once the greeting card is made, you would gift it to your mother. Are these steps relatable? __________________________________________________________________________________ __________________________________________________________________________________ Do you think your steps might differ? If so, write them down! __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ __________________________________________________________________________________ These steps show how we plan to execute the tasks around us. Consciously or Subconsciously our mind makes up plans for every task which we have to accomplish which is why things become clearer in our mind. Similarly, if we have to develop an AI project, the AI Project Cycle provides us with an appropriate framework which can lead us towards the goal. The AI Project Cycle mainly has 5 stages: * Images shown here are the property of individual organisations and are used here for reference purpose only. Starting with Problem Scoping, you set the goal for your AI project by stating the problem which you wish to solve with it. Under problem scoping, we look at various parameters which affect the problem we wish to solve so that the picture becomes clearer. To proceed, You need to acquire data which will become the base of your project as it will help you in understanding what the parameters that are related to problem scoping are. You go for data acquisition by collecting data from various reliable and authentic sources. Since the data you collect would be in large quantities, you can try to give it a visual image of different types of representations like graphs, databases, flow charts, maps, etc. This makes it easier for you to interpret the patterns which your acquired data follows. After exploring the patterns, you can decide upon the type of model you would build to achieve the goal. For this, you can research online and select various models which give a suitable output. You can test the selected models and figure out which is the most efficient one. The most efficient model is now the base of your AI project and you can develop your algorithm around it. Once the modelling is complete, you now need to test your model on some newly fetched data. The results will help you in evaluating your model and improving it. Finally, after evaluation, the project cycle is now complete and what you get is your AI project. Let us understand each stage of the AI Project Cycle in detail. Problem Scoping 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. A lot of times we are unable to observe any problem in our surroundings. In that case, we can take a look at the Sustainable Development Goals. 17 goals have been announced by the United nations which are termed as the Sustainable Development Goals. The aim is to achieve these goals by the end of 2030. A pledge to do so has been taken by all the member nations of the UN. * Images shown here are the property of individual organisations and are used here for reference purpose only. Here are the 17 SDGs. Let’s take a look: As you can see, many goals correspond to the problems which we might observe around us too. One should look for such problems and try to solve them as this would make many lives better and help our country achieve these goals. Scoping a problem is not that easy as we need to have a deeper understanding around it so that the picture becomes clearer while we are working to solve it. Hence, we use the 4Ws Problem Canvas to help us out. 4Ws Problem Canvas The 4Ws Problem canvas helps in identifying the key elements related to the problem. Who? What? Where? Why? Let us go through each of the blocks one by one. Who? The “Who” block helps in analysing the people getting affected directly or indirectly due to it. Under this, we find out who the ‘Stakeholders’ to this problem are and what we know about them. Stakeholders are the people who face this problem and would be benefitted with the solution. Here is the Who Canvas: * Images shown here are the property of individual organisations and are used here for reference purpose only. 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. Here is the What Canvas: 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. Here is the Where Canvas: 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 it will benefit them as well as the society. 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 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. [stakeholder(s)] Who Our has /have a [issue, problem, need] What problem that when / while [context, situation] Where An ideal [benefit of solution for them] Why solution would 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 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. 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 API Cameras Observations (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 offence. 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 Data Exploration 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 Representations Modelling 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 machines 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 in 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 Deep AI Models Learning Rule Based Rule Based Approach Refers to the AI 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 an elephant may be spotted or not while on safari. The parameters are: Outlook, Temperature, Humidity and Wind. Now, let’s take various possibilities of these parameters and see in which case the elephant may be spotted and in which case it may not. After looking through all the cases, we feed this data in to 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 elephant has been spotted before 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. That is, if you try testing the machine on a dataset which is different from the rules and data you fed it at the training stage, the machine will fail and will not learn from its mistake. Once trained, the model cannot improvise itself on the basis of feedbacks. Thus, machine learning gets introduced as an extension to this as in that case, the machine adapts to change in data and rules and follows the updated path only, while a rule-based model does what it has been taught once. Learning Based Approach Refers to the AI modelling where the machine learns by itself. Under the Learning Based approach, the AI 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 AI 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. In this way, the machine learns by itself by adapting to the new data which is flowing in. This is the machine learning approach which introduces the dynamicity in the model. The learning-based approach can further be Learning Based Approach divided into three parts: Supervised Learning Supervised In a supervised learning model, the dataset Learning which is fed to the machine is labelled. In other words, we can say that the dataset is Unsupervised known to the person who is training the Learning machine only then he/she is able to label the data. A label is some information which can Reinforcement be used as a tag for data. For example, students get grades according to the marks Learning they secure in examinations. These grades are labels which categorise the students according to their marks. There are two types of Supervised Learning models: Classification: Where the data is classified according to the labels. For example, in the grading system, students are classified on the basis of the grades they obtain with respect to their marks in the examination. This model works on discrete dataset which means the data need not be continuous. Regression: Such models work on continuous data. For example, if you wish to predict your next salary, then you would put in the data of your previous salary, any increments, etc., and would train the model. Here, the data which has been fed to the machine is continuous. Unsupervised Learning An unsupervised learning model works on unlabelled dataset. This means that the data which is fed to the machine is random and there is a possibility that the person who is training the model does not have any information regarding it. The unsupervised learning models are used to identify relationships, patterns and trends out of the data which is fed into it. It helps the user in understanding what the data is about and what are the major features identified by the machine in it. For example, you have a random data of 1000 dog images and you wish to understand some pattern out of it, you would feed this data into the unsupervised learning model and would train the machine on it. After training, the machine would come up with patterns which it was able to identify out of it. The Machine might come up with patterns which are already known to the user like colour or it might even come up with something very unusual like the size of the dogs. * Images shown here are the property of individual organisations and are used here for reference purpose only. Unsupervised learning models can be further divided into two categories: Clustering: Refers to the unsupervised learning algorithm which can cluster the unknown data according to the patterns or trends identified out of it. The patterns observed might be the ones which are known to the developer or it might even come up with some unique patterns out of it. Dimensionality Reduction: We humans are able to visualise upto 3-Dimensions only but according to a lot of theories and algorithms, there are various entities which exist beyond 3-Dimensions. For example, in Natural language Processing, the words are considered to be N-Dimensional entities. Which means that we cannot visualise them as they exist beyond our visualisation ability. Hence, to make sense out of it, we need to reduce their dimensions. Here, dimensionality reduction algorithm is used. As we reduce the dimension of an entity, the information which it contains starts getting distorted. For example, if we have a ball in our hand, it is 3-Dimensions right now. But if we click its picture, the data transforms to 2-D as an image is a 2-Dimensional entity. Now, as soon as we reduce one dimension, at least 50% of the information is lost as now we will not know about the back of the ball. Whether the ball was of same colour at the back or not? Or was it just a hemisphere? If we reduce the dimensions further, more and more information will get lost. Hence, to reduce the dimensions and still be able to make sense out of the data, we use Dimensionality Reduction. Evaluation 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: Accuracy Precision Recall F1 Score You will read more about this stage in Chapter 7. * Images shown here are the property of individual organisations and are used here for reference purpose only. Neural Networks Neural networks are loosely modelled after how neurons in the human brain behave. The key advantage of neural networks are that they are able to extract data features automatically without needing the input of the programmer. A neural network is essentially a system of organizing machine learning algorithms to perform certain tasks. It is a fast and efficient way to solve problems for which the dataset is very large, such as in images. As seen in the figure given, the larger Neural Networks tend to perform better with larger amounts of data whereas the traditional machine learning algorithms stop improving after a certain saturation point. This is a representation of how neural networks work. A Neural Network is divided into multiple layers and each layer is further divided into several blocks called nodes. Each node has its own task to accomplish which is then passed to the next layer. The first layer of a Neural Network is known as the input layer. The job of an input layer is to acquire data and feed it to the Neural Network. No processing occurs at the input layer. Next to it, are the hidden layers. Hidden layers are the layers in which the whole processing occurs. Their name essentially means that these layers are hidden and are not visible to the user. Each node of these hidden layers has its own machine learning algorithm which it executes on the data received from the input layer. The processed output is then fed to the subsequent hidden layer * Images shown here are the property of individual organisations and are used here for reference purpose only. of the network. There can be multiple hidden layers in a neural network system and their number depends upon the complexity of the function for which the network has been configured. Also, the number of nodes in each layer can vary accordingly. The last hidden layer passes the final processed data to the output layer which then gives it to the user as the final output. Similar to the input layer, output layer too does not process the data which it acquires. It is meant for user-interface. Some of the features of a Neural Network are listed below: Advance Python Recap In this section, we will go through a quick refreshing session around Python concepts and Jupyter notebook. Along with this we will talk about newer concepts like packages, virtual environments, etc. Recap 1: Jupyter Notebook The Jupyter Notebook is an incredibly powerful tool for interactively developing and presenting AI related projects. The Jupyter project is the successor to the earlier IPython What is Jupyter Notebook, which was first published as a prototype in 2010. Although it is possible to use many different programming Notebook? languages within Jupyter Notebooks, Python remains the most commonly used language for it. In other words, we can say that the Jupyter Notebook is an open source web application that can be used to create and share documents that contain live code, equations, visualizations, and text. The easiest way to install and start using Jupyter Notebook is through Anaconda. Anaconda is the most widely used Python distribution for data science and comes pre-loaded with all the most popular libraries and How to access tools. With Anaconda, comes the Anaconda Navigator Jupyter Notebook? through which we can scroll around all the applications which come along with it. Jupyter notebook can easily be accessed using the Anaconda Prompt with the help of a local host. To work with Jupyter Notebook, it is necessary to have a kernel on which it operates. A kernel provides programming Kernels in language support in Jupyter. IPython is the default kernel for Jupyter Jupyter Notebook. Therefore, whenever we need to work Notebook with Jupyter Notebook in a virtual environment, we first need to install a kernel inside the environment in which the Jupyter notebook will run. Introduction to Virtual Environments A virtual environment is a tool that helps to keep dependencies required by different projects separated, by creating isolated Python virtual environments What? for them. This is one of the most important tools that most of the Python developers use. Imagine a scenario where we are working on two Python-based projects and one of them works on Python 2.7 and the other uses Python 3.7. In such situations virtual environment can be really useful to maintain dependencies of both the projects as the virtual environments will make sure that these Why? dependencies are not conflicting with each other and no impact reaches the base environment at any point in time. Thus, different projects developed in the system might have another environment to keep their dependencies isolated from each other. Creating virtual environments is an easy task with Anaconda distribution. Steps How? to create one are: 1. Open Anaconda Prompt. 2. As we open the Anaconda prompt, we can see that in the beginning of the prompt message, the term (base) is written. This is the default environment in which the anaconda works. Now, we can create our own virtual environment and use it so that the base does not get affected by anything that is done in the virtual environment. 3. Let us now create a virtual environment named env. To create the environment, write conda create -n env python=3.7 This code will create an environment named env and will install Python 3.7 and other basic packages into it. 4. After some processing, the prompt will ask if we wish to proceed with installations or not. Type Y on it and press Enter. Once we press Enter, the packages will start getting installed in the environment. 5. Depending upon the internet speed, the downloading of packages might take varied time. The processing screen will look like this: 6. Once all the packages are downloaded and installed, we will get a message like this: 7. This shows that our environment called env has been successfully created. Once an environment has been successfully created, we can access it by writing the following: conda activate env This would activate the virtual environment and we can see the term written in brackets has changed form (base) to (env). Now our virtual environment is ready to be used. But, to open and work with Jupyter Notebooks in this environment, we need to install the packages which help in working with Jupyter Notebook. These packages get installed by default in the base environment when Anaconda gets installed. To install Jupyter Notebook dependencies, we need to activate our virtual environment env and write: conda install ipykernel nb_conda jupyter It will again ask if we wish to proceed with the installations, type Y to begin the installations. Once the installations are complete, we can start working with Jupyter notebooks in this environment. Recap 2: Introduction to Python In class 9, we were introduced to Python as the programming language which will be used for working around AI. Let us recall the basics of Python. Python is a programming language which was created by Guido Van Rossum in Centrum Wiskunde & Informatica. The language was publicly released in 1991 and it got its name from a BBC comedy series from 1970s – ‘Monty Python’s Flying What? Circus’. It can be used to follow both procedural approach and object-oriented approach of programming. Python has a lot of functionalities which makes it so popular to use. Artificial intelligence is the trending technology of the future. We can see so many applications around us. If we as individuals would also like to develop an AI application, we will need to know a programming language. There are various Why? programming languages like Lisp, Prolog, C++, Java and Python, which can be used for developing applications of AI. Out of these, Python gains a maximum popularity because of the following reasons: Easy to learn, read and maintain Python has few keywords, simple structure and a clearly defined syntax. Python allows anyone to learn the language quickly. A program written in Python is fairly easy-to-maintain. A Broad Standard library Python has a huge bunch of libraries with plenty of built-in functions to solve a variety of problems. Interactive Mode Python has support for an interactive mode which allows interactive testing and debugging of snippets of code. Portability and Compatibility Python can run on a wide variety of operating systems and hardware platforms, and has the same interface on all platforms. Extendable We can add low-level modules to the Python interpreter. These modules enable programmers to add to or customize their tools to be more efficient. Databases and Scalable Python provides interfaces to all major open source and commercial databases along with a better structure and support for much larger programs than shell scripting. Applications of Python There exist a wide variety of applications when it comes to Python. Some of the applications are: Recap 3: Python Basics In class 9, as Python was introduced, we also discussed about some basic Python syntaxes which can help us in writing codes in Python language. Let us brush up all the concepts once and see how we can use them in coding. 1. Printing Statements We can use Python to display outputs for any code we write. To print any statement, we use print() function in Python. 2. Python Statements and Comments Instructions written in the source code to execute are known as statements. These are the lines of code which we write for the computer to w

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