AI ebook

["6. Preven on: Preven on loop helps in avoiding any serious threat. For instance, in the previous scenario, if you are planning to a end the event during the wai ng me there is a chance of occurrence of any disaster like a storm, \ufb02ood or heavy rain. Then the AI system will inform you on me to reduce the impact of the disaster. 7. Situa onal Awareness: Situa onal awareness loop helps in providing relevant informa on to the people involved. In the modern age of technology, you or AI system can access a lot of informa on. The main objec ve of an AI system is to choose relevant informa on and discard the rest. For example, in the above scenario, AI controlled system will collect a lot of details about you but it can keep a backup of relevant informa on only like your name and room number. ICE-BREAKER ACTIVITY ABOUT AI SYSTEM The development in the \ufb01eld of AI is an ongoing process. Write any \ufb01ve areas that you have seen in your daily life which are completely based on AI technology. Also, explain why you think these systems follow AI technology? Skill Sets Needed for Jobs in Fields of AI The \ufb01eld of Ar \ufb01cial Intelligence is s ll at a developmental phase with its ongoing research and advancements. Due to this reason, many organiza ons will need a skilled workforce to build, test and deploy more and more Ar \ufb01cial Intelligence in the machines. Most of the AI-related jobs require a scien \ufb01c and logical approach to design computer systems that can think and learn. The speci\ufb01c skills that you will need to make a career in AI are varied, but all of them require an educated and trained person. The most prominent career in AI is developing and deploying AI systems. The development and deployment of AI systems is highly scien \ufb01c. These types of jobs require special and advanced training. The career opportuni es in the development and deployment of AI systems are as follows: a) So ware developers b) So ware analysts c) Algorithm developers d) Computer scien sts e) Hardware technicians f) Computer engineers The di\ufb00erent programming languages used to develop and deploy AI systems are as follows: a) Python b) Java c) R d) C++ List of Companies Hiring for Arti\ufb01cial Intelligence Roles As you know, the development and deployment of AI systems are highly scien \ufb01c. Due to this reason, companies need good AI talent. In the past few years, following companies are the leading employers to hire top AI talent: PM Publishers Pvt. Ltd. 51","AI Careers with Speci\ufb01c Skill Sets As you know, speci\ufb01c skills are needed to pursue a career in the development of AI \ufb01eld. Now, we are going to learn about the di\ufb00erent jobs available in AI along with the required skill set. 1. Data Scien st: Data Scien sts are scien sts who make value out of data. They fetch informa on from large data sets and analyze it for be er understanding. The skill sets needed for these jobs are as follows: a) Adequate knowledge of Big Data pla orms like Hive, MapReduce, etc. b) Knowledge of modern and sta s cal programming languages c) Strong analy cal skills 2. Machine Learning Engineer: Machine learning engineer is one of the most prominent professions in the \ufb01eld of AI. These engineers are responsible to develop and manage machine learning AI systems. The skill sets required to pursue a career in this \ufb01eld are: a) Good knowledge of programming languages b) Good knowledge of data science c) Strong mathema cal skills 3. Business Intelligence Developer: Business intelligence developer is also one of the most sought-a er careers these days. They are responsible to analyze data for the predic on of future market trends. The speci\ufb01c skill sets required for these jobs are: a) Good knowledge of query tools b) Knowledge of data modelling and data mining c) Ability to translate business requirements into technical requirements In a Nutshell 1. There are possibili es of AI in various \ufb01elds such as in military, research, healthcare, etc. 2. Ar \ufb01cial Intelligence consists of 7 di\ufb00erent loops: Percep on, No \ufb01ca on, Recommenda on, Automa on, Predic on, Preven on and Situa onal Awareness. 3. The speci\ufb01c skills that you will need to make a career in AI are varied, but all of them require an educated and trained person. 4. The most prominent career in AI is developing and deploying AI systems. 5. The di\ufb00erent career opportuni es in the development and deployment of AI systems are as follows: So ware Developers, So ware Analysts, Algorithm Developers, Computer Scien sts, Hardware Technicians and Computer Engineers. 6. Data scien sts are scien sts who make value out of data. They fetch informa on from large data sets and analyze it for be er understanding. 7. Machine learning engineer is one of the most prominent professions in the \ufb01eld of AI. These engineers are responsible to develop and manage machine learning AI systems. 8. Business intelligence developers are responsible to analyze data for the predic on of future market trends. Arti\ufb01cial Intelligence - 9 52","AI ETHICS Introduction to AI Ethics Scien \ufb01c advancement in the \ufb01eld of Ar \ufb01cial Intelligence technology impacts our daily life. This emerging technology raises various complex ethical issues like privacy concerns, transparency issues, etc. Let us learn about the implica ons of developing and using AI- controlled systems. Before learning about the term AI ethics, let us take a look at the term ethics. In general, the term \u2018ethics\u2019 refers to a system of moral principles and rules of conduct that govern an individual's acceptable behaviour or ac ons. The term \u2018ethics\u2019 is also known as moral philosophy. It is usually concerned with what is good or what is bad for individuals as well as for socie es. Now, let us understand about the term \u2018AI ethics\u2019. The term \u2018AI ethics\u2019 refers to a set of rules and principles related to AI systems. AI-enabled systems are becoming more capable day-by-day which raise complex ethical issues for the human world. These ethical issues or concerns cause individuals or socie es to evaluate di\ufb00erent op ons in terms of what is right or wrong. Let us understand with the help of an example. Suppose, a man is hit by an autonomous vehicle based on AI technology, when he is trying to cross a road. In such a case, we must think about the following ques ons: What should an autonomous vehicle do? Who is responsible for this accident \u2013 the manufacturers of autonomous vehicles or the developers of AI systems? In simple words, we can conclude that this emerging technology is just as much a new fron er for ethics and risk assessment. Every coin has two sides. Similarly, this digital transforma on technology also has two sides. On one side, you can see that chatbots are used by banks and other \ufb01nancial ins tu ons for customer support, which technically sounds good. But, on the other side, chatbots are the replacement of human workers i.e. they are elimina ng human jobs. The survival of human beings will be gravely impacted if AI technology replaces human workers at such an accelera ng pace. Let\u2019s explore this chapter to know more about the ethical concerns of AI technology. Ethical Concerns of AI Related to Data Nowadays, AI-controlled systems are being widely used in di\ufb00erent sectors like banking, educa on, healthcare, manufacturing, etc. because these systems have the ability to learn and can take decisions on their own without any human interven on. For example, in the \ufb01eld of transporta on, various tools are installed in semi-autonomous vehicles that allow drivers and vehicles to know about upcoming conges on or highway construc on. But these systems leave serious implica ons on the lives of people. PM Publishers Pvt. Ltd. 53","You have learned how AI-enabled systems like biometrics are used in organiza ons to manage the entry or a endance of employees by collec ng data on facial and demographic characteris cs. In the healthcare industry, the main goal of implemen ng Ar \ufb01cial Intelligence machines is to enhance produc vity in terms of improvement in pa ent care and reducing work pressure on healthcare workers. To achieve this, many healthcare industries have adopted Ar \ufb01cial Intelligence machines to make be er and faster diagnoses. A large set of real world data is used by AI system for analysing and learning purpose. This which could raise serious ethical concerns all over the world. Possible Bias in Data Collection As you know, AI systems are intelligent ar \ufb01cially. These systems can make decisions on the basis of data which is being fed by the programmer at the development stage. If the data fed into it is biased, the AI system will also be biased. Let us understand with the help of an example. Amazon, a big IT giant, developed an AI system for hiring so ware engineers in 2014. Soon, Amazon found out that their AI system was biased against women and was causing gender discrimina on. This system was based on the number of resumes submi ed over the past 10 years and the candidates hired. Since most of the candidates were men, the algorithm also favored men over women. Ul mately, Amazon was forced to scrap the AI recruitment system. In the beginning of compu ng, programmers understood the concept of GIGO. GIGO is an acronym that stands for Garbage In, Garbage Out. It means that you will get wrong output if you give wrong input to the computers. In simple words, the quality of the output received from a computer program depends on the quality of the informa on that was input. This principle is also applicable to AI systems. Problem of Inclusion in AI Systems Data is the core of AI systems. These AI systems can take their own decisions without any type of human interven on. To perform a speci\ufb01c task, these systems are equipped with real-world data which can be biased or unbiased. Most of the me, inclusion problems are found in those AI systems which make decisions on the basis of biased data. For example, facial recogni on technology is not so accurate to recognize darker skinned faces. This is a situa on in which AI systems exhibit racial bias which makes many darker skinned people feel inferior and excluded. This racial bias towards darker skinned people is due to lack of diversity in the images and other data used in training the AI systems. DO YOU KNOW ? Huawei\u2019s annual Global Industry Vision 2025 report predicts that by 2025, 14% of families across the globe will have a domes c robot. Arti\ufb01cial Intelligence - 9 54","Problem of Facts and their Interpretation Data is the heart of AI systems. These systems are able to learn and adapt due to the pre-equipped data fed into them but these systems are not able to understand the reason behind par cular learning or decisions made by them. Let us understand with the help of an example. In 2016, Microso created a Twi er bot named Tay. This chatbot was based on machine learning technique and was designed to understand user conversa ons. Targeted towards engaging millennials, it was supposed to speak like a teenager and interact with users of 18-24 age bracket in the US. According to Microso , this bot was designed to get smarter and develop more conversa onal understanding as it engaged more and more in conversa ons. In less than 24 hours a er its arrival on Twi er, Tay gained more than 50,000 followers and produced nearly 1,00,000 tweets. However, Tay soon started twee ng some very undesirable statements because some users on Twi er began twee ng incorrect phrases at Tay and taught it in\ufb02ammatory messages. Due to this, Tay began releasing racist and in\ufb02ammatory responses to the Twi er users. This was considered as a major drawback as Tay learned from the Twi er interac ons but was not embedded with the skills to interpret the facts of the inputs. This pi all within Tay\u2019s AI system caused Microso to shut down the service only a er 16 hours of its launch. Therefore, designers and engineers have to start thinking about codes of conduct regarding the future of chatbots and AI systems. Ethical Concerns Related to Implications of AI System As you read earlier, AI systems are raising serious ethical issues all over the world. These issues exist in two forms: Data for AI systems and Implica ons of AI systems. We have already discussed the ethical concerns of AI related to data in the previous sec on. In this sec on, we will learn about the ethical issues related to the implica ons of AI systems. These issues can be divided into two categories: i) Privacy Concern: Data collec on is one of the most serious implica ons of AI systems that comes under the category of privacy concern. Let us understand with the help of an example. Gmail, an app o\ufb00ered by Google, helps us to set up an e-mail account for sending e-mails across the world. You must have no ced that while crea ng an account on Gmail, you must have been asked to submit some informa on like your date of birth, phone number, etc. A er \ufb01lling all informa on, you will be asked to put a ck mark in the checkbox of a lengthy user agreement, which most users accept without even realizing the implica ons of such agreements on our privacy rights. You should remember that the data entered by you is stored somewhere in the large databases. Thus, the informa on entered by you can be used in any manner wherein the poten al risk is incredibly high. ii) Adop on Concern: One of the major concerns related to the adop on of AI systems is its impact on employment and the workforce. Let us understand more about adop on concerns of AI system through the following points: PM Publishers Pvt. Ltd. 55","a) Future of Jobs: Nowadays, there have been notable concerns regarding loss of future jobs due to the adop on of AI-enabled systems in various sectors. Di\ufb00erent researches on the topic \u201cJob Loss due to Automa on\u201d have been published by di\ufb00erent organiza ons, as given below: According to AI Expert Kai-Fu Lee, Chairman and CEO of Sinova on Ventures and author of the book AI Superpowers, \u201c50% of all jobs will be automated by AI inside of 15 years.\u201d According to Gartner Inc., leading global research and advisory \ufb01rm, \u201cAI related job crea on will reach two million net new jobs in 2025.\u201d According to McKinsey Global Ins tute, \u201cApproximately 40 to 160 million women worldwide may need to switch the jobs by 2030 because di\ufb00erent types of jobs like clerical, schedulers, bookkeepers, etc. are the most suscep ble areas which will be acquired by automa on. These types of jobs are women-dominated in developed countries.\u201d According to Oxford Economics, \u201cDue to adop on of automated robots, up to 20 million jobs in the manufacturing sector across the world will be lost by 2030.\u201d According to the World Economic Forum, \u201cThe adop on of AI systems will displace 75 million jobs but generate 133 million new ones worldwide by 2022.\u201d Thus, job loss is the major concern across the world due to adop on of automa on systems in various sectors. b) Income Inequali es: The adop on of AI systems could increase income inequality across the world. According to the research by Oxford Economics, \u201cThe nega ve consequences of automated systems are dispropor onately felt in the lower-income regions compared with higher-income regions of the same country.\u201d c) Security: Like other technologies, AI systems like bots can be used for both good and bad purposes. On the bad side, bots can be used by fraudsters to perform automated logins with the goal of compromising user accounts. These systems can also be misused by cyber criminals for hacking or damaging purposes. d) No reasoning: As you know, Ar \ufb01cial Intelligent systems are not moral agents. These systems can make decisions on the basis of real world data which is being fed into them but it is s ll unclear as to who is responsible for the outcome of the decisions made by them. In simple words, these systems are not able to give reasoning for the decisions made by them. Short Analysis of AI Systems With the technological advancement in the \ufb01eld of AI, we have seen how AI-based systems are transforming our lives. These systems play an important role in every sphere of our life. Some mes, a thought pops up in our minds that we are living in the golden age of technology. Though it is true, we must remember that every coin has two sides. Similarly, the use of AI systems can be bene\ufb01cial as well as destruc ve. To reduce the risk of any type of destruc on, all organiza ons must follow the rules and regula ons related to AI systems. Arti\ufb01cial Intelligence - 9 56","Worksheet 5: Based on Advantages and Ethical Concerns Here, the names of some important AI-enabled systems are given. Write at least two advantages and two ethical concerns related to these systems. S. No AI Systems Advantages Ethical Concerns 1. Biometric System 2. Smart Thermostats 3. Smart Security System 4. Smart Parking System 5. Clinical Trial Matching In a Nutshell 1. The term \u2018ethics\u2019 refers to a system of moral principles and rules of conduct that govern an individual's acceptable behaviour or ac ons. The term \u2018AI ethics\u2019 refers to a set of rules and principles related to AI systems. 2. AI systems are intelligent ar \ufb01cially. These systems can make decisions on the basis of data which is being fed by the programmer at the development stage. If the data fed into it is biased, the AI system will also be biased. 3. To perform a speci\ufb01c task, AI systems are equipped with real-world data which can be biased or unbiased. Most of the me, inclusion problems are found in those AI systems which make decisions on the basis of biased data. 4. AI systems are able to learn and adapt due to the pre-equipped data fed into them but these systems are not able to understand the reason behind par cular learning or decisions made by them. 5. Data collec on is one of the most serious implica ons of AI systems that comes under the category of privacy concern. 6. There are two types of ethical concerns related to implica ons of AI systems: privacy concern and adop on concern. 7. The major concerns related to the adop on of AI systems are future job security, income inequali es, compromised security and inability of AI systems to give reasoning for the decisions made by them. 8. The use of AI systems can be bene\ufb01cial as well as destruc ve. To reduce the risk of any type of destruc on, all organiza ons must follow the rules and regula ons related to AI systems. PM Publishers Pvt. Ltd. 57","Multiple Choice Questions 1. Auto-complete sugges on feature in search engines is an example of \u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.. . a) Deep Learning b) Neural Network c) Machine Learning d) Robo cs 2. Which of the following terms refers to the process of acquiring, interpre ng, selec ng and organizing sensory informa on? a) Reasoning b) Learning c) Percep on d) Problem solving 3. \u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.. systems use clustering and associa on techniques for processing the data. a) Weak AI b) Strong AI c) Applied AI d) Cogni ve AI 4. The \u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.. sensors are used as a water saving upgrade in smart homes. a) smoke sensors b) smart moisture c) smart grid d) smart thermostats 5. \u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.. has been developed by IBM Watson. a) Alexa b) Clinical Trial Matching (CTM) c) Eva d) AlphaGo 6. \u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.. is a service developed by Microso that uses the power of Natural Language Processing to solve the complex problem of extrac ng meaning from natural language input. a) Semantris b) Google Translator c) LUIS (Language Understanding Service) d) AlphaGo 7. Which of the following is an example of recommender system? a) Product recommenda ons on Amazon b) Movie recommenda ons on Ne lix c) Music sugges ons on YouTube d) All the these 8. Which of the following elements is not a part of sustainable development? a) Society b) Environment c) Ecology d) Ecosystem 9. The \u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.. wave will integrate everything and enhance the capability of machines to sense and respond to the world around them. a) \ufb01rst b) second c) third d) fourth 10. Among the following, which AI-related career demands strong mathema cal skills? a) Data Scien st b) Machine Learning Engineer c) Data Operator d) Business Intelligence Developer 11. Which of the following op ons is not a loop of AI? a) Predic on b) Reasoning c) No \ufb01ca on d) Preven on 58 Arti\ufb01cial Intelligence - 9","12. Which of the following is best for Big Data? a) MapReduce b) C c) Qbasic d) LOGO 13. Which of the following is an ethical concern related to implica ons of AI systems? a) Privacy concern b) Adop on concern c) Both (a) & (b) d) None of the above 14. Nowadays, many healthcare industries have adopted Ar \ufb01cial Intelligence machines to enhance produc vity in terms of \u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.. . a) faster diagnoses b) reducing work pressure c) me saving d) all of these 15. \u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.. is the name of Twi er chatbot developed by Microso in 2016. a) Tames b) Tay c) Thamy d) Eva Answers 1. c 2. c 3. b 4. b 5. b 6. c 7. d 8. c 9. d 10. b 11. b 12. a 13. c 14. d 15. b Fill in the Blanks 1. In self-driving cars, \u2026\u2026\u2026\u2026\u2026\u2026..\u2026\u2026\u2026\u2026\u2026.. is one of the key enabling technologies for detec ng tra\ufb03c signals, tra\ufb03c lights and weather condi ons. 2. The di\ufb00erent types of data used in AI systems are sound, text, \u2026\u2026\u2026..\u2026\u2026\u2026\u2026\u2026.. and video. 3. \u2026\u2026\u2026\u2026\u2026\u2026..\u2026\u2026\u2026\u2026\u2026.. can be de\ufb01ned as the ability of machines to store past experiences for a shorter period of me. 4. Various e-commerce organiza ons like \u2026\u2026\u2026\u2026\u2026\u2026..\u2026\u2026\u2026\u2026\u2026.. and \u2026\u2026\u2026\u2026\u2026\u2026..\u2026\u2026\u2026\u2026\u2026.. use AI technology to understand the perspec ve of customers or to automate customer services in an e\ufb00ec ve way. 5. \u2026\u2026\u2026\u2026\u2026\u2026..\u2026\u2026\u2026\u2026\u2026.. is India\u2019s \ufb01rst and largest AI-based banking chatbot. 6. Google Home or Nest is a Google Assistant based smart speaker which uses \u2026\u2026\u2026\u2026\u2026\u2026..\u2026....\u2026\u2026\u2026\u2026.. technology. 7. The social issues are complex and \u2026\u2026\u2026\u2026\u2026\u2026..\u2026\u2026\u2026\u2026\u2026.. . 8. According to SDG1, eradica ng poverty across the world has an interlinked rela onship with SDG2, SDG3 and \u2026\u2026\u2026\u2026\u2026\u2026..\u2026\u2026\u2026\u2026\u2026.. . 9. The development of \u2026\u2026\u2026\u2026\u2026\u2026..\u2026\u2026\u2026\u2026\u2026.. is considered as the second wave of AI. 10. \u2026\u2026\u2026\u2026\u2026\u2026..\u2026\u2026\u2026\u2026........................................\u2026.. are responsible to analyze data for the predic on of future market trends. 11. The development and deployment of AI systems are highly \u2026\u2026\u2026\u2026\u2026\u2026..\u2026\u2026\u2026\u2026\u2026.. . PM Publishers Pvt. Ltd. 59","12. \u2026\u2026\u2026\u2026\u2026\u2026..\u2026\u2026\u2026\u2026\u2026.. loop is an extension of the recommenda on and automa on loops. 13. \u2026\u2026\u2026\u2026\u2026\u2026..\u2026\u2026\u2026\u2026\u2026.. are used by banks and other \ufb01nancial ins tu ons for customer support which technically sounds good. 14. Many healthcare industries have adopted Ar \ufb01cial Intelligence machines to make be er and faster \u2026\u2026\u2026\u2026\u2026\u2026..\u2026\u2026\u2026\u2026\u2026.. . 15. A \u2026\u2026\u2026\u2026\u2026\u2026..\u2026\u2026\u2026\u2026\u2026.. recogni on technology is not so accurate to recognize darker skinned faces. Answers 1. Computer Vision 2. image 3. Limited Memory 6. speech recogni on 4. Flipkart, Amazon 5. Eva 9. mobile Internet 11. scien \ufb01c 7. interlinked 8. SDG4 14. diagnoses 10. Business Intelligence Developers 12. Predic on 13. Chatbots 15. facial Evaluate Yourself State T for True or F for False statements. 1. Problem solving is the process in which one perceives the problem and tries to arrive at a desired solu on for the present situa on by choosing the best path. It also includes decision making. 2. The daily rou ne tasks performed by humans are not di\ufb03cult for computers. 3. Computer Vision is the heart of AI systems. 4. AlphaGo is a computer game developed by Amazon. 5. Weak AI systems are those systems which use clustering and associa on techniques for processing the data. 6. The adop on of AI systems could increase income inequality across the world. 7. The term \u2018Ar \ufb01cial Intelligence\u2019 was coined in 1979 at Dartmouth College by John McCarthy, who is considered as the father of Ar \ufb01cial Intelligence. 8. Preven on loop is an extension of the recommenda on and automa on loops. 9. Media content producers are using AI so ware to improve the speed and e\ufb03ciency of the media produc on process. 10. Data collec on is one of the most serious implica ons of AI systems that comes under the category of Privacy Concern. Answers 1. T 2. F 3. F 4. F 5. F 6. T 7. F 8. F 9. T 10. T Arti\ufb01cial Intelligence - 9 60","Writing Subjective Responses 1. \u201cThe development in the \ufb01eld of Ar \ufb01cial Intelligence is s ll an ongoing process.\u201d Do you agree with the statement? Give reasons to support your answer. ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... 2. Write any two applica ons of AI in detail. ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... 3. \u201cTechnological advancement in the \ufb01eld of AI has brought tremendous change in our daily lives.\u201d Comment. ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... 4. \u201cSmart city is an evolu on of smart homes.\u201d Do you agree? If yes, explain your answer in 100 words. ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... 5. \u201cAn interlinked rela onship exists between Sustainable Development Goals.\u201d Describe how inclusive and quality educa on can empower the condi on of women in the society? ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... 6. \u201cSmart transport op ons help in protec ng the environment.\u201d Comment. ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... 7. \u201cAr \ufb01cial Intelligence has brought tremendous change in our lifestyles.\u201d Comment. ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... 8. \u201cModern programming languages and sta s cal programming languages play an important role in the \ufb01eld of AI.\u201d Explain how? ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... PM Publishers Pvt. Ltd. 61","9. What are the main ethical concerns related to the adop on of AI systems? ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... 10. \u201cAI systems can be used for bene\ufb01cial as well as destruc ve purposes.\u201d Comment. ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... Short Answer Questions 1. Write a short note on the following: a. Robo cs ..................................................................................................................................................... ..................................................................................................................................................... b. Problem of inclusion in AI systems ..................................................................................................................................................... ..................................................................................................................................................... c. Machine Learning ..................................................................................................................................................... ..................................................................................................................................................... d. GIGO ..................................................................................................................................................... ..................................................................................................................................................... 2. What are the main ethical concerns related to AI systems? ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... 3. What do you understand by the term \u2018AI Ethics\u2019? ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... 4. Name at least three companies which usually hire computer engineers for AI roles. ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... Arti\ufb01cial Intelligence - 9 62","Long Answer Questions 1. Di\ufb00eren ate between the following: a. Weak AI and Strong AI ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... b. Business AI and Percep on AI ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... c. Ar \ufb01cial General Intelligence and Ar \ufb01cial Narrow Intelligence ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... d. Privacy concern of AI and Adop on concern of AI ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... 2. Explain AI-related careers in brief. ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... 3. How will you achieve sustainable development goals using Ar \ufb01cial Intelligent systems? ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... 4. \u201cA smart ci zen is needed as the main driving force behind digital products that will produce a vast change in the \ufb01eld of AI technology.\u201d Comment. ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... PM Publishers Pvt. Ltd. 63","Application Based Questions 1. A teacher has asked Sohan to prepare an assignment on the topic \u201cTypes of data used by Ar \ufb01cial Intelligent machines\u201d. However, he doesn't know about the various types of data. Can you help him by giving appropriate sugges ons for the same? ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... 2. Rohit wants to interact with Amazon\u2019s Echo. However, he doesn\u2019t know how the interac on takes place? Can you help him to do the same? ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... 3. Rohan wants to connect all the smart devices in his smart home through the Internet. Which device would he use to do so? Also, write a short note on the suggested device. ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... 4. Priya\u2019s teacher has asked her to list the role of Ar \ufb01cial Intelligence in achieving Sustainable Development Goal 6 (sustainable management of water and sanita on for all). Can you help her for the same? ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... 5. Rishabh has strong mathema cal skills and a good knowledge of programming languages. Which career in the \ufb01eld of the Ar \ufb01cial Intelligence should he choose to pursue? ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... 6. \u201cAutoma on of jobs can lead to future job loss, insecurity and income inequality\u201d. Which ethical concerns related to implica on of Ar \ufb01cial Intelligence system has been discussed here? ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... ..................................................................................................................................................... Arti\ufb01cial Intelligence - 9 64","2 AI PROJECT CYCLE SESSION 1: INTRODUCTION TO AI PROJECT CYCLE Introduction In your daily life, you must have heard the word \u2018project\u2019 innumerable mes. Do you know what a project is? Let us discuss. A project can be de\ufb01ned as a set of inputs and outputs required to achieve a predetermined or speci\ufb01c goal. It can also be de\ufb01ned as a series of tasks that are needed to be performed in order to achieve a speci\ufb01c or predetermined goal within a de\ufb01ned me limit. Some mes, there is a requirement to break the task of the whole project into subtasks to achieve good produc vity in terms of me management, cost, etc. A project can be simple as well as complex. Like the human life cycle, a project also follows a life cycle in which various sequences of phases are available from beginning to end. The number and sequence of the cycle depend upon the needs of an organiza on, nature of the project and its area of applica on. Even though every project has a de\ufb01nite start and end, the par cular objec ves, deliverables, and ac vi es vary widely. The life cycle of a project gives us the ac on layout that has to be performed in the project, irrespec ve of the speci\ufb01c work involved. A project life cycle can vary from predetermined or plan-driven approaches to \ufb02exible or change-driven approaches. In a predetermined project life cycle, all things are de\ufb01ned at the star ng stage and any altera ons to scope are carefully addressed. In a \ufb02exible life cycle, the product is developed over mul ple itera ons. During the itera ons, the func onali es of the cycle will be discussed, implemented and, \ufb01nally, reviewed by the client. This life cycle is generally followed by the organiza ons to reduce risk of uncertain es in a project. In general, the four phases of a project life cycle are as follows: Initiation Phase Planning Phase Implementation Phase Execution Phase As you know, Ar \ufb01cial Intelligence is a branch of Computer Science that is a study of how the human brain thinks, learns, decides and works, when it tries to solve problems. Eventually, this study outputs intelligent so ware systems. In order to implement these systems, all organiza ons need to adopt a comprehensive approach to cover each step of the AI or machine learning lifecycle. The steps involved in an AI project life cycle are as follows: a) Problem Scoping: Problem Scoping is the \ufb01rst step of an AI project life cycle. In this step, you have to write about the nature and boundaries of a problem. In simple words, it is a process by which a problem is de\ufb01ned. b) Data Acquisi on: Data Acquisi on or Data Gathering refers to a process of iden fying and gathering all data required for an AI project. PM Publishers Pvt. Ltd. 65","c) Data Explora on: Data Explora on refers to a process of understanding the nature of the data you have to work 1) with, in terms of quality, characteris cs, etc. This step Problem provides you a be er understanding of data resul ng Scoping in an e\ufb00ec ve end product. d) Modelling: An appropriate analysis of collected 5) AI 2) data is an essen al requirement for modelling Evaluation Project Data data. In this step, you can select the datasets Acquisition which match your requirements and train the Life model using appropriate machine-learning Cycle algorithms or AI-enabled algorithms. 4) 3) Modelling Data e) Evalua on: Deployment is the last step of the AI Exploration project life cycle. But, before deploying any AI- enabled system, proper evalua on of a trained model is necessary as it determines the accuracy of the model according to the project requirements. Let us understand more about the AI project life cycle with the help of a simple example. Suppose you are going to organize a cultural fest of your school in a reputed hotel. Now, a list of guests is prepared and invita ons are sent to them. According to your school management team, no unauthorized person should be there. To ensure this, you can do the following things to collect data: a) Mandate entry in the hotel through an invita on card. b) Mandate entry through a secret code which is given by you to all invitees. c) Provide a separate ID card at the me of invita on. The data collected by you is available in visual form. This data should be reliable to ensure the smooth func oning of your school fest. In technical terms, the process of collec ng data is known as Data Acquisi on. A er collec ng data, now you would want to interpret some useful informa on of all invitees like designa on, level, etc. To do this, you can explore the collected data and put it in an organized way. The process of exploring data to interpret some useful informa on is known as Data Explora on. A er data explora on, now you would want to develop a system which detects the unique ID of a person at the me of entry. This ID should be matched with the exis ng ID that you have in your system. To do this, you can feed all the mandatory data in an AI intelligent system like biometric and train it in such a manner that it recognizes an unknown person immediately. In technical terms, the process of training a model using a machine learning algorithm based on accurate data is called the Modelling stage. A er training the model, you can deploy it and test it by pu ng accurate and inaccurate data. This stage is known as the Evalua on stage. DO YOU KNOW ? Data is the most founda onal element that makes AI highly powerful. Arti\ufb01cial Intelligence - 9 66","ICE-BREAKER ACTIVITY AI PROJECT LIFE CYCLE You have learnt various stages of the AI project life cycle in which the data acquisi on stage plays an important role to train a model. Now, explain the importance of data explora on in your own words. The detailed descrip on of AI project life cycle is as follows: Problem Scoping Problem Scoping is the ini al stage of the AI project life cycle. The term \u2018Problem Scoping\u2019 refers to a process of framing a problem in such a way that you will have a vision to solve it. In general, it involves a series of steps to narrow down to a problem statement from a broad theme. In simple words, it is basically selec ng a problem which we might want to solve using our AI knowledge. In this session, we\u2019ll learn how to scope a problem with the help of the following worksheets: Worksheet 1: Narrow Down a Problem Statement from a Broad Theme In your daily life, you must have seen various things around you like water, birds, animals, etc. Select any theme according to your interests or from the suggested themes given below: Women Sanitation Safety Environment Tra\ufb03c Themes Cyber Safety Disability Health Education From the given themes, select any one theme or think of any other theme on your own. In the previous chapter, you have learnt 17 goals of Sustainable Development which are also based on themes. Therefore, you can also select any theme using the 17 Sustainable PM Publishers Pvt. Ltd. 67","Development Goals. A er selec on, you can make a list of topics and \ufb01nd out various problems which exist in a par cular theme. These problems will now be very speci\ufb01c as they have been narrowed down from a broader sense. Write the goal of your project a er selec ng any one problem that you have scoped to get a clear vision of the problem that you are looking forward to solve using your AI knowledge. Now, \ufb01ll the following details on the basis of your interests: Selected theme for your project: \u2026\u2026...............................................\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026. List of topics: \u2026\u2026\u2026...............................................\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026 \u2026\u2026\u2026...............................................\u2026\u2026\u2026\u2026\u2026........................\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026 Choose any one problem that you listed down under the topic sec on. Now, you can set up the goal of your project by selec ng one problem out of the ones listed above, which you want to solve using your AI knowledge. This problem now becomes the target of your AI project and helps you to get a clear vision of what is to be achieved. Let us now state the selected problem as a goal. For example, a goal can be stated as, \u201cHow might we help teachers to determine the best method of inclusive educa on?\u201d Now, write the goal or aim of your project in the space given below: \u2026\u2026\u2026...............................................\u2026\u2026\u2026\u2026\u2026........................\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026 Worksheet 2: 4Ws Problem Canvas Consider the goal selected in Worksheet 1 as the problem of this ac vity. You can use 4Ws framework to understand the procedural steps of problem solving. The 4Ws are the basic ques ons used to collect informa on regarding a problem. So, what are the 4Ws? The 4Ws refer to Who, What, Where and Why. Who Why 4Ws What Framework Where Arti\ufb01cial Intelligence - 9 68","The detailed descrip ons of the 4Ws are as follows: a) Who: This block helps us in iden fying the stakeholders or the team that will work on the problem. At this stage, we are looking at the person who is ul mately bene\ufb01 ng from the project comple on. Various types of ques ons under this block are: Who are the stakeholders?, Find detailed informa on about the stakeholders, etc. b) What: This block helps us in determining the nature of the problem. What is the problem and how do you know that it is a problem? At this stage, a requirement to gather evidence to prove that the problem you have selected actually exists, is mandatory. You can gather evidence through various mediums like newspaper ar cles, media, announcements, Internet, etc. Various types of ques ons under this block are: What is the problem?, List at least two pieces of evidence which show that it is a problem, etc. c) Where: Under this block, you can focus on the situa on or the context of the problem. This block will help you look into the situa on in which the problem arises, its context, and the loca ons where it is notable. Various types of ques ons under this block are: At which point or loca on do you think it is a problem?, What is the situa on in which the stakeholders experience the problem?, etc. d) Why: Under this block, you can think about the bene\ufb01ts which the stakeholders would get from the project comple on and how it would bene\ufb01t them as well as the society. Various types of ques ons under this block are as follows: How would a stakeholder get a bene\ufb01t from the solu on?, Why will stakeholders prefer this solu on?, etc. A er understanding in brief about the 4Ws, you should be able to summarize all the key points of the 4Ws in a paragraph form. Use the following template if necessary: Our .............................................................................. [brief descrip on of the stakeholders] ....................................................... has\/have a problem that ........................................... [issue, problem, need] ............................................ when\/while ............................................ [context, situa on]. An ideal solu on would be ................................................. [bene\ufb01t of the solu on for them] .............................................................................. . Data Acquisition You have learnt that data is the core of any Ar \ufb01cial Intelligent system. Thus, data acquisi on plays an important role in the AI project life cycle. As you know, Ar \ufb01cial Intelligent systems have the ability to process enormous amounts of data and their accuracy increases along with the data volume. Data can be a piece of informa on or facts and sta s cs collected together for reference or analysis. We feed data into an AI model and train it to predict desired output. Let us understand the concept of dataset with the help of a simple illustra on. Suppose you want to develop an AI-enabled system which can predict the presence or absence of any employee on the basis of facial recogni on. To do this, you would feed the data of his\/her face into the machine. This is the data with which the machine can be trained. Now, once it is ready, it will predict his\/her a endance in an e\ufb00ec ve manner. The facial characteris cs of the PM Publishers Pvt. Ltd. 69","employee in this situa on would be known as the Training Data. You should always remember that the training data should be relevant and reliable. This is because AI systems work on a principle called GIGO (Garbage In, Garbage Out). The concept of GIGO implies that the quality of the output depends upon the quality of the input. In this example, if the training data was not relevant, then the AI system would have been unable to predict the employee\u2019s presence correctly or would have predicted faulty results, since the whole training went wrong. In simple words, if you want to develop an e\ufb03cient AI project, then the training data should be authen c and relevant to the problem statement scoped. The term data feature also plays a crucial role in the AI project life cycle. Data feature refers to the type of data that you want to collect for the problem scoped. In the above example, most appropriate data features would be facial characteris cs, names, employee IDs, date of joining, etc. Worksheet 3: Based on Data Features In the previous worksheet, you described the problem statement in summarized form on the basis of 4Ws canvas. Now, you have to iden fy the data features for your problem statement. Problem Statement Data Features A er \ufb01nding out the Data Features, we now need to acquire the same. You have read earlier about various sources from which the data can be acquired. According to you, is it possible to \ufb01nd reliable and authen c data from all sources? If you couldn't \ufb01nd appropriate data, then what can you do? Arti\ufb01cial Intelligence - 9 70","As you know, data is the heart of the AI system because it creates a founda on on which the AI project is built. One of the major challenges faced by data professionals in the data acquisi on stage is to understand where the data comes from and whether it is the latest data or not. Therefore, the data acquired should be authen c, reliable and correct. You should remember that the acquisi on methods should also be authen c so that your project does not create any sort of con\ufb02icts with anyone. You can collect a good quality data in the following ways: a) Sensors b) Surveys c) Cameras d) Observa ons e) Web Servers While extrac ng data from various sources, you should never collect data from random websites as there is no assurance of data authen city. You can extract data from open source websites like RBI database, Na onal Portal of India, etc. A er acquiring great informa on about data extrac on, write at least \ufb01ve ways of acquiring data for your project in the provided space: 1. .................................................................................................................................................. 2. .................................................................................................................................................. 3. .................................................................................................................................................. 4. .................................................................................................................................................. 5. .................................................................................................................................................. Worksheet 4: Based on Data Acquisition Suppose you are working as a computer teacher in a reputed school. Now, your school management adopted an Enterprise Resource Planning (ERP) system to manage all the func ons\/opera ons of your school in an e\ufb03cient and e\ufb00ec ve way. Now, the responsibility of acquiring mandatory data about students and teachers has been given to you. You have to iden fy at least 10 necessary data requirements. Also, \ufb01nd and write reliable sources which can be used to obtain the data. Data Requirements Reliable Sources PM Publishers Pvt. Ltd. 71","System Maps As you read earlier, data feature selec on plays an important role in the data acquisi on stage. Now, let us understand the concept of system maps. A system map is basically a tool that helps us in making decisions and ac ons in complex situa ons. It helps us to \ufb01nd rela onships between di\ufb00erent elements of the problem which we have scoped. In simple words, it helps us in formula ng the solu on for achieving the goal of our project. As you know, a system can be de\ufb01ned as an interconnected set of elements that is coherently organized in a way that achieves something. With the help of system maps, you can easily depict a rela onship amongst di\ufb00erent elements which come under a system. You will get a sense of clarity at an early stage of data analysis using system maps. The essen al components of a system map are as follows: a) Elements: Elements are the basic building blocks of any system. In an educa onal ins tu on system, elements might include di\ufb00erent types of departments and, of course, people (students, teachers, administra ve sta\ufb00, etc.). b) Links: The lines which connect all elements in a loop or system are known as links. These are used to depict the rela onship between di\ufb00erent elements under a system. In an educa onal system, interconnec ons might include networks and coali ons, rela onships between teachers and account sec ons, rela onships between students and account sec ons, etc. c) Func on or Purpose: This is some mes hard to iden fy, as the \u2018purpose\u2019 of a system is not explicitly stated anywhere and cannot be found by reading the mission statements of any single factor in the system. Some mes, when you solve a speci\ufb01c problem, many complex issues arise due to mul ple factors that a\ufb00ect each other. In such a case, we use system maps. As you know, every element is interconnected within a system through links, resul ng in looping structures. The iden \ufb01ca on of these looping structures in a system map is a very important and necessary task because they represent a speci\ufb01c chain of causes and e\ufb00ects. These system maps are also known as loop diagrams. Always use posi ve (+) and nega ve (-) signs to depict the nature of rela onship between the elements. The arrow lines are used to represent the direc on \ufb02ow and the sign shows the behaviour of rela onships. Let us understand the concept of arrow lines and signs with the help of an example. Here, you can see a system map with two elements named Lions (+) and Deers. If you draw an arrow with a posi ve sign from Deers to Lions, then it depicts that both Deers and Lions are directly Lions related to each other. This implies that increase in Deers (-) would lead to increase in Lions. Similarly, If you draw an Deers arrow with a nega ve sign from Lions to Deers, then it depicts that both Deers and Lions are inversely related to each other i.e., increase in Lions would lead to decrease in Deers. DO YOU KNOW ? There are a variety of data visualiza on tools such as Microso Excel, Tableau, QlikView and Google Data Studio which help in represen ng the data in an appealing way. Arti\ufb01cial Intelligence - 9 72","What is Process Simula on? Process simula on is used for the design, development, analysis, and visualiza on of process data. It is a method of crea ng a prototype of a real system. The goal of process simula on is to \ufb01nd op mal condi ons for a process and to understand the process of change in the behaviour of various opera ons of the system by changing the value of individual elements and their rela on with each other. Let\u2019s see the system map of Process Simula on de-mo va on fear of mistakes + -- + depression a possible doing CHALLENGE: anxiety systems-level things draw & simulate explana on for your OWN mental why depression & feedback loops anxiety are not just co-morbid, but MUTUALLY SELF- REINFORCING - ++ - feeling good accep ng Source: h ps:\/\/ncase.me\/loopy\/ mistakes In the above diagram, it has been depicted that doing things would lead to feeling good. The more you feel good, lesser is the chance of depression. Depression would lead to de- mo va on and the feeling of de-mo va on would badly a\ufb00ect your working capaci es. Similarly, doing things makes you open to accep ng mistakes. The more you accept mistakes, the lesser would be your level of anxiety. But, if you are more anxious, then it will increase the fear of mistakes. If your fear of mistakes increases, it would adversely a\ufb00ect your working capaci es. Worksheet 5: Based on System Map In Worksheet 3, you have found di\ufb00erent ways of acquiring data for your project. Now, in this ac vity, you have to consider all the data features for your problem and draw a system map on your computer with the help of the animated tool Loopy. It is an online tool that makes it easy to create interac ve animated simula ons to explain how mul ple elements of a system interact with each other. + PM Publishers Pvt. Ltd. - a tool for thinking in systems 73","Basic Requirements: You need the following things to draw a system map: a) A computer system such as PC, laptop, etc. b) An Internet connec on How to use Loopy Online? To draw a system map using Loopy, open a web browser and enter the following URL: h ps:\/\/ncase.me\/loopy\/ in the address bar and press Enter. You will get the following screen: Scroll down the page and click on TRY OUT LOOPY bu on. A new screen will appear. Here, you can draw your system map. Arti\ufb01cial Intelligence - 9 74","There are four tools in the tools pale e. These tools are Pencil, Text, Move and Erase. The detailed descrip ons of these tools are as follows: a) Pencil Tool: This tool is used to draw circles or nodes. Also, it is used to draw arrow lines between two nodes. b) Text Tool: Text tool is used to add labels on the nodes made by you. c) Move Tool: As its name implies, Move tool is used to move the nodes or rela onships between the nodes. d) Erase Tool: Erase tool is used to remove the nodes and arrows drawn by you. In a Nutshell 1. A project can be de\ufb01ned as a set of inputs and outputs required to achieve a predetermined or speci\ufb01c goal. 2. Like the human life cycle, a project also follows a life cycle in which various sequences of phases are available from beginning to end. 3. A project life cycle can vary from predetermined or plan-driven approaches to \ufb02exible or change-driven approaches. 4. Problem Scoping is the \ufb01rst step of an AI project life cycle. In this step, you have to write about the nature and boundaries of a problem. 5. Data Acquisi on or Data Gathering refers to a process of iden fying and gathering all data required for an AI project. 6. Deployment is the last step of the AI project life cycle. But, before deploying any AI- enabled system, proper evalua on of a trained model is necessary as it determines the accuracy of the model. 7. The 4Ws are the basic ques ons used to collect informa on regarding a problem. These ques ons are: Who, What, Where and Why. 8. Data feature refers to the type of data that you want to collect for the problem scoped. 9. A system map is basically a tool that helps us in making decisions and ac ons in complex situa ons. PM Publishers Pvt. Ltd. 75","SESSION 2: DATA EXPLORATION AND DATA VISUALISATION Introduction Data is an important part of AI systems. Without data, AI systems cannot perform the desired course of ac ons. In the previous session, you have learnt that data explora on refers to a process of exploring data to interpret useful informa on from the acquired raw facts in order to be er understand the nature of the data. This is a necessary step before training or modelling an AI system. Let us understand the concept of data explora on with the help of an example. Suppose you are a senior accountant in a school. In an Excel sheet, you have an enormous amount of data for the session 2019-2020 in which some data is useful while some is useless. You need to spend me in exploring the data for useful or relevant informa on as per your requirements. Similarly, when you have a lot of datasets about a par cular topic, you need to visualise it for the following things: \u25cf Quickly get a sense of the trends, rela onships and pa erns contained within the datasets. \u25cf De\ufb01ne strategy for which model to use at a later stage. \u25cf Communicate the same to others e\ufb00ec vely. Note that we can use various types of visualisa on methods or techniques to visualise data. In mathema cs, you must have represented data in the form of histograms, bar graphs, etc. many mes because it helps you to understand the signi\ufb01cance of data by framing huge amounts of data in a simple and organized way. Similarly, in compu ng, Data Visualisa on refers to a process of represen ng data in a pictorial or graphical format using various visualisa on tools. In simple words, Data Visualisa on is a quick, easy way to convey concepts in a universal manner. You can experiment with di\ufb00erent scenarios by making slight changes in the data. As you know, the decision making capability of an AI system can be biased or unbiased. Dataset plays a crucial role in the decision making of AI systems. Thus, data accuracy and reliability of datasets is very important while developing AI systems. Nowadays, various data visualisa on techniques are available which are free of cost. In this session, we will be exploring various types of graphs using an online open source website. You will learn about various new ways to visualise data. Worksheet 6: Data Visualisation Catalogue In mathema cs, you must have represented tabular data in graphical format like histogram, bar graphs, pie chart, etc. Now, you have to represent data (based on scenario) in graphical format on the computer with the help of an Internet connec on. Suppose you are working as a computer teacher in a school. You have been assigned a task by the school management to conduct examina ons of all classes online. A so ware company is hired by the school management for developing an ERP so ware for examina ons. Now, it's your responsibility to give all the necessary data that you want in the examina on module. Now, collect informa on and write what kind of data will be Arti\ufb01cial Intelligence - 9 76","required for a given task? A er collec ng the data, select an appropriate graphical format to represent the data acquired by you so that it will be user-friendly and e\ufb00ec ve. Nowadays, Data Visualisa on Catalogue, a handy guide and library of di\ufb00erent data visualisa on techniques, tools, and a learning resource for data visualisa on is available. Basic Requirements: To use Data Visualisa on Catalogue, you need the following things: a) A compu ng device like PC or laptop b) An Internet connec on How to Use Data Visualisa on Catalogue Online? Open a web browser and type the following URL: h ps:\/\/datavizcatalogue.com\/ in the address bar and press Enter. You will get the following screen: Here, you can see various types of graphical representa ons like arc diagram, area graph, etc. By clicking the icons, look at the various new ways of data visualisa on and iden fy the ones which interest you the most. Write down at least 5 new data visualisa on techniques in the format given below: Data Name of One line Suitable for How to Visualisa on the Graphical descrip on of which data draw it? Representa on the Graphical Technique Representa on type? PM Publishers Pvt. Ltd. 77","Worksheet 7: Based on Graphs In the previous worksheet, you have learnt about graphical representa on of data using Data Visualisa on Catalogue. As you know, you need to visualise data a er acquiring it from di\ufb00erent sources. In this ac vity, you will visualise the collected data in a graphical format for be er understanding. Before visualising data, you need to answer the following ques ons: a) What type of data feature do you want to represent? b) Which type of graphical representa on are you going to use for this data feature? Let us understand with the help of an illustra on. Suppose you have selected a problem statement: How can we predict whether a student will become a state level topper or not? We would require data features like: current percentage trends, performance of a student in all classes, performance of a student in the current class, behaviour of a student, etc. Now, to analyze a pa ern, we would plot a graph showing the performance of various students and the one who has the maximum chance of ge ng to the topper posi on. In this way, graphical representa on helps us to understand the trends and pa erns out of the data collected and to design a strategy around them for achieving the goal of the project. Now, take a scrapbook and start represen ng your data features using various types of graphs. A er comple on, present it in your class. DO YOU KNOW ? Data Analysts, one of the most sought-a er careers in AI, perform sta s cal analyses on large datasets. In a Nutshell 1. Data is an important part of AI systems. Without data, AI systems cannot perform the desired course of ac ons. 2. The representa on of data in graphical form helps us to understand the signi\ufb01cance of data by framing huge amounts of data in a simple and organized way. 3. Data visualisa on refers to a process of represen ng data in a pictorial or graphical format using various visualisa on tools. 4. Accuracy and reliability of datasets is very important while developing AI systems. 5. We need to visualise datasets for ge ng a sense of trends, rela onships and pa erns contained within the datasets. Arti\ufb01cial Intelligence - 9 78","SESSION 3: DATA MODELLING Introduction In the previous session, you have learned to collect and visualise data. To build an AI project, we need to work around Ar \ufb01cial Intelligent models or algorithms. This could be done either by designing your own model or by using a pre-exis ng model. AI Modelling refers to developing algorithms, also called models which can be trained to get intelligent outputs. It involves wri ng codes to make a machine ar \ufb01cially intelligent. Approaches to Arti\ufb01cial Intelligence The technological advancements in the \ufb01eld of Ar \ufb01cial Intelligence have transformed human lives at a great pace. In the previous module, you have learnt how AI-enabled systems can be used widely to tackle various challenges ranging from selec ng the op mal set of consumers to respond, learning their tastes and preferences, dynamic pricing, scheduling and control of devices, etc. As you know, AI is a branch of computer science which mainly deals with building Ar \ufb01cial Intelligent machines capable of performing speci\ufb01c tasks for which it is being programmed. AI is an interdisciplinary science with mul ple approaches, but digital advancements in the \ufb01eld of machine learning and deep learning are crea ng a paradigm shi in every sector of the IT industry. In this session, we will learn about di\ufb00erent AI approaches. In general, the two major approaches used in Ar \ufb01cial Intelligent systems are as follows: a) Rule-based Approach b) Learning-based Approach The detailed descrip ons of these approaches are as follows: a) Rule-based Approach: As its name implies, rule-based approach is a very simple approach based on EXPERT SYSTEM rules. In compu ng, those systems which follow a rule-based Sample User Rules Knowledge Input Interface Engine Base approach to deduce the desired Knowledge course of ac on are known as Non-expert Advice from an rule-based systems. Or, we can User Expert say that a rule-based system refers to a program that uses pre- Rule-based Approach de\ufb01ned rules to make deduc ons and choices to perform automated ac ons. This approach is widely used in developing expert systems. Under this approach, rules are de\ufb01ned in the form of IF-THEN statements that guide a system to perform the desired course of ac on. The main goal of a rule-based system is to capture the knowledge of a human expert in a specialized domain and embody it within a computer system. As we read PM Publishers Pvt. Ltd. 79","earlier, AI systems are ar \ufb01cially intelligent only. These systems cannot beat the intelligence of human beings due to limited learning capability. Thus, we can say that rule-based systems are the simplest form of AI based on rigid intelligence. Due to this reason, rule-based systems can only implement narrow AI at best because genera ng rules for a complex system is quite challenging and me consuming. The two main components of a rule-based system are as follows: i) Knowledge Base: Knowledge base is a set of facts which stores all relevant informa on, data, rules, cases, and rela onships used by the expert system. A knowledge base can combine the knowledge of mul ple human experts. ii) Inference Engine or Rules Engine: An inference engine seeks the desired informa on and rela onships from the knowledge base, and provides the desired course of ac on in the way a human expert would. b) Learning-based Approach: Learning-based approach is just opposite to rule-based input approach. In this approach, a programmer can Precipita on simulate intelligence in an Ar \ufb01cial Intelligent input Output machine as many mes as he\/she needs. Due to Humidity Sunny\/Rainy? this reason, this approach is also known as input Machine Learning Model adap ve intelligence approach. In general, Temperature adap ve intelligence means that the exis ng Learning-based Approach knowledge can be changed or discarded, and new knowledge can be acquired in a very e\ufb00ec ve way. Machine Learning technology, a subset of Ar \ufb01cial Intelligence, allows the systems to learn from the user interac ons, responses and ac vi es, and make decisions on the basis of user ac vi es. As you know, rule-based systems are sta c whereas systems based on learning approach are con nuously updated and more powerful with new user inputs and informa on. The major drawback of learning-based approach systems is frequent training which is required with new data and data-driven insights. Let us understand with the help of a simple example. Google, a big IT giant, uses machine learning algorithms to generate weather forecasts. This AI system has been designed to learn from the atmospheric examples daily using algorithms without applying any prior data fed to the system. Decision Trees \u2013 Rule-based Approach In the previous session, you have learnt how various types of Is a Person Fit? graphical representa on are used in the data visualisa on Age < 30 ? process. Decision tree, another form of graphical Yes? No? representa on is a well-known and widely used method for Ar \ufb01cial Intelligent machines. In general, a decision tree is a Eat\u2019s a lot Exercises in set of nodes or a direc onal graph which starts at the base of pizzas? the morning? with a single node and extends to many leaf nodes that Yes? No? Yes? No? represent the type of decision. Like \ufb02owchart, a decision tree Un\ufb01t! Fit Fit Un\ufb01t! is a graph in which the \ufb02ow begins from the root node and ends with an appropriate decision\/output made on the leaf nodes. In order to solve decision problems, data gathering and analysis is one of the necessary stages of decision support systems development with the use of Ar \ufb01cial Intelligence. Arti\ufb01cial Intelligence - 9 80","In simple words, a decision tree gives rules on the basis of which decisions are made and added to a knowledge base by Ar \ufb01cial Intelligent systems. Thus, we can say that decision trees follow rule-based approaches. The main components of a decision tree are as follows: a) Root Node: A root node is the top-level node that represents the ul mate goal. b) Branches: Branches are usually indicated with \ufb02ow lines or arrow lines that represent di\ufb00erent op ons. c) Leaf Node: A node that represents the possible outcomes for each ac on is called a leaf node. A er learning the basic concept of the decision tree, now try to visualise the decision tree given below: Here, you have seen that a decision tree contains a series of if-else decisions at each node and this leads us to one or more possible answers for the ques on. You should remember the following points while making a decision tree: a) Datasets: Like data, datasets are the core of the decision tree. So, take a look at the available datasets and try to calculate what pa ern does the output leaf follow. You should select only one output at the beginning stage. b) Parameters: You should only use those parameters which a\ufb00ect the output directly as it will give the best predic ons. c) Simple: While making a decision tree, some mes you face a situa on where mul ple decision trees lead to correct predic on for a single dataset. In such a case, you should select the simple one. Advantages of a Decision Tree The main advantages of a decision tree are as follows: a) Simple and Easy: The concept of decision trees and their working principle are very easy and simple to interpret. It can be easily understood by the people without analy cal or mathema cal backgrounds as compared to other complex machine learning algorithms. b) Flexible: The structure of decision trees is non-linear and \ufb02exible. Thus, you can easily explore, plan and predict several possible outcomes to your decisions, regardless of when they actually occur. c) No emo ons: A decision tree provides a balanced view of the decision making process. With the help of a decision tree, anyone can get a bias-free output as no emo ons are involved. Disadvantages of a Decision Tree In addi on to the advantages, some disadvantages of a decision tree are as follows: a) Di\ufb03cult to \ufb01nd an op mal set of rules: In a decision tree, \ufb01nding an op mal set of rules is very di\ufb03cult and it is computa onally ine\ufb03cient to \ufb01nd such a set of rules. b) Highly Sensi ve: The reproducibility of decision tree models is highly sensi ve as a small change in the data can result in a large change in the tree structure. c) Complexity: Decision tree model training me is rela vely more due to higher space and me complexity. PM Publishers Pvt. Ltd. 81","Worksheet 8: Based on Decision Tree As you know, a decision tree is usually a graphical representa on of data for predic ng outcomes on the basis of condi onal statements. Now, a decision tree is given below which starts from the \u2018Colour\u2019 a ribute. Colour = Red Model>2010 Colour=Yellow Yes No Yes No Buy Mileage<50000KM Make = Ferrari Don\u2019t Yes No Buy Yes No Buy Don\u2019t Buy Don\u2019t Buy Buy On the basis of your learning, answer the following ques ons: a) What is the label of the root node? b) How many branches or edges does the tree have? c) How many leaf nodes does the tree have? d) What are the condi ons available in the tree? Worksheet 9: Based on Given Data As you know, dataset plays a crucial role while making a decision tree. In the previous ac vity, you have found data from the decision tree. Now, you will learn how to draw a decision tree from the given dataset through this ac vity. Here, a dataset consis ng of 4 predictors and one target is given. The parameters or predictors which a\ufb00ect the target are: Outlook, Temperature, Humidity and Wind. The target is \u2018Playing Cricket\u2019. You have to draw a decision tree for this dataset. Outlook Temperature Humidity Wind Playing Cricket Rainy Cool High Strong No Rainy Cool Normal Weak No Sunny Hot High Weak Yes Overcast Mild High Strong Yes Arti\ufb01cial Intelligence - 9 82","Outlook Temperature Humidity Wind Playing Cricket Sunny Hot High Strong Yes Rainy Mild High Strong Yes Rainy Cool Normal Strong No Overcast Cool High Weak Yes Sunny Hot High Weak Yes Rainy Mild Normal Strong Yes Sunny Hot Normal Weak Yes Rainy Cool High Strong No Sunny Hot Normal Weak Yes Overcast Hot High Strong No Overcast Hot Normal Weak Yes Machine Learning (ML) Machine learning refers to a process of teaching computers to learn automa cally and improve from their past experiences without being explicitly programmed. These systems are able to make decisions with minimal human interference. In simple terms, machine learning is a speci\ufb01c subset of AI that trains a machine about how to learn and make decisions without human interven on. The various de\ufb01ni ons of machine learning given by various research organiza ons are as follows: According to Nvidia, \u201cMachine learning at its most basic is the prac ce of using algorithms to parse data, learn from it, and then make a determina on or predic on about something in the world.\u201d According to Stanford, \u201cMachine learning is the science of ge ng computers to act without being explicitly programmed.\u201d According to McKinsey & Co., \u201cMachine learning is based on algorithms that can learn from data without relying on rules-based programming.\u201d The ul mate goal of machine learning is to develop a computer program that can access data and use it to learn for building logics. In the present digital environment, anyone can classify things in a meaningful way with the help of machine learning algorithms. PM Publishers Pvt. Ltd. 83","Advantages of Machine Learning With the technological advancements in the \ufb01eld of Ar \ufb01cial Intelligence, the demand of machine learning systems is growing exponen ally due to the following advantages: a) Improvement with Data: As you know, machine learning algorithms are capable enough to learn from the data entered by the user. As new data is provided, the model\u2019s accuracy and e\ufb03ciency to make decisions improve with subsequent training. Let us understand with the help of an example: a product recommenda on engine. Nowadays, various e-commerce companies like Flipkart and Amazon use product recommenda on engines to know about the taste and preferences of the customer. The accuracy of \ufb01nding associated products or recommenda on engines improves with the huge amount of training data available. b) Easy Iden \ufb01ca on of Trends and Pa erns: Using machine learning algorithms, machines can easily discover trends and pa erns among the large database. For example, Amazon, a big IT giant uses machine learning algorithms to analyze the buying pa erns and search trends of its customers. It recommends products on the basis of these trends and pa erns. c) Automa on: A big advantage of machine learning systems is the ability to adapt and automate various decision-making tasks. For example, Eva, a chatbot launched by HDFC instantly replies as \ufb01rst-level customer support. d) Wide Range of Applica ons: Nowadays, machine learning systems are used for a wide range of applica ons like forecas ng sales, predic ng the future, pa ent care, automa c spell check, etc. because these systems are capable of delivering a much more personal experience to customers and organiza ons. e) E\ufb03cient U liza on of Resources: The u liza on of resources in an e\ufb03cient way is the prime mo ve of each and every organiza on. The machine learning systems are used widely in every \ufb01eld to enhance the produc vity of an organiza on, due to its nature of u lizing resources in an e\ufb03cient way. Disadvantages of Machine Learning In addi on to advantages, some disadvantages of machine learning system are as follows: a) Data Collec on: As you know, data plays an important role in machine learning systems. The collec on of data from various sources is a tedious job because you are not sure about the consistency and accuracy of data. Thus, data collec on process is one of the major disadvantages of machine learning systems. b) Selec on of Appropriate Algorithms: Di\ufb00erent algorithms are used to build a machine learning system but the selec on of appropriate algorithms is a complex process. c) High Chances of Error: The process of machine learning systems is autonomous but the chances of error are very high while processing the data. Suppose you train an algorithm with biased data sets. In such a case, you will get biased output because GIGO (Garbage In, Garbage Out) is the working principle of machine learning systems. d) Time Consuming: The capability of machine learning systems to process large chunks of data is considered as a big disadvantage because larger the amount of data, larger will be the processing me. Some mes, these systems also need addi onal resources to func on. e) Not Sure about Accurate Predic ons: The machine learning systems are not able to predict accurate output in every situa on because these systems are limited to answering ques ons. Arti\ufb01cial Intelligence - 9 84","Applications of Machine Learning The various applica ons of machine learning in our day-to-day life are as follows: a) Virtual Personal Assistant: The collec on and re\ufb01nement of informa on on the basis of past experience is the main role of machine learning algorithms in these personal assistants. Later, these datasets are used to produce results in which any type of altera ons\/modi\ufb01ca ons take place according to the user\u2019s behaviour. b) Global Posi oning System (GPS): Nowadays, we are using GPS for predic ng tra\ufb03c conges on. The working system of GPS is based on machine learning algorithms which help to es mate the areas where conges on can be found on the basis of daily experiences. c) Diagnose Diseases: Machine learning applica ons are widely used in detec ng diseases at a very early stage. Radiography is one of the most common examples. Also, these applica ons are u lized for various types of screening exams like mammography, chest CT, and colonography in the healthcare industry. d) Social Networking: You must have seen that when you upload a new picture on the social networking site Facebook, an automa c friend tagging sugges on appears. Have you ever thought about the technology used for this purpose? Facebook uses machine learning algorithms to recommend sugges ons automa cally. e) Developing Robots: Machine learning algorithms are widely being used to improve the working of intelligent systems like robots which can perform tasks with their own experiences without being explicitly programmed. Deep Learning (DL) Deep learning or deep neural learning is a subset of machine learning techniques which are inspired by the structure and func on of a brain, and are capable of learning by example. Like machine learning applica ons, deep learning applica ons also require a large amount of labelled data. For example, development of self-driving cars requires millions of images and thousands of hours of video. In the previous chapter, you have learnt about the Emoji Scavenger Hunt game which is a web browser-based game that uses deep learning neural network technique. In deep learning applica ons, the learning phase follows a deep neural network through which these applica ons are capable to learn. In simple words, a computer model learns to perform classi\ufb01ca on tasks directly from images, text, or sound using deep learning techniques. The concept of deep learning was \ufb01rst revolu onized in the 1980s. Nowadays, it is so popular because of two main reasons: a) Data: Deep learning, a subset of machine learning requires large amounts of labelled data. b) Complex applica ons: Deep learning has solved increasingly complicated applica ons with increasing accuracy over me. PM Publishers Pvt. Ltd. 85","Feature Extraction from Datasets Using Deep Learning Data is the heart of AI systems. You can collect data from various sources but \ufb01nding relevant Machine Learning and accurate data is a very tedious job. Before training a model, you need to extract many Car features from all the available datasets because Not Car a model will learn from the relevance of these Input Feature extraction Classi\ufb01cation Output features. Using deep learning, this work is so Deep Learning easy because it automa cally extracts all the relevant features from the training data without Car the explicit elabora on on feature extrac on Not Car and selec on. Neural networks operate by Input Feature extraction + Classi\ufb01cation Output passing the input informa on through layers of neurons that transform the input informa on into the desired output. In simple words, the working principle of deep learning is \u201cend-to-end learning\u201d, where a network is given raw data and a task to perform, such as classi\ufb01ca on, and it learns how to do this automa cally. Di\ufb00erence Between Arti\ufb01cial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) Ar \ufb01cial Intelligence is a branch of computer science which is made up of two main terminologies: Machine Learning and Deep Learning. Machine Learning is a sub\ufb01eld of AI whereas Deep Learning is the sub\ufb01eld of Machine Learning. The main di\ufb00erences between these three technical terms are as follows: S. No Basis Ar \ufb01cial Machine Deep Intelligence (AI) Learning (ML) Learning (DL) 1. Learning Power AI enables machines The ML systems Deep Learning or Deep to think without any can automa cally Neural Learning is a human interven on. learn and improve subset of Machine without explicitly Learning techniques. DL being programmed. systems are capable of learning by example. 2. Applica ons The main Product Driverless cars and applica ons of AI recommenda on autonomous vehicles are Siri, customer engine used by are examples of Deep support using various e-commerce Learning systems. chatbots, expert websites is an system, online game example of playing, intelligent Machine Learning humanoid robot, systems. etc. Arti\ufb01cial Intelligence - 9 86","S. No Basis Ar \ufb01cial Machine Deep Intelligence (AI) Learning (ML) Learning (DL) 3. Data AI systems give Machine Learning Deep learning systems systems give give excellent Dependencies excellent excellent performance on a big performance on a dataset. performance on a small\/medium dataset. big dataset. 4. Data Type The data required The data required The data required by by AI systems can by Machine Deep Learning systems be either structured, Learning systems is can be either structured unstructured or mostly in or unstructured because semi-structured. structured form. they rely on the layers of the Ar \ufb01cial Neural Network (ANN). 5. Problems\/ AI systems are able Machine Learning Deep Learning models Tasks to solve various models are suitable are suitable for solving complex problems. for solving simple complex problems. or a bit complex problems. Worksheet 10: Recognize the Relationship Between AI and Other Fields Here is an image which depicts the rela onship of AI with other \ufb01elds such as Machine Learning and Deep Learning. Fill up the venn diagram to depict the correct rela onship among these \ufb01elds. PM Publishers Pvt. Ltd. 87","Answer: 1. Deep Learning 2.. Machine Learning 3. Ar \ufb01cial Intelligence Also, with the help of the Internet, search more about these \ufb01elds and de\ufb01ne all of them in your own words. In a Nutshell 1. A rule-based system refers to a program that uses pre-de\ufb01ned rules to make deduc ons and choices to perform automated ac ons. 2. The main goal of a rule-based system is to capture the knowledge of a human expert in a specialized domain and embody it within a computer system. 3. A set of facts or the knowledge base stores all relevant informa on, data, rules, cases, and rela onships used by the expert system. 4. The inference engine seeks the desired informa on and rela onships from the knowledge base, and provides the desired course of ac on in the way a human expert would. 5. Machine Learning technology, a subset of Ar \ufb01cial Intelligence, allows the systems to learn from the user interac ons, responses and ac vi es, and make decisions on the basis of user ac vi es. 6. Rule-based systems are sta c whereas systems based on learning approach are con nuously updated and more powerful with new user inputs and informa on. 7. A decision tree provides a set of rules on the basis of which decisions are made and added to a knowledge base by Ar \ufb01cial Intelligent systems. 8. A node that represents the possible outcomes for each ac on is called a leaf node. 9. The concept of decision trees can be easily understood by the people without analy cal or mathema cal backgrounds as compared to other complex machine learning algorithms. 10. The ul mate goal of machine learning is to develop a computer program that can access data and use it to learn for building logics. 11. The process of machine learning systems is autonomous but the chances of error are very high while processing the data. 12. Deep learning or deep neural learning is a subset of machine learning techniques which are inspired by the structure and func on of a brain, and are capable of learning by example. The concept of deep learning was \ufb01rst revolu onized in the 1980s. 13. The data required by machine learning systems is mostly in structured form whereas data required by deep learning systems can be either structured or unstructured because they rely on the layers of the Ar \ufb01cial Neural Network. Arti\ufb01cial Intelligence - 9 88","Multiple Choice Questions 1. Which of the following terms refers to a set of inputs and outputs required to achieve a predetermined or speci\ufb01c goal? a) Project work b) Project c) Project life cycle d) IPO cycle 2. A predetermined approach is also known as a) Change-driven approach b) Flexible approach c) Plan-driven approach d) Adap ve approach 3. Which of the following stages is used by the programmers to write about the nature and boundaries of a problem? a) Data Acquisi on b) Problem Scoping c) Data Explora on d) Data Valida on 4. Which of the following terms refers to a process of exploring data to interpret some useful informa on? a) Data Acquisi on b) Data Valida on c) Exploring Data d) Data Explora on 5. Which of the following stages is considered as the last step of the AI project life cycle? a) Deployment b) Evalua on c) Modeling d) Training 6. In a system map, if someone depicts an arrow line from A to B with a +(plus) sign, it means that a) There exists a direct rela on between A and B b) There exists an inverse rela on between A and B c) There exists a bi-direc onal rela onship d) None of the above 7. Which of the following op ons is not a feature of Data Visualisa on technique? a) Easy b) User-friendly c) Complex d) Simple 8. ................................. helps you to understand the signi\ufb01cance of data by framing huge amounts of data in a simple and organized way. a) Data Acquisi on b) Data Explora on c) Data Visualisa on d) Data Experiment 9. When you have a lot of datasets about a par cular topic, you need to visualise it for understanding ................................. . a) pa erns b) trends c) strategy d) All of these PM Publishers Pvt. Ltd. 89","10. Which of the following is NOT an example of data visualisa on? a) A summarized weather report b) A network topology map c) A web page d) A system map 11. Which of the following approaches is not used in AI systems? a) Rule-based Approach b) Learning-based Approach c) Adap ve Intelligence Approach d) Situa onal Intelligence Approach 12. Which of the following op ons is true about rule-based systems? a) Sta c b) Dynamic c) Raw Text d) Summarized Text 13. The structure of decision trees is ............................................... . a) linear b) rigid c) non-linear d) symmetrical 14. The reproducibility of decision tree models is highly sensi ve as a small change in the data can result in a ............................................... change in the tree structure. a) small b) large c) Any of these d) None of these 15. ............................................... is one of the great applica ons of machine learning. a) Driverless cars b) Chatbots c) Expert system d) Product recommenda on engine Answers 1. b 2. c 3. b 4. d 5. a 6. a 7. c 8. c 9. d 10. c 11. d 12. a 13. c 14. b 15. d Fill in the Blanks 1. In a ...................................... AI project life cycle, the product is developed over mul ple itera ons. 2. The term \u2018Data Acquisi on\u2019 is also known as ...................................... . 3. The process of training a model using a machine learning algorithm based on accurate data is called the ...................................... stage. 4. The term ...................................... refers to a process of framing a problem in such a way that you will have a vision to solve it. 5. ...................................... refers to the type of data that you want to collect for the problem scoped. 6. Without ................................., AI systems cannot perform the desired course of ac ons. 7. ................................. is a necessary step before training or modelling an AI system. Arti\ufb01cial Intelligence - 9 90","8. The ................................. representa on helps us to understand the trends and pa erns out of the data collected and to design a strategy around them for achieving the goal of the project. 9. ................................. plays a crucial role in the decision making of AI systems. 10. ................................. and reliability of datasets is very important while developing AI systems. 11. ............................................... is a well-known and widely used method for Ar \ufb01cial Intelligent machines. 12. ...............................................-based approach is con nuously updated and more powerful with new user inputs and informa on. 13. ............................................... is a set of facts which stores all relevant informa on, data, rules, cases, and rela onships used by the expert system. 14. In order to solve decision problems, data gathering and ............................................... is one of the necessary stages of decision support systems development with the use of Ar \ufb01cial Intelligence. 15. Decision tree model training me is rela vely ............................................... due to higher space and me complexity. Answers 1. \ufb02exible 2. Data Gathering 3. Modelling 4. Problem Scoping 5. Data Feature 6. data 7. Data Explora on 8. graphical 9. Datasets 10. Accuracy 11. Decision tree 12. Learning 13. Knowledge base 14. analysis 15. more Evaluate Yourself State T for True or F for False for the following statements. 1. A project life cycle can vary from predetermined to plan-driven approaches. 2. An appropriate analysis of collected data is an essen al requirement for modelling data. 3. The process of exploring data to interpret some useful informa on is known as Data Visualisa on. 4. In the Evalua on stage, you can deploy the model and test it by pu ng accurate and inaccurate data. 5. The 4Ws are the complex ques ons used to collect informa on regarding a problem. 6. The training data should always be relevant and reliable. 7. AI systems work on a principle called GIGO. 8. Func ons are used to depict the rela onships between di\ufb00erent elements under a system. PM Publishers Pvt. Ltd. 91","9. AI systems cannot beat the intelligence of human beings due to limited learning capability. 10. The reproducibility of decision tree models is highly sensi ve as a small change in the data can result in a small change in the tree structure. Answers 1. F 2. T 3. F 4. T 5. F 6. T 7. T 8. F 9. T 10. F Writing Subjective Responses 1. Data feature plays an important role at the me of data acquisi on. Explain with the help of an example. ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... 2. Write a short note on the AI project life cycle. ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ...................................................................................................................................................... ...................................................................................................................................................... 3. \u201cData Explora on is a necessary step before training or modelling an AI system.\u201d Comment. ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... 4. What is the need of data visualisa on? ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... Arti\ufb01cial Intelligence - 9 92","5. Name at least three techniques used to visualise collected data. ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... 6. \u201cData feature plays an important role while visualising data.\u201d Explain in your own words. ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... 7. What do you understand by the term \u2018Decision Tree\u2019? Write any two advantages and disadvantages of Decision Trees. ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... 8. What is the main di\ufb00erence between Machine Learning and Deep Learning? ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... 9. Write any two applica ons of Machine Learning in detail. ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... 10. Write a short note on the following: a) Rule-based Approach: ............................................................................................................. ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... PM Publishers Pvt. Ltd. 93","b) Learning-based Approach: ....................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... 11. \u201cNowadays, the concept of deep learning is very popular.\u201d Explain. ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... Short Answer Questions 1. De\ufb01ne the term \u2018Project\u2019. ....................................................................................................................................................... ....................................................................................................................................................... ..................................................................................................................................................... 2. What do you understand by the term \u2018Problem Scoping\u2019? ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... 3. Write any two advantages of deep learning technique. ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... 4. What is machine learning? Name any two AI systems which are based on machine learning. ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... 5. What do you understand by the term \u2018Data Visualisa on\u2019? ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... Arti\ufb01cial Intelligence - 9 94","Long Answer Questions 1. Di\ufb00eren ate between the following: a) Data Acquisi on and Data Explora on ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... b) Data Modelling and Data Visualisa on ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... c) Modelling Stage and Evalua on Stage ....................................................................................................................................................... ...................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... 2. Explain 4Ws Canvas problem for eradica ng poverty across the world in brief. ....................................................................................................................................................... ....................................................................................................................................................... ...................................................................................................................................................... ...................................................................................................................................................... 3. \u201cOne of the most commonly used AI systems is pa ern recogni on.\u201d Comment. ....................................................................................................................................................... ....................................................................................................................................................... ...................................................................................................................................................... ...................................................................................................................................................... 4. Explain the components of the expert system in brief. ....................................................................................................................................................... ....................................................................................................................................................... ...................................................................................................................................................... ...................................................................................................................................................... PM Publishers Pvt. Ltd. 95","Application Based Questions 1. Anita has seen an AI system (biometrics) in her school. Now, she wants to know about the approaches used in biometric systems. Can you help her by giving a short descrip on of the approaches used in Ar \ufb01cial Intelligent machines? ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... 2. Sunita\u2019s teacher gave her an assignment to prepare a presenta on on the topic \u201cAI, Machine Learning and Deep Learning.\u201d While preparing the assignment, she became very confused about the di\ufb00erence between the three. Can you help her in preparing the assignment? ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... 3. Sohan heard a statement in his class that \u201cA Decision tree works on rule-based approach.\u201d Now, he is so curious to know about the detailed elabora on of the decision tree. Can you help him by sugges ng at least three main points about the decision tree? ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... 4. Anjali is so curious to know about the di\ufb00erence between problem scoping and data acquisi on technique. Can you help her by sugges ng at least two di\ufb00erences between these two? ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... Arti\ufb01cial Intelligence - 9 96","Project Writing 1. You have learnt about various stages of the AI project life cycle. Now, explain the importance of feature extrac on among the given data sets in your own words. ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... 2. As you read earlier, data is the core of AI systems. You can collect data from various sources but the accuracy and reliability of data plays an important role at the me of developing AI systems. How will you \ufb01nd whether the collected data is reliable or not? Explain. Also, write at least \ufb01ve points in favour of reliability and accuracy of data. ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... ....................................................................................................................................................... PM Publishers Pvt. Ltd. 97","Activity Section Pixel It (Based on Learning Approach) In this ac vity, you will learn how computers are to be instructed by the programmers to depict an image on the computer screen, as computer graphics play an important role in every applica on. Every image which is being fed to the computer is divided into pixels. Each pixel only displays one color, so computers combine thousands of pixels in a grid in order to display complex images. This ac vity is based on a machine learning approach that is typically used in \u2018Computer Vision\u2019 applica on. Let\u2019s understand how pixel vision works (Data Modelling). Step 1: Take 3 paper sheets and draw a table of 9 columns by 9 rows on each paper, as shown in \ufb01gure 1. (You can also use graph paper or kids\u2019 math copy paper.) Figure 1 12 23 34 4 5 Step 2: Put the columns values as shown in \ufb01gure 2. 56 67 78 89 9 10 Figure 2 Arti\ufb01cial Intelligence - 9 98","1 2 2 3 Step 3: Draw the le er \u2018R\u2019 on one of the 3 4 papers randomly as shown in \ufb01gure 3. 4 5 (Touch all the possible edges.) 5 6 6 7 7 8 8 9 9 10 Figure 3 2 3 Step 4: Fill each corresponding cell of the 1 4 le er with red marker or color as shown 2 5 in \ufb01gure 4. 3 6 4 7 5 8 6 9 7 10 8 9 2 Figure 4 3 Step 5: Cut the strips of papers as shown in 4 \ufb01gure 5. 1 5 2 6 3 7 4 8 5 9 6 10 7 99 8 9 Figure 5 PM Publishers Pvt. Ltd.","Step 6: Arrange all the cut strips one-by-one in order as shown. 3 5 1 22 7 3 44 9 5 66 7 88 9 10 Step 7: Take the value \u20181\u2019 of the box or cell color which is red and \u20180\u2019 where the box or cell color is empty as shown. 1 22 3 0111110 0100010 34 45 0100010 0100010 5 66 7 0111100 0110000 7 88 9 0111000 0101100 9 10 0100111 Arti\ufb01cial Intelligence - 9 100"]