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ARTIFICIAL INTELLIGENCE: STUDY MATERIAL
CLASS XI
______________________________________________
LEVEL 1: AI INFORMED (UNIT 1 – UNIT 5)
TEACHER INSTRUCTION MANUAL
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Artificial intelligence study material, Schemes and Mind Maps of Artificial Intelligence
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____________________________________________
ARTIFICIAL INTELLIGENCE: STUDY MATERIAL
CLASS XI
______________________________________________
LEVEL 1: AI INFORMED (UNIT 1 – UNIT 5)
TEACHER INSTRUCTION MANUAL
INDEX
- UNIT 1: INTRODUCTION: AI FOR EVERYONE ………………… Page 2 -
- UNIT 2 : AI APPLICATIONS & METHODOLOGIES ……………. Page 38 –
- UNIT 3: MATH FOR AI …………………………………………………… Page 77 -
- UNIT 4: AI VALUES (ETHICAL DECISION MAKING) ………… Page 129 -
- UNIT 5: INTRODUCTION TO STORYTELLING …………………. Page 140 -
1. What is Artificial Intelligence (AI)
- What movies have you seen about artificial intelligence?
- How intelligent will artificial intelligence become by 2030, any guess?
- At present, in what activities are computers better at than humans?
- At present, in what activities are human better at than computers? So, how do we define Artificial Intelligence [AI?] AI is a technique that facilitates a machine to perform all cognitive functions such as perceiving, learning and reasoning that are otherwise performed by humans. “The Science and Engineering of making intelligent machines, especially intelligent Computer programs is Artificial intelligence” – JOHN MC CARTHY [Father of AI] The yardstick to achieve true AI still seems decades away. Computers execute certain tasks way better than humans e.g.: Sorting, computing, memorizing, indexing, finding patterns etc. While identifying of
emotions, recognising faces, communication and conversation are unbeatable human skills. This is where AI will play a crucial role to enable machines achieving equalling human capabilities. World Famous AI Machines [naming a few of them]: IBM Watson (https://www.youtube.com/watch?v=s_wgf75GwCM ) Google’s Driverless car (https://www.youtube.com/watch?v=cdgQpa1pUUE) Sophia, the humanoid Robot (https://www.youtubeom/watch?v=cdgQpa1pUUE) The assistant / Chabot - Alexa, Siri, Google’s Home Honda Asimo (https://www.youtube.com/watch?v=1urL_X_vp7w)
2. History of AI
In 1950’s The modern-day AI got impetus since 50s of the previous centuries, once Alan Turning introduced “Turning Test” for assessment of intelligence. In 1955 John McCarthy known as the founder of Artificial Intelligence introduced the term ‘Artificial Intelligence’. McCarthy along with Alan Turing, Allen Newell, Herbert A. Simon, and Marvin Minsky too has the greatest contribution to present day machine intelligence. Alan suggested that if humans use accessible information, as well as reason, to solve problems to make decisions – then why can’t it be done with the help of machines? In 1970’s 70 s saw an upsurge of computer era. These machines were much quicker, affordable and stowed more information. They had an amazing character to think abstract, could self-recognize and accomplished natural language processing. In 1980’s These were the years that saw flow of funds for research and algorithmic tools. The learning skills were enhanced and computers improved with deeper user experience. In 2000’s Many unsuccessful attempts, Alas! The technology was successfully established by years 2000.The milestones were realised, that needed to be accomplished. AI could somehow manage to thrive despite lack of government funds and public appreciation. (Image Source: www.data-flair.com)
3. Machine Learning
Example 1: Let’s play a game. Find the missing number 2, 4, 8, 16, 32,? And I am sure, you would have guessed the correct answer which is 64. But how did you arrive at 64? This calculation must have taken place inside your brain cells and the technique you used to decipher this puzzle, has actually helped you to decode Machine Learning (ML). That’s exactly the kind of behaviour that we are trying to teach the machines. ‘Learn from experience’ is what we want machines to acquire. Example 2: Let us take another example from Cricket. Assume you are the batsman facing a baller. By looking at the baller’s body movement and action, you predict and move either left or right to hit the ball. But if the baller throws a straight ball, what will you do? Apart from the baller’s body movement, you also try to find out the patterns in baller’s bowling habit, that after 2 consecutive left side balls, he/she throws a straight ball and you prepare yourself to face the next ball. So what you are doing is learning from past experience in order to perform better in the future. When a computer does this, it is called Machine Learning. You let the computer to learn from its past experience / data. Example 3: Now let us go for a slightly more complicated example: I am Mr. XYZ and I want to buy a house. I try to calculate how much I need to save monthly for that. I did my research work and got to know that a new house would cost me anything between Rs. 30 Lakh to Rs. 100 Lakh. A 5-year old house would cost me between Rs. 20 Lakh to 50 Lakh, a house in Delhi would cost me ......and buying a house in Mumbai would be ......and so on. Now my brain starts working and suddenly I am able to make out a pattern: So, the price of the house depends on its age, location, built up area, facilities, depreciation (which means that price could drop by Rs. 2 Lakh every year, but it would not go below Rs. 20 Lakh.) In machine learning terms, Mr. XYZ has stumbled upon regression – he predicted a value (price) based on the available historical data. People do it all the time, when trying to estimate a reasonable cost for a used phone or a car or figure out how many cakes to buy for a birthday party, which might be 200 grams per person, so how many kilograms for a party of 50 persons?
If you haven’t realized as yet, then it is time for you to know that Machine learning is behind all the surprises sprung up by Google, Amazon and Flipkart. Even you can create this magic by learning a little about mathematics and a computer programming language. I am sure, by now you have some insight into ML. So, what is ML? “Machine Learning is a discipline that deals with programming the systems so as to make them automatically learn and improve with experience. Here, learning implies understanding the input data and taking informed decisions based on the supplied data”. In simple words, Machine Learning is a subset of AI which predicts results based on incoming data. The utilities of ML are numerous. So as to detect spam emails, forecast stock prices or to project class attendance one can achieve results by means of earlier collected spam messages, previous price history records or procure 5 years or more attendance data of a class. ML will predict the results based upon previous data base experience available with it. Activity Based on the understanding you have developed till now, how do you think Machine Learning could help some of the problems being faced currently by your school. Fill the problems in the blank circles given below:
3.1. Difference between Conventional programming and Machine Learning
Conventional programming and ML coding both are computer programs but their approach and objective are different. Like your school dress and your casual dress – both are clothes, made from threads but their purpose is different. If you ned to develop a website for your school, you will take the Conventional programming approach. But if you want to develop an application to forecast the attendance percentage of your school for a particular month (based on historical attendance data) you will use the ML approach. Conventional Programming Approach Conventional Programming refers to any manually created program which uses input data, runs on a computer and produces the output. What does it mean? Let us understand it by illustration below: A programmer accepts the input, gives the instruction (through Code / Computer language) to the computer to produce an output/destination. Take a look at an example. Below are the steps to convert Celcius scale to Fahrenheit scale Step - 1: Take input (Celcius) Step-2: Apply the conversion formula: Fahrenheit = Celcius * 1.8 + 32 Step - 3: Print the Output (Fahrenheit) Did you notice, we are telling the computer what to do on the input data i.e. multiply Celcius with 1. and then add 32 to obtain the value in Fahrenheit. Machine Learning (or AI) Approach On the contrary, in Machine Learning (ML), the input data and the output data are fed to an algorithm (Machine learning algorithm) to create a program. Unlike conventional programming, Machine Learning is an automated process where a programmer feeds the computer with ‘The Input + The Output’ and computer generates the algorithm as to how the ‘The Output’ was achieved.
4. Data
Modern day scholars have coined the phrase ‘Data is the new oil’. If everyone is talking so highly about data, then it must be something precious! But what is this data? Activity Let us create a students’ dataset for your class (the one given below is a sample, you can create one of your own) Name of Students Attendance (%) as of April, 2020 Gender Total Marks (%) obtained in Grade X Participation in Sports A 76 Male 92 N B 82 Male 88 Y C 57 Male 65 N D 97 Female 97 N E 56 Male 62 Y F 76 Female 85 N G 51 Male 56 Y Does this dataset tell you a story? Do you think it mirrors an association between marks obtained and attendance? Can you extract 5 observations from this dataset? [Although this is a very small dataset, can you still take a shot at it?]
Activity Open the URL https://data.gov.in/node/6721404 in your web browser. It should open the following page The page you opened, has a link Reference URL: https://myspeed.trai.gov.in/ - Click on this link. Now answer a few questions:
- Who owns and maintains this dataset?
- What kind of data does it hold?
- Why the Government of India stores these data?
- Why has the government made this data public?
- Do you see the use of such archives in Artificial Intelligence Machine Learning?
- Can you do a simple web search and find three other such sources of data? Now that we have engaged in two activities related to data, let us try and define Data. What is Data? Define it. Data can be defined as a representation of facts or instructions about some entity (students, school, sports, business, animals etc.) that can be processed or communicated by human or machines. Data is a collection of facts, such as numbers, words, pictures, audio clips, videos, maps, measurements, observations or even just descriptions of things.
it has no pre-defined model, meaning it cannot be organized in relational databases. Instead, non- relational, or NOSQI databases, are best fit for managing unstructured data.
5. Terminology and Related Concepts
5.1. Machine Learning “Machine learning is the science of getting computers to act without being explicitly programmed.”
- Stanford University “Machine learning algorithms can figure out how to perform important tasks by generalizing from examples.” – University of Washington Of late, machine learning has achieved a great deal of popularity, but the first attempt to develop a machine that imitated the behaviour of a living being was made in the 1930s by Thomas Ross. Machine Learning (ML) is a term used today to describe an application of AI which equips the system with the ability to learn and improve from experience using the data that is accessible to it. For more, please refer to section 3. 5.2. Supervised, Unsupervised and Reinforcement learning Machine learning is often divided into three categories – Supervised, Unsupervised and Reinforcement learning. 5.2.1. Supervised Learning As the name specifies, Supervised Learning occurs in the presence of a supervisor or a teacher. We train the machine with labeled data (i.e. some data is already tagged with correct answer). It is then compared to the learning which takes place in the presence of a supervisor or a teacher. A supervised learning algorithm learns from labelled training data, and then becomes ready to predict the outcomes for unforeseen data.
Example 1 Remember the time when you used to go to school? The time when you first learnt what an apple looked like? The teacher probably showed a picture of an apple and told you what it was, right? And you could identify the particular fruit ever since then. That’s exactly how supervised learning works. As you can see in the image below Step 1: You provide the system with data that contains photos of apples and let it know that these are apples. This is called labelled data. Step 2: The model learns from the labelled data and the next time you ask it to identify an apple, it can do it easily. Example 2 For instance, suppose you are given a basket full of different kinds of fruits. Now the first step is to train the machine to identify all the different fruits one by one in the following manner: If the shape of the object is round with depression at the top and its color being Red, then it will be labelled – Apple. If shape of object resembles a long-curved cylinder with tapering ends and its colour being Green or Yellow, then it will be labelled
- Banana. Now suppose after training, you bring a banana and ask the machine to identify it, the machine will classify the fruit on the basis of its shape and colour and would confirm the fruit to be BANANA and place it in the Banana category. Activity 1 Suppose you have a data set entailing images of different bikes and cars. Now you need to train the machine on how to classify all the different images. How will you create your labelled data?
Example 2 For instance, suppose the machine is given an image having both dogs and cats which it has not seen before. Logically the machine has no idea about the physical characteristics of dogs and cats and therefore it cannot categorize the animals. But it can surely categorize them according to their similarities, patterns, and differences i.e. we can easily categorize this above picture into two parts. First category may contain all pictures having dogs in it and second category may contain all pictures having cats in it. Here you didn’t learn anything before, means no training data or examples were provided for prior training. Let us take another example - a friend invites you to his party, where you meet a stranger. Now you will classify this person using unsupervised learning (without prior knowledge) and this classification can be on the basis of gender, age group, dressing style, educational qualification or whichever way you prefer. Why is this learning different from Supervised Learning? Since you didn't use any past/prior knowledge about the person and classified them "on-the-go". Activity 1 Let's suppose you have never seen a Cricket match before and by chance watch a video on the internet. Can you classify players on the basis of different criterion? Hint: [Players wearing similar outfits belong to a team, players performing different types of action – batting, bowling, fielding, and wicket keeping.] 5.2.3. Reinforcement Machine Learning Wikipedia defines Reinforcement learning as “Reinforcement learning (RL)” as an area of machine learning concerned with how software agents ought to take actions in an environment in order to maximize some notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.” In reinforcement learning, the machine is not given examples of correct input-output pairs, but a method is provided to the machine to measure its performance in the form of a reward_._ Reinforcement
learning methods resemble how humans and animals learn, the machine carries out numerous activities and gets rewarded whenever it does something well. Example 1 Let’s play a game: We have an agent, a robot, and a reward (diamond here) with many hurdles (fires) in between. The goal of the robot is to get the reward (diamond) and to avoid the hurdles (fire). The robot learns by trying all the possible paths and then chooses the path which reaches the reward while encountering the least hurdles. Each correct step will bring the robot closer to the diamond while accumulating some points and each wrong step will push the robot away from the diamond and will take away some of the accumulated points. The reward (diamond) will be assigned to the robot when it reaches the final stage of the game. Example 2 Imagine a small kid is given access to a laptop at home (environment). In simple terms, the baby (agent) will first observe and try to understand the laptop environment (state). Then the curious kid will take certain actions like hitting some random buttons (action) and observe how the laptop would respond (next state). As the non-responding laptop screen goes dull, the kid dislikes it (receiving a negative reward) and probably won’t like to repeat the actions that led to such a result (updating the policy) and vice versa. The kid will repeat the process until he/she finds a button which turns the laptop screen bright (rewards) and will be happy maximizing the total rewards. This is how reinforcement learning works! In reinforcement learning, artificial intelligence faces a game-like situation. The computer employs trial and error to come up with a solution to the problem. To get the machine to do what the programmer wants, the artificial intelligence gets either rewards or penalties for the actions it performs. Its goal is to maximize the total reward.