AI Module 1 Artificial Intelligence Lab programs 1. Write a Program to Implement

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2. Course objectives Gain a historical perspective of AI and its foundations Become familiar with basic principles of AI toward problem solving Familiarize with the basics of Machine Learning & Machine Learning process, basics of Decision Tree, and probability learning Understand the working of Artificial Neural Networks and basic concepts of clustering Algorithms 2

3. Course syllabus-Module-1 to 5 Module-1 Gain a historical perspective of AI and its foundation Become familiar with basic principles of AI toward problem solving s HKBKCE Module-1-21CS54 BY Dr.Pushpa Mohan 3

4. Module-2 Become familiar with basic principles of AI toward problem solving Familiarize with the basics of Machine Learning HKBKCE Module-1-21CS54 BY Dr.Pushpa Mohan 4

5. Module-3 & 4 Familiarize with Machine Learning process, basics of Decision Tree and probability learning HKBKCE Module-1-21CS54 BY Dr.Pushpa Mohan 5

6. Module-5 Understand the working of Artificial Neural Networks and basic concepts of clustering Algorithms HKBKCE Module-1-21CS54 BY Dr.Pushpa Mohan 6

7. Course Outcomes Apply the knowledge of searching and reasoning techniques for different applications. Have a good understanding of machine leaning in relation to other fields and fundamental issues and challenges of machine learning. Apply the knowledge of classification algorithms on various dataset and compare results Model the neuron and Neural Network , and to analyze ANN learning and its applications . Identifying the suitable clustering algorithm for different pattern HKBKCE Module-1-21CS54 BY Dr.Pushpa Mohan 7

8. Exam marks guidelines Continuous Internal Evaluation: 50 marks(100) Three internal test – 20 marks each(50) Assignments -10 marks(30) Quiz /Seminar/Lab Programs-20 marks(20) 2. Semester End Examination-50 marks(100) Question Paper Pattern -10 questions (3 sub questions) Answer any 5 questions. HKBKCE Module-1-21CS54 BY Dr.Pushpa Mohan 8

9. Textbooks Textbooks Stuart J. Russell and Peter Norvig , Artificial Intelligence, 3rd Edition, Pearson,2015 S. Sridhar, M Vijayalakshmi “Machine Learning”. Oxford ,2021 Reference: Elaine Rich, Kevin Knight, Artificial Intelligence, 3rdedition, Tata McGraw Hill,2013 George F Lugar, Artificial Intelligence Structure and strategies for complex, Pearson Education, 5th Edition, 2011 Tom Michel, Machine Learning, McGrawHill Publication. HKBKCE Module-1-21CS54 BY Dr.Pushpa Mohan 9

10. Weblinks https:// www.kdnuggets.com/2019/11/10-free-must-read-books-ai.html https:// www.udacity.com/course/knowledge-based-ai-cognitive-systems--ud409 https://nptel.ac.in/courses/106/105/106105077/ https:// www.javatpoint.com/history-of-artificial-intelligence https:// www.tutorialandexample.com/problem-solving-in-artificial-intelligence https://techvidvan.com/tutorials/ai-heuristic-search/ https:// www.analyticsvidhya.com/machine-learning/ https:// www.javatpoint.com/decision-tree-induction https:// www.hackerearth.com/practice/machine-learning/machine-learning-algorithms/ml- decision-tree/tutorial/ https:// www.javatpoint.com/unsupervised-artificial-neural-networks HKBKCE Module-1-21CS54 BY Dr.Pushpa Mohan 10

11. Why AI Artificial intelligence (AI) makes it possible for machines to learn from experience , adjust to new inputs and perform human-like tasks. Most AI examples Chess-playing computers( Garry Kasparov-IBM-DEEP BLUE) to self-driving cars(TESLA) – rely heavily on deep learning and  natural language processing . Using these technologies , computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data. HKBKCE Module-1-21CS54 BY Dr.Pushpa Mohan 11

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13. Reasons behind why learn AI: Bright career- Decent Salary - work as a Machine Learning Engineer, Data Scientist, Business Intelligence Developer, Research Engineer AI i s versatile-  It is applicable to any industry -healthcare, automobile, and even banking and finance sector. for example ,   PathAI , which is a technology that will assist pathologists in reducing error rates in cancer diagnosis Skill of the century -create many and different job opportunities in related fields. Ingests huge amounts of data -Humans generate more than 2.5 quintillion bytes  of data every day. machines and AI-enabled systems that are able to handle this big data. The information regarding the AADHAR Cards of Indian citizens can be an  example of big data . The posts that we like, view, share, or comment on Facebook are also an example of big data. AI has enabled programs to analyze trends in these data and act accordingly . HKBKCE Module-1-21CS54 BY Dr.Pushpa Mohan 13

14. Reasons behind why learn AI: Improved disaster management -Many a time, the victims of disasters record videos and share them on social media platforms, like Facebook and Twitter . These platforms have AI-enabled programs in them, which serve as a carrier in spreading the news about these disasters. Benefit of the society- Farmlogs  are software that simplifies the work of farmers by providing them information about the weather, fields, and soil . It is also helping them track irregular plant growth. This is helping them in achieving better profits. Governments are implementing AI in their smart city applications , which is helping them in improving environmental planning, crime prevention, and better resource managemen t . HKBKCE Module-1-21CS54 BY Dr.Pushpa Mohan 14

15. Reasons behind why learn AI: AI improvises user experience- AI is not a technology that requires a separate app or device. It is adding intelligence to the products we are using regularly in our lives. A combination of different types of AI technologies like  chatbots , automation, virtual assistants like Google assistant is helping improve user experience by adding multiple useful features to a previously existing product. Siri , the voice assistant that Apple provides specifically for iPhone and iPad users HKBKCE Module-1-21CS54 BY Dr.Pushpa Mohan 15

16. Define Intelligence Artificial Intelligence Agent Rationality Logical reasoning HKBKCE Module-1-21CS54 BY Dr.Pushpa Mohan 16

17. Define Intelligence: Intelligence is the ability to think, learn and act according to a situation and the environment It is a process of applying knowledge . It can also be defined as the ability to adapt to the changes in the environment . Artificial Intelligence: Artificial Intelligence is the study of making computers as act intelligently like humans . It is the capability of a system to perform the functions similar to a human . Agent : An agent is something that acts. The word ‘ agent’ came from the Latin word “ agree ”, which means, to do . An agent acts on behalf of a person . It is an entity that acts in response to the environmental issues . Rationality : Rationality   means doing the right thing , given what it knows . A rational approach involves a combination of mathematics and engineering Logical reasoning : It is the way of thinking and taking decisions derived from the conclusions and inferences. HKBKCE Module-1-21CS54 BY Dr.Pushpa Mohan 17

18. What do you understand by Artificial Intelligence? Artificial intelligence is computer science technology that emphasizes creating intelligent machine that can mimic human behavior "Artificial" and "Intelligence," which means the " man-made thinking ability." Do not need to pre-program the machine to perform a task ; instead, create a machine with the programmed algorithms, and it can work on its own. HKBKCE Module-1-21CS54 BY Dr.Pushpa Mohan 18

19. What is AI? Some definition of AI organized into 4 categories HKBKCE Module-1-21CS54 BY Dr.Pushpa Mohan 19 Thinking Humanly Thinking Rationally “The exciting new effort to make computers think . . . machines with minds , in the full and literal sense.” ( Haugeland , 1985) “[The automation of] activities that we associate with human thinking, activities such as decision-making, problem solving, learning . . .” (Bellman, 1978) “The study of mental faculties through the use of computational models .” ( Charniak and McDermott, 1985) “The study of the computations that make it possible to perceive( conscious) , reason( way to infer facts from existing data) , and act .” (Winston, 1992) Acting Humanly Acting Rationally “The art of creating machines that perform functions that require intelligence when performed by people.” (Kurzweil, 1990) “The study of how to make computers do things at which, at the moment, people are better .” (Rich and Knight, 1991) “Computational Intelligence is the study of the design of intelligent agents .” (Poole et al. , 1998) “AI . . . is concerned with intelligent behavior in artifacts ( describe the output created by the training process) .” (Nilsson, 1998)

20. Views of AI fall into 4 categories which indicates Eight definitions of AI , laid out along two dimensions (a) and (b) are concerned with thought process and reasoning (c) and (d) address behaviour . (a) and (c) measure success in terms of human performance (b) and (d) measure the ideal concept of intelligence called rationality( maths and engineering A system is rational if it does the “ right thing ,” given what it knows. HKBKCE Module-1-21CS54 BY Dr.Pushpa Mohan 20

21. Thinking humanly: The Cognitive modeling approach The cognitive modeling approach under "thinking humanly" involves creating explicit, computer-based models that simulate human thought processes, such as problem-solving or decision-making. An example of a cognitive modeling approach is the development of expert systems in artificial intelligence CaDet (Cancer Decision Support Tool) is used to identify cancer in its earliest stages. HKBKCE Module-1-21CS54 BY Dr.Pushpa Mohan 21

22. Acting humanly: The Turing Test approach Turing test is one of the popular intelligence tests in Artificial intelligence. The Turing test was introduced by Alan Turing in the year 1950. It is a test to determine that if a machine can think like a human or not. According to this test, a computer can only be said to be intelligent if it can mimic human responses under some particular conditions.  This test is that verbal behavior is sufficient to determine whether an agent is intelligent enough. HKBKCE Module-1-21CS54 BY Dr.Pushpa Mohan 22

23. Turing test process involves In the test, a human interrogator interacts with two players, A and B , by exchanging written messages (in a chat). If the interrogator cannot determine which player, A or B, is a computer and which is a human , the computer is said to pass the test. The argument is that if a computer is indistinguishable from a human HKBKCE Module-1-21CS54 BY Dr.Pushpa Mohan 23

24. The computer would need to possess the following capabilities to do Turing test NATURAL LANGUAGE PROCESSING • natural language processing to enable it to communicate successfully in English KNOWLEDGE REPRESENTATION • knowledge representation to store what it knows or hears; AUTOMATED REASONING• automated reasoning to use the stored information to answer questions and to draw new conclusions ; MACHINE LEARNING • machine learning to adapt to new circumstances and to detect and extrapolate patterns. HKBKCE Module-1-21CS54 BY Dr.Pushpa Mohan 24

25. Total Turing test The Total Turing Test takes this a step further by including not just the ability to engage in natural language conversation but also other human abilities , such as vision, hearing, and manual skill in performing tasks In essence, the Total Turing Test seeks to evaluate a machine's overall capability to perform any intellectual task that a human can, including perception( taking data from cameras ) and physical actions . So it requires COMPUTER VISION • computer vision to perceive objects( image processing, object detections) ROBOTICS • robotics to manipulate objects and move about. HKBKCE Module-1-21CS54 BY Dr.Pushpa Mohan 25

26. Major components of AI knowledge, reasoning, language understanding, learning, computer vision & Robotics. 26

27. Thinking rationally : The “laws of thought” approach The "Laws of Thought" are fundamental principles in classical logic that help guide rational thinking and reasoning.  For example, “Ganesh is a man; all men are mortal; therefore, Ganesh is mortal.” 27

28. Acting rationally: The rational agent approach A rational agent is one that takes actions to maximize the expected outcome based on its knowledge and goals. Computer program : Code that we have written in the dark is executed by a conventional computer software. It is not acting on its own; rather, it is changing in response to the outcomes. Agent program A so-called agent program is supposed to function significantly better than traditional computer programs. Its predicted behaviors include setting out and achieving goals , changing into a new stage of being, and functioning on its own . Example : Play chess. In this context, a rational agent is one that makes moves to maximize its chances of winning the game 28

29. The foundations of Artificial Intelligence The foundation provides the disciplines that contributed ideas, view points and techniques to Al Philosophy Mathematics & Statistics Economics Neuroscience Psychology Computer Science and Engineering Control Theory and Cybernetics Linguistics 29

30. Philosophy Philosophy is the very basic foundation of Al. One important aspect of artificial intelligence is the study of the underlying nature of reality, existence, and knowledge as they relate to addressing a particular problem .. Philosophy defines that how can the formal rules be used to draw valid conclusions. With out philosophy it is difficult to answer the following questions. In what way does a physical brain give rise to intellect? Where does the knowledge come from? How does the knowledge lead to action? 30

31. Mathematics and statistics Al required Formal Logic and Probability for planning and learning . Computation required for analyzing relation and implementation . Knowledge in Formal Representation are most required for writing actions for agents. In Al,the Mathematics & Statistics are most important for Proving theorems, Writing algorithms, Computation, Decidability, Tractability, Modeling uncertainty, Learning from data What are the formal rules to draw valid conclusions? What can be computed? How do we reason with uncertain information? 31

32. Deals with investing the amount of money and Maximization of utility with minimal investment . While developing an Al product, we should make decisions for When to invest? How to invest? How much to invest? Where to invest? To answer these questions one should have knowledge about Decision Theory, Game Theory, Operation Research etc 32 Economics

33. Neuroscience Neuroscience is the study of the nervous system particularly the human brain. Human brains are somehow different , when compared to other creatures , man has the largest brain in proportion to his size. Since neurons, or nerve cells make up the majority of the brain , understanding a single neuron can provide information about thinking, acting, and brain consciousness. How do brains process information? 33

34. Neurons , also known as nerve cells, send and receive signals from brain . Specialized projections called axons allow neurons to transmit electrical and chemical signals to other cells. Neurons can also receive these signals via root like extensions known as dendrites. Synapses  connect neurons in the brain to neurons in the rest of the body and from those neurons to the muscles HKBKCE Module-1-21CS54 BY Dr.Pushpa Mohan 34

35. Psychology/Cognitive Science The scientific method to the study of human vision Problem-solving abilities: how can people make decisions in complex situations? What are people's actions in unexpected circumstances ? Perceive: How do you look around you to solve problems ? Process cognitive information to represent knowledge. How do humans and animals think and act? 35 •

36. Computer Science & Engineering Important areas of computer science include algorithms, programming languages, logic and inference theory, and software system development. Modern programming tools, operating systems, and programming languages were provided by the software branch of computer science. Al has founded many ideas in modern and mainstream computer science including Time sharing, Interactive Interpreters, Personal computers with windows, rapid development environments, the linked-list data type, automatic storage management key concepts of symbolic, functional, declarative and object­ oriented programming Every 100 days, the amount of CPU power used to train the best machine learning applications doubles. The Super computers and quantum computers can solve very complicated Al problems Computer hardware gradually changed for A l applications , such as the graphics processing unit (GPU), tensor processing unit (TPU),and wafer scale engine (WSE) How can we build fast and efficient computer? . 36

37. Control theory 37 Control theory helps the system to analyze, define, debug and fix errors by itself . Developing self-controlling machine, self-regulating feedback control systems and the submarine are some examples of control theory The basis of Algebra is calculus, matrix algebra are the tool of control theory methods that apply to systems that may be described by fixed sets of continuous variables . – to build robot Knowledge representation, grammars, computational linguistics or natural language processing ( N LP) are significant to developing Al applications. Agent programming uses the language, vision, and symbolic planning of computation and logical inference as its instruments. How can artifacts(model-output created by training process) operate under their own control?

38. Linguistics c:: Speech recognition is a technology which enables a machine to understand the spoken language and translate into a machine­ readable format. It is a way to talk with a computer and on the basis of that command, a computer can perform a specific task. It includes Speech to Text, Text to Speech. How does language relate to thought? 38

39. History of AI The Gestation of AI(1945–1955): In the early 1950s, Turing published a paper called “ can a machine think? ”. In 1943, McCulloch and Pitts implemented a neural model as the very first intelligent program. The birth of artificial intelligence (1956): MacCarthy said that intelligent machines need a separate discipline and he named it Artificial intelligence . According to MacCarthy’s definition , AI is the science and engineering of building intelligent machines. 39

40. History of AI Early enthusiasm, great expectations (1952–1969): Logic-based intelligence programs were implemented under symbolic AI such as problem solvers, game players, and theorem provers .  Minsky criticized the neural network and because of that, there was no development related to the neural network until 1986 Other reasons Since AI research was funded by the military , all the details were kept as secrets. General people didn't know. Power and technology both are in hands of AI machines . So, people were afraid of that. At that time AI was not a science 40

41. History of AI Knowledge-based systems(1969–1974): Model the knowledge for developing intelligent machine . Expert systems, Natural language processing, game players, and problem solvers are knowledge-intensive systems. AI become an industry(1980-present): It has taken more than 25 years to gain recognition from the industry. Xcon was the very first industrial application of AI. The neural network was reborn with a backpropagation training algorithm. The best example of that is the DART expert system which is used in the gulf war. AI becomes a science(1995-): After the 1980s AI also adopted scientific methods that are experimental explanations for theories. Being a science AI won a big recognition . 41

42. History of AI Large datasets, 2001–present: Throughout the 60-year history of computer science , the emphasis has been on the algorithm as the main subject of study. But some recent work in AI suggests that for many problems, it makes more sense to worry about the data and be less picky about what algorithm to apply. 42

43. Intelligent agent: The intelligent agent can be any autonomous entity that perceives its environment through the sensors and act on it using the actuators for achieving its goal. These Intelligent agents in AI are used in the following applications: Information Access and Navigations such as Search Engine Repetitive Activities Domain Experts Chatbots , etc Intelligent agents are supposed to maximize their performance measure 43

44. Goal-based agent A goal-based agent is an artificial intelligence agent that responds to its environment and adjusts accordingly to achieve a goal Example: Google's Waymo driverless cars  are good examples of a goal-based agent when they are programmed with an end destination, or goal in mind 44

45. Define : 1. Problem formulation 2.Search 3. Execution Phase Problem formulation: The process of selecting what actions and states to consider given a goal is known as problem formulation. Search :The process SEARCH of looking for a sequence of actions that reaches the goal is called search .A search algorithm takes a problem as input and returns a solution in the form of an action sequence. Execution Phase: Once a solution is found, the actions it recommends can be carried out. This EXECUTION is called the execution phase. “formulate, search, execute” are the design for the agent 45

46. Questions Define in your own words: (a) intelligence, (b) artificial intelligence, (c) agent, (d) rationality, (e) logical reasoning. HKBKCE Module-1-21CS54 BY Dr.Pushpa Mohan 46

47. “Chinese room” argument [Searle 1980] Person who knows English but not Chinese sits in room Receives notes in Chinese Has systematic English rule book for how to write new Chinese characters based on input Chinese characters, returns his notes Person=CPU, rule book=AI program, really also need lots of paper (storage) Has no understanding of what they mean But from the outside, the room gives perfectly reasonable answers in Chinese! Searle’s argument: the room has no intelligence in it! image from http://www.unc.edu/~prinz/pictures/c-room.gif