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1. Unit-I Introduction to AI & ML Unit-I INTRODUCTION Introduction to AI: Artificial Intelligence is an approach to make a computer, a robot, or a product to think how smart human think. AI is a study of how human brain think, learn, decide and work, when it tries to solve problems. And finally this study outputs intelligent software systems.The aim of AI is to improve computer functions which are related to human knowledge, for example, reasoning, learning, and problem-solving. The intelligence is intangible. It is composed of Reasoning Learning Problem Solving Perception Linguistic Intelligence The objectives of AI research are reasoning, knowledge representation, planning, learning, natural language processing, realization, and ability to move and manipulate objects. There are long-term goals in the general intelligence sector. Approaches include statistical methods, computational intelligence, and traditional coding AI. During the AI research related to search and mathematical optimization, artificial neural networks and methods based on statistics, probability, and economics, we use many tools. Computer science attracts AI in the field of science, mathematics, psychology, linguistics, philosophy and so on. Applications of AI  Gaming − AI plays important role for machine to think of large number of possible positions based on deep knowledge in strategic games. for example, chess,river crossing, N-queens problems and etc.  Natural Language Processing − Interact with the computer that understands natural language spoken by humans.

2.  Expert Systems − Machine or software provide explanation and advice to the users.  Vision Systems − Systems understand, explain, and describe visual input on the computer.  Speech Recognition − There are some AI based speech recognition systems have ability to hear and express as sentences and understand their meanings while a person talks to it. For example Siri and Google assistant.  Handwriting Recognition − The handwriting recognition software reads the text written on paper and recognize the shapes of the letters and convert it into editable text.  Intelligent Robots − Robots are able to perform the instructions given by a human. Major Goals  Knowledge reasoning  Planning  Machine Learning  Natural Language Processing  Computer Vision  Robotics Foundations of Artificial Intelligence: 1941- First Electronic Computer. 1941-49: First Commercial Program Stored Computer. 1949-1956: Birth of AI. 1956-1958: LISP language developed. 1963: Start of DOD advance research project. 1968: Micro World Program. 1970: First expert system. 1972: PROLOG language developed. 1991: AI system beats human chess master. History of AI Here is the history of AI during 20th century − Year Milestone / Innovation 1923 Karel Čapek play named “Rossum's Universal Robots” (RUR) opens in London,

3. first use of the word "robot" in English. Foundations for neural networks laid. 1943 Isaac Asimov, a Columbia University alumni, coined the term Robotics. 1945 Alan Turing introduced Turing Test for evaluation of intelligence and published Computing Machinery and Intelligence.Claude Shannon published Detailed 1950 Analysis of Chess Playing as a search. John McCarthy coined the term Artificial Intelligence. Demonstration of the first 1956 running AI program at Carnegie Mellon University. John McCarthy invents LISP programming language for AI. 1958 Danny Bobrow's dissertation at MIT showed that computers can understand 1964 natural language well enough to solve algebra word problems correctly. Joseph Weizenbaum at MIT built ELIZA, an interactive problem that carries on a 1965 dialogue in English. Scientists at Stanford Research Institute Developed Shakey, a robot, equipped 1969 with locomotion, perception, and problem solving. The Assembly Robotics group at Edinburgh University built Freddy, the Famous 1973 Scottish Robot, capable of using vision to locate and assemble models. The first computer-controlled autonomous vehicle, Stanford Cart, was built. 1979 Harold Cohen created and demonstrated the drawing program, Aaron. 1985 1990 Major advances in all areas of AI −

4.  Significant demonstrations in machine learning  Case-based reasoning  Multi-agent planning  Scheduling  Data mining, Web Crawler  natural language understanding and translation  Vision, Virtual Reality  Games The Deep Blue Chess Program beats the then world chess champion, Garry 1997 Kasparov. Interactive robot pets become commercially available. MIT displays Kismet, a robot with a face that expresses emotions. The robot Nomad explores remote 2000 regions of Antarctica and locates meteorites. Comparision of Data Science and Artificial Intelligence: The Basic Of Comparisons Data Science Artificial Intelligence Data Science is of curating Artificial Intelligence is Meaning mass data for analytics and implementing this data in visualization Machine Statistical technique design Algorithm technique design Skills and development and development Data Science is a Data Artificial Intelligence is a Technique Analytics technique Machine learning technique Data Science use statistical Artificial Intelligence is of Use of Knowledge learning for Analysis Machine Learning Observation Patterns in Data for decision Intelligence in Data for

5. decision Artificial intelligence Data science tends to use represents the loop of Solving parts of this loop to solve perception and Planning with specific problems action Data Science Medium level Artificial Intelligence high Processing processing of Data for Data order processing scientific Manipulation data for manipulation Data science involved in Artificial intelligence involves Graphic data representation in the in algorithm network node various graphical format representation Data control and Robotic control with artificial Control manipulation with Data intelligence and machine Science technique learning techniques According to a survey by Gartner, number of enterprises using Artificial Intelligence (A.I.) have increased by 270%, creating a shortage of AI professionals. AI is going to affect every profession, but how will mechanical engineering get along in this future scenario? There are some mechanical engineering fields in which AI is about to give a paradigm shift. AI Helping in Complex CAD AI used in Computer-Aided Design (CAD) generally works on knowledge-based systems. Design artefacts, rules, and problems in CAD are stored which later assist CAD designers. Merging of AI and CAD is done through Model-Based Reasoning (MBR). Many new releases of software packages are using knowledge-based systems. A major field for the application of AI is Generative Design. Generative design tool takes design requirements as input and gives possible designs as output. SolidWorks gives a feature of topology optimization in its 2018 version by using different algorithm based on generative design. Autodesk launched a project named Dreamcatcher which offers the feature of generative design. Using this utility, instead of designing by the hit-and-trail method, engineers can select a design provided by software after observing suitable trade-offs for any features.

6. Artificial Neural Networks in CFD Computational Fluid Dynamics has been of great interest among scientists, engineers and mathematicians. The turbulence and chaos associated with fluid mechanics have made it a lot difficult to solve with Direct Numerical Simulation (DNS). There are some models available, namely Reynold’s-Averaged Navier-Stokes equation (RANS) and Large Eddy Simulation (LES), which approximates flow behaviour and AI also found its way among them. Artificial Neural Networks(ANN) are gaining interest in academia for their potential to give approximations of flow with less computing power, time and dimensional reduction of problems. They are also showing good agreement with traditional CFD models. The challenge is to train ANN with lots of example simulations. Also, you can’t get an insight of flow mechanism with neural networks. IoT and Data Analysis 4th industrial revolution is going to connect all machinery in a production plant and consumer products, so engineers can analyse, optimize and ensure quality of the product. Managing such technical data will require engineers who could read between the lines of sensor data. Mechanical engineers with AI skills would be required to work on software which can handle data provided by sensors in components of power plant, production facility or consumer products. One example of data science use in power plant optimization. Data collected from Supervisory Control And Data Acquisition (SCADA) can help predict failures, avoiding any loss of money or life. A US-based company Sparkcognition is providing solutions to power companies that detect anomalies in plant data and predict any failure sufficient time ahead, avoiding downtime and loss of revenue. [3] AI is creating strides in self-driving cars as well as industrial robotics. Introduction to Machine Learning: In the real world, we are surrounded by humans who can learn everything from their experiences with their learning capability, and we have computers or machines which work on our instructions. But can a machine also learn from experiences or past data like a human does? So here comes the role of Machine Learning.

7. Machine Learning is said as a subset of artificial intelligence that is mainly concerned with the development of algorithms which allow a computer to learn from the data and past experiences on their own. The term machine learning was first introduced by Arthur Samuel in 1959. We can define it in a summarized way as: Machine learning enables a machine to automatically learn from data, improve performance from experiences, and predict things without being explicitly programmed With the help of sample historical data, which is known as training data, machine learning algorithms build a mathematical model that helps in making predictions or decisions without being explicitly programmed. Machine learning brings computer science and statistics together for creating predictive models. Machine learning constructs or uses the algorithms that learn from historical data. The more we will provide the information, the higher will be the performance. Reasoning It is the set of processes that enables us to provide basis for judgement, making decisions, and prediction. There are broadly two types Inductive Reasoning Deductive Reasoning It starts with a general statement and It conducts specific observations to examines the possibilities to reach a specific, makes broad general statements. logical conclusion. Even if all of the premises are true in a If something is true of a class of things in statement, inductive reasoning allows general, it is also true for all members of that for the conclusion to be false. class. Example − "Nita is a teacher. Nita is Example − "All women of age above 60 years studious. Therefore, All teachers are are grandmothers. Shalini is 65 years. studious." Therefore, Shalini is a grandmother." Learning− It is the activity of gaining knowledge or skill by studying, practising, being taught, or experiencing something. Learning enhances the awareness of the subjects of

8. the study. The ability of learning is possessed by humans, some animals, and AI-enabled systems. Learning is categorized as − Auditory Learning− It is learning by listening and hearing. For example, students listening to recorded audio lectures. Episodic Learning− To learn by remembering sequences of events that one has witnessed or experienced. This is linear and orderly. Motor Learning− It is learning by precise movement of muscles. For example, picking objects, Writing, etc. Observational Learning− To learn by watching and imitating others. For example, child tries to learn by mimicking her parent. Perceptual Learning− It is learning to recognize stimuli that one has seen before. For example, identifying and classifying objects and situations. Relational Learning− It involves learning to differentiate among various stimuli on the basis of relational properties, rather than absolute properties. For Example, Adding ‘little less’ salt at the time of cooking potatoes that came up salty last time, when cooked with adding say a tablespoon of salt. Spatial Learning− It is learning through visual stimuli such as images, colors, maps, etc. For Example, A person can create roadmap in mind before actually following the road. Stimulus-Response Learning− It is learning to perform a particular behavior when a certain stimulus is present. For example, a dog raises its ear on hearing doorbell. Problem Solving− It is the process in which one perceives and tries to arrive at a desired solution from a present situation by taking some path, which is blocked by known or unknown hurdles. Problem solving also includes decision making, which is the process of selecting the best suitable alternative out of multiple alternatives to reach the desired goal are available. Perception− It is the process of acquiring, interpreting, selecting, and organizing sensory information.

9. Perception presumes sensing. In humans, perception is aided by sensory organs. In the domain of AI, perception mechanism puts the data acquired by the sensors together in a meaningful manner. Knowledge representation Knowledge representation and knowledge engineering allow AI programs to answer questions intelligently and make deductions about real world facts. Planning An intelligent agent that can plan makes a representation of the state of the world, makes predictions about how their actions will change it and makes choices that maximize the utility (or "value") of the available choices.In classical planning problems, the agent can assume that it is the only system acting in the world, allowing the agent to be certain of the consequences of its actions.However, if the agent is not the only actor, then it requires that the agent reason under uncertainty, and continuously re-assess its environment and adapt.Multi-agent planning uses the cooperation and competition of many agents to achieve a given goal. Emergent behavior such as this is used by evolutionary algorithms and swarm intelligence. Motion and manipulation AI is heavily used in robotics.Localization is how a robot knows its location and maps its environment. When given a small, static, and visible environment, this is easy; however, dynamic environments, such as (in endoscopy) the interior of a patient's breathing body, pose a greater challenge. Motion planning is the process of breaking down a movement task into "primitives" such as individual joint movements. Such movement often involves compliant motion, a process where movement requires maintaining physical contact with an object. Robots can learn from experience how to move efficiently despite the presence of friction and gear slippage.