Introduction to Artificial Intelligence, Lecture notes of Artificial Intelligence

Download Introduction to Artificial Intelligence and more Lecture notes Artificial Intelligence in PDF only on Docsity! Introduction to Computational Intelligence -Presented By: Shawni Dutta, Department of Computer Science, The BES College This Photo by Unknown author is licensed under CC BY. What is Artificial Intelligence? • Artificial Intelligence (AI) is a branch of Science which deals with helping machines finding solutions to complex problems in a more human-like fashion. • This generally involves borrowing characteristics from human intelligence, and applying them as algorithms in a computer friendly way. • This definition is restrictive as it excludes a major area ; problems that cannot be solved well either by computers or by people at the moment. HISTORY OF AI • The origin of artificial intelligence lies in the earliest days of machine computations. • During the 1940s and 1950s, AI begins to grow with the emergence of the modern computer. • Prof. Peter Jackson (University of Edinburgh) classified the history of AI into three periods as: 1. Classical 2. Romantic 3. Modern Classical Period • It was started from 1950. In 1956, the concept of Artificial Intelligence came into existence. • During this period, the main research work carried out includes • game playing, • theorem proving and • concept of state space approach for solving a problem. Romantic Period • It was started from the mid 1960 and continues until the mid 1970. • During this period people were interested in making machine understand, that is usually mean the understanding of natural language. AI in Healthcare • In the last, five to ten years, AI becoming more advantageous for the healthcare industry and going to have a significant impact on this industry. • Healthcare Industries are applying AI to make a better and faster diagnosis than humans. AI can help doctors with diagnoses and can inform when patients are worsening so that medical help can reach to the patient before hospitalization. AI in Gaming • AI can be used for gaming purpose. • The AI machines can play strategic games like chess, where the machine needs to think of a large number of possible places. AI in Finance • AI and finance industries are the best matches for each other. • The finance industry is implementing automation, chatbot, adaptive intelligence, algorithm trading, and machine learning into financial processes. This Photo by Unknown author is licensed under CC BY-NC-ND. AI in Travel & Transport •AI is becoming highly demanding for travel industries. AI is capable of doing various travel related works such as from making travel arrangement to suggesting the hotels, flights, and best routes to the customers. •Travel industries are using AI-powered chatbots which can make human-like interaction with customers for better and fast response. AI in Robotics •Artificial Intelligence has a remarkable role in Robotics. Usually, general robots are programmed such that they can perform some repetitive task, but with the help of AI, we can create intelligent robots which can perform tasks with their own experiences without pre-programmed. •Humanoid Robots are best examples for AI in robotics, recently the intelligent Humanoid robot named as Erica and Sophia has been developed which can talk and behave like humans. AI in education •AI can automate grading so that the tutor can have more time to teach. AI chatbot can communicate with students as a teaching assistant. •AI in the future can be work as a personal virtual tutor for students, which will be accessible easily at any time and any place. WEAK AND STRONG AI • There are two conceptual thoughts about AI namely the • Weak AI and • Strong AI. Strong AI The strong AI is very much promising about the fact that the machine is almost capable of solve a complex problem like an intelligent man. Strong AI is the supposition that some forms of artificial intelligence can truly reason and solve problems. They claim that a computer is much more efficient to solve the problems than some of the human experts Weak AI • In contrast, the weak AI is not so enthusiastic about the outcomes of AI and it simply says that some thinking like features can be added to computers to make them more useful tools. • It says that computers to make them more useful tools. It says that computers cannot be made intelligent equal to human being, unless constructed significantly differently. • They claim that computers may be similar to human experts but not equal in any cases. • Generally weak AI refers to the use of software to study or accomplish specific problem solving that do not encompass the full range of human cognitive abilities. • An example of weak AI would be a chess program. Weak AI programs cannot be called “intelligent” because they cannot really think. Artificial Super Intelligence •Oxford philosopher Nick Bostrom defines superintelligence as “any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest” Artificial Super Intelligence (ASI) will surpass human intelligence in all aspects — from creativity, to general wisdom, to problem-solving. Machines will be capable of exhibiting intelligence that we haven’t seen in the brightest amongst us. Artificial super intelligence (ASI), is the hypothetical AI that doesn’t just mimic or understand human intelligence and behaviour. Task Domain of AI Mundane Tasks • Perception • Vision • Speech •Natural language • Understanding • Generation • Translation •Commonsense reasoning •Robot Control Formal Tasks •Games • Chess • Backgammon • Checkers-Go • Mathematics • Geometry • Logic • Integral Calculus • Proving properties of programs TASK DOMAIN OF AI: Common Sense Reasoning • AI focused on this sort of problem solving that we do every day when we decide how to get to work in the morning often called us common sense reasoning. • It includes reasoning about physical objects and their relationships to each other (for example an object can be in only one place at a time) as well as reasoning about actions and their consequences (such as if you let go of something it will fall onto the floor and may be broken). • To investigate this sort of reasoning Newell Shaw and Seaman built the general problem solver GPS which they applied to several common sense tasks as well as to the problems of performing symbolic manipulations of logical expressions. • Again no attempt was made to create a program with a large amount of knowledge about a particular problem domain. Only simple tasks were selected. TASK DOMAIN OF AI: Natural Language Understandin g • The ability to use language to communicate a wide variety of ideas is perhaps the most important thing that separates humans from other animals the problem of understanding spoken language it's a perceptual problem and it is hard to solve for reasons well. • But suppose we simplify the problem by restricting it to written language this problem, usually referred to as natural language understanding, is still extremely difficult in order to understand about the topic it is necessary to know not only a lot about the language itself (its vocabulary and grammar) but also a good deal about the topic so that unstated assumptions can be recognized. TASK DOMAIN OF AI: Natural Language Understandin g • In addition to two this mundane task, many people can also perform one or maybe more specialized tasks in which carefully acquired expertise is necessary. Examples of such tasks include engineering design, scientific discovery, medical diagnosis, and financial planning. Programs that can solve in these domains also fall under the category of artificial intelligence. AI TECHNIQUE • Intelligence requires knowledge but knowledge possesses less desirable properties such as • It is voluminous • it is difficult to characterize accurately • it is constantly changing • it differs from data by being organized in a way that corresponds to its application AI TECHNIQUE • An AI technique is a method that exploits knowledge that is represented so that • The knowledge captures generalizations; situations that share properties, are grouped together, rather than being allowed separate representation. • It can be understood by people who must provide it; although for many programs the bulk of the data may come automatically, such as from readings. In many AI domains people must supply the knowledge to programs in a form the people understand and in a form that is acceptable to the program. • It can be easily modified to correct errors and reflect changes in real conditions. • It can be widely used even if it is incomplete or inaccurate. • It can be used to help overcome its own sheer bulk by helping to narrow the range of possibilities that must be usually considered. Perception and Pattern Recognition • In AI systems the task of perceiving sound or image is divided into two main phases. •The first phase concerns of receiving a corresponding signal with the help of a sensory device (e.g., a camera or a microphone), its preprocessing, and its coding in a certain format. •The methods used in this phase belong to conventional areas of computer science (also automatics and electronics) such as signal processing theory and image processing theory.