Lecture 1. Introduction to AI and it's applications.ppt
- 1. 1 Deepak Gaur Assistant Professor, Department of Computer Science & Engineering ASET, AUUP, Noida Module 1 : Problem Solving & Scope of AI
- 2. Topics to be covered 2 • Introduction to Artificial Intelligence. • Applications- Games, Theorem proving, Natural language processing, Vision and speech processing, Robotics, Expert systems. • AI techniques- search knowledge, • State space search, Production systems • Search space control: depth-first, breadth-first search. Heuristic search - Hill climbing, best-first search, branch and bound. • Problem Reduction, • Constraint Satisfaction, • Means-End Analysis
- 3. Artificial Intelligence 3 • “Artificial Intelligence is the science and engineering of making intelligent machines”. • Artificial Intelligence is the study of how to make computers do things which, at the moment, people do better. • Artificial Intelligence is the branch of computer science that is concerned with the automation of intelligent behavior. • Artificial Intelligence is the study and design of intelligent agents, where an intelligent agent is a system that perceives its environment and takes actions that maximize its chance of success.
- 4. Artificial Intelligence 4 • “Artificial intelligence is concerned with the design of intelligence in an artificial device”. • Artificial Intelligence term was coined by McCarthy in 1956. • There are two idea in definition: Intelligence and artificial. • The term artificial is easy to understand. But it’s very difficult to define intelligence.
- 5. Intelligence 5 • Intelligence is what we use when we don’t know what to do. • Intelligence relates to tasks involving higher mental process. • Examples: 1) Creativity, 2) Solving problems 3) Pattern Recognition 4) Classification 5) Learning 6) Induction and deduction. 7) Building analogies, Optimization 8) Language processing and knowledge extraction etc.
- 6. Approaches to Artificial Intelligence 6 • Hard or Strong AI. • Soft or Weak AI. • Applied AI. • Cognitive AI.
- 7. Hard or Strong AI 7 • Hard or Strong AI refers to a machine that approaches or supersedes human intelligence -- if it can do typically human tasks. -- if it can apply a wide range of background knowledge and -- if it has some degree of self-consciousness. • Strong AI aims to build machines whose overall intellectual ability is indistinguishable from that of human being.
- 8. Soft or Weak AI 8 • Weak AI refers to the use of software to study or accomplish specific problem solving or reasoning tasks that do not encompass the full range of human cognitive abilities. Example: a chess program such as Deep Blue • Weak AI does not achieve self-awareness; • Weak AI demonstrates wide range of human level cognitive abilities; • Weak AI is merely an intelligent, a specific problem-solver .
- 9. Applied AI 9 • Aims to produce commercially viable "smart" systems such as, for example, a security system that is able to recognize the faces of people who are permitted to enter a particular building. • Applied artificial intelligence is more successful.
- 10. Cognitive AI 10 • Computer that are used to test theories about how the human mind works. • For example- theories about how we recognize faces and other objects or about how we solve abstract problems. • Cognitive science aims to develop, explore and evaluate theories of how human mind works through the use of computational models. • The important is not what is done, but how it is done. Means, intelligent behavior is not enough, the program must operate in an intelligent manner. • Application of cognitive AI: --Smart IOT, -- AI enabled Cybersecurity, -- Content AI, -- Cognitive AI in Healthcare.
- 11. Goals of Artificial Intelligence 11 1 • The definition of AI gives four possible goals to pursue: 1. Systems that think like humans 2. Systems that think rationally 3. Systems that act like humans 4. Systems that act rationally • Traditionally, all four goals have been followed and the approaches were: • Most of AI works falls into category 2 and 4. Human-IiiIke Rati onally Tlhinlk (I) Cognitive science Approach (2) ILaws of thought Approach A1ct (3) Turing Test Approach (4) Rational agent Approach
- 12. System that thinks like human 12 • Most of the time it is a black box where we are not clear about our thought process. • One has to know functioning of brain and its mechanism for possessing information. • It is an area of cognitive science. –The stimuli are converted into mental representation. –Cognitive processes manipulate representation to build new representations that are used to generate actions. • Neural network is a computing model for processing information similar to brain.
- 13. System that act like human 13 • The overall behaviour of the system should be human like. • It could be achieved by observation.
- 14. System that thinks rationally 14 • Such systems rely on logic rather than human to measure correctness. • For thinking rationally or logically, logic formulas and theories are used for synthesizing outcomes. • For example, • –given John is a human and all humans are mortal then one can conclude logically that John is mortal. • Not all intelligent behavior are mediated by logical deliberation.
- 15. System that act rationally 15 • Rational behavior means that doing right things. • Goal is to develop systems that are rational and sufficient.
- 16. General AI goals 16 • Replicate human intelligence. • Solve knowledge intensive tasks. • Make an intelligent connection between perception and action. • Enhance human-human, human-computer and computer to computer interaction/communication.
- 17. AI goals 17 • Engineering based AI Goal: -Develop concepts, theory and practice of building intelligent machines. -Emphasis is on system building. • Science based AI Goal: -Develop concepts, mechanisms and vocabulary to understand biological intelligent behavior. -Emphasis is on understanding intelligent behavior.
- 18. Major component of AI 18
- 19. Applications area of AI 19 • Perception –Machine vision –Speech understanding –Touch ( tactile or haptic) sensation • Robotics • Natural Language Processing –Natural Language Understanding –Speech Understanding –Language Generation –Machine Translation • Planning • Expert Systems • Machine Learning • Theorem Proving • Symbolic Mathematics • Game Playing
- 20. Thank You 20