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Slide 1 - Prof Saroj Kaushik 1 Introduction to Artificial Intelligence
Slide 2 - Prof Saroj Kaushik 2 Contents Artificial Intelligence Characterstics of AI Program Categories of System Turing Test Foundations of AI Views of AI Goals Components of AI Programs Sub-areas of AI Applications Latest Perception of AI
Slide 3 - Prof Saroj Kaushik 3 Artificial Intelligence Quick Answer from Academia: Modeling human cognition or mental faculty using computers Study of making computers do things which at the moment people better Making computers do things which require intelligence
Slide 4 - Prof Saroj Kaushik 4 More Formal Definition of AI AI is a branch of computer science which is concerned with the study and creation of computer systems that exhibit some form of intelligence OR those characteristics which we associate with intelligence in human behavior
Slide 5 - Prof Saroj Kaushik 5 AI is a broad area consisting of different fields, from machine vision, expert systems to the creation of machines that can "think". In order to classify machines as "thinking", it is necessary to define intelligence.
Slide 6 - Prof Saroj Kaushik 6 What is Intelligence? Intelligence is a property of mind that encompasses many related mental abilities, such as the capabilities to reason plan solve problems think abstractly comprehend ideas and language and learn
Slide 7 - Prof Saroj Kaushik 7 Characteristics of AI systems learn new concepts and tasks reason and draw useful conclusions about the world around us remember complicated interrelated facts and draw conclusions from them (inference) understand a natural language or perceive and comprehend a visual scene look through cameras and see what's there (vision), to move themselves and objects around in the real world (robotics)
Slide 8 - Prof Saroj Kaushik 8 Contd.. plan sequences of actions to complete a goal offer advice based on rules and situations may not necessarily imitate human senses and thought processes but indeed, in performing some tasks differently, they may actually exceed human abilities capable of performing intelligent tasks effectively and efficiently perform tasks that require high levels of intelligence
Slide 9 - Prof Saroj Kaushik 9 Understanding of AI AI techniques and ideas seem to be harder to understand than most things in computer science AI shows best on complex problems for which general principles don't help much, though there are a few useful general principles
Slide 10 - Prof Saroj Kaushik 10 Artificial intelligence is also difficult to understand by its content. Boundaries of AI are not well defined. Often it means the advanced software engineering, sophisticated software techniques for hard problems that can't be solved in any easy way. AI programs - like people - are usually not perfect, and even make mistakes.
Slide 11 - Prof Saroj Kaushik 11 It often means, nonnumeric ways of solving problems, since people can't handle numbers well. Nonnumeric ways are generally "common sense" ways, not necessarily the best ones. Understanding of AI also requires an understanding of related terms such as intelligence, knowledge, reasoning, thought, cognition, learning, and a number of other computer related terms.
Slide 12 - Prof Saroj Kaushik 12 Categories of AI System Systems that think like humans Systems that act like humans Systems that think rationally Systems that act rationally
Slide 13 - Prof Saroj Kaushik 13 Systems that think like humans 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.
Slide 14 - Prof Saroj Kaushik 14 Systems that act like humans The overall behaviour of the system should be human like. It could be achieved by observation.
Slide 15 - Prof Saroj Kaushik 15 Systems that think rationally 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.
Slide 16 - Prof Saroj Kaushik 16 Systems that act rationally Rational behavior means doing right thing. Even if method is illogical, the observed behavior must be rational.
Slide 17 - Prof Saroj Kaushik 17 The Turing Test Turing proposed operational test for intelligent behavior in 1950. Human Human ? Interrogator AI system
Slide 18 - Prof Saroj Kaushik 18 ELIZA Eliza was first program developed by Joseph Wiezbaum to converse in English in mid 1960's It passed turing test. The following passage shows Eliza’s talking to a teenage girl. Blue text Eliza’s response Men are all alike. IN WHAT WAY They're always bugging us about something or other. CAN YOU THINK OF A SPECIFIC EXAMPLE
Slide 19 - Prof Saroj Kaushik 19 Well, my boyfriend made me come here. YOUR BOYFRIEND MADE YOU COME HERE He says that I am depressed all the time. I AM SORRY TO HEAR THAT YOU ARE DEPRESSED It's true that I am unhappy DO YOU THINK COMING HERE WILL HELP YOU NOT TO BE UNHAPPY I need help, that seems certain. WHAT WOULD IT MEAN IF YOU GOT SOME HELP Perhaps I could learn to get along with my mother. TELL ME MORE ABOUT YOUR FAMILY
Slide 20 - Prof Saroj Kaushik 20 My mother takes care of me. WHO ELSE IN YOUR FAMILY TAKES CARE OF YOU My father. YOUR FATHER You are like my father in some ways. WHAT RESEMBLANCE DO YOU SEE You are not very aggressive but I think you don't want me to notice that. WHAT MAKES YOU THINK THAT I AM NOT VERY AGGRESSIVE
Slide 22 - Prof Saroj Kaushik 22 Foundations of AI Foundation of AI is based on Mathematics Neuroscience Control Theory Linguistics
Slide 23 - Prof Saroj Kaushik 23 Foundations - Mathematics More formal logical methods Boolean logic Fuzzy logic Uncertainty The basis for most modern approaches to handle uncertainty in AI applications can be handled by Probability theory Modal and Temporal logics
Slide 24 - Prof Saroj Kaushik 24 Foundations - Neuroscience How do the brain works? Early studies (1824) relied on injured and abnormal people to understand what parts of brain work More recent studies use accurate sensors to correlate brain activity to human thought By monitoring individual neurons, monkeys can now control a computer mouse using thought alone Moore’s law states that computers will have as many gates as humans have neurons in 2020 How close are we to have a mechanical brain? Parallel computation, remapping, interconnections,….
Slide 25 - Prof Saroj Kaushik 25 Foundations – Control Theory Machines can modify their behavior in response to the environment (sense/action loop) Water-flow regulator, steam engine governor, thermostat The theory of stable feedback systems (1894) Build systems that transition from initial state to goal state with minimum energy In 1950, control theory could only describe linear systems and AI largely rose as a response to this shortcoming
Slide 26 - Prof Saroj Kaushik 26 Foundations - Linguistics Speech demonstrates so much of human intelligence Analysis of human language reveals thought taking place in ways not understood in other settings Children can create sentences they have never heard before Language and thought are believed to be tightly intertwined
Slide 27 - Prof Saroj Kaushik 27 Two Views of AI Goals AI is about duplicating what the (human) brain DOES Cognitive Science AI is about duplicating what the (human) brain SHOULD do Rationality (doing things logically)
Slide 28 - Prof Saroj Kaushik 28 Cool Stuff in AI Game playing agents Machine learning Speech Language Vision Data Mining Web agents …….
Slide 29 - Prof Saroj Kaushik 29 Useful Stuff Medical Diagnosis Fraud Detection Object Identification Space Shuttle Scheduling Information Retrieval..
Slide 30 - Prof Saroj Kaushik 30 AI Techniques Rule-based Fuzzy Logic Neural Networks Genetic Algorithms
Slide 31 - Prof Saroj Kaushik 31 Components of AI Program AI techniques must be independent of the problem domain as far as possible. AI program should have knowledge base navigational capability inferencing
Slide 32 - Prof Saroj Kaushik 32 Knowledge Base AI programs should be learning in nature and update its knowledge accordingly. Knowledge base consists of facts and rules. Characteristics of Knowledge: It is voluminous in nature and requires proper structuring It may be incomplete and imprecise It may keep on changing (dynamic)
Slide 33 - Prof Saroj Kaushik 33 Navigational Capability Navigational capability contains various control strategies Control Strategy determines the rule to be applied some heuristics (thump rule) may be applied
Slide 34 - Prof Saroj Kaushik 34 Inferencing Inferencing requires search through knowledge base and derive new knowledge
Slide 35 - Prof Saroj Kaushik 35 Sub-areas of AI Sub areas of AI are: Knowledge representation Theorem proving Game playing Vommon sense reasoning dealing with uncertainty and decision making Learning models, inference techniques, pattern recognition, search and matching etc. Logic (fuzzy, temporal, modal) in AI Planning and scheduling
Slide 36 - Prof Saroj Kaushik 36 Sub-areas of AI – Contd.. Natural language understanding Computer vision Understanding spoken utterances Intelligent tutoring systems Robotics Machine translation systems Expert problem solving Neural Networks, AI tools etc
Slide 37 - Prof Saroj Kaushik 37 Applications Some of the applications are given below: Business : Financial strategies, give advice Engineering: check design, offer suggestions to create new product Manufacturing: Assembly, inspection & maintenance Mining: used when conditions are dangerous Hospital : monitoring, diagnosing & prescribing Education : In teaching household : Advice on cooking, shopping etc. farming : prune trees & selectively harvest mixed crops.
Slide 38 - Prof Saroj Kaushik 38 Latest Perception of AI Three typical components of AI Systems THE WORLD Perception Action Reasoning
Slide 39 - Prof Saroj Kaushik 39 Recent AI Heavy use of probability theory decision theory statistics logic (fuzzy, modal, temporal)
Slide 40 - Prof Saroj Kaushik 40 Thank You