Artificial intelligence(introduction)
Download as PPT, PDF0 likes181 views
- The document discusses artificial intelligence, including its history, key areas such as knowledge representation and learning, and applications in areas like consumer marketing, identification technologies, predicting stock markets, and machine translation. - While progress has been made in areas like recognition and learning, challenges remain in full natural language understanding, human-level planning and decision making. AI is being applied across many industries but remains an active area of research.
![04/08/19
Federal Urdu University of Arts Science and
Technology (FUUAST)
Acting rationally: rational agent
• Rational behavior: Doing that was is expected to maximize
one’s “utility function” in this world.
• An agent is an entity that perceives and acts.
• A rational agent acts rationally.
• Abstractly, an agent is a function from percept histories to
actions:
[f: P* A]
• For any given class of environments and tasks, we seek the
agent (or class of agents) with the best performance
• Caveat: computational limitations make perfect rationality
unachievable
design best program for given machine resources](https://image.slidesharecdn.com/artificialintelligenceintroduction-190408185145/85/Artificial-intelligence-introduction-11-320.jpg)
Artificial intelligence(introduction)
- 1. Artificial Intelligence - Introduction - Sir Hafiz Syed Muhammad Rafi Federal Urdu University of Arts Science and Technology (FUUAST) 04/08/19
- 2. Federal Urdu University of Arts Science and Technology (FUUAST) Textbooks: • Artificial Intelligence, A Modern Approach, 2nd Edition, Stuart Russell and Peter Norving. 04/08/19 • “Fundamentals of Neural Networks”, Architecture, Algorithms and Applications. By Laurene Fausett • “Neural Networks. A Comprehensive Foundation”, 2nd Edition, Simon Haykin, Prentice Hall, 1999. • “Digital Image Processing”, 2nd Edition, Rafael C. Gonzalez, Richard E. Woods.
- 3. 04/08/19 Federal Urdu University of Arts Science and Technology (FUUAST) Semester Policy: Sessional: 60% * Practical 50% * Attendance 10% Final: 40% * MCQs based
- 4. 04/08/19 Federal Urdu University of Arts Science and Technology (FUUAST) Meet HAL • 2001: A Space Odyssey – classic science fiction movie from 1969 • HAL – part of the story centers around an intelligent computer called HAL – HAL is the “brains” of an intelligent spaceship – in the movie, HAL can • speak easily with the crew • see and understand the emotions of the crew • navigate the ship automatically • diagnose on-board problems • make life-and-death decisions • display emotions • In 1969 this was science fiction: is it still science fiction? http://www.youtube.com/watch?v=LE1F7d6f1Qk
- 5. Main Areas of AI • Knowledge representation (including formal logic) • Search, especially heuristic search (puzzles, games) • Planning • Reasoning under uncertainty, including probabilistic reasoning • Learning • Agent architectures • Robotics and perception • Natural language processing Search Knowledge rep. Planning Reasoning Learning Agent Robotics Perception Natural language ... Expert Systems Constraint satisfaction 04/08/19 Federal Urdu University of Arts Science and Technology (FUUAST)
- 6. What is AI? Views of AI fall into four categories: Thinking humanly Thinking rationally Acting humanly Acting rationally The textbook advocates "acting rationally" • • 04/08/19 Federal Urdu University of Arts Science and Technology (FUUAST)
- 7. Acting humanly: Turing Test • Turing (1950) "Computing machinery and intelligence": • "Can machines think?" "Can machines behave intelligently?" • Operational test for intelligent behavior: the Imitation Game • Predicted that by 2000, a machine might have a 30% chance of fooling a lay person for 5 minutes • Anticipated all major arguments against AI in following 50 years • Suggested major components of AI: knowledge, reasoning, language understanding and learning • 04/08/19
- 8. Thinking humanly: cognitive modeling 1960s "cognitive revolution": information-processing psychology Requires scientific theories of internal activities of the brain - How to validate? Requires 1- Predicting and testing behavior of human subjects (top-down) or 2- Direct identification from neurological data (bottom-up). Both approaches (roughly, Cognitive Science and Cognitive Neuroscience) are now distinct from AI 04/08/19
- 9. Thinking rationally: "laws of thought" • Aristotle: what are correct arguments/thought processes? • Several Greek schools developed various forms of logic: notation and rules of derivation for thoughts; may or may not have proceeded to the idea of mechanization • Direct line through mathematics and philosophy to modern AI • Problems: 1. Not all intelligent behavior is mediated by logical deliberation 2. What is the purpose of thinking? What thoughts should I have? 04/08/19
- 10. Acting rationally: rational agent 04/08/19
- 11. 04/08/19 Federal Urdu University of Arts Science and Technology (FUUAST) Acting rationally: rational agent • Rational behavior: Doing that was is expected to maximize one’s “utility function” in this world. • An agent is an entity that perceives and acts. • A rational agent acts rationally. • Abstractly, an agent is a function from percept histories to actions: [f: P* A] • For any given class of environments and tasks, we seek the agent (or class of agents) with the best performance • Caveat: computational limitations make perfect rationality unachievable design best program for given machine resources
- 12. Academic Disciplines important to AI. • Philosophy Logic, methods of reasoning, mind as physical system, foundations of learning, language, rationality. • Mathematics Formal representation and proof, algorithms, computation, (un)decidability, (in)tractability, probability. • Economics utility, decision theory, rational economic agents • Neuroscience neurons as information processing units. • Psychology/ how do people behave, perceive, process • Cognitive Science information, represent knowledge. • Computer building fast computers engineering • Control theory design systems that maximize an objective function over time • Linguistics knowledge representation, grammar
- 13. History of AI • 1943 McCulloch & Pitts: Boolean circuit model of brain • 1950 Turing's "Computing Machinery and Intelligence" • 1956 Dartmouth meeting: "Artificial Intelligence" adopted • 1950s Early AI programs, including Samuel's checkers program, Newell & Simon's Logic Theorist, Gelernter's Geometry Engine • 1965 Robinson's complete algorithm for logical reasoning • 1966—73 AI discovers computational complexity Neural network research almost disappears • 1969—79 Early development of knowledge-based systems • 1980-- AI becomes an industry • 1986-- Neural networks return to popularity • 1987-- AI becomes a science • 1995-- The emergence of intelligent agents
- 14. Consider what might be involved in building a “intelligent” computer…. • What are the “components” that might be useful? – Fast hardware? – Foolproof software? – Chess-playing at grandmaster level? – Speech interaction? • speech synthesis • speech recognition • speech understanding – Image recognition and understanding ? – Learning? – Planning and decision-making?
- 15. Can we build hardware as complex as the brain? • How complicated is our brain? – a neuron, or nerve cell, is the basic information processing unit – estimated to be on the order of 10 11 neurons in a human brain – many more synapses (10 14 ) connecting these neurons – cycle time: 10 -3 seconds (1 millisecond) • How complex can we make computers? – 106 or more transistors per CPU – supercomputer: hundreds of CPUs, 10 9 bits of RAM – cycle times: order of 10 -8 seconds • Conclusion – YES: in the near future we can have computers with as many basic processing elements as our brain, but with • far fewer interconnections (wires or synapses) than the brain • much faster updates than the brain – but building hardware is very different from making a computer behave like a brain!
- 16. Must an Intelligent System be Foolproof? • A “foolproof” system is one that never makes an error: – Types of possible computer errors • hardware errors, e.g., memory errors • software errors, e.g., coding bugs • “human-like” errors – Clearly, hardware and software errors are possible in practice – what about “human-like” errors? • An intelligent system can make errors and still be intelligent – humans are not right all of the time – we learn and adapt from making mistakes • e.g., consider learning to surf or ski – we improve by taking risks and falling – an intelligent system can learn in the same way • Conclusion: – NO: intelligent systems will not (and need not) be foolproof
- 17. Can Computers play Humans at Chess? • Chess Playing is a classic AI problem – well-defined problem – very complex: difficult for humans to play well • Conclusion: YES: today’s computers can beat even the best human 1200 1400 1600 1800 2000 2200 2400 2600 2800 3000 1966 1971 1976 1981 1986 1991 1997 Ratings Garry Kasparov (current World Champion) Deep Blue Deep Thought PointsRatings
- 18. Can Computers Talk? • This is known as “speech synthesis” – translate text to phonetic form • e.g., “fictitious” -> fik-tish-es – use pronunciation rules to map phonemes to actual sound • e.g., “tish” -> sequence of basic audio sounds • Difficulties – sounds made by this “lookup” approach sound unnatural – sounds are not independent • e.g., “act” and “action” • modern systems (e.g., at AT&T) can handle this pretty well – a harder problem is emphasis, emotion, etc • humans understand what they are saying • machines don’t: so they sound unnatural • Conclusion: NO, for complete sentences, but YES for individual words
- 19. Can Computers Recognize Speech? • Speech Recognition: – mapping sounds from a microphone into a list of words. – Hard problem: noise, more than one person talking, occlusion, speech variability,.. – Even if we recognize each word, we may not understand its meaning. • Recognizing single words from a small vocabulary • systems can do this with high accuracy (order of 99%) • e.g., directory inquiries – limited vocabulary (area codes, city names) – computer tries to recognize you first, if unsuccessful hands you over to a human operator – saves millions of dollars a year for the phone companies
- 20. Recognizing human speech (ctd.) • Recognizing normal speech is much more difficult – speech is continuous: where are the boundaries between words? • e.g., “John’s car has a flat tire” – large vocabularies • can be many tens of thousands of possible words • we can use context to help figure out what someone said – try telling a waiter in a restaurant: “I would like some dream and sugar in my coffee” – background noise, other speakers, accents, colds, etc – on normal speech, modern systems are only about 60% accurate • Conclusion: NO, normal speech is too complex to accurately recognize, but YES for restricted problems – (e.g., recent software for PC use by IBM, Dragon systems, etc)
- 21. Can Computers Understand speech? • Understanding is different to recognition: – “Time flies like an arrow” • assume the computer can recognize all the words • but how could it understand it? – 1. time passes quickly like an arrow? – 2. command: time the flies the way an arrow times the flies – 3. command: only time those flies which are like an arrow – 4. “time-flies” are fond of arrows • only 1. makes any sense, but how could a computer figure this out? – clearly humans use a lot of implicit commonsense knowledge in communication • Conclusion: NO, much of what we say is beyond the capabilities of a computer to understand at present
- 22. Can Computers Learn and Adapt ? • Learning and Adaptation – consider a computer learning to drive on the freeway – we could code lots of rules about what to do – and/or we could have it learn from experience – machine learning allows computers to learn to do things without explicit programming • Conclusion: YES, computers can learn and adapt, when presented with information in the appropriate way Darpa’s Grand Challenge. Stanford’s “Stanley” drove 150 without supervision in the Majove dessert
- 23. • Recognition v. Understanding (like Speech) – Recognition and Understanding of Objects in a scene • look around this room • you can effortlessly recognize objects • human brain can map 2d visual image to 3d “map” • Why is visual recognition a hard problem? • Conclusion: mostly NO: computers can only “see” certain types of objects under limited circumstances: but YES for certain constrained problems (e.g., face recognition) Can Computers “see”?
- 24. In the computer vision community research compete to improve recognition performance on standard datasets
- 25. Can Computers plan and make decisions?• Intelligence – involves solving problems and making decisions and plans – e.g., you want to visit your cousin in Boston • you need to decide on dates, flights • you need to get to the airport, etc • involves a sequence of decisions, plans, and actions • What makes planning hard? – the world is not predictable: • your flight is canceled or there’s a backup on the 405 – there is a potentially huge number of details • do you consider all flights? all dates? – no: commonsense constrains your solutions – AI systems are only successful in constrained planning problems • Conclusion: NO, real-world planning and decision-making is still beyond the capabilities of modern computers – exception: very well-defined, constrained problems: mission planning for satellites.
- 26. Intelligent Systems in Your Everyday Life• Post Office – automatic address recognition and sorting of mail • Banks – automatic check readers, signature verification systems – automated loan application classification • Telephone Companies – automatic voice recognition for directory inquiries • Credit Card Companies – automated fraud detection • Computer Companies – automated diagnosis for help-desk applications • Netflix: – movie recommendation • Google: – Search Technology
- 27. AI Applications: Consumer Marketing • Have you ever used any kind of credit/ATM/store card while shopping? – if so, you have very likely been “input” to an AI algorithm • All of this information is recorded digitally • Companies like Nielsen gather this information weekly and search for patterns – general changes in consumer behavior – tracking responses to new products – identifying customer segments: targeted marketing, e.g., they find out that consumers with sports cars who buy textbooks respond well to offers of new credit cards. – Currently a very hot area in marketing • How do they do this? – Algorithms (“data mining”) search data for patterns – based on mathematical theories of learning – completely impractical to do manually
- 28. AI Applications: Identification Technologies• ID cards – e.g., ATM cards – can be a nuisance and security risk: • cards can be lost, stolen, passwords forgotten, etc • Biometric Identification – walk up to a locked door • camera • fingerprint device • microphone • iris scan – computer uses your biometric signature for identification • face, eyes, fingerprints, voice pattern, iris pattern
- 29. AI Applications: Predicting the Stock Market • The Prediction Problem – given the past, predict the future – very difficult problem! – we can use learning algorithms to learn a predictive model from historical data • prob(increase at day t+1 | values at day t, t-1,t-2....,t-k) – such models are routinely used by banks and financial traders to manage portfolios worth millions of dollars ? ? time in days Value of the Stock
- 30. AI-Applications: Machine Translation • Language problems in international business – e.g., at a meeting of Japanese, Korean, Vietnamese and Swedish investors, no common language – or: you are shipping your software manuals to 127 countries – solution; hire translators to translate – would be much cheaper if a machine could do this! • How hard is automated translation – very difficult! – e.g., English to Russian – “The spirit is willing but the flesh is weak” (English) – “the vodka is good but the meat is rotten” (Russian) – not only must the words be translated, but their meaning also! • Nonetheless.... – commercial systems can do alot of the work very well (e.g.,restricted vocabularies in software documentation) – algorithms which combine dictionaries, grammar models, etc. – see for example babelfish.altavista.com
- 31. Summary • Artificial Intelligence involves the study of: – automated recognition and understanding of speech, images, etc – learning and adaptation – reasoning, planning, and decision-making • AI has made substantial progress in – recognition and learning – some planning and reasoning problems • AI Applications – improvements in hardware and algorithms => AI applications in industry, finance, medicine, and science. • AI Research – many problems still unsolved: AI is a fun research area!