![12/22/2020
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
• Caution: computational limitations make perfect
rationality unachievable
design best program for given machine resources](https://image.slidesharecdn.com/ailecture-01introduction-240321150224-c441d93c/85/AI-Lecture-01-Introduction-NN-and-Fuzzy-10-320.jpg)
AI Lecture-01 (Introduction) NN and Fuzzy
- 1. Neural Networks and Fuzzy Logic CS-452 (3+0) Department of Software Engineering Fall 2020 12/22/2020 SYED MUHAMMAD RAFI LECTURER, DEPARTMENT OF SOFTWARE ENGINEERING FACULTY OF ENGINEERING SCIENCE AND TECHNOLOGY, ZIAUDDIN UNIVERSITY
- 2. Lecture # 01 Artificial Intelligence Introduction 12/22/2020
- 3. 12/22/2020 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
- 4. 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 12/22/2020 Federal Urdu University of Arts Science and Technology (FUUAST)
- 5. What is AI? Views of AI fall into four categories: Thinking humanly Thinking rationally Acting humanly Acting rationally The textbook advocates "acting rationally" 12/22/2020 Federal Urdu University of Arts Science and Technology (FUUAST)
- 6. 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 12/22/2020
- 7. 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 12/22/2020
- 8. 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? 12/22/2020
- 9. Acting rationally: rational agent ⦿ Rational behavior: doing the right thing ⦿ The right thing: that which is expected to maximize goal achievement, given the available information ⦿ Doesn't necessarily involve thinking – e.g., blinking reflex – but thinking should be in the service of rational action 12/22/2020
- 10. 12/22/2020 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 • Caution: computational limitations make perfect rationality unachievable design best program for given machine resources
- 11. 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
- 12. History of AI • 1943 McCulloch & Pitts: Boolean circuit model of brain • 1950 Turing's "Computing Machinery and Intelligence" • 1956 Dartmouth meeting: "Artificial Intelligence" adopted • 1950sEarly 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
- 13. 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?
- 14. 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!
- 15. 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
- 16. 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 Magnus Carlsen (current World Champion 2016) Deep Blue Deep Thought Points Ratings Garry Kasparov (Champion-1980s to 1990s)
- 17. 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
- 18. 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
- 19. 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)
- 20. 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
- 21. 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
- 22. • 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”?
- 23. In the computer vision community research compete to improve recognition performance on standard datasets
- 24. 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.
- 25. 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
- 26. 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
- 27. 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
- 28. 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
- 29. 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
- 30. 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!