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Slide 1 - Introduction to Artificial Intelligence CS 271, Fall 2007 Instructor: Professor Padhraic Smyth
Slide 2 - Goals of this Course This class is a broad introduction to artificial intelligence (AI) AI is a very broad field with many subareas We will cover many of the primary concepts/ideas But in 10 weeks we can’t cover everything Other classes in AI you may want to consider: Belief Networks, 276 Winter: Probabilistic Learning, 274A Spring: Machine Learning, 273A If you have taken another class (e.g., undergrad) in AI, you may want to consider waiving this class and taking a more specialized AI class (feel free to ask me about this).
Slide 3 - Class Overview Class Web page http://www.ics.uci.edu/~smyth/courses/cs271/ Review Organizational details Textbook Schedule and syllabus Homeworks, exams, grading Academic honesty
Slide 4 - Academic Honesty It is each student’s responsibility to be familiar with UCI’s current policies on academic honesty Violations can result in getting an F in the class (or worse) Please take the time to read the UCI academic honesty policy See also the class Web page Academic dishonesty is defined as: Cheating Dishonest conduct Plagiarism Collusion You can discuss problems verbally – otherwise, the work you hand in should be entirely your own
Slide 5 - Assigned Reading Chapter 1 in the text Papers on Web page http://www.ics.uci.edu/~smyth/courses/cs271/schedule.html Paper by Sebastian Thrun et al on robot driving Slides or video by Peter Stone on autonomous agents
Slide 6 - Why taking 271 could change your life….. As we begin the new millenium science and technology are changing rapidly “old” sciences such as physics are relatively well-understood computers are ubiquitous Grand Challenges in Science and Technology understanding the brain reasoning, cognition, creativity creating intelligent machines is this possible? what are the technical and philosophical challenges? arguably AI poses the most interesting challenges and questions in computer science today
Slide 7 - Today’s Lecture What is intelligence? What is artificial intelligence? A very brief history of AI Modern successes: Stanley the driving robot An AI scorecard How much progress has been made in different aspects of AI AI in practice Successful applications The rational agent view of AI
Slide 8 - What is Intelligence? Intelligence: “the capacity to learn and solve problems” (Websters dictionary) in particular, the ability to solve novel problems the ability to act rationally the ability to act like humans Artificial Intelligence build and understand intelligent entities or agents 2 main approaches: “engineering” versus “cognitive modeling”
Slide 9 - What is Artificial Intelligence? (John McCarthy, Stanford University) What is artificial intelligence? It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable. Yes, but what is intelligence? Intelligence is the computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals and some machines. Isn't there a solid definition of intelligence that doesn't depend on relating it to human intelligence? Not yet. The problem is that we cannot yet characterize in general what kinds of computational procedures we want to call intelligent. We understand some of the mechanisms of intelligence and not others. More in: http://www-formal.stanford.edu/jmc/whatisai/node1.html
Slide 10 - What’s involved in Intelligence? Ability to interact with the real world to perceive, understand, and act e.g., speech recognition and understanding and synthesis e.g., image understanding e.g., ability to take actions, have an effect Reasoning and Planning modeling the external world, given input solving new problems, planning, and making decisions ability to deal with unexpected problems, uncertainties Learning and Adaptation we are continuously learning and adapting our internal models are always being “updated” e.g., a baby learning to categorize and recognize animals
Slide 11 - Academic Disciplines relevant 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/Statistics modeling uncertainty, learning from data Economics utility, decision theory, rational economic agents Neuroscience neurons as information processing units. Psychology/ how do people behave, perceive, process cognitive Cognitive Science information, represent knowledge. Computer building fast computers engineering Control theory design systems that maximize an objective function over time Linguistics knowledge representation, grammars
Slide 12 - History of AI 1943: early beginnings McCulloch & Pitts: Boolean circuit model of brain 1950: Turing Turing's "Computing Machinery and Intelligence“ 1956: birth of AI Dartmouth meeting: "Artificial Intelligence“ name adopted 1950s: initial promise Early AI programs, including Samuel's checkers program Newell & Simon's Logic Theorist 1955-65: “great enthusiasm” Newell and Simon: GPS, general problem solver Gelertner: Geometry Theorem Prover McCarthy: invention of LISP
Slide 13 - History of AI 1966—73: Reality dawns Realization that many AI problems are intractable Limitations of existing neural network methods identified Neural network research almost disappears 1969—85: Adding domain knowledge Development of knowledge-based systems Success of rule-based expert systems, E.g., DENDRAL, MYCIN But were brittle and did not scale well in practice 1986-- Rise of machine learning Neural networks return to popularity Major advances in machine learning algorithms and applications 1990-- Role of uncertainty Bayesian networks as a knowledge representation framework 1995-- AI as Science Integration of learning, reasoning, knowledge representation AI methods used in vision, language, data mining, etc
Slide 14 - Success Stories Deep Blue defeated the reigning world chess champion Garry Kasparov in 1997 AI program proved a mathematical conjecture (Robbins conjecture) unsolved for decades During the 1991 Gulf War, US forces deployed an AI logistics planning and scheduling program that involved up to 50,000 vehicles, cargo, and people NASA's on-board autonomous planning program controlled the scheduling of operations for a spacecraft Proverb solves crossword puzzles better than most humans Robot driving: DARPA grand challenge 2003-2007 2006: face recognition software available in consumer cameras
Slide 15 - Example: DARPA Grand Challenge Grand Challenge Cash prizes ($1 to $2 million) offered to first robots to complete a long course completely unassisted Stimulates research in vision, robotics, planning, machine learning, reasoning, etc 2004 Grand Challenge: 150 mile route in Nevada desert Furthest any robot went was about 7 miles … but hardest terrain was at the beginning of the course 2005 Grand Challenge: 132 mile race Narrow tunnels, winding mountain passes, etc Stanford 1st, CMU 2nd, both finished in about 6 hours 2007 Urban Grand Challenge This November in Victorville, California
Slide 16 - Stanley Robot Stanford Racing Team www.stanfordracing.org Next few slides courtesy of Prof. Sebastian Thrun, Stanford University
Slide 17 - Touareg interface Laser mapper Wireless E-Stop Top level control Laser 2 interface Laser 3 interface Laser 4 interface Laser 1 interface Laser 5 interface Camera interface Radar interface Radar mapper Vision mapper UKF Pose estimation Wheel velocity GPS position GPS compass IMU interface Surface assessment Health monitor Road finder Touch screen UI Throttle/brake control Steering control Path planner laser map vehicle state (pose, velocity) velocity limit map vision map vehicle state obstacle list trajectory RDDF database driving mode pause/disable command Power server interface clocks emergency stop power on/off Linux processes start/stop heart beats corridor SENSOR INTERFACE PERCEPTION PLANNING&CONTROL USER INTERFACE VEHICLE INTERFACE RDDF corridor (smoothed and original) Process controller GLOBAL SERVICES health status data Data logger File system Communication requests vehicle state (pose, velocity) Brake/steering Communication channels Inter-process communication (IPC) server Time server road center
Slide 18 - Planning = Rolling out Trajectories
Slide 19 - 2004: Barstow, CA, to Primm, NV 150 mile off-road robot race across the Mojave desert Natural and manmade hazards No driver, no remote control No dynamic passing Fastest vehicle wins the race (and 2 million dollar prize)
Slide 20 - 2005 Semi-Finalists: 43 Teams
Slide 21 - The Grand Challenge Race
Slide 22 - HAL: from the movie 2001 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?
Slide 23 - Hal and AI HAL’s Legacy: 2001’s Computer as Dream and Reality MIT Press, 1997, David Stork (ed.) discusses HAL as an intelligent computer are the predictions for HAL realizable with AI today? Materials online at http://mitpress.mit.edu/e-books/Hal/contents.html The website contains full text and abstracts of chapters from the book links to related material and AI information sound and images from the film
Slide 24 - Consider what might be involved in building a computer like Hal…. What are the components that might be useful? Fast hardware? Chess-playing at grandmaster level? Speech interaction? speech synthesis speech recognition speech understanding Image recognition and understanding ? Learning? Planning and decision-making?
Slide 25 - 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 12 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? 108 or more transistors per CPU supercomputer: hundreds of CPUs, 1012 bits of RAM cycle times: order of 10 - 9 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!
Slide 26 - Can Computers beat 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 Human World Champion Deep Blue Deep Thought Points Ratings
Slide 27 - 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 YES, for individual words
Slide 28 - Can Computers Recognize Speech? Speech Recognition: mapping sounds from a microphone into a list of words classic problem in AI, very difficult “Lets talk about how to wreck a nice beach” (I really said “________________________”) 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
Slide 29 - 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 thousands of possible words we can use context to help figure out what someone said e.g., hypothesize and test 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 60-70% accurate Conclusion: NO, normal speech is too complex to accurately recognize YES, for restricted problems (small vocabulary, single speaker)
Slide 30 - Can Computers Understand speech? Understanding is different to recognition: “Time flies like an arrow” assume the computer can recognize all the words how many different interpretations are there?
Slide 31 - Can Computers Understand speech? Understanding is different to recognition: “Time flies like an arrow” assume the computer can recognize all the words how many different interpretations are there? 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
Slide 32 - Can Computers Understand speech? Understanding is different to recognition: “Time flies like an arrow” assume the computer can recognize all the words how many different interpretations are there? 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
Slide 33 - Can Computers Learn and Adapt ? Learning and Adaptation consider a computer learning to drive on the freeway we could teach it lots of rules about what to do or we could let it drive and steer it back on course when it heads for the embankment systems like this are under development (e.g., Daimler Benz) e.g., RALPH at CMU in mid 90’s it drove 98% of the way from Pittsburgh to San Diego without any human assistance machine learning allows computers to learn to do things without explicit programming many successful applications: requires some “set-up”: does not mean your PC can learn to forecast the stock market or become a brain surgeon Conclusion: YES, computers can learn and adapt, when presented with information in the appropriate way
Slide 34 - 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 YES for certain constrained problems (e.g., face recognition) Can Computers “see”?
Slide 35 - Can computers plan and make optimal decisions? Intelligence involves solving problems and making decisions and plans e.g., you want to take a holiday in Brazil 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 are 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
Slide 36 - Summary of State of AI Systems in Practice Speech synthesis, recognition and understanding very useful for limited vocabulary applications unconstrained speech understanding is still too hard Computer vision works for constrained problems (hand-written zip-codes) understanding real-world, natural scenes is still too hard Learning adaptive systems are used in many applications: have their limits Planning and Reasoning only works for constrained problems: e.g., chess real-world is too complex for general systems Overall: many components of intelligent systems are “doable” there are many interesting research problems remaining
Slide 37 - 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 Customer Service automatic voice recognition The Web Identifying your age, gender, location, from your Web surfing Automated fraud detection Digital Cameras Automated face detection and focusing Computer Games Intelligent characters/agents
Slide 38 - 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! is this problem “AI-complete”? Nonetheless.... commercial systems can do a lot of the work very well (e.g.,restricted vocabularies in software documentation) algorithms which combine dictionaries, grammar models, etc. Recent progress using “black-box” machine learning techniques
Slide 39 - AI and Web Search
Slide 40 - What’s involved in Intelligence? (again) Perceiving, recognizing, understanding the real world Reasoning and planning about the external world Learning and adaptation So what general principles should we use to achieve these goals?
Slide 41 - Different Types of Artificial Intelligence Modeling exactly how humans actually think Modeling exactly how humans actually act Modeling how ideal agents “should think” Modeling how ideal agents “should act” Modern AI focuses on the last definition we will also focus on this “engineering” approach success is judged by how well the agent performs
Slide 42 - 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 Suggests major components required for AI: - knowledge representation - reasoning, - language/image understanding, - learning * Question: is it important that an intelligent system act like a human?
Slide 43 - Thinking humanly Cognitive Science approach Try to get “inside” our minds E.g., conduct experiments with people to try to “reverse-engineer” how we reason, learning, remember, predict Problems Humans don’t behave rationally e.g., insurance The reverse engineering is very hard to do The brain’s hardware is very different to a computer program
Slide 44 - Thinking rationally Represent facts about the world via logic Use logical inference as a basis for reasoning about these facts Can be a very useful approach to AI E.g., theorem-provers Limitations Does not account for an agent’s uncertainty about the world E.g., difficult to couple to vision or speech systems Has no way to represent goals, costs, etc (important aspects of real-world environments)
Slide 45 - Acting rationally Decision theory/Economics Set of future states of the world Set of possible actions an agent can take Utility = gain to an agent for each action/state pair An agent acts rationally if it selects the action that maximizes its “utility” Or expected utility if there is uncertainty Emphasis is on autonomous agents that behave rationally (make the best predictions, take the best actions) on average over time within computational limitations (“bounded rationality”)
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Slide 47 - Summary of Today’s Lecture Artificial Intelligence involves the study of: automated recognition and understanding of signals reasoning, planning, and decision-making learning and adaptation AI has made substantial progress in recognition and learning some planning and reasoning problems …but many open research problems AI Applications improvements in hardware and algorithms => AI applications in industry, finance, medicine, and science. Rational agent view of AI Reading: chapter 1 in text, Thrun paper, Stone lecture