CS 771 Artificial Intelligence. Introduction to AI

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1 CS 771 Artificial Intelligence Introduction to AI

2 Outline Course overview What is AI? A brief history State of the Art

3 Course overview Intro to AI (chapter 1) Intelligent agents (chapter 2) Goal based agents and uninformed search(chapter ) Informed Search : A* (chapter ) Beyond classical search (chapter 4) Adversarial search alpha-beta pruning (chapter 5) Constraint satisfaction problem (chapter 6) Midterm 1 (chapter 1, 2, 3,4,5,6) Logical agents and propositional logic (chapter 7) First-order logic (chapter 8) Inference in first order logic (chapter 9) Midterm 2 (chapter 7, 8, 9) Quantifying uncertainty (chapter 13) Probabilistic reasoning using Bayes net (chapter 14) Probabilistic reasoning over time (chapter 15)

4 Where is AI in Computer Science? Computer science : problem solving using computers Computer Architecture and Operating System study how to build good computers. Computation and Complexity Theory study what can be computed, what cannot be computed, i.e., the limits of different computing devices. Programming Languages study how to use computers conveniently and efficiently. Algorithms and Data Structures study how to solve popular computation problems efficiently. Artificial Intelligence is relevant to any intellectual tasks, e.g., playing chess, proving mathematical theorems, writing poetry, driving a car on a crowded street, diagnosing diseases

5 What is AI? A scientific and engineering discipline devoted to: understanding principles that make intelligent behavior possible in natural or artificial systems developing methods for the design and implementation of useful intelligent artifacts

6 What is AI? Views of AI fall into four categories 1. Thinking humanly 2. Acting humanly 3. Thinking rationally 4. Acting rationally

7 AI definition 1: Thinking humanly Need to study the brain as an information processing machine: cognitive science and neuroscience

8 AI definition 1: Thinking humanly Can we build a brain? Source: L. Zettlemoyer

9 AI definition 1: Thinking humanly Can we build a brain?

10 AI definition 2: Acting humanly Turing test : proposed and designed by Alan Turing in 1950 to provide a satisfactory operational definition of intelligence What capabilities would a computer need to have to pass the Turing Test? Natural language processing Knowledge representation Automated reasoning Machine learning

11 Turing predicted that by the year 2000, machines would be able to fool 30% of human judges for five minutes Loebner prize The Turing Test 2008 competition: each of 12 judges was given five minutes to conduct simultaneous, split-screen conversations with two hidden entities (human and chatterbot). The winner, Elbot of Artificial Solutions, managed to fool three of the judges into believing it was human [Wikipedia].

12 A better Turing test?

13 Total Turing test Turing s test deliberately avoided direct physical interactions between interrogator and the computer because physical simulation of a person is unnecessary for intelligence A total Turing test includes a video signal so that interrogator can test the subject s perceptual abilities To pass a total Turing test the computer will need Computer vision : to perceive object Robotics : to manipulate objects and move about These six disciplines compose most of the AI

14 Relevance of Turing Test Turing deserves a credit for designing a test that remains relevant 60+ years later Yet AI researchers have devoted little effort to pass the Turing test believing that it is more important to study underlying principles of intelligence than to duplicate an exemplar Here is an analogy Quest for artificial flight succeeded when Wright brothers and others stopped imitating birds and started using wind tunnels and learning about aerodynamics In fact, aerospace/aeronautical engineering practitioners do not define the goal of their field as machines that fly so exactly like pigeons that they can fool even other pigeons

15 AI definition 3: Thinking rationally The law of thought approach Idealized or right way of thinking Logic: patterns of argument that always yield correct conclusions when supplied with correct premises Socrates is a man; all men are mortal; therefore Socrates is mortal. Logicist approach to AI: describe problem in formal logical notation and apply general deduction procedures to solve it Problems with the logicist approach Computational complexity of finding the solution Describing real-world problems and knowledge in logical notation Dealing with uncertainty A lot of rational behavior has nothing to do with logic

16 AI definition 4: Acting rationally An agent is just something that acts A rational agent is one that acts so as to achieve the best outcome or acts to optimally achieve its goals Goals are application-dependent and are expressed in terms of the utility of outcomes Being rational means maximizing your utility or maximizing expected utility under uncertainty This definition of rationality only concerns the decisions/actions that are made, not the cognitive process behind them

17 Justification for acting rationally The law of thought process approach to AI emphasize on correct inference Making correct inference is sometimes part of being a rational agent because One way to act rationally is to reason logically to the conclusion that a given action will achieve one s goals On the other hand correct inference is not all of rationality In some situations there may not be any provably correct thing to do yet something still must be done The rational agent approach has some advantages over other approaches It is more general than law of thought process It is more amenable to scientific development than are approaches based on human behavior or human thought The standard of rationality is mathematically well defined, completely general and can be used to generate agent designs that provably achieve it Therefore, in this course we will concentrate on rational agent

18 History of AI Image source

19 What are some successes of AI today?

20 IBM Watson NY Times article Trivia demo IBM Watson wins on Jeopardy (February 2011)

21 Self-driving cars Google s self-driving car passes 300,000 miles (Forbes, 8/15/2012) Nissan pledges affordable self-driving car models by 2020 (CNET, 8/27/2013)

22 Natural Language Speech technologies Google voice search Apple Siri Machine translation translate.google.com Comparison of several translation systems

23 Vision OCR, handwriting recognition Face detection/recognition: many consumer cameras, Apple iphoto Visual search: Google Goggles, search by image Vehicle safety systems: Mobileye

24 Mathematics In 1996, a computer program written by researchers at Argonne National Laboratory proved a mathematical conjecture unsolved for decades NY Times story: [The proof] would have been called creative if a human had thought of it Mathematical software:

25 Games IBM s Deep Blue defeated the reigning world chess champion Garry Kasparov in : Kasparov Beats Deep Blue I could feel I could smell a new kind of intelligence across the table. 1997: Deep Blue Beats Kasparov Deep Blue hasn't proven anything. In 2007, checkers was solved (though checkers programs had been beating the best human players for at least a decade before then)

26 Logistics, scheduling, planning 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 Remote Agent software operated the Deep Space 1 spacecraft during two experiments in May 1999 In 2004, NASA introduced the MAPGEN system to plan the daily operations for the Mars Exploration Rovers

27 Mars rovers Autonomous vehicles DARPA Grand Challenge Self-driving cars Autonomous helicopters Robot soccer RoboCup Personal robotics Humanoid robots Robotic pets Personal assistants? Robotics

28 Towel-folding robot YouTube Video J. Maitin-Shepard, M. Cusumano-Towner, J. Lei and P. Abbeel, Cloth Grasp Point Detection based on Multiple-View Geometric Cues with Application to Robotic Towel Folding, ICRA 2010 More clothes folding

29 Origins of AI: Early excitement 1940s First model of a neuron (W. S. McCulloch & W. Pitts) Hebbian learning rule Cybernetics 1950s Turing Test Perceptrons (F. Rosenblatt) Computer chess and checkers (C. Shannon, A. Samuel) Machine translation (Georgetown-IBM experiment) Theorem provers (A. Newell and H. Simon, H. Gelernter and N. Rochester) 1956 Dartmouth meeting: Artificial Intelligence adopted

30 Herbert Simon, 1957 It is not my aim to surprise or shock you but there are now in the world machines that think, that learn and that create. Moreover, their ability to do these things is going to increase rapidly until in a visible future the range of problems they can handle will be coextensive with the range to which human mind has been applied. More precisely: within 10 years a computer would be chess champion, and an important new mathematical theorem would be proved by a computer. Simon s prediction came true but forty years later instead of ten

31 Harder than originally thought 1966: Eliza chatbot (Weizenbaum) mother Tell me more about your family I wanted to adopt a puppy, but it s too young to be separated from its mother. 1954: Georgetown-IBM experiment Completely automatic translation of more than sixty Russian sentences into English Only six grammar rules, 250 vocabulary words, restricted to organic chemistry Promised that machine translation would be solved in three to five years (press release) Automatic Language Processing Advisory Committee (ALPAC) report (1966): machine translation has failed The spirit is willing but the flesh is weak. The vodka is strong but the meat is rotten.

32 Blocks world (1960s 1970s) Larry Roberts, MIT, 1963???

33 History of AI: Taste of failure 1940s 1950s Rochester) Late 1960s Early 1970s Late 1970s First model of a neuron (W. S. McCulloch & W. Pitts) Hebbian learning rule Cybernetics Turing Test Perceptrons (F. Rosenblatt) Computer chess and checkers (C. Shannon, A. Samuel) Machine translation (Georgetown-IBM experiment) Theorem provers (A. Newell and H. Simon, H. Gelernter and N. Machine translation deemed a failure Neural nets deprecated (M. Minsky and S. Papert, 1969)* Intractability is recognized as a fundamental problem The first AI Winter

34 History of AI to the present day 1980s Late 1980s- Early 1990s Mid-1980s Late 1980s 1990s-Present Expert systems boom Expert system bust; the second AI winter Neural networks and back-propagation Probabilistic reasoning on the ascent Machine learning everywhere Big Data Deep Learning

35 NY Times article

36 What accounts for recent successes in AI? Faster computers The IBM 704 vacuum tube machine that played chess in 1958 could do about 50,000 calculations per second Deep Blue could do 50 billion calculations per second a million times faster! Dominance of statistical approaches, machine learning Big data Crowdsourcing

37 Historical themes Moravec s paradox It is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility [Hans Moravec, 1988] Why is this? Early AI researchers concentrated on the tasks that they themselves found the most challenging, abilities of animals and two-year-olds were overlooked We are least conscious of what our brain does best Sensorimotor skills took millions of years to evolve, whereas abstract thinking is a relatively recent development

38 Historical themes Silver bulletism (Levesque, 2013): The tendency to believe in a silver bullet for AI, coupled with the belief that previous beliefs about silver bullets were hopelessly naïve Conceptual dichotomies (Newell, 1983): Symbolic vs. continuous High-level vs. low-level modeling of mental processes Serial vs. parallel Problem solving vs. recognition Performance vs. learning Boom and bust cycles Periods of (unjustified) optimism followed by periods of disillusionment and reduced funding Image problems AI effect: As soon as a machine gets good at performing some task, the task is no longer considered to require much intelligence

39 Philosophy of this class Our goal is to use machines to solve hard problems that traditionally would have been thought to require human intelligence We will try to follow a sound scientific/engineering methodology Consider relatively limited application domains Use well-defined input/output specifications Define operational criteria amenable to objective validation Zero in on essential problem features Focus on principles and basic building blocks

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