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CS 416 Artificial Intelligence

CS 416 Artificial Intelligence. Lecture 1 Introduction. What is AI?. Discussion exercise for class Think of example AI systems (applications that are intelligent) Think of example AI Techniques. Textbook. This is a great book 2 nd edition released one year ago

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CS 416 Artificial Intelligence

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  1. CS 416Artificial Intelligence Lecture 1 Introduction

  2. What is AI? • Discussion exercise for class • Think of example AI systems (applications that are intelligent) • Think of example AI Techniques

  3. Textbook • This is a great book • 2nd edition released one year ago • Most widely used in U.S. universities • It’s so good…. • I’m going to make you read it! • Homework • Read chapters 1 and 2

  4. Syllabus • Instructor • David BroganOlsson 217982-2211dbrogan@cs.virginia.edu • Office hours: TBA • TA • Ben Hocking • Office hours: TBA

  5. Syllabus • Class web page: • http://www.cs.virginia.edu/~cs416 • Class discussion forum: • http://www.cs.virginia.edu/~humper/forums/ • Grading • 3 programming assignments (10% each) • 1 programming project (15%) • 3 homework assignments (5% each) • Midterm and Final (20% each)

  6. What is expected of you • You’ll have to do math • Neural network update function • Multidimensional function minimization • Probability – Bayes’ Rule • We will teach necessary parts ofstatistics and linear algebra Calculus expected.Probability and Linear Algebra beneficial.

  7. What is expected of you • You have to program • The programming assignments are non-trivial • C++ • Requires integration with existing code libraries • Input/output handling (images, for example) • We do not teach programming in this course CS 216 expected.Additional programmingexperience beneficial.

  8. Turn in papers

  9. This thing • Asimo AI Systems • Thermostat • Tic-Tac-Toe • Your car • Chess • Google • Babblefish

  10. Examples • Chess: Deep Junior (IBM) tied Kasparov in 2003 match ATR’s DB Android Ritsumeikan University RHex Hexapod Honda’s Asimo

  11. AI Techniques • Rule-based • Fuzzy Logic • Neural Networks • Genetic Algorithms • Exhaustive search • Expert Systems • Logic

  12. How to Categorize These Systems • Systems that think like humans • Systems that act like humans • Systems that think rationally • Systems that act rationally

  13. How to Categorize These Systems

  14. Systems that think/act like humans • It’s hard to study things you can’t observe… • How can I know how you think? • Observation is difficult (changing with fMRI). For the most part, you are a “black box” • Cognitive Science • How can I know how you act? • Observation is possible, but hard to control all aspects of experimental conditions. • Turing Test

  15. Alan Turing – “Building a Brain” • World War II motivated computer advances • Code breaking (1943, Colossus) – Used to decipher telegrams encrypted using Germany’s encryption machine • Electronic Numerical Integrator and Computer (ENIAC, 1946) • Turing greatly involved with British efforts to build computers and crack codes (Bletchley Park) • Arrested for being a homosexual in 1952 and denied security clearance • Committed suicide in 1954

  16. Systems that think/act rationally • Rely on logic itself rather than human to measure correctness • Thinking rationally (logically) • Socrates is a human; All humans are mortal; Socrates is mortal • Logic formulas for synthesizing outcomes • Acting rationally (logically) • Even if method is illogical, the observed behavior must be rational

  17. Perspective of this Course • We will investigate the general principles of rational agents • Not restricted to human actions and human environments • Not restricted to human thought • Not confined to only using laws of logic • Anything goes so long as it produces rational behavior

  18. What is AI? • The use of computers to solve problems that previously could only be solved by applying human intelligence…. thus something can fit this definition today, but, once we see how the program works and understand the problem, we will not think of it as AI anymore (David Parnas)

  19. Foundations - Philosophy • Aristotle (384 B.C.E.) – Author of logical syllogisms • da Vinci (1452) – designed, but didn’t build, first mechanical calculator • Descartes (1596) – can human free will be captured by a machine? Is animal behavior more mechanistic? • Necessary connection between logic and action is discovered

  20. Foundations - Mathematics • More formal logical methods • Boolean logic (Boole, 1847) • Analysis of limits to what can be computed • Intractability (1965) – time required to solve problem scales exponentially with the size of problem instance • NP-complete (1971) – Formal classification of problems as intractable • Uncertainty (Cardano 1501) • The basis for most modern approaches to AI • Uncertainty can still be used in logical analyses

  21. Foundations - Economics • Humans are peculiar so define generic happiness term: utility • Game Theory – study of rational behavior in small games • Operations Research – study of rational behavior in complex systems • Herbert Simon (1916 – 2001) – AI researcher who received Nobel Prize in Economics for showing people accomplish satisficing solutions, those that are good enough

  22. Foundations - Neuroscience • How do brains work? • Early studies (1824) relied on injured and abnormal people to understand what parts of brain do • 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 computers will have as many gates as humans have neurons in 2020 • How close are we to having a mechanical brain? • Parallel computation, remapping, interconnections, binary vs. gradient…

  23. Foundations - Psychology • Helmholtz and Wundt (1821) – started to make psychology a science by carefully controlling experiments • The brain processes information (1842) • stimulus converted into mental representation • cognitive processes manipulate representation to build new representations • new representations are used to generate actions • Cognitive science started at a MIT workshop in 1956 with the publication of three very influential papers

  24. Foundations – Control Theory • Machines can modify their behavior in response to the environment (sense / action loop) • Water-flow regulator (250 B.C.E), steam engine governor, thermostat • The theory of stable feedback systems (1894) • Build systems that transition from initialstate to goal state with minimum energy • In 1950, control theory could only describelinear systems and AI largely rose as aresponse to this shortcoming

  25. 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

  26. History of AI • Read the complete story in text • Alan Turing (1950) did much to define the problems and techniques • John McCarthy helped coordinate the players (1956) • Alan Newell and Herbert Simon (1956) did much to demonstrate first solutions • Marvin Minsky (student of von Neumann) built a neural network (1951) from 3000 vacuum tubes and the “autopilot” from a B-24 bomber

  27. Why is AI in Computer Science? • Uses computer as a tool more than psychologists, mathematicians (operations research), or mechanical engineers (control theory)

  28. History of AI: 1952- 1969 • Great successes! • Logic programs were replicating human logic in many cases • Solving hard math problems • game playing • LISP was invented by McCarthy (1958) • second oldest language in existence • could accept new axioms at runtime • McCarthy went to MIT and Marvin Minsky started lab at Stanford • Both powerhouses in AI to this day

  29. History of AI: 1966 - 1973 • A dose of reality – Overhyped • Systems fail to play chess and translate Russian • Computers were ignorant to context of their logic • Problems were intractable • algorithms that work in principle may not work in practice • Combinatorial Explosion / Curse of Dimensionality • Fatal flaw in neural networks was exposed • though flaw was first resolved in 1969, neural networks did not return to vogue until late 1980s

  30. AI History: 1969 - 1979 • Knowledge-based Systems • Previous systems knocked because general logical algorithms could not be applied to realistic problems • Answer: accumulate specific logical algorithms • DENDRAL – infer chemical structure • knowledge of scientists boiled down to cookbook logic • large number of special purpose rules worked well • Researchers work on ways to accumulate and store facts for expert systems

  31. AI History: 1980 - present • Let the good times roll • The demonstrated success of AI invited investments • from millions to billions of dollars in 10 years • extravagant AI promises again led to “AI Winter” when investments in technology dropped (1988) • Neural Networks come back from the dead (1986)

  32. AI History: 1987 - present • AI becomes a science • More repeatability of experiments • More development of mathematical underpinnings • Reuse of time-tested models • Intelligent Agents (1994) • AI systems exist in real environments with real sensory inputs • Niches of AI need to be reorganized

  33. AI History: Where are We Now? • Autonomous planning: scheduling operations aboard a spacecraft • Some notable failures (Dante falls in a crater after one step) and shining successes (Mars Spirit Rover) • Game playing: Kasparov lost to IBM’s Big Blue in chess • Rules were changed to prevent computer from retraining over night and to provide human players with more examples of computerized play

  34. AI History: Where are We Now? • Autonomous Control: CMU’s NAVLAB drove from Pittsburgh to San Francisco under computer control 98% of time • Logistics: deployment of troops to Iraq • Robotics: remote heart operations • human genome, protein folding, drug discovery • stock market

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