Transcript What is AI?
PPT presentation made by David Brogan ITCS 3153 Artificial Intelligence Lecture 1 Introduction Textbook This is a great book • I will use its 2nd edition • Most widely used in U.S. universities Homework • Read chapters 1 and 2 What is AI? Discussion exercise for class • Think of example AI systems (applications that are intelligent) • Think of example AI Techniques AI Systems • Thermostat • Tic-Tac-Toe • Your car • Chess • Google • Babblefish • This thing – Asimo AI Techniques • Rule-based • Fuzzy Logic • Neural Networks • Genetic Algorithms How to Categorize These Systems Systems that think like humans Systems that act like humans Systems that think rationally Systems that act rationally Distinctions How one thinks vs. How one acts • How can I know how you think? – For the most part, you are a “black box” – Cognitive Science • How can I know how you act? – Observation – Turing Test Alan Turing – “Building a Brain” World War II motivated computer advances • Code breaking (Colossus) • Computing missile trajectories (Mark I) • Electronic Numerical Integrator and Computer (ENIAC) 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 Rational vs. Human Thinking/acting rationally vs. Thinking/acting like a human • Rely on logic 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 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 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) 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 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 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 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… 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 three very influential papers 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 initial state to goal state with minimum energy – In 1950, control theory could only describe linear systems and AI largely rose as a response to this shortcoming 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 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 Why is AI in Computer Science? • Uses computer as a tool more than psychologists, mathematicians (operations research), or mechanical engineers (control theory) 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 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 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 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) 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 AI History: Where are We Now? • Autonomous planning: scheduling operations aboard a spacecraft – Dante falls in an ice crater after one step – Mars Rover never deploys • 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 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