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Published on : Feb 10, 2014
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Slide 1 - Artificial Intelligence
Slide 2 - What is AI? Can machines “think”? Can machines be truly autonomous? Can machines program themselves? Can machines learn? Will they ever be “conscious”, and is that necessary?
Slide 3 - media depictions of AI (science fiction) HAL in 2001: Space Odessey Spielberg’s AI Data on Star Trek Next Generation ... real AI has many practical applications credit evaluation, medical diagnosis guidance systems, surveillance manufacturing (robotics, logistics) information kiosks, computer-aided tutoring AI in video games (also: Deep Blue, chess) driverless vehicles, UAVs Mars rover, Hubble telescope
Slide 4 - AI has a long history, and draws on many fields mathematics, computability, formal logic control theory optimization cognitive science linguistics
Slide 5 - Perspectives on AI Philosophical What is the nature of intelligence? Psychological How do humans think? Engineering advanced methods for building complex systems that solve hard real-world problems
Slide 6 - Philosophical Perspective started with Greek philosophers (e.g. Aristotle) syllogisms natural categories 1700-1800s: development of logic, calculus Descartes, Liebnitz, Boole, Frege, Tarski, Russell what are concepts? existence, intention, causality... reductionist approaches to try to mechanize reasoning 1900s: development of computers input/output model is intelligence a “computable function”? Turing, von Neumann, Gödel
Slide 7 - Does “intelligence” require a physical brain? Programmed devices will probably never have “free will” Or is it sufficient to produce intelligent behavior, regardless of how it works? The Turing Test first published in 1950 a panel of human judges asks questions through a teletype interface (restricted to topic areas) a program is intelligent if it can fool the judges and look indistinguishable from other humans annual competition at MIT: the Loebner Prize chatterbots
Slide 8 - Psychological Perspective AI is about understanding and modeling human intelligence Cognitive Science movement (ca. 1950s) replace stimulus/response model internal representations mind viewed as “information processor” (sensory perceptionsconceptsactions)
Slide 9 - Are humans a good model of intelligence? strengths: interpretation, dealing with ambiguity, nuance judgement (even for ill-defined situations) insight, creativity adaptiveness weaknesses: slow error-prone limited memory subject to biases influenced by emotions
Slide 10 - Optimization AI draws upon (and extends) optimization remember NP-hard problems? there is (probably) no efficient algorithm that solves them in polynomial time but we can have approximation algorithms run in polynomial time, but don’t guarantee optimal solution classic techniques: linear programming, gradient descent Many problems in AI are NP-hard (or worse) AI gives us techniques for solving them heuristic search use of expertise encoded in knowledge bases AI relies heavily on greedy algorithms, e.g. for scheduling custom algorithms for search (e.g. constraint satisfaction), planning (e.g. POP, GraphPlan), learning (e.g. rule generation), decision making (MDPs)
Slide 11 - Planning Autonomy – we want computers to figure out how to achieve goals on their own Given a goal G and a library of possible actions Ak find a sequence of actions A1..An that changes the state of the worlds to achieve G current state of world desired state of world pickup(A) puton(A,table) pickup(C) puton(C,A) pickup(B) pickup(B,C)
Slide 12 - Examples: Blocks World – stack blocks in a desired way traveling from College Station to Statue of Liberty rescuing a victim in a collapsed building cooking a meal The challenges of planning are: for each task, must invoke sub-tasks to ensure pre-conditions are satisfied in order to nail 2 pieces of wood together, I have to have a hammer sub-tasks might interact with each other if I am holding a hammer and nail, I can’t hold the boards so planning is a combinatorial problem
Slide 13 - Intelligent Agents agents are: 1) autonomous, 2) situated in an environment they can change, 3) goal-oriented agents focus on decision making incorporate sensing, reasoning, planning sense-decide-act loop rational agents try to maximize a utility function (rewards, costs)
Slide 14 - agents often interact in multi-agent systems collaborative teamwork, task distribution information sharing/integration contract networks voting remember Dr. Shell’s multi-robots competitive agents will maximize self utility in open systems negotiation auctions, bidding game theory design mechanisms where there is incentive to cooperate
Slide 15 - Core Concepts in AI Symbolic Systems Hypothesis intelligence can be modeled as manipulating symbols representing discrete concepts like Boolean variables for CupEmpty, Raining, LightsOn, PowerLow, CheckmateInOneMove, PedestrianInPath... inference and decision-making comes from comparing symbols and producing new symbols Herbert Simon, Allan Newell (CMU, 1970s) (A competing idea: Connectionism) neural networks maybe knowledge can’t be represented by discrete concepts, but is derived from associations and their strengths good model for perception and learning
Slide 16 - Core Concepts in AI Search everything in AI boils down to discrete search search space: different possible actions branch out from initial state finding a goal weak methods: depth-first search (DFS), breadth-first search (BFS), constraint satisfaction (CSP) strong methods: use ‘heuristics’, A* search goal nodes
Slide 17 - Applications of search game search (tic-tac-toe, chess) planning natural language parsing learning (search for logical rules that describe all the positive examples and no negative examples by adding/subtracting antecedents)
Slide 18 - Knowledge-representation attempt to capture expertise of human experts build knowledge-based systems, more powerful than just algorithms and code “In the knowledge lies the power” (Ed Feigenbaum, Turing Award: 1994 ) first-order logic p vegetarian(p)↔(f eats(p,f)m meat(m)contains(f,m)) x,y eat(joe,x)contains(x,y)fruit(y)vegetable(y)  vegetarian(joe) inference algorithms satisfiability, entailment, modus ponens, backward-chaining, unification, resolution Core Concepts in AI
Slide 19 - Expert Systems Medical diagnosis: rules for linking symptoms with diseases, from interviews with doctors Financial analysis: rules for evaluating credit score, solvency of company, equity-to-debt ratio, sales trends, barriers to entry Tutoring – rules for interpreting what a student did wrong on a problem and why, taxonomy of possible mis-conceptions Science – rules for interpreting chemical structures from mass-spectrometry data, rules for interpreting well logs and finding oil
Slide 20 - Major problem with many expert systems: brittleness Major issue in AI today: Uncertainty using fuzzy logic for concepts like “good management team” statistics: conditional probability that a patient has meningitis given they have a stiff neck Markov Decision Problems: making decisions based on probabilities and payoffs of possible outcomes
Slide 21 - Sub-areas within AI Natural language parsing sentences, representing meaning, metaphor, answering questions, translation, dialog systems Vision cameraimagescorners/edges/surfaces objectsstate description occlusion, shading, textures, face recognition, stereo(3D), motion(video) Robotics – configuration/motion planning Machine Learning (machines can adapt!) decision trees, neural networks, linear classifiers extract characteristic features from a set of examples