CS 771 Artificial Intelligence. Introduction to AI
|
|
- Blaze Francis
- 7 years ago
- Views:
Transcription
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
CS440/ECE448: Artificial Intelligence. Course website: http://slazebni.cs.illinois.edu/fall15/
CS440/ECE448: Artificial Intelligence Course website: http://slazebni.cs.illinois.edu/fall15/ Last time: What is AI? Definitions from Chapter 1 of the textbook: 1. Thinking humanly 2. Acting humanly 3.
More informationCOMP 590: Artificial Intelligence
COMP 590: Artificial Intelligence Today Course overview What is AI? Examples of AI today Who is this course for? An introductory survey of AI techniques for students who have not previously had an exposure
More informationWhat is Artificial Intelligence?
CSE 3401: Intro to Artificial Intelligence & Logic Programming Introduction Required Readings: Russell & Norvig Chapters 1 & 2. Lecture slides adapted from those of Fahiem Bacchus. 1 What is AI? What is
More informationCSC384 Intro to Artificial Intelligence
CSC384 Intro to Artificial Intelligence What is Artificial Intelligence? What is Intelligence? Are these Intelligent? CSC384, University of Toronto 3 What is Intelligence? Webster says: The capacity to
More informationActing humanly: The Turing test. Artificial Intelligence. Thinking humanly: Cognitive Science. Outline. What is AI?
Acting humanly: The Turing test Artificial Intelligence Turing (1950) Computing machinery and intelligence : Can machines think? Can machines behave intelligently? Operational test for intelligent behavior:
More informationArtificial Intelligence
Artificial Intelligence Chapter 1 Chapter 1 1 Outline What is AI? A brief history The state of the art Chapter 1 2 What is AI? Systems that think like humans Systems that think rationally Systems that
More informationIntroduction to Artificial Intelligence
Introduction to Artificial Intelligence Kalev Kask ICS 271 Fall 2014 http://www.ics.uci.edu/~kkask/fall-2014 CS271/ Course requirements Assignments: There will be weekly homework assignments, a project,
More informationCSE 517A MACHINE LEARNING INTRODUCTION
CSE 517A MACHINE LEARNING INTRODUCTION Spring 2016 Marion Neumann Contents in these slides may be subject to copyright. Some materials are adopted from Killian Weinberger. Thanks, Killian! Machine Learning
More information21 st Century Knowledge Worker: the Centaur
21 st Century Knowledge Worker: the Centaur Daniel Kiss Introduction The centaur is a well-known mythological creature, half-human half-horse. The most famous of centaurs was Chiron, the teacher of Asclepius,
More informationCHAPTER 15: IS ARTIFICIAL INTELLIGENCE REAL?
CHAPTER 15: IS ARTIFICIAL INTELLIGENCE REAL? Multiple Choice: 1. During Word World II, used Colossus, an electronic digital computer to crack German military codes. A. Alan Kay B. Grace Murray Hopper C.
More informationCOMP-424: Artificial intelligence. Lecture 1: Introduction to AI!
COMP 424 - Artificial Intelligence Lecture 1: Introduction to AI! Instructor: Joelle Pineau (jpineau@cs.mcgill.ca) Class web page: www.cs.mcgill.ca/~jpineau/comp424 Unless otherwise noted, all material
More informationApplications of Artificial Intelligence. Omark Phatak
Applications of Artificial Intelligence Omark Phatak Applications of artificial intelligence (AI) are a convergence of cutting edge research in computer science and robotics. The goal is to create smart
More informationHistory of Artificial Intelligence. Introduction to Intelligent Systems
History of Artificial Intelligence Introduction to Intelligent Systems What is An Intelligent System? A more difficult question is: What is intelligence? This question has puzzled philosophers, biologists
More informationLearning is a very general term denoting the way in which agents:
What is learning? Learning is a very general term denoting the way in which agents: Acquire and organize knowledge (by building, modifying and organizing internal representations of some external reality);
More informationIntroduction to Machine Learning and Data Mining. Prof. Dr. Igor Trajkovski trajkovski@nyus.edu.mk
Introduction to Machine Learning and Data Mining Prof. Dr. Igor Trakovski trakovski@nyus.edu.mk Neural Networks 2 Neural Networks Analogy to biological neural systems, the most robust learning systems
More informationEXECUTIVE SUPPORT SYSTEMS (ESS) STRATEGIC INFORMATION SYSTEM DESIGNED FOR UNSTRUCTURED DECISION MAKING THROUGH ADVANCED GRAPHICS AND COMMUNICATIONS *
EXECUTIVE SUPPORT SYSTEMS (ESS) STRATEGIC INFORMATION SYSTEM DESIGNED FOR UNSTRUCTURED DECISION MAKING THROUGH ADVANCED GRAPHICS AND COMMUNICATIONS * EXECUTIVE SUPPORT SYSTEMS DRILL DOWN: ability to move
More informationVorlesung Grundlagen der Künstlichen Intelligenz
Vorlesung Grundlagen der Künstlichen Intelligenz Reinhard Lafrenz / Prof. A. Knoll Robotics and Embedded Systems Department of Informatics I6 Technische Universität München www6.in.tum.de lafrenz@in.tum.de
More informationCOMP-424: Artificial intelligence. Lecture 2: Introduction to AI!
COMP 424 - Artificial Intelligence Lecture 2: Introduction to AI! Instructor: Joelle Pineau (jpineau@cs.mcgill.ca) Class web page: www.cs.mcgill.ca/~jpineau/comp424 Unless otherwise noted, all material
More informationArtificial Intelligence for ICT Innovation
2016 ICT 산업전망컨퍼런스 Artificial Intelligence for ICT Innovation October 5, 2015 Sung-Bae Cho Dept. of Computer Science, Yonsei University http://sclab.yonsei.ac.kr Subjective AI Hype Cycle Expert System Neural
More informationWatson. An analytical computing system that specializes in natural human language and provides specific answers to complex questions at rapid speeds
Watson An analytical computing system that specializes in natural human language and provides specific answers to complex questions at rapid speeds I.B.M. OHJ-2556 Artificial Intelligence Guest lecturing
More informationAppendices master s degree programme Artificial Intelligence 2014-2015
Appendices master s degree programme Artificial Intelligence 2014-2015 Appendix I Teaching outcomes of the degree programme (art. 1.3) 1. The master demonstrates knowledge, understanding and the ability
More informationComputation Beyond Turing Machines
Computation Beyond Turing Machines Peter Wegner, Brown University Dina Goldin, U. of Connecticut 1. Turing s legacy Alan Turing was a brilliant mathematician who showed that computers could not completely
More informationThe Turing Test! and What Computer Science Offers to Cognitive Science "
The Turing Test and What Computer Science Offers to Cognitive Science " Profs. Rob Rupert and Mike Eisenberg T/R 11-12:15 Muenzinger D430 http://l3d.cs.colorado.edu/~ctg/classes/cogsci12/ The Imitation
More informationFall 2012 Q530. Programming for Cognitive Science
Fall 2012 Q530 Programming for Cognitive Science Aimed at little or no programming experience. Improve your confidence and skills at: Writing code. Reading code. Understand the abilities and limitations
More informationArtificial Intelligence and Robotics @ Politecnico di Milano. Presented by Matteo Matteucci
1 Artificial Intelligence and Robotics @ Politecnico di Milano Presented by Matteo Matteucci What is Artificial Intelligence «The field of theory & development of computer systems able to perform tasks
More informationComputers and the Creative Process
Computers and the Creative Process Kostas Terzidis In this paper the role of the computer in the creative process is discussed. The main focus is the investigation of whether computers can be regarded
More informationMachine Learning. 01 - Introduction
Machine Learning 01 - Introduction Machine learning course One lecture (Wednesday, 9:30, 346) and one exercise (Monday, 17:15, 203). Oral exam, 20 minutes, 5 credit points. Some basic mathematical knowledge
More informationCS91.543 MidTerm Exam 4/1/2004 Name: KEY. Page Max Score 1 18 2 11 3 30 4 15 5 45 6 20 Total 139
CS91.543 MidTerm Exam 4/1/2004 Name: KEY Page Max Score 1 18 2 11 3 30 4 15 5 45 6 20 Total 139 % INTRODUCTION, AI HISTORY AND AGENTS 1. [4 pts. ea.] Briefly describe the following important AI programs.
More informationBowdoin Computer Science
Bowdoin Science What is computer science, what are its applications in other disciplines, and its impact in society? 101: Introduction to CS Pre-requisites: none Assumes no prior knowledge of programming
More informationFinal Assessment Report of the Review of the Cognitive Science Program (Option) July 2013
Final Assessment Report of the Review of the Cognitive Science Program (Option) July 2013 Review Process This is the second program review of the Cognitive Science Option. The Cognitive Science Program
More informationNeural Networks and Back Propagation Algorithm
Neural Networks and Back Propagation Algorithm Mirza Cilimkovic Institute of Technology Blanchardstown Blanchardstown Road North Dublin 15 Ireland mirzac@gmail.com Abstract Neural Networks (NN) are important
More informationDraft dpt for MEng Electronics and Computer Science
Draft dpt for MEng Electronics and Computer Science Year 1 INFR08012 Informatics 1 - Computation and Logic INFR08013 Informatics 1 - Functional Programming INFR08014 Informatics 1 - Object- Oriented Programming
More informationSchool of Computer Science
School of Computer Science Computer Science - Honours Level - 2014/15 October 2014 General degree students wishing to enter 3000- level modules and non- graduating students wishing to enter 3000- level
More informationFive High Order Thinking Skills
Five High Order Introduction The high technology like computers and calculators has profoundly changed the world of mathematics education. It is not only what aspects of mathematics are essential for learning,
More informationCourse Outline Department of Computing Science Faculty of Science. COMP 3710-3 Applied Artificial Intelligence (3,1,0) Fall 2015
Course Outline Department of Computing Science Faculty of Science COMP 710 - Applied Artificial Intelligence (,1,0) Fall 2015 Instructor: Office: Phone/Voice Mail: E-Mail: Course Description : Students
More informationLONG BEACH CITY COLLEGE MEMORANDUM
LONG BEACH CITY COLLEGE MEMORANDUM DATE: May 5, 2000 TO: Academic Senate Equivalency Committee FROM: John Hugunin Department Head for CBIS SUBJECT: Equivalency statement for Computer Science Instructor
More informationCS Master Level Courses and Areas COURSE DESCRIPTIONS. CSCI 521 Real-Time Systems. CSCI 522 High Performance Computing
CS Master Level Courses and Areas The graduate courses offered may change over time, in response to new developments in computer science and the interests of faculty and students; the list of graduate
More informationGame Playing in the Real World. Next time: Knowledge Representation Reading: Chapter 7.1-7.3
Game Playing in the Real World Next time: Knowledge Representation Reading: Chapter 7.1-7.3 1 What matters? Speed? Knowledge? Intelligence? (And what counts as intelligence?) Human vs. machine characteristics
More informationARTIFICIAL INTELLIGENCE: DEFINITION, TRENDS, TECHNIQUES, AND CASES
ARTIFICIAL INTELLIGENCE: DEFINITION, TRENDS, TECHNIQUES, AND CASES Joost N. Kok, Egbert J. W. Boers, Walter A. Kosters, and Peter van der Putten Leiden Institute of Advanced Computer Science, Leiden University,
More informationWriting a Project Report: Style Matters
Writing a Project Report: Style Matters Prof. Alan F. Smeaton Centre for Digital Video Processing and School of Computing Writing for Computing Why ask me to do this? I write a lot papers, chapters, project
More information(Academy of Economic Studies) Veronica Adriana Popescu (Academy of Economic Studies) Cristina Raluca Popescu (University of Bucharest)
24 (Academy of Economic Studies) Veronica Adriana Popescu (Academy of Economic Studies) Cristina Raluca Popescu (University of Bucharest) Abstract: the importance of computer science, with the most important
More information3. Mathematical Induction
3. MATHEMATICAL INDUCTION 83 3. Mathematical Induction 3.1. First Principle of Mathematical Induction. Let P (n) be a predicate with domain of discourse (over) the natural numbers N = {0, 1,,...}. If (1)
More informationHyper-connectivity and Artificial Intelligence
Hyper-connectivity and Artificial Intelligence How hyper-connectivity changes AI through contextual computing Chuan (Coby) M 04/03/15 Description of Major Sections Background Artificial intelligence (AI)
More informationCSE841 Artificial Intelligence
CSE841 Artificial Intelligence Dept. of Computer Science and Eng., Michigan State University Fall, 2014 Course web: http://www.cse.msu.edu/~cse841/ Description: Graduate survey course in Artificial Intelligence.
More informationAnalecta Vol. 8, No. 2 ISSN 2064-7964
EXPERIMENTAL APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS IN ENGINEERING PROCESSING SYSTEM S. Dadvandipour Institute of Information Engineering, University of Miskolc, Egyetemváros, 3515, Miskolc, Hungary,
More informationFeed-Forward mapping networks KAIST 바이오및뇌공학과 정재승
Feed-Forward mapping networks KAIST 바이오및뇌공학과 정재승 How much energy do we need for brain functions? Information processing: Trade-off between energy consumption and wiring cost Trade-off between energy consumption
More informationCPSC 211 Data Structures & Implementations (c) Texas A&M University [ 313]
CPSC 211 Data Structures & Implementations (c) Texas A&M University [ 313] File Structures A file is a collection of data stored on mass storage (e.g., disk or tape) Why on mass storage? too big to fit
More informationArtificial Neural Networks and Support Vector Machines. CS 486/686: Introduction to Artificial Intelligence
Artificial Neural Networks and Support Vector Machines CS 486/686: Introduction to Artificial Intelligence 1 Outline What is a Neural Network? - Perceptron learners - Multi-layer networks What is a Support
More informationLevels of Analysis and ACT-R
1 Levels of Analysis and ACT-R LaLoCo, Fall 2013 Adrian Brasoveanu, Karl DeVries [based on slides by Sharon Goldwater & Frank Keller] 2 David Marr: levels of analysis Background Levels of Analysis John
More informationA Few Basics of Probability
A Few Basics of Probability Philosophy 57 Spring, 2004 1 Introduction This handout distinguishes between inductive and deductive logic, and then introduces probability, a concept essential to the study
More informationApplying Deep Learning to Car Data Logging (CDL) and Driver Assessor (DA) October 22-Oct-15
Applying Deep Learning to Car Data Logging (CDL) and Driver Assessor (DA) October 22-Oct-15 GENIVI is a registered trademark of the GENIVI Alliance in the USA and other countries Copyright GENIVI Alliance
More informationCAD and Creativity. Contents
CAD and Creativity K C Hui Department of Automation and Computer- Aided Engineering Contents Various aspects of CAD CAD training in the university and the industry Conveying fundamental concepts in CAD
More informationWhat is Learning? CS 391L: Machine Learning Introduction. Raymond J. Mooney. Classification. Problem Solving / Planning / Control
What is Learning? CS 391L: Machine Learning Introduction Herbert Simon: Learning is any process by which a system improves performance from experience. What is the task? Classification Problem solving
More informationLimitations of Human Vision. What is computer vision? What is computer vision (cont d)?
What is computer vision? Limitations of Human Vision Slide 1 Computer vision (image understanding) is a discipline that studies how to reconstruct, interpret and understand a 3D scene from its 2D images
More informationPredicate logic Proofs Artificial intelligence. Predicate logic. SET07106 Mathematics for Software Engineering
Predicate logic SET07106 Mathematics for Software Engineering School of Computing Edinburgh Napier University Module Leader: Uta Priss 2010 Copyright Edinburgh Napier University Predicate logic Slide 1/24
More informationBrain-in-a-bag: creating an artificial brain
Activity 2 Brain-in-a-bag: creating an artificial brain Age group successfully used with: Abilities assumed: Time: Size of group: 8 adult answering general questions, 20-30 minutes as lecture format, 1
More informationLearning and Teaching
B E S T PRACTICES NEA RESEARCH BRIEF Learning and Teaching July 2006 This brief outlines nine leading research-based concepts that have served as a foundation for education reform. It compares existing
More informationDoctor of Philosophy in Computer Science
Doctor of Philosophy in Computer Science Background/Rationale The program aims to develop computer scientists who are armed with methods, tools and techniques from both theoretical and systems aspects
More informationCourse 395: Machine Learning
Course 395: Machine Learning Lecturers: Maja Pantic (maja@doc.ic.ac.uk) Stavros Petridis (sp104@doc.ic.ac.uk) Goal (Lectures): To present basic theoretical concepts and key algorithms that form the core
More informationOur Future in the Driver s Seat
February 14, 2014 * Page 1 of 7 The NAViSection System: Uniting the Champions of Safe Driving Somebody is discussing the end of driving with their mother or father today. At the same time, a national discussion
More informationCognitive Development
LP 9C Piaget 1 Cognitive Development Piaget was intrigued by the errors in thinking children made. To investigate how these errors and how thinking changes as we grow older, Jean Piaget carefully observed
More informationTHE BLASTER METHOD: MATH GAMES TO MAKE YOU MATH SMART
THE BLASTER METHOD: MATH GAMES TO MAKE YOU MATH SMART Math fundamentals for a technological age: What makes students math smart? What makes a student math smart? What kind of mathematical competencies
More informationMEng, BSc Computer Science with Artificial Intelligence
School of Computing FACULTY OF ENGINEERING MEng, BSc Computer Science with Artificial Intelligence Year 1 COMP1212 Computer Processor Effective programming depends on understanding not only how to give
More informationProblem-Based Group Activities for a Sensation & Perception Course. David S. Kreiner. University of Central Missouri
-Based Group Activities for a Course David S. Kreiner University of Central Missouri Author contact information: David Kreiner Professor of Psychology University of Central Missouri Lovinger 1111 Warrensburg
More informationMAN VS. MACHINE. How IBM Built a Jeopardy! Champion. 15.071x The Analytics Edge
MAN VS. MACHINE How IBM Built a Jeopardy! Champion 15.071x The Analytics Edge A Grand Challenge In 2004, IBM Vice President Charles Lickel and coworkers were having dinner at a restaurant All of a sudden,
More informationDesign and Development of Electronic Prescription and Patient Information Systems for Developing World By
Design and Development of Electronic Prescription and Patient Information Systems for Developing World By Dr Boniface Ekechukwu* and Chidi Obi **Dr Arinze Nweze* *Department of Computer Science, Nnamdi
More informationAppendices master s degree programme Human Machine Communication 2014-2015
Appendices master s degree programme Human Machine Communication 2014-2015 Appendix I Teaching outcomes of the degree programme (art. 1.3) 1. The master demonstrates knowledge, understanding and the ability
More informationSelf-Improving Supply Chains
Self-Improving Supply Chains Cyrus Hadavi Ph.D. Adexa, Inc. All Rights Reserved January 4, 2016 Self-Improving Supply Chains Imagine a world where supply chain planning systems can mold themselves into
More informationNew Predictive Analysis Solutions for Health Care
New Predictive Analysis Solutions for Health Care More accurate forecasts of risk and cost for health care providers, payers, ACOs and Medical Home programs Leveraging HIT and Innovation to Support New
More informationDepth-of-Knowledge Levels for Four Content Areas Norman L. Webb March 28, 2002. Reading (based on Wixson, 1999)
Depth-of-Knowledge Levels for Four Content Areas Norman L. Webb March 28, 2002 Language Arts Levels of Depth of Knowledge Interpreting and assigning depth-of-knowledge levels to both objectives within
More informationCSCI 101 - Historical Development. May 29, 2015
CSCI 101 - Historical Development May 29, 2015 Historical Development 1. IBM 2. Commodore 3. Unix and Linux 4. Raspberry pi IBM IBM or International Business Machine Corporation began in the late 1800's,
More informationSchoo\ of Computing. Middlesbrough. Teesside University. 22 June 2015. To whom it may concern
Dr Simon Stobart Dean School of Compuling Teesside University Middlesbrough Tees Val\ey TS l 3BA Ul< T: 44 (0) 1642 342631 F: 44 (0) 1642 230527 tees.ac.uk Teesside University 22 June 2015 To whom it may
More informationExtending Semantic Resolution via Automated Model Building: applications
Extending Semantic Resolution via Automated Model Building: applications Ricardo Caferra Nicolas Peltier LIFIA-IMAG L1F1A-IMAG 46, Avenue Felix Viallet 46, Avenue Felix Viallei 38031 Grenoble Cedex FRANCE
More informationPUSD High Frequency Word List
PUSD High Frequency Word List For Reading and Spelling Grades K-5 High Frequency or instant words are important because: 1. You can t read a sentence or a paragraph without knowing at least the most common.
More informationChess Algorithms Theory and Practice. Rune Djurhuus Chess Grandmaster runed@ifi.uio.no / runedj@microsoft.com October 3, 2012
Chess Algorithms Theory and Practice Rune Djurhuus Chess Grandmaster runed@ifi.uio.no / runedj@microsoft.com October 3, 2012 1 Content Complexity of a chess game History of computer chess Search trees
More informationLearning Styles and Aptitudes
Learning Styles and Aptitudes Learning style is the ability to learn and to develop in some ways better than others. Each person has a natural way of learning. We all learn from listening, watching something
More informationMaster of Science in Computer Science
Master of Science in Computer Science Background/Rationale The MSCS program aims to provide both breadth and depth of knowledge in the concepts and techniques related to the theory, design, implementation,
More informationResearch in the cognitive sciences is founded on the assumption
Aporia vol. 24 no. 1 2014 Conceptual Parallels Between Philosophy of Science and Cognitive Science: Artificial Intelligence, Human Intuition, and Rationality Research in the cognitive sciences is founded
More informationChapter 2: Intelligent Agents
Chapter 2: Intelligent Agents Outline Last class, introduced AI and rational agent Today s class, focus on intelligent agents Agent and environments Nature of environments influences agent design Basic
More informationNEURAL NETWORK FUNDAMENTALS WITH GRAPHS, ALGORITHMS, AND APPLICATIONS
NEURAL NETWORK FUNDAMENTALS WITH GRAPHS, ALGORITHMS, AND APPLICATIONS N. K. Bose HRB-Systems Professor of Electrical Engineering The Pennsylvania State University, University Park P. Liang Associate Professor
More informationA Client-Server Interactive Tool for Integrated Artificial Intelligence Curriculum
A Client-Server Interactive Tool for Integrated Artificial Intelligence Curriculum Diane J. Cook and Lawrence B. Holder Department of Computer Science and Engineering Box 19015 University of Texas at Arlington
More information2013 International Symposium on Green Manufacturing and Applications Honolulu, Hawaii
Green Robotics, Automation, and Machine Intelligence; a new Engineering Course in Sustainable Design Joseph T. Wunderlich, PhD College, PA, USA 2013 International Symposium on Green Manufacturing and Applications
More informationQuine on truth by convention
Quine on truth by convention March 8, 2005 1 Linguistic explanations of necessity and the a priori.............. 1 2 Relative and absolute truth by definition.................... 2 3 Is logic true by convention?...........................
More informationProgram Your Own Game
Program Your Own Game Provided by TryEngineering - Lesson Focus Lesson focuses on how software engineers design computer games and other software. Student teams work together to develop a simple computer
More informationArtificial Intelligence I. Introduction: what s AI for? Homo Sapiens = Man the wise. Dr Mateja Jamnik. Computer Laboratory, Room FC18
Artificial Intelligence I Dr Mateja Jamnik Computer Laboratory, Room FC18 Telephone extension 63587 Email: mj201@cl.cam.ac.uk http://www.cl.cam.ac.uk/users/mj201/ Notes I: General introduction to artificial
More informationPage 1 of 5. (Modules, Subjects) SENG DSYS PSYS KMS ADB INS IAT
Page 1 of 5 A. Advanced Mathematics for CS A1. Line and surface integrals 2 2 A2. Scalar and vector potentials 2 2 A3. Orthogonal curvilinear coordinates 2 2 A4. Partial differential equations 2 2 4 A5.
More informationRegulating AI and Robotics
Regulating AI and Robotics Steve Omohundro, Ph.D. PossibilityResearch.com SteveOmohundro.com SelfAwareSystems.com http://i791.photobucket.com/albums/yy193/rokib50/sculpture/lady-justice-frankfurt_zps970c5d8f.jpg
More information010200 - «Mathematics and Computer Science»
Institute of Applied Mathematics and Mechanics Telematics Department (under the Central Scientific Research Institute of Robotics and Technical Cybernetics) announces admission to bachelor's and master's
More informationLearning to Process Natural Language in Big Data Environment
CCF ADL 2015 Nanchang Oct 11, 2015 Learning to Process Natural Language in Big Data Environment Hang Li Noah s Ark Lab Huawei Technologies Part 1: Deep Learning - Present and Future Talk Outline Overview
More informationBackground Biology and Biochemistry Notes A
Background Biology and Biochemistry Notes A Vocabulary dependent variable evidence experiment hypothesis independent variable model observation prediction science scientific investigation scientific law
More informationImplementation of hybrid software architecture for Artificial Intelligence System
IJCSNS International Journal of Computer Science and Network Security, VOL.7 No.1, January 2007 35 Implementation of hybrid software architecture for Artificial Intelligence System B.Vinayagasundaram and
More informationThe Relationship between Artificial Intelligence and Finance
Material 1 The Relationship between Artificial Intelligence and Finance University of Tokyo, Yutaka Matsuo Provisional Translation by the Secretariat Please refer to the original material in Japanese 1
More informationI N T E L L I G E N T S O L U T I O N S, I N C. DATA MINING IMPLEMENTING THE PARADIGM SHIFT IN ANALYSIS & MODELING OF THE OILFIELD
I N T E L L I G E N T S O L U T I O N S, I N C. OILFIELD DATA MINING IMPLEMENTING THE PARADIGM SHIFT IN ANALYSIS & MODELING OF THE OILFIELD 5 5 T A R A P L A C E M O R G A N T O W N, W V 2 6 0 5 0 USA
More informationCognitive and Motor Development. Four Domains. Interaction. Affective Cognitive Motor Physical. Why organize into domains?
Cognitive and Motor Development There is a strong relationship between human intellectual function and movement: Any intellectual change is also accompanied by a change in motor function Four Domains Interaction
More informationHow To Use Neural Networks In Data Mining
International Journal of Electronics and Computer Science Engineering 1449 Available Online at www.ijecse.org ISSN- 2277-1956 Neural Networks in Data Mining Priyanka Gaur Department of Information and
More informationUnlocking Big Data: The Power of Cognitive Computing. James Kobielus, IBM
Unlocking Big Data: The Power of Cognitive Computing James Kobielus, IBM James Kobielus IBM's big data evangelist IBM senior program director for product marketing in big data analytics Editor-in-chief
More informationCHAPTER 2. Logic. 1. Logic Definitions. Notation: Variables are used to represent propositions. The most common variables used are p, q, and r.
CHAPTER 2 Logic 1. Logic Definitions 1.1. Propositions. Definition 1.1.1. A proposition is a declarative sentence that is either true (denoted either T or 1) or false (denoted either F or 0). Notation:
More informationArtificial Intelligence (AI)
Overview Artificial Intelligence (AI) A brief introduction to the field. Won t go too heavily into the theory. Will focus on case studies of the application of AI to business. AI and robotics are closely
More informationEFFICIENT DATA PRE-PROCESSING FOR DATA MINING
EFFICIENT DATA PRE-PROCESSING FOR DATA MINING USING NEURAL NETWORKS JothiKumar.R 1, Sivabalan.R.V 2 1 Research scholar, Noorul Islam University, Nagercoil, India Assistant Professor, Adhiparasakthi College
More information