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COSC 4368 and “What is AI?”

COSC 4368 and “What is AI?”. Introduction to AI (today, and WE) What is AI? Sub-fields of AI / Example problems investigated by AI research What is going on with respect to AI? Course Information. Part1a: Definitions of AI. “AI centers on the simulation of intelligence using computers”

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COSC 4368 and “What is AI?”

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  1. COSC 4368 and “What is AI?” • Introduction to AI (today, and WE) • What is AI? • Sub-fields of AI / Example problems investigated by AI research • What is going on with respect to AI? • Course Information

  2. Part1a: Definitions of AI • “AI centers on the simulation of intelligence using computers” • “AI develops programming paradigms, languages, tools, and environments for application areas for which conventional programming fails” • Symbolic programming (LISP) • Functional programming • Heuristic Programming • Logical Programming (PROLOG) • Rule-based Programming (Expert system shells) • Soft Computing (Belief network tools, fuzzy logic tool boxes,…) • Object-oriented programming (Smalltalk)

  3. More Definitions of AI • Rich/Knight: ”AI is the study of how to make computers do things which, at the moment, people do better” • Winston: “AI is the study of computations that make it possible to perceive, reason, and act. • Turing Test: If an artificial intelligent system is not distinguishable from a human being, it is definitely intelligent. • https://en.wikipedia.org/wiki/Turing_test • Eugene Goostman Winner 2014 Touring Test: https://en.wikipedia.org/wiki/Eugene_Goostman • Please read: http://www.zdnet.com/article/beyond-cortana-what-artificial-intelligence-means-for-the-future-of-microsoft/

  4. Physical Symbol System Hypothesis • “What the brain does can be thought of at some level as a kind of computation” • Physical Symbol System Hypothesis (PSSH): A physical symbol system has the sufficient and necessary means for general, intelligent actions. Remarks PSSH: • Subjected to empirical validation • If false  AI is quite limited • Important for psychology and philosophy

  5. Questions/Thoughts about AI • What are the limitations of AI? Can computers only do what they are told? Can computers be creative? Can computers think? What problems cannot be solved by computers today? • Computers show promise to control the current waste of energy and other natural resources. • Computer can work in environment that are unsuitable for human beings. • If computers control everything --- who controls the computers? • If computers are intelligent what civil rights should be given to computers? • If computers can perform most of our work; what should the human beings do? • Only those things that can be represented in computers are important. • It is fun to play with computers.

  6. AI https://en.wikipedia.org/wiki/Watson_%28computer%299 https://en.wikipedia.org/wiki/Watson_%28computer%29 CS221/Autumn2018/Liang

  7. Companies ”Animportantshiftfromamobilefirstworld toanAIfirstworld”[CEOSundarPichai@GoogleI/O2017] CreatedAIandResearchgroupas 4th engineeringdivision,now8Kpeople[2016] CreatedFacebookAIResearch,MarkZuckerbergveryoptimisticandinvested Others: IBM,Amazon,Apple,Uber,Salesforce,Baidu,Tencent,etc. CS221/Autumn2018/Liang

  8. Governments ”AI holds the potentialtobeamajordriverofeconomicgrowthandsocialprogress”[WhiteHousereport,2016] https://www.whitehouse.gov/briefings-statements/artificial-intelligence-american-people/ ReleaseddomesticstrategicplantobecomeworldleaderinAIby2030[2017]https://www.nytimes.com/2017/07/20/business/china-artificial-intelligence.html ”Whoeverbecomestheleaderinthissphere[AI]willbecome theruleroftheworld”[Putin,2017]https://www.theverge.com/2017/9/4/16251226/russia-ai-putin-rule-the-world https://yourstory.com/2018/02/budget-2018-artificial-intelligence-fuel-indian-economy/

  9. AIindex:numberofpublishedAI papers

  10. AIindex:numberofAIstartups CS221/Autumn2018/Liang

  11. Stanford CS221enrollments 800 600 400 200 0 2012 2013 2014 2015 2016 2017 2018 Slowingdown?ProbablyduetotheCS221springoffering... 14 CS221/Autumn2018/Liang

  12. Topics Covered in COSC 4368 • More general topics: • Exposure to many search algorithms • Making sense out of data (kind of Data Science) • AI-specific Topics: • Reasoning in uncertain environments and belief networks • Heuristic search, Constraint Satisfaction Problems, and Games • Learning from examples, reinforcement learning and deep learning (short) • Evolutionary Computing • Multi-Agent Systems (?!?) • Logical Reasoning and Classical Planning • Ethical and philosophical aspects of AI • Exposure to AI tools (belief networks, maybe ANN and multi-agent systems tools)

  13. 2019 Organization COSC 4368 • January 14+16: 1. Introduction to AI (covers chapter 1 and chapter 2 in part) 1.5 lectures • January 16+23+28+30, February 4: 2. Problem Solving (covering chapter 3, 4 in part, 5, and 6 in part, centering on uninformed and informed search , adversarial search and games, A*, alpha-beta search and solving constraint satisfaction problems) 4.5 lectures • February 6+11: Evolutionary Computing (use material different from textbook) 2 lectures • February 13+18+20+25+27 March 4+17+19+24 4. Machine Learning (covering reinforcement Learning (chapter 21, chapter17 in part), learning from examples (chapter 18 in part;), and deep learning (short using extra material) and 4.5 lectures • February 27, March 6, March 11+13+25: 3. Knowledge, Reasoning and Planning centering on introduction to first order predicate logic, inference in First Order Logic, and Classical Planning (short) (Chapter 7-10 in part) 3.5 lectures • Reviews: February 27, April 3, April 29; 3x0.5=1.5 lectures total • Monday, March 4: Midterm1 Exam • March 25+27+30: Multi Agent Systems (other teaching material) 2 Lectures • March 30+April 1: Philosophical Foundations of AI (Chapter 26) 1.5 Lectures • April 3+10+15+17+22: 5. Reasoning and Learning in Uncertain Environments (covers chapters 13, 14, 15 in part, and 20 in part, centering on “basics” in probabilistic reasoning, naïve Bayesian approaches, belief networks and hidden markov models (HMM)) 4.5 lectures • Monday April 8: Midterm2 Exam • April 22+24: TBDL 1.5 lectures • April 29: Course Summary 0.5 lectures • May ??, 2p: Final Exam Remarks: • Schedule is tentative and subject to change

  14. IJCAI 2017 Competitions 1. IJCAI-17 Video Competition Maybe short student presentation … More details can be found here.Video Playlist: https://www.youtube.com/playlist?list=PLv7SuAt_Vfa8vHX8g8_Ju9rzPhtLeurQQ 2. The Data Mining Contest Winners are announced here: http://tb.am/s0a3oMore details can be found here. 3. The Eighth International Automated Negotiating Agent Competition (ANAC) Student presentation on Sept. 21 http://web.tuat.ac.jp/~katfuji/ANAC2017/More details can be found here. 4. Angry Birds AI Competition Student presentation on Sept. 14 (19)http://aibirds.org/

  15. Positive Forces for AI • Data Science &Data Mining (KDD) / Learning for Examples • AI for the Web • Robotics https://www.wsj.com/articles/robot-hotel-loses-love-for-robots-11547484628?mod=itp_wsj&ru=yahoo • Multi-Agent Systems • AI and NLP: Chatbots, intelligent user interfaces that can communicate in natural language, doing intelligent things with text • Planning, Routing and Scheduling • Computer Chess/Go and Computer Games in General • Speech Recognition, Image Annotation • Computer Vision and Video Analytics • Deep Learning • Reinforcement Learning • AI for Social Impact • Reasoning under Uncertainty • Intelligent “this and that”

  16. 6368 Homepagehttp://www2.cs.uh.edu/~ceick/4368.html Textbook Code Repository https://github.com/aimacode IJCAI 2017 Homepagehttp://ijcai-17.org/index.html AAAI 2019 Homepagehttps://aaai.org/Conferences/AAAI-19/

  17. Course Elements • 23 Lectures • 3 Exams (2 midterms and final exam) • 3 Problem Sets (review questions, homework-style paper and pencil problems, tasks that involve using AI-tools and tasks that involve some programming) • A larger size Course Project: • Discussion of Problem Set Solutions • Three 45 minute Reviews before the three exams • Will try to use demos, videos and animations --- we have to see if this turns out to be useful; your input is appreciated!

  18. Tentative Course Schedule • February 12: deadline Problem Set1 • March 4: Midterm1 Exam • March 16: deadline Problem Set2 • April 8: Midterm2 Exam • April 13: Deadline Course Project • April 30: Deadline Problem Set3 • Monday, May 6, 2p: Final Exam

  19. Knowledge Representation Knowledge-based and Expert Systems AI Planning Coping with Vague, Incomplete and Uncertain Knowledge Searching Intelligently Logical Reasoning & Theorem Proving Communicating, Perceiving and Acting Intelligent Agents & Distributed AI AI Programming Learning & Knowledge Discovery

  20. AAAI 2019 #Session Counts • NLP: 20 • Vision (and Video Analytics): 12 • Game Theory and Economic Paradigms:10 surprise, surprise • AI and the Web: 9 • Machine Learning: 7 • AI for Social Impact: 7 • Search, Constraint Satisfaction and Optimization: 7 • Knowledge Representation and Reasoning: 6 • Deep Learning: 5 • Planning, Routing and Scheduling: 4 • Reinforcement Learning: 3 • Multi-Agent Systems: 3 • Reasoning under Uncertainty: 3 Remarks: Only topics with 3 AAAI sessions are mentioned; NLP, vision and AI&Web were aggregated into a single category! AAAI 2018 received 3900 papers, and AAAI 2019 received 7764 paper; about 2500 reviewers were needed to review the papers (Dr. Laszka and Dr. Eick were reviewing papers for AAAI 2019)

  21. Part1b: Examples of Problems Investigated by Different Subfields of AI IJCAI 2017 link: http://ijcai-17.org/index.html

  22. Knowledge Representation Problem: Can the above chess board that misses the NW&SE corner be covered by 31 domino pieces that cover 2 fields on a chess board? AI’s contribution: object-oriented and frame-based systems, ontology languages, logical knowledge representation frameworks, belief networks, semantic web, PROLOG,…

  23. Natural Language Understanding • I saw the Golden Gate Bridge flying to San Francisco. • I ate dinner with a friend. I ate dinner with a fork. • John went to a restaurant. He ordered a steak. After an hour John left happily. • I went to three dentists this morning.

  24. http://sanjonmotel.com/wp-content/uploads/2016/10/free-fire-evacuation-plan-template-free-business-template-free-fire-evacuation-plan-template.gifhttp://sanjonmotel.com/wp-content/uploads/2016/10/free-fire-evacuation-plan-template-free-business-template-free-fire-evacuation-plan-template.gif Planning Objective: Construct a sequence of actions that will achieve a goal. Example: John wants to buy a house Characteristics of Planning: • Goals and Subgoals • Operators that potentially make goal predicate true • Parallelism • Dependency between goals / subgoals • Plan and sub-plans might fail, requiring plan modification

  25. Heuristic Search • Heuristo (greek): I find • Copes with problems for which it is not feasible to look at all solutions • Heuristics: rules a thumb (help you to explore the more promising solutions first), based on experience, frequently fuzzy • Main ideas of heuristics: search space reduction, ordering solutions intelligently, simplifications of computations Example problems: puzzles, traveling salesman problem, chess,…

  26. Figure

  27. Evolutionary Computing • http://www2.cs.uh.edu/~ceick/6367/6367.html • Evolutionary algorithms are global search techniques. • They are built on Darwin’s theory of evolution by natural selection. • Numerous potential solutions are encoded in structures, called chromosomes. • During each iteration, the EA evaluates solutions adn generates offspring based on the fitness of each solution in the task. • Substructures, or genes, of the solutions are then modified through genetic operators such as mutation or recombination. • The idea: structures that led to good solutions in previous evaluations can be mutated or combined to form even better solutions.

  28. Soft Computing Conventional Programming: • Relies on two-valued logic • Mostly uses a symbolic (non-numerical knowledge representation framework) Soft Computing (e.g. Fuzzy Logic, Belief Networks, Hidden Markov Models): • Tolerance for uncertainty and imprecision • Uses weights, probabilities, possibilities • Strongly relies on numeric approximation and interpolation Remark: There seem to be two worlds in computer science; one views the world as consisting of numbers; the other views the world as consisting of symbols.

  29. Different Forms of Learning • Learning agent receives feedback with respect to its actions (e.g. using a teacher) • Supervised Learning/Learning from Examples/Inductive Learning: feedback is received with respect to all possible actions of the agent • Reinforcement Learning: feedback is only received with respect to the taken action of the agent • Unsupervised Learning: Learning without feedback

  30. Training Data Classifier (Model) Machine Learning Classification- Model Construction (1) Classification Algorithms IF rank = ‘professor’ OR years > 6 THEN tenured = ‘yes’

  31. Classifier Testing Data Unseen Data Classification Process (2): Use the Model in Prediction (Jeff, Professor, 4) Tenured?

  32. Knowledge Discovery in Data [and Data Mining] (KDD) • Definition := “KDD is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data” (Fayyad) Let us find something interesting!

  33. Flying SWARM Robots • http://arstechnica.com/science/2012/03/robots-swarm-the-stage-at-ted/ • Watch First 2 minutes. 4:30, 10:15. 15:30 • Requires: • Planning • Multi-Agent System and Distributed AI • Search • Reasoning in uncertain Environments • Machine Leaning • Computer Vision • ……

  34. 2. General Course Information Course Id: COSC 4368: Fundamentals of Artificial Intelligence Time:MO&WE 1-2:30p Instructor: Christoph F. Eick(573 PGH) Homepage:http://www2.cs.uh.edu/~ceick Office Hours MO 10:45a-noon WE 2:30-3:15p TAs RomitaBanejee(313 PGH) and Khadija Khaldi(550E PGH) Office Hours WE 12-1(both), Mo 10-11(Ro.), Mo 2:30-3:30(Kh.) Classroom:GAR 205 E-mail: ceick@uh.edu /

  35. Prerequisites COSC 4368 • Prerequisite: COSC 2320 or COSC 2430 • Otherwise, the course is self-contained • Some experience in writing programs with 400+ lines in some programming language (C, C++, Java,…) • Basic knowledge of data structures (particularly trees and graphs); what is taught in an introductory undergraduate data structure course; e.g. COSC 2430; will need to do some programming involving (search trees in the first course project). • basic data structures, complexity… • No knowledge of LISP, PROLOG and other AI languages is required • Ability to deal with “abstract mathematical concepts” • Basic knowledge of probability theory is helpful, but I will give a very basic review in early April…

  36. Textbook http://aima.cs.berkeley.edu/

  37. Other Things • There will be some group activities • I am contemplating giving students or groups of students small tasks (mostly giving presentations about an uprising subject of AI) that contribute to the course, and students will receive 3-4% credit for those tasks and present their results during the lecture. This might happen or not…

  38. Docollaborateanddiscusstogether,butwriteupandcodeindependently.Docollaborateanddiscusstogether,butwriteupandcodeindependently. • Solve Homework-style and AI tool problems yourself; however, you can discuss what is required to do with other students, but you cannot solve the problems jointly. A few course activities will be group activities. • Donotlookatanyoneelse’swriteuporcode. • Donotshowanyoneelseyourwriteuporcodeorpostitonline(e.g.,GitHub). • Whendebugging,onlylookatinput-outputbehavior. • WewillrunMOSSperiodicallytodetectplagarism.

  39. 2019 Grading Weights COSC 4368 3 Exams 50% 3 Problem Sets 24% 1 Project 20% Small Task/Extra Credit 0-4% Attendance 2% Remark: Weights are subject to change NOTE: PLAGIARISM IS NOT TOLERATED.

  40. Exams • Will be open notes/textbook • Will get a review list before the exam • Exams will center (80% or more) on material that was covered in the lecture • Exam scores will be immediately converted into number grades • As Dr. Eick taught this course the last time in Fall 2008, not many example exams will be available.

  41. Questionnaires • There will be a few questionnaires during the course of the semester, inquiring • Your programming experience and what languages you use… • Background knowledge from other courses • About your expectations • What things you like/ do not like when taking a course (e.g. making presentations, group project ) • What do you think about the graduate program you are part of? What do you expect from the graduate program you are part of?

  42. 2. General Course Information Course Id: COSC 4368: Fundamentals of Artificial Intelligence Time:MO&WE 1-2:30p Instructor: Christoph F. Eick(573 PGH) Homepage:http://www2.cs.uh.edu/~ceick Office Hours MO 10:45a-noon WE 2:30-3:15p TAs RomitaBanejee(313 PGH) and Khadija Khaldi(550E PGH) Office Hours WE 12-1(both), Mo 10-11(Ro.), Mo 2:30-3:30(Kh.) Classroom:GAR 205 E-mail: ceick@uh.edu /

  43. TwoviewsofAI AIagents:howcanwere-createintelligence? AItools:howcanwebenefitsociety? CS221/Autumn2018/Liang

  44. AnIntelligentAgent • Thestartingpointfortheagent-basedviewisourselves. • Ashumans,wehave tobeableperceivetheworld(computervision),performactionsinit(robotics),andcommunicatewithotheragents. • Wealsohaveknowledgeabouttheworld(fromhowtorideabiketoknowingthecapitalofFrance),andusingthisknowledgewecandrawinferencesandmakedecisions. • Finally,lastbutnotleast, welearnandadaptovertime.IndeedmachinelearninghasbecometheprimarydriverofmanyoftheAIapplicationsweseetoday. PerceptionRoboticsLanguage Knowledge Reasoning Learning CS221/Autumn2018/Liang

  45. Motivation:virtualassistant Tellinformation Askquestions • Usenaturallanguage! • [demo] • Needto: • Digestheterogenousinformation • Reasondeeplywiththatinformation 82 CS221/Autumn2018/Liang

  46. AI tools... • Approach: Provide AI techniques in a non-agent tool setting, e.g. • Learn models from examples • Annotate images with categories • Predict poverty from satellite images • Translate from language to another language • Tools, e.g. belief networks, for probabilistic reasoning • Planning and Scheduling Tools • … 33

  47. Whatinspiresyoumore? Buildingagentswithhuman-levelintelligence Developingtoolsthatcanbenefitsociety

  48. What we discussed so far • The AIdreamofachievinghuman-levelintelligenceisongoing • Stilllotsofopenresearchquestions • AIishavinghugesocietalimpact • Needtothinkcarefullyaboutreal-worldconsequences

  49. HowdowesolveAItasks?

  50. Paradigms Modeling Inference Learning 50

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