Lecture 1 – Introduction

1. Lecture 1 – Introduction Shuaiqiang Wang (王帅强) School of Computer Science and Technology Shandong University of Finance and Economics http://alpha.sdufe.edu.cn/swang/ shqiang.wang@gmail.com

2. About Me • Office: SDFIE center (舜耕校区 金融信息工程中心) • Education: • 2000.09 – 2009.12, Shandong Univ. (B.Sc. & Ph.D.) • 2009.07 – 2009.09, Hong Kong Baptist Univ. (visit) • Work Experience: • 2010.01 – 2011.02, Texas State Univ. (Postdoc) • 2011.03 – Current, SDUFE (Associate Prof.) • Research Interests • Data mining; Machine learning; Information retrieval

3. About This Course • I prepared everything carefully from several relevant courses! • I removed those out-of-date contents while introduced some state-of-the-art, useful and interesting chapters! • So, enjoy it! • Part I: Optimization • Part II: Frequent Pattern Mining • Part III: Clustering • Part IV: Classification • Part V: Search Engine and Recommender Systems

4. What is AI?

5. Acting Humanly: Turing Test • Turing (1950) "Computing machinery and intelligence": • "Can machines think?"  "Can machines behave intelligently?" • Operational test for intelligent behavior: the Imitation Game • Predicted that by 2000, a machine might have a 30% chance of fooling a lay person for 5 minutes • Suggested major components of AI: knowledge, reasoning, language understanding, learning

6. Thinking Humanly: Cognitive Modeling • 1960s "cognitive revolution": information-processing psychology • Requires scientific theories of internal activities of the brain • -- How to validate? Requires 1) Predicting and testing behavior of human subjects (top-down) or 2) Direct identification from neurological data (bottom-up) Both approaches (roughly, Cognitive Science and Cognitive Neuroscience) are now distinct from AI!

7. Thinking Rationally: “Laws of Thought" • Aristotle: what are correct arguments/thought processes? • Several Greek schools developed various forms of logic: notation and rules of derivation for thoughts; may or may not have proceeded to the idea of mechanization • Direct line through mathematics and philosophy to modern AI • Problems: • Not all intelligent behavior is mediated by logical deliberation • What is the purpose of thinking? What thoughts should I have?

8. Acting Rationally: Rational Agent • Rational behavior: doing the right thing • The right thing: that which is expected to maximize goal achievement, given the available information • Doesn't necessarily involve thinking – e.g., blinking reflex – but thinking should be in the service of rational action

9. History of AI (1) • 1943 McCulloch & Pitts: Boolean circuit model of brain • 1950 Turing’s “Computing Machinery and Intelligence” • 1950s Early AI programs, including Samuel’s checkers program, • Newell & Simon’s Logic Theorist, Gelernter’s Geometry Engine • 1956 Dartmouth meeting: “Artificial Intelligence” adopted

10. History of AI(2) • 1965 Robinson’s complete algorithm for logical reasoning • 1966–74 AI discovers computational complexity • Neural network research almost disappears • 1969–79 Early development of knowledge-based systems • 1980–88 Expert systems industry booms

11. History of AI(3) • 1988–93 Expert systems industry busts: “AI Winter” • 1985–95 Neural networks return to popularity • 1988– Resurgence of probability; general increase in technical depth • “Nouvelle AI”: ALife, GAs, soft computing • 1995– Agents, agents, everywhere . . . • 2003– Human-level AI back on the agenda

12. State-of-the-art • Decision Support • Data Mining • Machine Learning • Natural Language Processing • Web Intelligence • Information Retrieval • Pattern Recognition • Intelligent City

13. Important Issues • The ultimate goal of AI • E.g., machine translation can be done based on dictionaries, data and rules, without any understanding of languages • “How old are you?” • 怎么老是你？ • Representation • Logic or Probability?

14. ThankYou!