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Introduction to AI: Foundations, History, and Key Concepts

This course, led by Dr. Shuaiqiang Wang, explores the fundamentals of Artificial Intelligence (AI), encompassing critical areas such as optimization, data mining, machine learning, and information retrieval. The lectures cover historical milestones in AI, from Turing's early theories to modern advancements, addressing challenges and the ultimate goal of creating intelligent systems. The course aims to provide a comprehensive understanding of both theoretical principles and practical applications, making AI accessible and engaging for all learners.

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Introduction to AI: Foundations, History, and Key Concepts

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  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 • I removed those out-of-date contents while introduced some state-of-the-art, useful and interesting chapters!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!

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