Lecture 1  Introduction

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# Lecture 1  Introduction - PowerPoint PPT Presentation

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. About Me. Office: SDFIE center ( 舜耕校区 金融信息工程中心 ) Education:

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### 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

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• I removed those out-of-date contents while introduced some state-of-the-art, useful and interesting chapters!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

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?
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
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
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
History of AI(3)
• 1988–93 Expert systems industry busts: “AI Winter”
• 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
State-of-the-art
• Decision Support
• Data Mining
• Machine Learning
• Natural Language Processing
• Web Intelligence
• Information Retrieval
• Pattern Recognition
• Intelligent City
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?