Transcript Lecture 5 – Perception

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/
[email protected]
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
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• I removed those out-of-date contents while introduced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!
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Part I: Optimization
Part II: Frequent Pattern Mining
Part III: Clustering
Part IV: Classification
Part V: Search Engine and Recommender Systems
What is AI?
Thinking humanly
Acting humanly
Thinking rationally
Acting rationally
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 (topdown)
or 2) Direct identification from neurological data (bottomup)
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:
1. Not all intelligent behavior is mediated
by logical deliberation
2. 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”
• 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
State-of-the-art
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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?