Introduction to Artificial Intelligence

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Introduction to Artificial Intelligence

• Prof. Kathleen McKeown
• 722 CEPSR, 939-7118
• TAs
• Kapil Thadani
• 724 CEPSR, 939-7120
• Phong Pham
• TA Room

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Today

• What is artificial intelligence anyway?
• Requirements and assignments for class
• Examples of AI systems

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What is intelligence?

• Intelligence
• The ability to learn and solve problems
  (Websters Dictionary)
• The ability to think and act rationally
• Goal in artificial intelligence
• Build and understand intelligent systems/agents

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2001
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What is involved in intelligence?

• Ability to interact with the real world
• Reasoning and planning
• Learning and adaptation

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Definitions
Systems that think like humans Systems that think rationally
The exciting new effort to make computers think .. Machines with minds, in the full and literal sense (Haugeland, 1985) ..systems that exhibit the characteristics we associate with intelligence in human behavior understanding language, learning, reasoning, solving problems and so on (Handbook of AI)
Systems that act like humans Systems that act rationally
The study of how to make computers do things which, at the moment, humans do better (Rich and Knight) ..the study of rational agents that exist in an environment and perceive and act. (Russell and Norvig)
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• Systems that think like humans versus
• Systems that act like humans

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• Systems that think rationally versus
• Systems that act rationally

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Different Approaches to AI

• Building exact models of human cognition
• The view from psychology and cognitive science
• The logical thought approach
• Emphasis on correct inference
• Building rational agents
• Agent something that perceives and acts
• Emphasis on developing systems to match or exceed
  human performance, often in limited domains

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Class focus

• Systems that act
• Like humans
• Rationally

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AI is a smorgasbord of topics

• Core areas
• Knowledge representation
• Reasoning/inference
• Machine learning
• Perception
• Vision
• Natural language
• Robotics
• Uncertainty
• Probabilistic approaches

• General algorithms
• Search
• Planning
• Constraint satisfaction
• Applications
• Game playing
• AI and education
• Distributed agents
• Decision theory
• Electronic commerce
• Auctions
• Reasoning with symbolic data

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AI is a smorgasbord of topics

• Core areas
• Knowledge representation
• Reasoning/inference
• Machine learning
• Perception
• Vision
• Natural language
• Robotics
• Uncertainty
• Probabilistic approaches

• General algorithms
• Search
• Planning
• Constraint satisfaction
• Applications
• Game playing
• AI and education
• Distributed agents
• Decision theory
• Electronic commerce
• Auctions
• Reasoning with symbolic data

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AI used to be

• Expert systems
• Medical expert systems diagnosis
• Computer systems design
• Theorem proving/software verification
• Inheritance, class-based systems

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AI is interdisciplinary

• Psychology
• Cognitive Science
• Linguistics
• Neuroscience
• Economics
• Philosophy
• Physics

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What will we study in the course?
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Assignments

• 2 programming assignments
• Search (1.5 weeks)
• Game playing (3.5 weeks)
• Tournament
• 1 light programming/using tool plus paper (3
  weeks) machine learning
• 1 purely written assignment (1 week)
• Each programming assignment has written questions
  too

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Grading

• 45 homeworks homeworks are important. You
  cant pass without doing them.
• 5 class participation
• Notes will be posted on the web
• There will be board work in addition to slides.
  The slides dont tell the whole story.
• Class is a social experience there will be
  discussion
• End of Class Questions
• 20 midterm
• 30 final

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Undergrad vs. MS

• Separate grading curves
• Separate game tournaments
• MS students picked to raise discussion issues
  undergrads expected to respond

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Reading

• Chapters from the required text Artificial
  Intelligence A Modern Approach, Russell and
  Norvig, 2003. Columbia University Bookstore.
• Selected papers. Watch for papers on reserve.
• Will be posted on the Reading Section of the web

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Other AI Classes this semester

• 4701 NLP (Hirschberg)
• 4731 Computer Vision (Nayar)
• 4737 Biometrics (Belhumeur)
• 6733 3D Photography (Allen)
• 6998 Section 4 Search Engine Technology (Radev)

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Some Examples

• Natural language processing
• Question answering on the web
• Automatic news summarization
• Robotics
• Robocup soccer
• Roomba robotics meets the real world
• Vision
• Modeling the real world

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Machine Learning

• Learning to play pool
• Talking robots

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Todays Assignment

• Fill out on courseworks
• Survey worth 5 points towards total homework
  grade
• Answer the following questions
• UNI
• Degree BA BS MS PhD non-degree
• Year at Columbia (e.g., freshman, sophomore,
  junior, senior, 1st year MS, etc)
• Major
• Why are you taking this class?
• What do you want to get out of the class?
• What programming languages do you know?

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End of Class Questions