Introduction to Artificial Intelligence

Prof. Kathleen McKeown722 CEPSR, 939-7118TAs:Kapil Thadani724 CEPSR, 939-7120Phong PhamTA 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”

(Webster’s 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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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 can’t 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 don’t 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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Today’s 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