cs 440: introduction to arti cial intelligence...what you need to know de ning arti cial...

What you need to knowDefining Artificial Intelligence

Course TopicsFinal Note

CS 440: Introduction to Artificial IntelligenceLecture 1, Sep 2, 2010

Matthew Stone

September 2, 2010

Matthew Stone CS 440: Introduction to Artificial Intelligence



What you need to know

Fun, practical classTextbooks are well-reviewed O’Reilly guides designed to teachcomputer professionals useful and important techniques.




AI for Game Developers

David M. Bourg, Glenn Seemann




Programming Collective Intelligence

Toby Segaran




Getting the books

Will be at bookstore soon.Also available on Amazon (Bourg & Seemann $22, Segaran $26).Also available electronically through O’Reilly Safari digital library($20/month subscription).




Programming in Java

Four projects

I They won’t be easy, and you might need help

I But you’ll understand something new and importantafter you finish them




Academic in focus

Emphasis on careful thinking

I Most important and difficult question in the course:How can I tell if my program works?

Lots of connections

I To more advanced computer science

I To other scientific fields—linguistics, psychology, philosophy, neuroscience

I To other technical fields—statistics, information science, engineering




Course Organization

Lecture

I Big picture

I Focuses on techniques, ideas, background, etc.

Recitations

I Hands on

I Led by Tarek El-Gaaly

I Focuses on skills, programming, exercises, etc.




Course Organization

A few details still being worked out

I Office hours— mine will likely be Monday

I Sakai site— with syllabus, reading, lectures, technical infoYou will be added automatically as soon as it’s up




Plan for today

More details about the course topicMore details about course activitiesBy the end of the class you should have a clear idea of three points

I What makes something AI

I Why it’s easier to do AI now than ever

I What the class will be like




So what is AI?

The name comes with a lot of baggage

I academic history

I popular culture




AI in science fiction

HAL Still from 2001 Terminator Still from Terminator 3

C3P0 and R2D2 Still from Star Wars Data Star Trek TNG

David Still from AI (2001)Matthew Stone CS 440: Introduction to Artificial Intelligence



So what is AI?

The name comes with a lot of baggage

I academic history

I popular culture

I we’ll return to this

The usual definition is

I “creating systems that do things that would requireintelligence if done by a person”




Not such a good description these days

Example of state-of-the-art AI

I http://berkeley.edu/news/media/releases/2010/04/02_robot%20.shtml

This would not be an intellectual achievement if a person did it.


http://berkeley.edu/news/media/releases/2010/04/02_robot%20.shtml

http://berkeley.edu/news/media/releases/2010/04/02_robot%20.shtml



What is AI?

A better definition is

I “techniques for writing programs to carry out tasks that wecan’t specify exactly in advance”

Why is laundry robot like this?

I we don’t know where the laundry will be

I we only discover how to move the arm, gripper, etc., after webuild it

I physics says where you can pick up fabric, how it folds, etc.




What are the alternatives to preprogramming behavior?

Perception

I Give the system the ability to understand the situation inwhich it has to act.

Problem solving

I Give the system strategies that it can use to create its ownresponse to an unexpected situation.

Learning

I Design the system to tune the steps that it carries out basedon experience that it has that was not available to theprogrammer.




This class covers some concrete, useful strategies

A toolbox for writing powerful, general, versatile programs

I understanding the world

I acting in the world

I solving problems

I learning from experience




Acting in the world

Introduce “agents”—a general way of thinking about complexgeneral programsGet a sense of how AI problems of perception, planning, learning,action fit together.Project: Create and analyze a “flocking” simulation of simplecreatures

I A common strategy for animating “non-player characters” invideo games

I Foundations of simulations in psychology, economics, and“artificial life”




Household robotics

Roomba 500 (2007)”[the new model] will be able to sense when it’s about to hit a wall, so it’ll slow down and not whack itself into it.”—dvice.com

www.irobot.com




Solving problems

Introduce the ideas of problems, solutions, and search.Understand how to encode and optimize search algorithms to workeffectively.Project (plan): Path planning for Pac-Man

I Real connection to navigation and control applications

I Also used for many other optimization problems




Computer chess

Picture from 2009 world computer chess competition in Mainz, Germany From rybkachess.com




Learning and Perception

Introduce the problem of classification: relating a new example toprevious experience.Describe models based on statistics and based on similarity.Project (plan): Movie recommendation

I Inspired by the technology actually used by Netflix to makerecommendations

I Good example of the role of data in new AI applications




Recommendations




Combining Learning and Problem Solving

Understand how to use AI tools in a larger applicationLearn more about the strengths and limitations of differenttechniquesProject (plan): Work in small groups on a larger problemSelected from a short menu, likely including

I Play chess

I Play a video game (Pac-Man, Super Mario)

I Learn and recognize patterns in web data




Google translate




Google query processing




Speech recognition

Actual transcript: Hi Matthew. It sounds like we... missed paths and it’s probably my fault. My email’s been

defunct and I had the wrong number for you. Um, but maybe we can - I’ll see you in a second, and we can meet in

New York. Okay bye.




This class is a work in progress

AI is traditionally a research class

I These classes teach you how to read, critique, develop andtest new methods

Why?

I Because nothing really worked.




The challenge of AI implementations

The problem was that real applications were expensive

I Need a lot of compute power

I Need a lot of data

I Need ”practical know-how” to put it all together

That made the class kind of depressing

I Difficult ideas

I Difficult programming

I Lame results




This class is a work in progress

Things have changed

I Computers are faster— no surprise there

I Lots of data is available— web, sharing

I More know-how— e.g., toolkits for learning


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