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Human Computation

Steven Emory CS 575. Human Computation. Overview. What is Human Computation? History of Human Computation Examples of Human Computation Bad Example Good Example Challenges in Human Computation. Definition of Computation. Normally we rely on computers to do all the work On input:

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Human Computation

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  1. Steven Emory CS 575 Human Computation

  2. Overview • What is Human Computation? • History of Human Computation • Examples of Human Computation • Bad Example • Good Example • Challenges in Human Computation

  3. Definition of Computation • Normally we rely on computers to do all the work • On input: • Step #1 • Step #2 • ... • Step #n • Ouput

  4. Definition of Human Computation • Some or all steps are solved by human(s)‏ • On input: • Step #1 • Step #2 (ask a human)‏ • Step #3 (ask a human)‏ • ... • Step #n • Ouput

  5. History of Human Computation • 1980's: Interactive Genetic Algorithms • 2000's: Human-Based Genetic Algorithms • 2000's: Outsourced Human Spam • 2000's: Interactive Guessing Games

  6. Quicksort Example • Bad Example: Quicksort • Input: Unsorted array • Select a pivot (human selection)‏ • Swap last element with pivot element • Partition array using pivot element • Insert pivot element into correct position • Repeat above steps for left/right side partitions • Output: Sorted array

  7. Quicksort Problems • Solved faster by computer alone • Not rewarding • Boring • Painful

  8. Photomosaic Example • Photomosaic Algorithm • Can be solved by computer alone. • On input “image gallery,” “source image”1.) Tile source image.2.) From left-to-right, top-to-bottom, compare each image in the image gallery to each tile in the source image, inserting the gallery image with the lowest error.3.) Output photomosaic.

  9. Photomosaic Problems • Noticable visual artifacts • Could use a randomized algorithm instead • Can we do better using Human Computation? • Of course!

  10. Photomosaic Solutions • Optimize important features first • Objects (eyes, nose, mouth)‏ • Edges (chin, facial edges, hair, body)‏ • Revised algorithm: • On input “image gallery,” “source image”1.) Tile source image.2.) Select important features (ask human).3.) Optimize (randomly) important features.4.) Optimize (randomly) unimportant features.5.) Output photomosaic.

  11. When to Use • When the problem: • is hard for a computer, but easy for a human • can be done better by a human • needs a human-like (artistic/creative) solution • is not boring to a human (music, art, games)‏ • is rewarding (financially or emotionally)‏ • is one and done

  12. Human Computation Challenges • User interface design • Coordinating many human participants • Analagous to distributed computing • Honesty • Prolonged computation

  13. reCAPTCHA Example • Used to digitize old books (make e-books)‏ • OCR normally works 99% of the time • OCR accuracy drops for older books • Old paper • Old printing techniques • Solution: Ask humans to determine words OCR fails to classify • When enough humans agree, consider it solved

  14. reCAPTCHA Example

  15. Metadata Example • Algorithm: Assign metadata to images • Useful for content/multimedia management systems (i.e. Istockphoto)‏ • No algorithm exists for image labeling • Luis Von Ahn's solution: The ESP Gamehttp://www.espgame.org • ESP Game Demo

  16. Metadata Example • Problem: • Getting humans to agree correctly

  17. Electric Sheep Example • Brief fractal explanation: • Iterative process based on chaos, dynamical systems • Newton's Method FractalSolve az3 + bz2 + cz + d = 0 for complex numbersa, b, c, d are fractal parameters • Cubic equation has 3 rootsRed = converges to root #1Green = converges to root #2Blue = converges to root #3Black = fails to converge

  18. Electric Sheep Example

  19. Electric Sheep Example • Interactive genetic algorithm • Humans evaulate fitness • Animated fractal parameters are mutated • Algorithm has been running for years • Implemented as a screensaver • http://www.electricsheep.org/

  20. Electric Sheep Example

  21. Conclusions • There many hard/impossible to solve problems • Nothing shameful about using Human Computation • Applications in art, music, computer vision, security, content management

  22. References • Luis Von Ahn • Google Talk lecture on Human Computation • http://www.cs.cmu.edu/~biglou/ • http://www.espgame.org • The Art of Artificial Evolution (2008)‏ • http://www.springerlink.com/content/r68831/?p=d04c460aa17749eb8153fec3d0507f68&pi=9

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