Human Computation / Google TechTalks

Recently I proposed an alternative method for human identification on my blog so when a friend sent me this video via Del.icio.us, I was absolutely delighted:

Google Video: Human Computation / Google TechTalks 2006-07-26


Luis von Ahn

• Assistant Professor, Computer Science Department, Carnegie Mellon University
• Ph.D. Computer Science, Carnegie Mellon University, 2005
• B.S. Mathematics, Duke University, 2000
• Recipient, Microsoft Research Fellowship

Key points

• Tasks like image recognition are trivial for humans, but continue to challenge even the most sophisticated computer programs.
• Utilize human processing power to solve problems that computers cannot yet solve.
• Traditional approaches to solving such problems focus on improving software.
• Luis von Ahn advocates a novel approach: constructivly channel human brainpower using computer games.
• ESP Game = an enjoyable online game that help label images on the Web with descriptive keywords
• These keywords can be used to improve the accuracy of image search.
• People play the game not because they want to help, but because they enjoy it.

Fun Facts

• Number of human-hours spent playing Solitaire in 2003 = 9 billion human-hours
• Number of human-hours spent building the Empire State Building = 7 million human-hours (6.8 hours of Solitaire)
• Number of human-hours spent building the Panama Canal = 20 million human-hours (less than a day of Solitaire)

Related Web Sites

• www.captcha.net
• www.espgame.org
• www.peekaboom.org

Related SML

• FlickrAnalytics.com
• SML Pro Blog: Flickr Analytics / SML Analytics
• SML Pro Blog: Human Identification Algortihm

SML Copyright Notice

©2007 See-ming Lee 李思明 SML / SML Pro Blog / SML Universe. All rights reserved.

SML Google: Web History: Interesting Items / 2007-09-26T21:41-04:00 / SML

SML Google: Web History: Interesting Items / 2007-09-26T21:41-04:00 / SML, originally uploaded by See-ming Lee 李思明 SML.

SML.Screenshot: SML.Google.Web-History.Interesting-Items.20070926T2141-0400.png

SML.to.Google.2007-09-26T20:09-04:00

1. It appears that your algorithm on machine-learning is doing quite well. These items are indeed interesting!

2. I recommend that your tweak your modifiers. For example, in Top-Queries.5, Propellerhead.Reason.4 is obviously related to my audio software searches, but you should be smart enough to know that I don't torrent.

Torrent, like data, information, website, etc. is a modifier and as such should probably not be used. Focus on the subject, which, in this case, should be Reason 4. In other words, make sets out of your queries for (reason 4)* instead.

3. SML.initHHI(Google.Algorithm.Machine-Learning.Author);

Cheers,
See-ming

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Google Gadgets
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SML Copyright Notice
©2007 See-ming Lee / SML Flickr / SML Universe. All rights reserved.

SML Copyright Notice
©2007 See-ming Lee 李思明 SML / SML Pro Blog / SML Universe. All rights reserved.