hunch_net hunch_net-2006 hunch_net-2006-209 knowledge-graph by maker-knowledge-mining

209 hunch net-2006-09-19-Luis von Ahn is awarded a MacArthur fellowship.


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Introduction: For his work on the subject of human computation including ESPGame , Peekaboom , and Phetch . The new MacArthur fellows .


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1 For his work on the subject of human computation including ESPGame , Peekaboom , and Phetch . [sent-1, score-1.129]


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Introduction: For his work on the subject of human computation including ESPGame , Peekaboom , and Phetch . The new MacArthur fellows .

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Introduction: Luis has released Peekaboom a successor to ESPgame ( game site ). The purpose of the game is similar—using the actions of people playing a game to gather data helpful in solving AI. Peekaboom gathers more detailed, and perhaps more useful, data about vision. For ESPgame, the byproduct of the game was mutually agreed upon labels for common images. For Peekaboom, the location of the subimage generating the label is revealed by the game as well. Given knowledge about what portion of the image is related to a label it may be more feasible learn to recognize the appropriate parts. There isn’t a dataset yet available for this game as there is for ESPgame, but hopefully a significant number of people will play and we’ll have one to work wtih soon.

3 0.25318003 159 hunch net-2006-02-27-The Peekaboom Dataset

Introduction: Luis von Ahn ‘s Peekaboom project has yielded data (830MB). Peekaboom is the second attempt (after Espgame ) to produce a dataset which is useful for learning to solve vision problems based on voluntary game play. As a second attempt, it is meant to address all of the shortcomings of the first attempt. In particular: The locations of specific objects are provided by the data. The data collection is far more complete and extensive. The data consists of: The source images. (1 file per image, just short of 60K images.) The in-game events. (1 file per image, in a lispy syntax.) A description of the event language. There is a great deal of very specific and relevant data here so the hope that this will help solve vision problems seems quite reasonable.

4 0.18192123 396 hunch net-2010-04-28-CI Fellows program renewed

Introduction: Lev Reyzin points out the CI Fellows program is renewed . CI Fellows are essentially NSF funded computer science postdocs for universities and industry research labs. I’ve been lucky and happy to have Lev visit me for a year under last year’s program , so I strongly recommend participating if it suits you. As with last year, the application timeline is very short, with everything due by May 23.

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Introduction: Lev and Hal point out the CI Fellows program is on again for this year. Lev visited me for a year under this program, and I quite enjoyed it. Due May 31.

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Introduction: For his work on the subject of human computation including ESPGame , Peekaboom , and Phetch . The new MacArthur fellows .

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Introduction: Luis has released Peekaboom a successor to ESPgame ( game site ). The purpose of the game is similar—using the actions of people playing a game to gather data helpful in solving AI. Peekaboom gathers more detailed, and perhaps more useful, data about vision. For ESPgame, the byproduct of the game was mutually agreed upon labels for common images. For Peekaboom, the location of the subimage generating the label is revealed by the game as well. Given knowledge about what portion of the image is related to a label it may be more feasible learn to recognize the appropriate parts. There isn’t a dataset yet available for this game as there is for ESPgame, but hopefully a significant number of people will play and we’ll have one to work wtih soon.

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Introduction: Luis von Ahn ‘s Peekaboom project has yielded data (830MB). Peekaboom is the second attempt (after Espgame ) to produce a dataset which is useful for learning to solve vision problems based on voluntary game play. As a second attempt, it is meant to address all of the shortcomings of the first attempt. In particular: The locations of specific objects are provided by the data. The data collection is far more complete and extensive. The data consists of: The source images. (1 file per image, just short of 60K images.) The in-game events. (1 file per image, in a lispy syntax.) A description of the event language. There is a great deal of very specific and relevant data here so the hope that this will help solve vision problems seems quite reasonable.

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Introduction: Luis von Ahn has been running the espgame for awhile now. The espgame provides a picture to two randomly paired people across the web, and asks them to agree on a label. It hasn’t managed to label the web yet, but it has produced a large dataset of (image, label) pairs. I organized the dataset so you could explore the implied bipartite graph (requires much bandwidth). Relative to other image datasets, this one is quite large—67000 images, 358,000 labels (average of 5/image with variation from 1 to 19), and 22,000 unique labels (one every 3 images). The dataset is also very ‘natural’, consisting of images spidered from the internet. The multiple label characteristic is intriguing because ‘learning to learn’ and metalearning techniques may be applicable. The ‘natural’ quality means that this dataset varies greatly in difficulty from easy (predicting “red”) to hard (predicting “funny”) and potentially more rewarding to tackle. The open problem here is, of course, to make

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