Want to help the AI community? Play games!

Last week I heard Luis von Ahn‘s talk on CMU’s Human Computation project, which utilizes Human cycles to solve significant AI-Complete problems like Computer Vision and NLP. How do they enlist the help of free human labor? By wrapping these problems into addictive, interactive, web based games. Genius. For example, their ESP Game gives two remote players a series of identical images to label. For each label they both match, the higher their scores.

So, how does this help advance AI research? Well, despite our major breakthroughs in computer hardware and theory, we haven’t quite figured out a robust method to understanding the contents of a picture. This is a big problem because most pictures on the web don’t come with sufficient metadata and often have insensitive file names like ‘3.jpg’. However, unlike machines, us humans can easily categorize a set of images. These labels would significantly help search engines like Google improve their image rankings and even enable users to search for related images.

Other interesting remarks from the talk – In just the past year humans spent over 9 billion hours playing Windows Solitaire. 9 billion! ASDFLKQOW!! To put this into perspective, it took 5 million hours to construct the Empire State building and 20 million hours for the Panama Canal. That’s ridiculous. Luis estimates that if just 5,000 humans played ESP for a 2 month’s time, all the images in Google search engine could be accurately labeled.

So, the moral of this post – if you’re bored and got a few human cycles to spare, try playing these ‘productive’ games.


One thought on “Want to help the AI community? Play games!

  1. well to think about it, why can’t a search engine scour the web for images, just like it would for text ? Find out what the same image is called in 10 different locations, like and image would be called 3.jpg or car.jpg or ferrari.jpg in different locations. This could tell you more about what the image is all about ? This will automatically bring the “most copied” image to the top of relevancy , just like in text where the most linked page comes to the top of search results. More here

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