Sunday, June 10, 2007

Human computation

Human computation is the concept of using humans to solve problems that computers suck at. This is the same sort of goal that artificial intelligence has; namely, solving problems that humans are good at but are difficult for computers.

Human computation might well be the next big paradigm of Artificial Intelligence, even though this sounds like a paradox. Efficient machine learning algorithms usually require supervised training to learn how to solve a given problem, but training can be a daunting task. In unsupervised learning, some sort of fitness or reward function is required. Writing a good fitness function can be really complex. For instance, it would definitely not be trivial writing a fitness function which determines if a picture contains pornographic material. Humans, on the other hand, have little trouble doing this. Karl Sims has suggested using humans as fitness functions in genetic algorithms, and applied the technique to evolving beautiful art.

I think that the potential effect of Human Computation in an AI context can be compared to the effect that web 2.0 has had on content on the web.

The difficult part is motivating people to collaborate in the training process.

Money for nothing and cycles for free

Human cycles are usually more expensive than computer cycles. That is, unless you get them for free. Luis von Ahn has found a clever way to get those cycles for free: By designing computer games where the players sort a specific kind of problems. The first batch of games he has designed deals with image classification. For instance the ESP Game is two player game where both players have to agree on a word for an image. The result is a set of classifications tags for the image.

The other games are Peekaboom and Phetch.

In this video Luis von Ahn explains the concept and gives some examples of "human computation" games he has designed.

I am especially excited about a not-yet-released game, Verbosity, which deals with the problem of creating a large corpus of common-sense knowledge. The MIT project ConceptNet has attempted to do this using web-collaboration. It's currently the best corpus around, but there is certainly room for improvement. Entering common-sense knowledge seems really boring, but this game might actually make the process fun enough to get people to collaborate.

1 comment:

hthth said...

The latest AAAI magazine discusses mixed-initative assistants. Systems that deal with human-computer collaboration. Interesting read.

I think we can also be sure that, given semantic web applications for the public take off, we'll get an entirely new set of tools for making the masses aid intelligent systems more efficiently.