Chruser
2009-04-04, 07:00 PM
Cornell University researchers have created a program (http://blog.wired.com/wiredscience/2009/04/newtonai.html) that can find relationships in large amounts of data. It sounds like simple data processing, but it is not:
http://blog.wired.com/photos/uncategorized/2009/04/01/doublependulum.jpg
The Cornell program came up with an formula describing the physics of a two-part pendulum. It did in a day what some of the most brilliant physicist minds took centuries to do. The program accomplished this feat without any knowledge of physics or geometry!
This is only an example of what the researchers are hoping to do with such programs: To help human scientists analyze infinitely large data sets.
“One of the biggest problems in science today is moving forward and finding the underlying principles in areas where there is lots and lots of data, but there’s a theoretical gap. We don’t know how things work,” said Hod Lipson, the Cornell University computational researcher who co-wrote the program. “I think this is going to be an important tool.”
Condensing rules from raw data has long been considered the province of human intuition, not machine intelligence. It could foreshadow an age in which scientists and programs work as equals to decipher datasets too complex for human analysis.
http://blog.wired.com/photos/uncategorized/2009/04/01/doublependulum.jpg
The Cornell program came up with an formula describing the physics of a two-part pendulum. It did in a day what some of the most brilliant physicist minds took centuries to do. The program accomplished this feat without any knowledge of physics or geometry!
This is only an example of what the researchers are hoping to do with such programs: To help human scientists analyze infinitely large data sets.
“One of the biggest problems in science today is moving forward and finding the underlying principles in areas where there is lots and lots of data, but there’s a theoretical gap. We don’t know how things work,” said Hod Lipson, the Cornell University computational researcher who co-wrote the program. “I think this is going to be an important tool.”
Condensing rules from raw data has long been considered the province of human intuition, not machine intelligence. It could foreshadow an age in which scientists and programs work as equals to decipher datasets too complex for human analysis.