newton-principia-mathematica_smallAn algorithm from researchers at Cornell has managed to data-mine the underlying laws of physics in just under one day.

For the past 50 years, it has been postulated that computer learning algorithms would out-pace the human mind in deriving laws of behavior from large, complicated datasets. Prizes (like the Leibniz Prize) were even been offered for the first program to fundamentally change mathematics. Despite many earnest attempts, Hal has yet to be created.

However, the modern availability of cheap memory and speed has meant that the processing power of researchers has grown exponentially, allowing for more sophisticated learning algorithms. Researchers have been able to learn from these algorithms, refine them and create even more elaborate methods until voila! A computer has been able to derive laws of physics with nothing more than experimental data.

To be more specific, the researchers, Schmidt and Lipson, fed experimental data on several simple systems such as a weight and spring or a 2-arm pendulum into the program. The program also had knowledge of simple mathematical operations. Using a method similar to Monte Carlo simulations, it kept trying and optimizing different forms of equations until it had derived equations that described the systems. In the process, it also expressed some underlying laws like conservation of momentum and Newton’s 2nd law of motion.

indusvalleyseals_smallIn another example of data-mining, researchers were able to answer one part of a hieroglyphic mystery that has perplexed archeologists to this day. The Indus Script from 4,000 years ago has remained undeciphereable. Some linguists have insisted that it is no language at all, but merely political ciphers (like the Democtratic donkey or Republican elephant) that were important in that day. The problem is that the longest chain of Indus Script contains only 27 characters, making it extremely difficult to crack. A group of researchers has now managed to “prove” that it is a real language by showing that the entropy level of the order of the Indus characters is very similar to human language.

Rao, a machine learning expert, and his team, fed their learning algorithm several different modern and ancient languages and measured the entropy of the word order. They also fed it non-verbal communication like DNA and FORTRAN code. These non-verbal communications turned out to have either extremely low or high entropy. The Indus language matched the mid-entropy level of human languages.

knots_smallIn other fascinating news, did you know that you can’t tie your shoelaces? If you don’t believe me, visit Ian’s shoelace site. Apparently, most of us use Granny knots to tie our shoelaces instead of the “correct” Shoelace knot.

Now you know.

 

 

 

Citations:

“Distilling Free-Form Natural Laws from Experimental Data.” By Michael Schmidt and Hod Lipson.  Science, Vol. 324, April 3, 2009.

Wired Science article on the topic: http://www.wired.com/wiredscience/2009/04/newtonai

“Entropic Evidence for Linguistic Structure in the Indus
Script.” By Rajesh P. N. Rao, Nisha Yadav, Mayank N. Vahia, Hrishikesh
Joglekar, R. Adhikari and Iravatham Mahadevan. Science, Vol. 324 Issue
5926, April 24, 2009.

Wired Science article on the Indus language:   http://www.wired.com/wiredscience/2009/04/indusscript#comment-152291563

Ian’s shoelace website: http://www.fieggen.com/shoelace/index.htm