The carpet sensors can detect the tiniest of differences in a person's step, and thus indicate that something isn't right with the patient before a possible fall. Scientists are also experimenting with other logical extensions of the concept. For instance, chips and sensors with these kinds of alarm functions can also be incorporated into artificial hips. The Algorithm Builder Stefan Henss, a student, had initially hoped to earn a little extra cash by betting on sporting events. Hoping to outsmart the bookies, he wrote a program that was supposed to precisely predict football scores. But it didn't work very well.
Henss was 10 when he got his first computer, and he started programming at 13. Three years ago, when he was in his early 20s and studying at the Technical University of Darmstadt, he happened upon Kaggle, a platform where companies tender data projects. The companies are interested in obtaining the most precise predictions possible, as well as solving difficult problems for which they are unable to find solutions on their own.
Henss chose a task that had been posted by a car dealership platform, which was searching for a way to predict the resale prospects of used cars. He built an algorithm, in which he inserted a large number of details about the cars "into a context that made sense," as he describes it. The information included data such as original registration dates, mileage and annual distance driven.
He submitted his solution and came in sixth among the 571 teams from around the world competing for the $10,000 award. The challenge had awakened his ambition. "Competing with others was incredibly motivating. I knew that I was onto something," says Henss, who has since become one of the most successful algorithm builders on Kaggle. Nowadays he chooses his challenges more strategically, partly based on the amount of the award. Computer Grading
The approach led him to his biggest triumph to date. The challenge was to write a program that could automatically and reliably evaluate student essays -- essentially a grading machine. Using various standard algorithms, Henss built a program that takes the wealth of language into account, determines the number of spelling errors per word and recognizes grammatical errors. The program can draw conclusions on the content of essays. His algorithm can even detect how levelheaded the writer was or whether emotions were at play.
He spent a month and a half working exclusively on the program, which eventually consisted of 12,000 lines of code. A week before the end of the contest, he joined forces with two other competitors to increase his chances of winning. His new partners, an Englishman and an American who only knew each other through the platform, combined their solutions. They won the contest and split the prize money of $60,000.
"Tests have shown that our evaluations did not differ significantly from the teacher evaluations," says Henss. The trio has since sold the software to Pacific Metrics, a US company. Henss, who is now writing his master's thesis, can look forward to a bright future.
But there are also people whose lives are made more difficult by Big Data applications.