If a robot read a novel, how would it feel? You might get a sense from these little jingles. Below are some songs that were automatically created by a series of algorithms that turn the emotions in novels into short pieces of music. If the songs remind you, traumatically, of your untalented little sister practicing piano… well, you can't say I didn't warn you.
With just an fMRI, an algorithm, and the internet, researchers from Kyoto, Japan predicted with 60 per cent accuracy what a person was dreaming about, Smithsonian magazine reports. The idea, like the process, isn't all that complicated: Our brains react measurably differently to different stimuli: looking at a book or a building doesn't cause the same reaction. So the Kyoto team had three people sleep in an fMRI for three-hour stints over 10 days, and hooked them up to an EEG, which used electrical signals from the body to track which stage of sleep the were in were in.
When we feel there's a situation out of our control, we often fall back on superstition to account for it. ("Nothing else is working, why not blame it on that black cat?") But when enough of us rely on superstition, it's not just an individual comfort; it starts to have real repercussions. Now a designer has created an algorithm trades stock superstitiously, and it's going to see if gambling based on full moons and thirteens can pay off.
An algorithm called the fast Fourier transform is one of the most important aspects of your digital life that you never think about. It's a core concept in information technology, making possible the signal processing, image and audio compression, and other complex mathematics necessary for you to cram every episode of Breaking Bad onto your mobile device alongside every track Jay-Z ever made, and then play it all back without a hitch.
In 2006, US movie streaming and DVD-by-post company Netflix made its vast database of user-generated movie ratings available to the public, offering US$1 million to the first team that could improve the accuracy of the company's recommendations by 10 percent. That's a lot of money-but Netflix could have spent much more on in-house development, with no guarantees. By 2009, the top team had its prize, and Netflix had its algorithm. Other groups took notice and are now holding their own contests, asking statisticians, computer scientists and basement hobbyists alike to mine complex data sets for solutions to some difficult problems.
