Math breakthroughs don’t often capture the headlines—but MIT researchers have just made one that could lead to all sorts of amazing technological breakthroughs that in just a few years will touch every hour of your life.
Here’s a quickie explainer: Fourier transforms are a mathematical trick to simplify how you represent a complicated signal—say the waves of sound made by speaking. They work by reducing the complex wave pattern to a simple and pretty short list of numbers that, when run through the system again, result in a very good approximation of the original signal. FFTs (Fast Fourier Transforms) are simply a way of making this magic happen in a digital computer, but the combination of math and machine means the FFT has revolutionized science and many industries that have technology at their core. Which is why it’s been labeled the “most important algorithm of our lifetime.”
Now, you should remember that sound waves, and both picture and video signals, are all handled by processors in your TV, PC, and phone, and that the radio waves that whizz through the air to keep us all connected to the Internet need digital processing too. That’s every compressed sound signal that you listen to as an MP3 or similar format, most every image that you snap with your smartphone or DSLR, every image frame in the video you’re watching on your TV streamed over the Net, many images—such as those from an MRI—your doctor uses to diagnose your disease and every burst of radio that connects your cell phone to the nearest tower or your PC to its Wi-Fi router.
So calculating FFTs up to ten times faster is a big deal. It means that if you use existing hardware to do the math, it’ll be quicker at solving the problem you’ve set—so you need less compute time to do the task. If you’re talking about a portable computer like the one in your smartphone, that means it can spend more time doing other things instead. And with the valuable computing and battery resources of these portable devices under such pressure (you wouldn’t want your phone to be laggy now, would you?) that’s a good thing.