Last week at the Association for Computing Machinery’s Symposium on Discrete Algorithms (SODA) a new way of calculating Fast Fourier Transforms was presented by a group of MIT researchers. It’s possible that under certain situations it may be up to ten times faster than the current way we do these. At this point you are probably wondering: What the hell is he talking about? Let me explain, because improving these three little letters–FFT–may change 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.”

How so? Well, here’s just one example plucked from an average interaction with our daily tech: You’re certainly familiar with a type of image format called JPEG. They’re much smaller than other sorts of digital image format, which is why they’re used all over web pages like this one (that way less data has to get to your home from the Net speedily). The magic happens because the original complicated digital picture–an array of pixels with color and brightness–is squeezed by some clever math so that the JPEG looks at lot like it, with small errors you normally ignore, but it takes up less memory space. The core bit of this transformation is an FFT, treating the original image as a complicated signal.

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.

On the other hand, it also could let you use slower, cheaper computing hardware to do many of the same tasks we use today’s hardware to do–meaning the cost could tumble on some everyday objects.