Traditional algorithms power complicated computational tools like machine learning. A new approach, called algorithms with predictions, uses the power of machine learning to improve algorithms.
lgorithms — the chunks of code that allow programs to sort, filter and combine data, among other things — are the standard tools of modern computing. Like tiny gears inside a watch, algorithms execute well-defined tasks within more complicated programs.
They’re ubiquitous, and in part because of this, they’ve been painstakingly optimized over time. When a programmer needs to sort a list, for example, they’ll reach for a standard “sort” algorithm that’s been used for decades.
Now researchers are taking a fresh look at traditional algorithms, using the branch of artificial intelligence known as machine learning. Their approach, called algorithms with predictions, takes advantage of the insights machine learning tools can provide into the data that traditional algorithms handle. These tools have, in a real way, rejuvenated research into basic algorithms.
Machine learning and traditional algorithms are “two substantially different ways of computing, and algorithms with predictions is a way to bridge the two,” said Piotr Indyk, a computer scientist at the Massachusetts Institute of Technology. “It’s a way to combine these two quite different threads.”