Algorithms — 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.”

The recent explosion of interest in this approach began in 2018 with a paper by Tim Kraska, a computer scientist at MIT, and a team of Google researchers. In it, the authors suggested that machine learning could improve a well-studied traditional algorithm called a Bloom filter, which solves a straightforward but daunting problem.

Imagine you run your company’s IT department and you need to check if your employees are going to websites that pose a security risk. Naively, you might think you’ll need to check every site they visit against a blacklist of known sites. If the list is huge (as is likely the case for undesirable sites on the internet), the problem becomes unwieldly — you can’t check every site against a huge list in the tiny amount of time before a webpage loads.

The Bloom filter provides a solution, allowing you to quickly and accurately check whether any particular site’s address, or URL, is on the blacklist. It does this by essentially com