| When I tell people that I write about computer science, they're often not sure what I mean. Do I cover software? Cybersecurity? The latest Silicon Valley gadgets? Actually, I rarely touch on any of these. A pithy quote attributed to the pioneering researcher Edsger Dijkstra helps me explain: "Computer science is no more about computers than astronomy is about telescopes."
As a journalist, I focus mostly on theoretical computer science, a branch of the field that predates the invention of modern digital computers by decades. It grew out of the efforts of Alan Turing and other researchers to mathematically formalize the process of doing mathematics, and parts of the field still have a similar self-referential character that I find equally bewildering and delightful.
Real computers can lead to developments in theoretical computer science, just as telescopes led to progress in astronomy. Once researchers started using computers to solve problems, they realized they'd need precise mathematical language to describe the procedures they were developing, called algorithms. That inspired new questions about the behavior and fundamental limits of algorithms — questions that proved subtle enough that researchers are still trying to answer them half a century later.
If you strip away dense mathematical notation and acronyms that'll make your eyes glaze over, you'll find that these questions are often quite simple. What makes some problems harder than others? What does it mean for something to be random? What do the laws of physics have to do with information? And how can we study algorithms whose inner workings we don't understand?
When I started covering computer science as a journalist in 2022, I knew next to nothing about the subject. In my past life as an experimental physicist, my programming experience mainly involved hunting for bugs in poorly documented code. It wasn't the most intellectually stimulating work, and I wasn't sure I would find the theoretical side of the field more compelling. But it quickly became clear to me that these fears were unfounded. Two years in, I'm still learning fascinating new things every time I write a story. Here are some of my favorites from 2024.
What's New and Noteworthy
Since the 1970s, computer scientists have used a unified mathematical framework called computational complexity theory to study the difficulty of different problems. In the 1980s and 1990s, new insights from quantum physics challenged conventional wisdom about how hard certain problems were, but they didn't change which problems were solvable in principle. Now, a string of recent results in theoretical cryptography has offered tantalizing evidence that some problems about quantum physics might be outside the framework of complexity theory entirely. The landscape of quantum complexity may be much weirder than researchers thought.
It may be unsurprising that these discoveries about quantum complexity theory only happened recently — after all, these are pretty heady subjects. But researchers have also made startling new discoveries about more down-to-earth problems, like finding the shortest routes through a road network. In 1956, the eminently quotable Dijkstra developed a simple algorithm for solving this problem. By the 1980s researchers had proved that his algorithm was the best one possible, in one specific theoretical sense: In a worst-case scenario, it finds those shortest paths faster than any other approach. But recently, a team of researchers discovered that a tiny tweak to Dijkstra's algorithm makes it provably unbeatable in many other cases as well. It's a nice illustration that researchers can still learn new things about classic algorithms that have been studied for decades.
My favorite computer science breakthrough of 2024, though, wasn't about fast algorithms but about extremely slow ones known affectionately as "busy beavers." Researchers study these computational critters to understand the most complicated things that simple-looking computer programs can do. By 1974, researchers had discovered a sequence of four busy beavers, each busier than its predecessor. This year, a ragtag band of beaver hunters working together online definitively identified the fifth in the series. It's a spectacular story, and "the cast of characters seems right out of a Michael Lewis book," in the words of one reader. If you read one story by me this year, make it this one. | 
