Humans and machines organize the world in very different ways. People are good at fitting small data sets into larger patterns: Eggs, chocolate, butter, sugar, and flour? Sounds like a brownie mix. Computers, on the other hand, are good at sorting huge data sets into clusters without supervision: All of these ingredients show up in recipes for sweets. But they can struggle with details: Wait, what's this Tootsie Roll doing in my brownie? A new machine-learning model out of MIT aims to close that gap.

Until now, if scientists asked a computer to sort data points unsupervised, the best it could do was throw similar-looking stuff into a big pile—a process known as topic modeling. The MIT model asks the computer to limit the size of each pile, and organize it around a prototype. In other words, the algorithm is more likely to throw Tootsie Rolls in a separate pile from brownies, because they don't appear in the set's most typical example of a brownie recipe.