Join me on my ML Journey from novice to expert! with some nomadic meanderings along the way.

Machine Learning

The ML Glossary: Five years of new language

Over guacamole and corn chips at a party, a friend mentions that her favorite phone game uses augmented reality. Another friend points her phone at the host and shouts, “Watch out—a t-rex is sneaking up behind you.” Eager to join the conversation, you blurt, “My blender has an augmented reality setting.

If only you had looked up augmented reality in Google’s Machine Learning Glossary, which defines over 460 terms related to artificial intelligence, you’d know what the heck your friends are talking about. If you’ve ever wondered what a neural network is, or if you chronically confuse the negative class with the positive class at the doctor’s office (“Wait, the negative class means I’m healthy?”), the Glossary has you covered.

AI is increasingly intertwined with our future, and as the language of AI sneaks its way into household conversation, learning AI’s specialized vocabulary could be helpful to understanding many key technological advances — or what’s being said at a guacamole party.

A team of technical writers and AI experts produces the definitions. Sure, the definitions have to be technically accurate, but they also have to be as clear as possible. Clarity is rare in a field as notoriously complicated as artificial intelligence, which is why we created Google’s Machine Learning glossary in 2016. Since then, we’ve published nine full revisions, providing almost 300 additional terms.

Good glossaries are quicksand for the curious. You’ll come for the accuracy, stay for the class-imbalanced datasets, and then find yourself an hour later embedded in overfitting. It’s fun, educational, and blessedly blender free.