Glossary of AI Glossaries

The language used to describe AI is evolving just as quickly as the technology itself. While there are many glossaries of AI terminology, some are better maintained than others. When learning about the field, it’s best to cross-reference definitions to keep up with the rapidly-evolving senses of these words. This is a glossary of AI glossaries (shout out Goold Brown, shout out PPA) that define the technical terms, emerging methods, and cultural formations surrounding AI. It was last updated 2026-03-24.

• Alan Turing Institute’s Data Science & AI Glossary
• AI for Humanists glossary, an NEH-funded project directed by Matt Wilkens, David Mimno, and Melanie Walsh
• DHRIFT (Digital Humanities Resource Infrastructure for Teaching Technology) glossary
• KPLEX (Knowledge Complexity) Project glossary, funded by the European Commission’s Horizon 2020 research program
• Sasha Luccioni, Bruna Trevelin, Margaret Mitchell, glossary for “The Environmental Impacts of AI – Primer”, Hugging Face Blog
• Elements of AI, University of Helsinki and MinnaLearn
• Yale’s Machine Learning Words and Concepts, part of their DH Library Guide
• Glossary of GenAI Terms, Centre for Teaching, Learning, and Technology, University of British Columbia
• Google’s Machine Learning Glossary
• A New AI Lexicon, project of AI Now Institute, 2021. 42 terms devoted to “alternate narratives, positionalities, and understandings to the better known and widely circulated ways of talking about AI.” Includes neologisms and loan words like algolinguicism and tequiologies.
• Open Encyclopedia of Cognitive Science (MIT), especially the thematic collection on AI and Cognitive Modeling
• Tables and figures where “terminological disarray is untangled" in Olivia Guest, “Against the Uncritical Adoption of ‘AI’ Technologies in Academia,” available here.