In the Berkeley Speech & Computation Lab he develops interpretable machine-learning models, including an “artificial baby” that learns speech from raw audio the way infants do. Beguš leads efforts to develop techniques that help us better understand the inner workings of AI.

As Linguistics Lead of Project CETI, he applies similar tools to the click sequences of sperm whales and recently showed that whales produce sound patterns analogous to human vowels.  

Beguš works with industry through InterpretAI to make neural networks more transparent, and he serves as College Principal of Bowles Hall, leading UC Berkeley’s oldest residential college. 

His research has been covered by National Geographic, Financial Times, The Atlantic, Quanta, Harvard Magazine, Noema Magazine, and others, and he has spoken at venues ranging from the National Science Foundation to the Centre Pompidou. 

Beguš regularly appears as an invited speaker in diverse venues such as NYU Stern School of Business, Centre Pompidou, the National Science Foundation, and the Santa Fe Institute. His models inspired parts of the La Biennale di Venezia exhibition and a science fiction book for young audiences.