Is the GPT-3 a Good Model to Understand the Human Brain?

Joining SlatorPod this week is Ev Fedorenko, Associate Professor of Neuroscience at the Department of Brain and Cognitive Sciences, MIT. Ev also runs EvLab, an MIT language lab that discovers how the human brain creates language.

Ev talks about the different hypotheses concerning the origin of language and how it has likely been a gradual evolution. She shares a number of intriguing research findings on the relationship between language and abstract representations of structure (i.e., complex thought).

Ev discusses how language processing takes place and how we can use brain imaging to compare language with other non-linguistic tasks, such as solving math problems and composing music. She questions whether specific languages can be objectively easy or difficult to learn as an adult.

She also considers what sets polyglots apart when it comes to learning languages and some of the generalizations made in research. Ev talks about how language processing in machines like GPT-3 compares to that in humans. She argues that it would be more fruitful to build language systems that are structured similar to the human brain.

Ev concludes with the collaboration between academia and the booming field of applied AI, despite different goals. She touches on the MIT Quest for Intelligence, which brings together scientists and engineers to build better human-like models for the benefit of society.