Uploaded August 2022 | Updated September 2026, 1 day ago
Patreon for full episodes and Discord community:
patreon.com/braininspired
Free Video Series: Open Questions in AI and Neuroscience:
braininspired.co/open
Apple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2
Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLe
Music by: The New Year: thenewyear.net
Show notes: braininspired.co/podcast/144
Large language models, often now called "foundation models", are the model de jour in AI, based on the transformer architecture. In this episode, I bring together Evelina Fedorenko and Emily M. Bender to discuss how language models stack up to our own language processing and generation (models and brains both excel at next-word prediction), whether language evolved in humans for complex thoughts or for communication (communication, says Ev), whether language models grasp the meaning of the text they produce (Emily says no), and much more.
Evelina Fedorenko is a cognitive scientist who runs the EvLab at MIT. She studies the neural basis of language. Her lab has amassed a large amount of data suggesting language did not evolve to help us think complex thoughts, as Noam Chomsky has argued, but rather for efficient communication. She has also recently been comparing the activity in language models to activity in our brain's language network, finding commonality in the ability to predict upcoming words.
Emily M. Bender is a computational linguist at University of Washington. Recently she has been considering questions about whether language models understand the meaning of the language they produce (no), whether we should be scaling language models as is the current practice (not really), how linguistics can inform language models, and more.
0:00 - Intro
5:04 - Language and cognition
16:09 - Grasping for meaning
22:05 - Are language models producing language?
23:42 - Next-word prediction in brains and models
32:50 - Interface between language and thought
36:00 - Studying language in nonhuman animals
42:40 - Do we understand language enough?
46:41 - What do language models need?
52:34 - Are LLMs teaching us about language?
55:48 - Is meaning necessary, and does it matter how we learn language?
1:00:47 - Is our biology important for language?
1:05:57 - Future outlook
Patreon for full episodes and Discord community:
patreon.com/braininspired
Free Video Series: Open Questions in AI and Neuroscience:
braininspired.co/open
Apple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2
Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLe
Music by: The New Year: thenewyear.net
Show notes: braininspired.co/podcast/144
Large language models, often now called "foundation models", are the model de jour in AI, based on the transformer architecture. In this episode, I bring together Evelina Fedorenko and Emily M. Bender to discuss how language models stack up to our own language processing and generation (models and brains both excel at next-word prediction), whether language evolved in humans for complex thoughts or for communication (communication, says Ev), whether language models grasp the meaning of the text they produce (Emily says no), and much more.
Evelina Fedorenko is a cognitive scientist who runs the EvLab at MIT. She studies the neural basis of language. Her lab has amassed a large amount of data suggesting language did not evolve to help us think complex thoughts, as Noam Chomsky has argued, but rather for efficient communication. She has also recently been comparing the activity in language models to activity in our brain's language network, finding commonality in the ability to predict upcoming words.
Emily M. Bender is a computational linguist at University of Washington. Recently she has been considering questions about whether language models understand the meaning of the language they produce (no), whether we should be scaling language models as is the current practice (not really), how linguistics can inform language models, and more.
0:00 - Intro
5:04 - Language and cognition
16:09 - Grasping for meaning
22:05 - Are language models producing language?
23:42 - Next-word prediction in brains and models
32:50 - Interface between language and thought
36:00 - Studying language in nonhuman animals
42:40 - Do we understand language enough?
46:41 - What do language models need?
52:34 - Are LLMs teaching us about language?
55:48 - Is meaning necessary, and does it matter how we learn language?
1:00:47 - Is our biology important for language?
1:05:57 - Future outlook
