E40: How can brain activity improve how we teach computers to talk?
Updated: Apr 17, 2022
The Ask AI interview with Dr. Alona Fyshe, Assistant Professor at University of Alberta
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Dr. Alona Fyshe is a leading researcher whose interests include Computational Linguistics, Machine Learning and Neuroscience and her work combines all three areas to study the way the human brain processes language.
Computer models of language are typically built using large bodies of text (corpora) collected from the Internet. These models have become very large and very accurate, but they still fall short in a few ways. First, they need a huge amount of data to train. Secondly, they misjudge meaning when inference or common sense is required.
Using data from brain imaging technologies like EEG and fMRI, Dr. Fyshe's research takes inspiration from what we know about the brain in order to improve language models, making them more efficient and more effective.
Tune in to this episode of the Ask AI podcast as host Melissa Kargiannakis sits down with Dr. Alona Fyshe, Assistant Professor at University of Alberta to ask the question:
How can brain activity improve how we teach computers to talk?
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GLB to OBJ was a fascinating listen, especially the part about how current models misjudge meaning when inference is required. It's exciting to think about using brain activity to bridge that gap.