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The Language Neuroscience Podcast
Deep learning algorithms, natural language processing, and the brain, with Jean-Rémi King
Jul 10, 2023
Jean-Rémi King, research scientist and team leader at Meta AI, discusses deep learning algorithms, natural language processing, and the brain. They explore the similarities between brain responses and deep net activations, factors for algorithm similarity to the brain, and the need for nonlinear composition in language processing. They also delve into the size of predicted chunks, the functional similarity between deep neural networks and brain voxels, and the importance of choosing the right model architecture and parameters for predicting brain activity.
01:35:15
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Quick takeaways
- Language models' ability to predict the next word drives their similarity to brain activity in language processing.
- Different layers in language models align with specific brain regions, revealing a hierarchical organization in language processing.
Deep dives
Predicting brain activity through language models
This podcast episode discusses the use of large language models and deep learning in investigating the neural basis of language. The guest, John Remi King, shares his groundbreaking work on training language models with raw audio waveform data. The study reveals that the similarity between language models and brain activity is driven by the models' ability to predict the next word. The paper demonstrates that even randomly initialized language models exhibit a high level of similarity with brain activity, highlighting the importance of prediction in language processing. Additionally, the episode touches on the potential impact of AI research labs like META in advancing the field of language neuroscience.
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Discover
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