Nathan Labenz sits down with Lili Yu, a researcher of Meta AI to discuss the paper she authored: MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformers. In this conversation, they discuss the architecture and breakthroughs of their research, and the opportunity to eliminate the need for tokenization.
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LINK:
MEGABYTE Paper: https://arxiv.org/pdf/2305.07185.pdf
TIMESTAMPS:
(00:00) Episode preview
(07:41) Takeaways from Lili Yu's paper: MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformers
(17:00) Architecture
(24:59) Embeddings
(27:43) Different local models
(34:23) Encoder model
(36:35) Transformer Architecture
(48:10) Choosing patch size
(01:08) What happens when you scale up?
(01:19:20) Big picture for Meta AI
(01:22:57) Responsible AI
(01:27:02) China and AI
TWITTER:
@labenz (Nathan)
@liliyu_lili (Lili)
@eriktorenberg (Erik)
@cogrev_podcast
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