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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