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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