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Scaling Multi-Modal Generative AI with Luke Zettlemoyer - #650
Manage episode 379287122 series 2355587
Today we’re joined by Luke Zettlemoyer, professor at University of Washington and a research manager at Meta. In our conversation with Luke, we cover multimodal generative AI, the effect of data on models, and the significance of open source and open science. We explore the grounding problem, the need for visual grounding and embodiment in text-based models, the advantages of discretization tokenization in image generation, and his paper Scaling Laws for Generative Mixed-Modal Language Models, which focuses on simultaneously training LLMs on various modalities. Additionally, we cover his papers on Self-Alignment with Instruction Backtranslation, and LIMA: Less Is More for Alignment.
The complete show notes for this episode can be found at twimlai.com/go/650.
722 episoade
Scaling Multi-Modal Generative AI with Luke Zettlemoyer - #650
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Manage episode 379287122 series 2355587
Today we’re joined by Luke Zettlemoyer, professor at University of Washington and a research manager at Meta. In our conversation with Luke, we cover multimodal generative AI, the effect of data on models, and the significance of open source and open science. We explore the grounding problem, the need for visual grounding and embodiment in text-based models, the advantages of discretization tokenization in image generation, and his paper Scaling Laws for Generative Mixed-Modal Language Models, which focuses on simultaneously training LLMs on various modalities. Additionally, we cover his papers on Self-Alignment with Instruction Backtranslation, and LIMA: Less Is More for Alignment.
The complete show notes for this episode can be found at twimlai.com/go/650.
722 episoade
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