Scaling Multi-Modal Generative AI with Luke Zettlemoyer - #650
Published:Oct 9, 2023 18:54
•1 min read
•Practical AI
Analysis
This article summarizes a podcast episode featuring Luke Zettlemoyer, a prominent researcher in the field of AI. The discussion centers on multi-modal generative AI, exploring the impact of data on model performance, and the importance of open-source principles. Key topics include the grounding problem, the need for visual grounding, and the benefits of discretization tokenization in image generation. The episode also delves into Zettlemoyer's research on scaling laws for mixed-modal language models and self-alignment techniques. The focus is on the technical aspects of developing and improving large language models (LLMs) that can handle multiple data types.
Key Takeaways
- •The episode discusses the challenges and advancements in multi-modal generative AI.
- •It highlights the importance of data and open-source approaches in AI research.
- •Key research areas include visual grounding, discretization tokenization, and scaling laws for mixed-modal language models.
Reference
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