No Priors: Artificial Intelligence | Technology | Startups Personalizing AI Models with Kelvin Guu, Senior Staff Research Scientist, Google Brain
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May 4, 2023 Kelvin Guu, a Senior Staff Research Scientist at Google Brain, leads innovations in retrieval-augmented language models. In this discussion, he unpacks how to personalize AI models efficiently, exploring the advantages of using external data sources for training. Kelvin also shares insights on the REALM model's architecture, emphasizing its modular and adaptive nature. The conversation dives into the complexities of knowledge representation in AI and the importance of aligning models with user values, providing a glimpse into the future of AI innovations.
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Retrieval-Augmented Models
- Retrieval-augmented models enhance memorization and modularity in AI.
- This allows swapping data sources like databases, crucial for various applications.
REALM Architecture
- REALM embeds user input and documents into vectors, enabling nearest neighbor search for relevant info retrieval.
- This approach uses cross-attention to process retrieved documents and predict outputs.
Retrieval vs. Expert Models
- Retrieval is best for specific facts, while expert models handle specialized language.
- Memorization in dense models improves with scale, making retrieval more useful for modularity and personalization.