Today we’re joined by Ed Anuff, chief product officer at DataStax. In our conversation, we discuss Ed’s insights on RAG, vector databases, embedding models, and more. We dig into the underpinnings of modern vector databases (like HNSW and DiskANN) that allow them to efficiently handle massive and unstructured data sets, and discuss how they help users serve up relevant results for RAG, AI assistants, and other use cases. We also discuss embedding models and their role in vector comparisons and database retrieval as well as the potential for GPU usage to enhance vector database performance.
The complete show notes for this episode can be found at twimlai.com/go/664.
Quantizing Transformers by Helping Attention Heads Do Nothing with Markus Nagel - #663
Responsible AI in the Generative Era with Michael Kearns - #662
Edutainment for AI and AWS PartyRock with Mike Miller - #661
Data, Systems and ML for Visual Understanding with Cody Coleman - #660
Patterns and Middleware for LLM Applications with Kyle Roche - #659
AI Access and Inclusivity as a Technical Challenge with Prem Natarajan - #658
Building LLM-Based Applications with Azure OpenAI with Jay Emery - #657
Visual Generative AI Ecosystem Challenges with Richard Zhang - #656
Deploying Edge and Embedded AI Systems with Heather Gorr - #655
AI Sentience, Agency and Catastrophic Risk with Yoshua Bengio - #654
Delivering AI Systems in Highly Regulated Environments with Miriam Friedel - #653
Mental Models for Advanced ChatGPT Prompting with Riley Goodside - #652
Multilingual LLMs and the Values Divide in AI with Sara Hooker - #651
Scaling Multi-Modal Generative AI with Luke Zettlemoyer - #650
Pushing Back on AI Hype with Alex Hanna - #649
Personalization for Text-to-Image Generative AI with Nataniel Ruiz - #648
Ensuring LLM Safety for Production Applications with Shreya Rajpal - #647
What’s Next in LLM Reasoning? with Roland Memisevic - #646
Is ChatGPT Getting Worse? with James Zou - #645
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