AI + a16z

Vector Databases and the Power of RAG

Apr 26, 2024

Edo Liberty, CEO of Pinecone, discusses the challenges and potential of RAG technology in AI. He compares RAG to early transformers, highlighting its sharp edges but amazing capabilities. The conversation covers the evolution of infrastructure in machine learning, semantic search, and the future of AI knowledgeability.

36:41

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Edo Liberty

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Quick takeaways

RAG, while promising, is still in early stages resembling 2017 transformers, requiring innovation and refinement for optimal usage.

Vector databases and models like BERT are advancing semantic search and recommendation engines, enhancing user experiences and information retrieval capabilities.

Deep dives

Evolution of Machine Learning: From Basic Coding to Automated Processes

Machine learning has evolved from manual model building requiring months of coding complex optimization processes to today's more automated frameworks like TensorFlow and auto differentiation tools. The focus on making machines smarter and more predictive has remained consistent throughout this progression. Challenges such as building complex big data models have been met with continuous advancements to train bigger models efficiently and effectively.

Expanding Cognitive Bandwidth with RAG Technology

01:22

Success through Imperfect but Functional Solutions

01:18

Efficient Infrastructure in AI and Machine Learning

01:11

Introduction

4min

Evolution of Infrastructure and Impact on Machine Learning

11min

Evolution of Semantic Search and Recommendation Engines with Vector Databases

5min

The Evolution of RAG Technology in Information Retrieval and AI

14min

Exploring the Future of AI Knowledgeability and Problem Solving

2min

Pinecone Founder and CEO Edo Liberty joins a16z's Satish Talluri and Derrick Harris to discuss the promises, challenges, and opportunities for vector databases and retrieval augmented generation (RAG). He also shares insights and highlights from a decades-long career in machine learning, which includes stints running research teams at both Yahoo and Amazon Web Services.

Because he's been at this a long time, and despite its utility, Edo understands that RAG — like most of today's popular AI concepts — is still very much a progress:

"I think RAG today is where transformers were in 2017. It's clunky and weird and hard to get right. And it has a lot of sharp edges, but it already does something amazing. Sometimes, most of the time, the very early adopters and the very advanced users are already picking it up and running with it and lovingly deal with all the sharp edges ...

"Making progress on RAG, making progress on information retrieval, and making progress on making AI more knowledgeable and less hallucinatory and more dependable, is a complete greenfield today. There's an infinite amount of innovation that will have to go into it."

More about Pinecone and RAG:

Investing in Pinecone

Retrieval Augmented Generation (RAG)

Emerging Architectures for LLM Applications

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Edo Liberty

Satish Talluri

Derrick Harris

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