No Priors Ep. 67 | With Voyage AI Co-Founder and CEO

After Tengyu Ma spent years at Stanford researching AI optimization, embedding models, and transformers, he took a break from academia to start Voyage AI which allows enterprise customers to have the most accurate retrieval possible through the most useful foundational data. Tengyu joins Sarah on this week’s episode of No priors to discuss why RAG systems are winning as the dominant architecture in enterprise and the evolution of foundational data that has allowed RAG to flourish. And while fine-tuning is still in the conversation, Tengyu argues that RAG will continue to evolve as the cheapest, quickest, and most accurate system for data retrieval. They also discuss methods for growing context windows and managing latency budgets, how Tengyu’s research has informed his work at Voyage, and the role academia should play as AI grows as an industry. Show Notes: 0:00 Introduction 1:59 Key points of Tengyu’s research 4:28 Academia compared to industry 6:46 Voyage AI overview 9:44 Enterprise RAG use cases 15:23 LLM long-term memory and token limitations 18:03 Agent chaining and data management 22:01 Improving enterprise RAG 25:44 Latency budgets 27:48 Advice for building RAG systems 31:06 Learnings as an AI founder 32:55 The role of academia in AI

Sarah GuohostTengyu Maguest

Jun 6, 202436mWatch on YouTube ↗

EPISODE INFO

Released: June 6, 2024
Duration: 36m
Channel: No Priors
Watch on YouTube: ▶ Open ↗

EPISODE DESCRIPTION

After Tengyu Ma spent years at Stanford researching AI optimization, embedding models, and transformers, he took a break from academia to start Voyage AI which allows enterprise customers to have the most accurate retrieval possible through the most useful foundational data. Tengyu joins Sarah on this week’s episode of No priors to discuss why RAG systems are winning as the dominant architecture in enterprise and the evolution of foundational data that has allowed RAG to flourish. And while fine-tuning is still in the conversation, Tengyu argues that RAG will continue to evolve as the cheapest, quickest, and most accurate system for data retrieval. They also discuss methods for growing context windows and managing latency budgets, how Tengyu’s research has informed his work at Voyage, and the role academia should play as AI grows as an industry. Show Notes: 0:00 Introduction 1:59 Key points of Tengyu’s research 4:28 Academia compared to industry 6:46 Voyage AI overview 9:44 Enterprise RAG use cases 15:23 LLM long-term memory and token limitations 18:03 Agent chaining and data management 22:01 Improving enterprise RAG 25:44 Latency budgets 27:48 Advice for building RAG systems 31:06 Learnings as an AI founder 32:55 The role of academia in AI

SPEAKERS

Sarah Guo
host
Tengyu Ma
guest

EPISODE SUMMARY

In this episode of No Priors, featuring Sarah Guo and Tengyu Ma, No Priors Ep. 67 | With Voyage AI Co-Founder and CEO explores voyage CEO Explains Why RAG Beats Long Context For Enterprise AI Stanford professor and Voyage AI co-founder Tengyu Ma discusses his research journey from matrix completion and early sentence embeddings to modern contrastive learning, LLM optimizers, and domain-specific retrieval systems.

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