24 April 202459 min 4 sec

About the Session

In this episode of Leading with Data, we have with us Vijay Karunamurthy, the field Chief Technology Officer at Scale AI, where he works to democratize the capabilities of Generative AI and Large Language Models, partnering with large model providers, governments, and Fortune 500 companies. Prior to Scale, Vijay was a Director of Engineering at Apple and Google, where he worked on AI applications for personalizing human interfaces.

 

Join us as Vijay takes us through his journey on some fascinating topics:

 

➡️Explore the evolution of search and discovery on the world's biggest video platform - YouTube.

➡️Discover how Apple is pioneering practices to protect your privacy while harnessing the power of machine learning.

➡️Vijay’s recent talk at GTC '24 offers insights into red teaming, reinforcement learning from human feedback, and frameworks for responsible AI.

➡️Discover the latest advancements and frameworks shaping the future of AI.

➡️Vijay’s entrepreneurial journey and his excitement about the future of self-driving cars with AI advancements!

About the Speaker

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Vijay Karunamurthy

Field Chief Technology Officer at Scale AI

Vijay Karunamurthy is the field Chief Technology Officer at Scale AI, where he works to democratize the capabilities of Generative AI and Large Language Models, partnering with large model providers, governments, and Fortune 500 companies. Prior to Scale, Vijay was a Director of Engineering at Apple and Google, where he worked on applications of AI for personalizing human interfaces. Vijay was an early employee at YouTube (prior to the acquisition by Google), and led Search and Discovery as well as collaborations with media, entertainment, and advertising agencies. He holds a BA in Mathematics from the University of Illinois at Urbana-Champaign, a MS in Computer Science from Stanford University, and an MBA from the UC Berkeley Haas School of Business.

Keynotes/transcript

Arnav Garg (15:32 min)

Arnav Garg (15:32 min)

Arnav Garg (15:32 min)

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