Exploring Open-Source vs Closed-Source Models in AI Development

In this episode of Gradient Dissent, Together AI co-founder and Stanford Associate Professor Percy Liang joins host, Lukas Biewald, to discuss advancements in AI benchmarking and the pivotal role that open-source plays in AI development.

He shares his development of HELM—a robust framework for evaluating language models. The discussion highlights how this framework improves transparency and effectiveness in AI benchmarks. Additionally, Percy shares insights on the pivotal role of open-source models in democratizing AI development and addresses the challenges of English language bias in global AI applications. This episode offers in-depth insights into how benchmarks are shaping the future of AI, highlighting both technological advancements and the push for more equitable and inclusive technologies.

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Connect with Percy Liang:

• https://www.linkedin.com/in/percy-liang-717b8a/ 
• https://twitter.com/percyliang 

 Anticipatory Music Composer:

• https://stanford.io/3y5VycN 

Blog Post:

• https://crfm.stanford.edu/2024/02/18/helm-instruct.html

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