Evolving AI Systems Gracefully with Stefano Soatto

EPISODE 502
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About this Episode

Today we're joined by Stefano Soatto, VP of AI applications science at AWS and a professor of computer science at UCLA. Our conversation with Stefano centers on recent research of his called Graceful AI, which focuses on how to make trained systems evolve gracefully. We discuss the broader motivation for this research and the potential dangers or negative effects of constantly retraining ML models in production. We also talk about research into error rate clustering, the importance of model architecture when dealing with problems of model compression, how they've solved problems of regression and reprocessing by utilizing existing models, and much more.
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Thanks to our sponsor Amazon Web Services

You know AWS as a cloud computing technology leader, but did you realize the company offers a broad array of services and infrastructure at all three layers of the machine learning technology stack? AWS has been focused on making ML accessible to customers of all sizes and across industries, and over 100,000 of them trust AWS for machine learning and artificial intelligence services. AWS is constantly innovating across all areas of ML including infrastructure, tools on Amazon SageMaker, and AI services, such as Amazon CodeWhisperer, an AI-powered code companion that improves developer productivity by generating code recommendations based on the code and comments in an IDE. AWS also created purpose-built ML accelerators for the training (AWS Trainium) and inference (AWS Inferentia) of large language and vision models on AWS. 

To learn more about AWS ML and AI services, and how they’re helping customers accelerate their machine learning journeys, visit twimlai.com/go/awsml.

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