Synthetic Data Training
Saurabh discusses the evolution of self-driving technology and the importance of extensive annotation and guidance in training models. He emphasizes the value of generating synthetic data to address various scenarios and failure modes, allowing models to learn efficiently without relying solely on real-world data. By leveraging techniques from gaming engines, the approach can enhance model performance across diverse conditions, mirroring the advancements seen in self-driving vehicles.In this clip
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Machine Learning Street Talk (MLST)
Cohere's SVP Technology - Saurabh Baji
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