One of the most active areas in AI research is the use of synthetic data, where simulations are used in place of real-world sensor collection to train AI algorithms. This may look like a straightforward process of running simulations in bulk. In reality, successful creation of synthetic data requires additional technologies, broad arrays of content, novel data pipelines, AI-centric quality metrics, and workflows that are relevant to a new (and very different) class of users. In this session, hear about some of the leading work being done in synthetic data with an eye toward how these gaps can be closed.

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