Generative models can now produce realistic and diverse synthetic data in many domains. This makes them a viable choice as a data source for training downstream AI systems. Unlike real data, synthetic data can be steered and optimized via interventions in the generative process. I will share my view on how this makes synthetic data act like data++, data with additional capabilities. I will discuss the advantages and disadvantages of this setting, and show several applications toward problems in computer vision and robotics.
Phillip Isola Associate Professor, Department of Electrical Engineering and Computer Science
Generative Models as a Data Source for AI Systems
William Fischer Senior Lecturer, Sloan School of Management
Explore cutting-edge human-computer interface technology and product advancements, covering areas such as soft materials development, digital fabrication methods, wearable bioadhesive ultrasound devices, emotion AI technologies, and the translation of wearables for monitoring vital functions and neurobiomarkers, with discussions on challenges, opportunities, and patient experience implications.