Phillip Isola Associate Professor, Department of Electrical Engineering and Computer Science
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.
Generative Models as a Data Source for AI Systems
Nan-Wei Gong and figur8 are spurring the growth of wearable technology within the sports medicine and digital health sectors, where they aim to commercialize digitized 3-D body movement technologies.
Management of Technology: Strategy & Portfolio Analysis is designed to expand and build upon the knowledge acquired by professionals in Management of Technology: Roadmapping & Development. In this continuation of the first program, the comprehensive goal is to lead participants in their quest to develop the necessary skills and knowledge to effectively manage technology within their organizations.