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Associate Professor Jonathan Ragan-Kelley, an MIT-IBM Watson AI Lab and CSAIL researcher, optimizes how computer graphics and images are processed for the hardware of today and tomorrow. Ragan-Kelley specializes in high-performance, domain-specific programming languages and machine learning.
Machine-learning models trained to classify human actions using synthetic data can outperform models trained using real data in certain situations. This could help scientists identify when it’s better to use synthetic data for training, which could eliminate bias, privacy, security, and copyright issues that often impact real datasets.
A machine learning model called VALHALLA uses a trained neural network to understand and digest a source sentence in one language, hallucinate an image of what that sentence describes, and translate into a target language. The work was led by researchers from MIT, IBM, and UC San Diego.