A branch of machine learning called deep learning has helped computers surpass humans at well-defined visual tasks like reading medical scans, but as the technology expands into interpreting videos and real-world events, the models are getting larger and more computationally intensive. By one estimate, training a video-recognition model can take up to 50 times more data and eight times more processing power than training an image-classification model. That's a problem as demand for processing power to train deep learning models continues to rise exponentially and concerns about AI's massive carbon footprint grow. Running large video-recognition models on low-power mobile devices, where many AI applications are heading, also remains a challenge. Song Han, an assistant professor at MIT's Department of Electrical Engineering and Computer Science (EECS), is tackling the problem by designing more efficient deep learning models.