By blending 2D images with foundation models to build 3D feature fields, a new MIT method helps robots understand and manipulate nearby objects with open-ended language prompts.
The scientists used a natural language-based logical inference dataset to create smaller language models that outperformed much larger counterparts.
The CSAIL scientist describes natural language processing research through state-of-the-art machine-learning models and investigation of how language can enhance other types of artificial intelligence.
Award is given each year by the School of Engineering to an outstanding educator up for promotion to associate professor without tenure.
MIT researchers uncover the structural properties and dynamics of deep classifiers, offering novel explanations for optimization, generalization, and approximation in deep networks.
MIT researchers trained logic-aware language models to reduce harmful stereotypes like gender and racial biases.
Built on recent advances in machine learning, the model predicts how well individuals will produce and comprehend sentences.
Researchers used a powerful deep-learning model to extract important data from electronic health records that could assist with personalized medicine.
Yilun Du, a PhD student and MIT CSAIL affiliate, discusses the potential applications of generative art beyond the explosion of images that put the web into creative hysterics.