LLMs trained primarily on text can generate complex visual concepts through code with self-correction. Researchers used these illustrations to train an image-free computer vision system to recognize real photos.
Researchers at MIT, NYU, and UCLA develop an approach to help evaluate whether large language models like GPT-4 are equitable enough to be clinically viable for mental health support.
MIT CSAIL researchers combine AI and electron microscopy to expedite detailed brain network mapping, aiming to enhance connectomics research and clinical pathology.
Google AI’s Jeff Dean has a seemingly straightforward objective: he wants to use a collection of trainable mathematical units organized in layers to solve complicated tasks that will ultimately benefit many parts of society.
Through the Multidisciplinary University Research Initiative, the US Department of Defense supports research projects in areas of critical importance to national defense.
Dina Katabi was recognized by her peers for her outstanding contributions to research.
MIT CSAIL and GIST researchers were awarded for their augmented reality method of evaluating algorithmic explanations in autonomous vehicles. The study found that users trust AVs more when they offer timely, relevant information about perception and traffic risks.
FinTech@CSAIL industry collaboration will work to improve business models, access to data, and security in the finance sector.
A new tool brings the benefits of AI programming to a much broader class of problems.
Experts convene to peek under the hood of AI-generated code, language, and images as well as its capabilities, limitations, and future impact.