Citation tool offers a new approach to trustworthy AI-generated content
Researchers develop “ContextCite,” an innovative method to track AI’s source attribution and detect potential misinformation.
Researchers develop “ContextCite,” an innovative method to track AI’s source attribution and detect potential misinformation.
Researchers have developed a web plug-in to help those looking to protect their mental health make more informed decisions.
MIT engineers developed the largest open-source dataset of car designs, including their aerodynamics, that could speed design of eco-friendly cars and electric vehicles.
Researchers propose a simple fix to an existing technique that could help artists, designers, and engineers create better 3D models.
This new device uses light to perform the key operations of a deep neural network on a chip, opening the door to high-speed processors that can learn in real-time.
The method could help communities visualize and prepare for approaching storms.
The technique could make AI systems better at complex tasks that involve variability.
The Tree-D Fusion system integrates generative AI and genus-conditioned algorithms to create precise simulation-ready models of 600,000 existing urban trees across North America.
MIT CSAIL researchers used AI-generated images to train a robot dog in parkour, without real-world data. Their LucidSim system demonstrates generative AI’s potential for creating robotics training data.
An AI method developed by Professor Markus Buehler finds hidden links between science and art to suggest novel materials.