Engineering household robots to have a little common sense
With help from a large language model, MIT engineers enabled robots to self-correct after missteps and carry on with their chores.
With help from a large language model, MIT engineers enabled robots to self-correct after missteps and carry on with their chores.
Researchers demonstrate a technique that can be used to probe a model to see what it knows about new subjects.
Single-cell gene expression patterns in the brain, and evidence from follow-up experiments, reveal many shared cellular and molecular similarities that could be targeted for potential treatment.
Novel method makes tools like Stable Diffusion and DALL-E-3 faster by simplifying the image-generating process to a single step while maintaining or enhancing image quality.
FeatUp, developed by MIT CSAIL researchers, boosts the resolution of any deep network or visual foundation for computer vision systems.
MIT CSAIL postdoc Nauman Dawalatabad explores ethical considerations, challenges in spear-phishing defense, and the optimistic future of AI-created voices across various sectors.
A new algorithm reduces travel time by identifying shortcuts a robot could take on the way to its destination.
By enabling models to see the world more like humans do, the work could help improve driver safety and shed light on human behavior.
Faster and more accurate than some alternatives, this approach could be useful for robots that interact with humans or work in tight spaces.
Fellows honored for creativity, innovation, and research accomplishments.
Adaptive smart glove from MIT CSAIL researchers can send tactile feedback to teach users new skills, guide robots with more precise manipulation, and help train surgeons and pilots.
Innovative AI system from MIT CSAIL melds simulations and physical testing to forge materials with newfound durability and flexibility for diverse engineering uses.
Two professors and three additional alumni recognized for “dreaming up solutions to global challenges — advancing health, sustainability, and human connection.”
Exploiting the symmetry within datasets, MIT researchers show, can decrease the amount of data needed for training neural networks.
The ambient light sensors responsible for smart devices’ brightness adjustments can capture images of touch interactions like swiping and tapping for hackers.