The 27 finalists — representing every school at MIT — will explore the technology’s impact on democracy, education, sustainability, communications, and much more.
Researchers use multiple AI models to collaborate, debate, and improve their reasoning abilities to advance the performance of LLMs while increasing accountability and factual accuracy.
With Style2Fab, makers can rapidly customize models of 3D-printable objects, such as assistive devices, without hampering their functionality.
The machine-learning method works on most mobile devices and could be expanded to assess other motor disorders outside of the doctor’s office.
Although computer scientists may initially treat data bias and error as a nuisance, researchers argue it’s a hidden treasure trove for reflecting societal values.
The system could improve image quality in video streaming or help autonomous vehicles identify road hazards in real-time.
“Lightning” system connects photons to the electronic components of computers using a novel abstraction, creating the first photonic computing prototype to serve real-time machine-learning inference requests.
A one-week summer program aims to foster a deeper understanding of machine-learning approaches in health among curious young minds.
With a new technique, a robot can reason efficiently about moving objects using more than just its fingertips.
MIT system demonstrates greater than 100-fold improvement in energy efficiency and a 25-fold improvement in compute density compared with current systems.