MIT researchers make language models scalable self-learners
The scientists used a natural language-based logical inference dataset to create smaller language models that outperformed much larger counterparts.
The scientists used a natural language-based logical inference dataset to create smaller language models that outperformed much larger counterparts.
All together, a core group of MIT.nano staffers has more than 400 years of technical experience in nanoscale characterization and fabrication.
A new multimodal technique blends major self-supervised learning methods to learn more similarly to humans.
Using insights into how people intuit others’ emotions, researchers have designed a model that approximates this aspect of human social intelligence.
Selecting the right method gives users a more accurate picture of how their model is behaving, so they are better equipped to correctly interpret its predictions.
Researchers develop an algorithm that decides when a “student” machine should follow its teacher, and when it should learn on its own.
A two-day conference at MIT reflected on the impact of the Institute for Data, Systems, and Society since its launch, as founding Director Munther Dahleh prepares to step down.
The current MEng student is one of 175 students nationwide honored for nonpartisan democratic engagement work.
The machine-learning algorithm identified a compound that kills Acinetobacter baumannii, a bacterium that lurks in many hospital settings.
It’s more important than ever for artificial intelligence to estimate how accurately it is explaining data.
Violence Prevention and Response and the Institute Discrimination and Harassment Response Office celebrate students and employees for their efforts in combating sexual misconduct.
Researchers create a new simulation tool for robots to manipulate complex fluids in a step toward helping them more effortlessly assist with daily tasks.
By mapping the volumes of objects, rather than their surfaces, a new technique could yield solutions to computer graphics problems in animation and CAD.
A new study finds human supervisors have the potential to reduce barriers to deploying autonomous vehicles.
This machine-learning method could assist with robotic scene understanding, image editing, or online recommendation systems.