To build the experimental stations of the future, scientists at the National Synchrotron Light Source II (NSLS-II), a U.S. Department of Energy (DOE) Office of Science user facility at DOE's Brookhaven National Laboratory, are learning from some of the challenges that face them today. As light source technologies and capabilities continue to advance, researchers must navigate increasingly complex workflows and swiftly evolving experimental demands.
Researchers developed a more efficient way to control the outputs of a large language model, guiding it to generate text that adheres to a certain structure, like a programming language, and remains error free.
Combining two different kinds of signals could help engineers build prosthetic limbs that better reproduce natural movements, according to a new study. A combination of electromyography and force myography is more accurate at predicting hand movements than either method by itself.
An international team has explored how in future aerial robots could process construction materials precisely in the air -- an approach with great potential for difficult-to-access locations or work at great heights. The flying robots are not intended to replace existing systems on the ground, but rather to complement them in a targeted manner for repairs or in disaster areas, for instance.
New research can transform how hospitals triage, risk-stratify, and counsel patients to save lives.
Helping music professionals explore the potential of generative AI
Helping music professionals explore the potential of generative AI
Helping music professionals explore the potential of generative AI
Helping music professionals explore the potential of generative AI
Helping music professionals explore the potential of generative AI
Helping music professionals explore the potential of generative AI
Helping music professionals explore the potential of generative AI
For a robot, the real world is a lot to take in. Making sense of every data point in a scene can take a huge amount of computational effort and time. Using that information to then decide how to best help a human is an even thornier exercise.
The core innovation of our new patent lies in its self-supervised approach to depth estimation. What that means is, the AI learns to judge the depth of an item it needs to pick up by using its own sensor data and the feedback it gets from its actions.
New research led by Imperial College London and co-authored by the University of Bristol, has revealed that aerial robotics could provide wide-ranging benefits to the safety, sustainability and scale of construction.