This blog is adapted from an original post by Groq CEO and Founder, Jonathan Ross.
Category: robotics/AI – Page 99
Feline-inspired vision technology enhances accuracy in challenging environments, paving the way for smarter, more efficient autonomous systems.
Researchers have unveiled a vision system inspired by feline eyes to enhance object detection in various lighting conditions. Featuring a unique shape and reflective surface, the system reduces glare in bright environments and boosts sensitivity in low-light scenarios. By filtering unnecessary details, this technology significantly improves the performance of single-lens cameras, representing a notable advancement in robotic vision capabilities.
Autonomous systems like drones, self-driving cars, and robots are becoming more common in our daily lives. However, they often struggle to “see” well in different environments — like bright sunlight, low light, or when objects blend into complex backgrounds. Interestingly, nature may already have the solution to this problem.
By using sensor-embedded sponges and data, Vienna researchers quickly trained robots to clean washbasins.
Thanks to researchers at TU Wein in Vienna, the promise of housecleaning robots is one step closer. The team has developed a self-learning robot to mimic humans to complete simple tasks like cleaning washbasins.
While this might sound mundane, the development is very significant as hard coding a robot to move a sponge over the complex curved edges of a washbasin would be a monumental task. To this end, the research team found a hack by blending observation with tactile data from human teachers to train robots to copy the same task.
With its unique design consisting of two wheels and gripper arms, evoBOT keeps itself balanced while performing a range of tasks.
EvoBOT, a uniquely shaped autonomous mobile robot, recently completed its first test runs at the Munich Airport.
A study introduces AI2BMD, an artificial intelligence-based dynamics simulation program that uses protein fragmentation with a machine learning force field to accurately and efficiently model the folding and unfolding of large proteins.
Rapid advances in applying artificial intelligence to simulations in physics and chemistry have some people questioning whether we will even need quantum computers at all.
Part of a groundbreaking effort to harness artificial intelligence (AI) to unlock the mysteries of the cosmos, the U.S. Department of Energy’s (DOE) Argonne National Laboratory is a key collaborator in the newly launched NSF-Simons AI Institute for the Sky (SkAI, pronounced “sky”), led by Northwestern University.
Jointly funded by a $20 million grant from the U.S. National Science Foundation (NSF) and the Simons Foundation, SkAI aims to revolutionize how researchers explore the universe by developing innovative AI technologies capable of handling the vast data generated by astronomical surveys.
The U.S. Department of Energy (DOE) has awarded DOE’s Argonne National Laboratory funding as part of its Artificial Intelligence (AI) for Scientific Research program.
Supported by DOE funding, two projects will drive innovations by improving how data is processed and protected, leading to faster and more secure discoveries.
