Researchers have developed a computational framework that allows robots to adapt their physical form and plan movements for complex tasks, paving the way for more versatile and efficient automation.
A new visualization technique reveals nonlinear connections and feature importance, offering a clearer path to understanding multifaceted datasets.
A new approach ensures multiple robots can collaboratively build complex structures with flexibility and scalability.
New research proves that spiking neural networks possess a universal representation property, allowing them to efficiently approximate a broad class of temporal functions.
Researchers are pushing the boundaries of artificial intelligence by challenging models to understand and navigate complex urban environments using only visual and textual cues.
Researchers have developed a framework that automatically builds interpretable and provably-convex mathematical models directly from data, bypassing traditional modeling limitations.
Researchers have developed a new system that uses artificial intelligence to intelligently choose the best viewpoints for robots, dramatically improving their ability to grasp and manipulate objects in 3D space.
New research explores whether dividing the task of geometric reasoning between specialized AI agents improves problem-solving performance.
A new system, MobiMem, empowers AI agents to evolve and improve their performance through sophisticated memory management, bypassing the need for constant retraining.
A new study examines how well existing AI ethics tools help developers address potential biases and harms in language technologies.