To build a true artificial mind, first map out how thinking works. Enter the Common Model of Cognition.
Category: robotics/AI – Page 1,285
Staff Scientist Daniele Filippetto working on the High Repetition-Rate Electron Scattering Apparatus. (Credit: Thor Swift/Berkeley Lab)
– By Will Ferguson
Scientists have developed a new machine-learning platform that makes the algorithms that control particle beams and lasers smarter than ever before. Their work could help lead to the development of new and improved particle accelerators that will help scientists unlock the secrets of the subatomic world.
With our brand new documentary premiering at #SIGGRAPH 2022, you’ll get to take a look behind the scenes of the 2022 Spring GTC and discover how NVIDIA’s creative, engineering, and research teams pushed the limits of NVIDIA GPUs, AI, USD, and @NVIDIA Omniverse to deliver our most watched GTC ever.
Global Documentary Premiere: Wednesday, August 10, at 10:00 a.m. PT
Add the event to your calendar: https://nvda.ws/3z9kltq
Energy, mass, velocity. These three variables make up Einstein’s iconic equation E=MC2. But how did Einstein know about these concepts in the first place? A precursor step to understanding physics is identifying relevant variables. Without the concept of energy, mass, and velocity, not even Einstein could discover relativity. But can such variables be discovered automatically? Doing so could greatly accelerate scientific discovery.
This is the question that researchers at Columbia Engineering posed to a new AI program. The program was designed to observe physical phenomena through a video camera, then try to search for the minimal set of fundamental variables that fully describe the observed dynamics. The study was published on July 25 in Nature Computational Science.
The researchers began by feeding the system raw video footage of phenomena for which they already knew the answer. For example, they fed a video of a swinging double pendulum known to have exactly four “state variables”—the angle and angular velocity of each of the two arms. After a few hours of analysis, the AI produced the answer: 4.7.
A chess-playing robot fractured the finger of its 7-year-old opponent during a tournament in Moscow last week.
The incident happened after the boy hurried the artificial intelligence-powered robot, the president of the Moscow Chess Federation told the Russian state news agency Tass. “The robot broke the child’s finger — this, of course, is bad,” Sergey Lazarev said.
Video of the incident, which occurred at the Moscow Chess Open competition Tuesday, went viral on social media after a post by the local outlet Baza News.
Researchers at Ulm University in Germany have recently developed a new framework that could help to make self-driving cars safer in urban and highly dynamic environments. This framework, presented in a paper pre-published on arXiv, is designed to identify potential threats around the vehicle in real-time.
The team’s paper builds on one of their previous studies, featured in IEEE Transactions on Intelligent Vehicles earlier this year. This previous work was aimed at providing autonomous vehicles with situation-aware environment perception capabilities, thus making them more responsive in complex and dynamic unknown environments.
“The core idea behind our work is to allocate perception resources only to areas around an automated vehicle that are relevant in its current situation (e.g., its current driving task) instead of the naive 360° perception field,” Matti Henning, one of the researchers who carried out the study, told TechXplore. “In this way, computational resources can be saved to increase the efficiency of automated vehicles.”
Bonsai brain is one of the projects of Microsoft that aims to use trained AI models for automation and major tasks with lesser coding. Deep RL is used by bonsai brain to simulate and train the Bonsai platform.
A new artificial intelligence system has been asked to produce an image from inside of a black hole, and the results are stunning.