Raytheon tapped for self-evaluating machine learning system by Ed Adamczyk Washington DC (UPI) Jan 13, 2020 Raytheon Co. announced on Monday it has begun work on a machine-learning technology allowing machines to teach machines through artificial intelligence use. The $6 million contract is one of four, valued at a total of $20.9 million, between the U.S. Defense Research Projects Agency and Raytheon BBN Technologies, SRI International, BBN Technologies, Teledyne Scientific & Imaging and BAE Systems. The new deal calls for development of systems able to communicate information and the conditions of the initial learning, and recommended strategies and situations calling for those strategies. Known as CAML, or Categorical Abstract Machine Language, it uses a process similar to that in a video game; instead of rules, the system offers a list of choices and identification of a goal. By repeatedly playing the game, the system will learn the best way to achieve the goal. CAML focuses on competency awareness machine learning, so that a machine, using artificial intelligence, can self-assess its capability and strategy, and express them in a way understandable to humans. "The CAML system turns tools into partners," said Ilana Heintz, a principle investigator for CAML at Raytheon. "It will understand the conditions where it makes decisions and communicate the reasons for those decisions." The four-year DARPA program will involve three years of research and one year of technology demonstration.
Can sea star movement inspire better robots? Los Angeles CA (SPX) Jan 13, 2020 Have you ever seen a sea star move? To many of us, sea star seem motionless, like a rock on the ocean's floor, but in actuality, they have hundreds of tube feet attached to their underbelly. These feet stretch and contract to attach to rough terrain, hold on to prey and, of course, move. Any one tube foot on a sea star can act autonomously in responding to stimuli, but coupled together, they can synchronize their motion to produce a bouncing motion - their version of running. For years, researcher ... read more
|
|
The content herein, unless otherwise known to be public domain, are Copyright 1995-2024 - Space Media Network. All websites are published in Australia and are solely subject to Australian law and governed by Fair Use principals for news reporting and research purposes. AFP, UPI and IANS news wire stories are copyright Agence France-Presse, United Press International and Indo-Asia News Service. ESA news reports are copyright European Space Agency. All NASA sourced material is public domain. Additional copyrights may apply in whole or part to other bona fide parties. All articles labeled "by Staff Writers" include reports supplied to Space Media Network by industry news wires, PR agencies, corporate press officers and the like. Such articles are individually curated and edited by Space Media Network staff on the basis of the report's information value to our industry and professional readership. Advertising does not imply endorsement, agreement or approval of any opinions, statements or information provided by Space Media Network on any Web page published or hosted by Space Media Network. General Data Protection Regulation (GDPR) Statement Our advertisers use various cookies and the like to deliver the best ad banner available at one time. All network advertising suppliers have GDPR policies (Legitimate Interest) that conform with EU regulations for data collection. By using our websites you consent to cookie based advertising. If you do not agree with this then you must stop using the websites from May 25, 2018. Privacy Statement. Additional information can be found here at About Us. |