El programa SAFRON (Safe and Assured Foundations Robots for Open Environment) que lleva adelante DARPA, la Agencia de Proyectos Avanzados de\u00a0Defensa\u00a0de EEUU,\u00a0convoca a la industria a presentar propuestas, sobre distintas posibilidades de incorporaci\u00f3n segura de Inteligencia Artificial (AI) y Machine Learning (ML) en sistemas de uso militar. SAFRON tiene por objetivo buscar alternativas que maximicen la confiabilidad de la IA \/ ML aplicada a los robots militares. Las propuestas deber\u00edan permitir a los expertos, desarrollar programas que incorporen IA y ML de la manera m\u00e1s r\u00e1pida, flexible, al menor costo y que fundamentalmente, otorguen la mayor seguridad en el accionar de los sistemas aut\u00f3nomos de todo tipo.<\/p>\n
ARLINGTON, Va. \u2013<\/strong>\u00a0U.S. military researchers are asking industry for ways of assuring trust in complex\u00a0military artificial intelligence (AI)<\/a>\u00a0programming, so that military leaders could use sophisticated robotics for crucial jobs without concern for erroneous and dangerous results.<\/p>\n Officials of the U.S. Defense Advanced Research Projects Agency (DARPA) in Arlington, Va., issued a advanced research concepts opportunity (DARPA-EA-24-01-0) last week for the Safe and Assured Foundation Robots for Open Environments (SAFRON) project.<\/p>\n SAFRON seeks to enhance trust in AI-based robots that use sophisticated foundation models, which can help computer experts develop AI and\u00a0machine learning<\/a>\u00a0for military applications quickly and cost-effectively.<\/p>\n Foundation models are trained on extremely large datasets, and are large deep-learning neural networks that can form the foundation of new AI applications that do not require scientists to develop AI programs from scratch.<\/p>\n Foundation models describe machine learning that is trained on a broad spectrum of generalized and unlabeled data that is capable of performing a wide variety of general tasks such as understanding language, generating text and images, and conversing in natural language.<\/p>\n Foundation models, for example, can enable robots to parse natural-language directions for complex tasks and then execute those tasks in real-world conditions. Although this represents a dramatic break from existing autonomous systems, natural-language direction for open-world autonomy presents a safety and assurance challenge.<\/p>\n