![]() ![]() ![]() The main target of their research is to employ data analytics and machine learning algorithms to monitor nuclear materials that could be used to produce nuclear weapons. PNNL researchers are employing machine learning for national security, too, as the laboratory's experts are combining their knowledge in nuclear nonproliferation and "artificial reasoning" to detect and (possibly) mitigate nuclear threats. Generative AI tools like ChatGPT are just the latest public face of a technology that has had many decades to mature and evolve. Today, PNNL said, machine learning is everywhere as it powers personalized shopping recommendations and voice-driven assistants like Siri and Alexa. The system was able to learn by itself, thanks to the aforementioned algorithm, without being explicitly programmed to change its strategy against chess player Robert Nealey. The official public debut of an ML algorithm dates back to 1962, PNNL said, when an IBM 7094 computer won against a human opponent in checkers. PNNL, which is one of the United States Department of Energy national laboratories, said that ML is everywhere now, and that it can be used to create "secure, trustworthy, science-based systems" designed to give people and nations answers to different kinds of difficult scientific challenges. The Pacific Northwest National Laboratory (PNNL) is trying to hunt for unknown nuclear threats by using machine learning (ML) algorithms. In context: While the entire technology world is focused on generative AI and its alleged capabilities to destroy the economy and the job market, researchers are employing neural networks to tackle challenges in science, energy, health and security, such as detection of rogue nuclear weapons.
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