Three Chinese nationals have been arrested in Georgia on suspicion of attempting to illegally purchase 2kg of uranium. Lasha Maghradze, deputy head of the nation's State Security Service (SSG), told a news briefing the group planned to pay $400,000 (£300,570) for the nuclear material in the capital, Tblisi, before transporting it to China via Russia. The alleged plot was unearthed by intelligence agents while one member of the group was attempting to buy the radioactive substance on the black market, he said. The three pleaded not guilty at a court in Tblisi and have been placed in custody to prevent them fleeing the country, according to public broadcaster Georgia Today. They face up to five years in prison under a provision of Georgia's criminal code banning the purchasing of nuclear material.

Deep learning still struggles with certain kinds of scientific data. Notably, pretraining data may not provide coverage of relevant distribution shifts (e.g., shifts induced via the use of different measurement instruments). We consider deep learning models trained to classify the synthesis conditions of uranium ore concentrates (UOCs) and show that model editing is particularly effective for improving generalization to distribution shifts common in this domain. In particular, model editing outperforms finetuning on two curated datasets comprising of micrographs taken of U$_{3}$O$_{8}$ aged in humidity chambers and micrographs acquired with different scanning electron microscopes, respectively.

Millions of shipments of nuclear and other radiological materials are moved in the U.S. every year for good reasons, including health care, power generation, research and manufacturing. But there remains the threat that bad actors in possession of stolen or illegally produced nuclear materials or weapons will try to smuggle them across borders for nefarious purposes. Texas A&M University researchers are making it harder for them to succeed. If border agents intercept illicit nuclear materials, investigators need to know who produced them and where they came from. Fortunately, nuclear materials carry certain forensic markers that can reveal valuable information, much like fingerprints can identify criminals.

Millions of shipments of nuclear and other radiological materials are moved in the U.S. every year for good reasons, including health care, power generation, research and manufacturing. But there remains the threat that bad actors in possession of stolen or illegally produced nuclear materials or weapons will try to smuggle them across borders for nefarious purposes. Texas A&M University researchers are making it harder for them to succeed. If border agents intercept illicit nuclear materials, investigators need to know who produced them and where they came from. Fortunately, nuclear materials carry certain forensic markers that can reveal valuable information, much like fingerprints can identify criminals.

San Francisco (CNN Business)A new artificial intelligence system is so good at composing text that the researchers behind it said they won't release it for fear of how it could be misused. Created by nonprofit AI research company OpenAI (whose backers include Tesla CEO Elon Musk and Microsoft), the text-generating system can write page-long responses to prompts, mimicking everything from fantasy prose to fake celebrity news stories and homework assignments. It builds on an earlier text-generating system the company released last year. Researchers have used AI to generate text for decades with varying levels of success. In recent years, the technology has gotten particularly good.

The use of fake news stories for political disinformation has become a major concern for governments around the world in the wake of the 2016 U.S. presidential election. The Federal Bureau of Investigation concluded Russia used false news reports, spread through social media, to try to sway voters. Writing these stories still needed someone to sit behind a keyboard. Now OpenAI, a non-profit artificial intelligence research group in San Francisco, has unveiled a machine learning algorithm that can generate coherent text, including fake news articles, after being given just a small sample to build on. The algorithm can be tuned to imitate the writing style of the sample text.

I find it appropriate that my first post on this platform will be on a topic that is in many ways central to my life and ever-more so to the lives of us all-- namely, artificial intelligence, or AI. This is actually just going to be a repost of my response to a Quora question asked by someone, where a number of other people, including famed Professor Andrew Ng, have responded. This topic of AI regulation is complex and multi-faceted, involving many (all?) people from various fields. However, as someone who's used AI in his work extensively, and as a long-time technologist and programmer, I wonder at some of the responses this question has received in the wider media, in particular by the AI community and other technologists-the most common view (and I exaggerate and simplify here) is that technological progress is good and must continue as fast as possible. Most of the rest of the technological and scientific world has a variety of monitoring systems and regulatory systems in place.

NEW YORK – Terrorists flying drones to spread highly radioactive material over a civilian area: That's part of the nightmare scenario President Barack Obama urged world leaders to consider as they debated better ways of controlling nuclear material. With the aid of apocalyptic fake newscasts, Obama told the group of 50 heads of state and foreign ministers in Washington Friday to imagine that a terrorist group had bought isotopes through brokers on the so-called dark Web. One shipment was picked up in transit by radiation monitors, but others were thought to be still on the move. The terrorists were believed to be planning to use a drone to distribute the material. Would authorities react in time?