Saudi Arabia has appealed to the United States and its allies in Europe and the Gulf for resupplies of ammunition it uses to defend the kingdom against drone and missile attacks, the Wall Street Journal reported on Tuesday (paywall), citing US and Saudi officials. Riyadh has been using its Patriot surface-to-air missile system over the past several months to thwart weekly ballistic missile and drone attacks launched by Houthi rebels based in Yemen, the officials told the WSJ. But the kingdom's stock of Patriot missiles to intercept aerial attacks has run dangerously low. The call for resupplies comes after the US has scaled back a large of portion its military presence in the Middle East that shored up the kingdom's security as the administration of President Joe Biden pivots to counter China's growing prowess on the global stage. Though the US is expected to approve the Saudi request for more Patriot interceptors, Saudi officials told the Journal they are concerned that insufficient stocks could result in a successful missile or drone attack, costing lives in the kingdom or harming the Saudi economy by damaging its critical oil infrastructure.

USB drives are used by ransomware attackers to distribute malware across the air gap that all industrial distribution, manufacturing, and utility firms rely on as their first line of defense against cyber attacks. According to Honeywell's Industrial Cybersecurity USB Threat Report 2021, 79 percent of USB assaults have the potential to damage operational technologies (OT) that power industrial processing plants. The incidence of malware-based USB attacks is one of the most rapidly developing and difficult-to-detect threat vectors that process industries such as public utilities confront today, according to the research. As the Colonial Pipeline and JBS Foods demonstrate, this type of attack vector is particularly effective. Utility companies are also being targeted by ransomware criminals, as the thwarted water treatment plant attacks in Florida and Northern California illustrate.

Elon Musk has said he does not want to "raise hopes unreasonably" but is "increasingly confident" his brain-machine interface startup Neuralink will have its first implant devices in humans by 2022. The billionaire said he is "cautiously optimistic" the implants can restore full-body functionality for tetraplegics and quadriplegics, adding that the company's standards for implanting the brain-computer interface device are higher than what the FDA requires. "I think we have a chance with Neuralink to restore full-body functionality to someone who has a spinal cord injury," Musk said in a video interview during the Wall Street Journal's CEO Council Conference. "I don't want to raise hopes unreasonably, but I'm increasingly convinced that this could be done," he added. Crypto market enters'extreme fear' following bitcoin crash – follow live France to mimic Elon Musk with own SpaceX-style launcher Suicide machine that kills users in the blink of an eye'legalised in Switzerland' Suicide machine that kills users in the blink of an eye'legalised in Switzerland' In April, the startup released a video of a monkey named Pager with the brain-computer interface chip that was able to play video games using only its mind.

The British government has published a new standard for transparency in the algorithms that organisations use to support decision-making.

Officials at the Department of Veterans Affairs are looking to increase efficiency and optimize their clinicians' professional capabilities, featuring advanced artificial intelligence and machine learning technologies. In a November presolicitation, the VA seeks to gauge market readiness for advanced healthcare device manufacturing, ranging from prosthetic solutions, surgical instruments, and personalized digital health assistant technology, as well as artificial intelligence and machine learning capabilities. "What we are trying to do is utilize AI and machine learning to remove administrative burden of tasks," she told Nextgov. The technology requested by the department will be tailored to areas where a computer can do a better, more efficient job than a human, and thereby give people back time to complete demanding tasks that require human judgement. Some of these areas the AI and machine learning technology could be implemented include surgical preplanning, manufacturing submissions, and 3D printing, along with injection molding to produce plastic medical devices and other equipment.

A new paper from the University of California and Google Research has found that a small number of'benchmark' machine learning datasets, largely from influential western institutions, and frequently from government organizations, are increasingly dominating the AI research sector. The researchers conclude that this tendency to'default' to highly popular open source datasets, such as ImageNet, brings up a number of practical, ethical and even political causes for concern. Among their findings – based on core data from the Facebook-led community project Papers With Code (PWC) – the authors contend that'widely-used datasets are introduced by only a handful of elite institutions', and that this'consolidation' has increased to 80% in recent years. '[We] find that there is increasing inequality in dataset usage globally, and that more than 50% of all dataset usages in our sample of 43,140 corresponded to datasets introduced by twelve elite, primarily Western, institutions.' Criteria for inclusion is where the institution or company accounts for more than 50% of known usages.

Humans are pretty good at looking at a single two-dimensional image and understanding the full three-dimensional scene that it captures. Artificial intelligence agents are not. Yet a machine that needs to interact with objects in the world -- like a robot designed to harvest crops or assist with surgery -- must be able to infer properties about a 3D scene from observations of the 2D images it's trained on. While scientists have had success using neural networks to infer representations of 3D scenes from images, these machine learning methods aren't fast enough to make them feasible for many real-world applications. A new technique demonstrated by researchers at MIT and elsewhere is able to represent 3D scenes from images about 15,000 times faster than some existing models.

Artificial intelligence operates on a different plane than human reason. As described by various futurists and technologists, AI is literally an "alien mind." In advanced artificial neural networks, the modes of cognition--the logical steps behind any given conclusion--are completely incomprehensible, even to their creators. In the coming years, this nonhuman intelligence will change everything about our personal lives, our social organization, and how we think. That's the premise of two books published back-to-back this year--one from the West, the other from the East. The Age of AI: And Our Human Future is a primer on technetronic civilization for Western policy makers.

Humans and artificial intelligences (AI) will increasingly participate digitally and physically in conflicts, yet there is a lack of trusted communications across agents and platforms. For example, humans in disasters and conflict already use messaging and social media to share information, however, international humanitarian relief organisations treat this information as unverifiable and untrustworthy. AI may reduce the 'fog-of-war' and improve outcomes, however AI implementations are often brittle, have a narrow scope of application and wide ethical risks. Meanwhile, human error causes significant civilian harms even by combatants committed to complying with international humanitarian law. AI offers an opportunity to help reduce the tragedy of war and deliver humanitarian aid to those who need it. In this paper we consider the integration of a communications protocol (the 'Whiteflag protocol'), distributed ledger technology, and information fusion with artificial intelligence (AI), to improve conflict communications called 'Protected Assurance Understanding Situation and Entities' (PAUSE). Such a trusted human-AI communication network could provide accountable information exchange regarding protected entities, critical infrastructure; humanitarian signals and status updates for humans and machines in conflicts.

Machine learning plays a crucial role in enhancing and accelerating the search for new fundamental physics. We review the state of machine learning methods and applications for new physics searches in the context of terrestrial high energy physics experiments, including the Large Hadron Collider, rare event searches, and neutrino experiments. While machine learning has a long history in these fields, the deep learning revolution (early 2010s) has yielded a qualitative shift in terms of the scope and ambition of research. These modern machine learning developments are the focus of the present review.