In the United States, hurricanes are the most devastating natural disasters causing billions of dollars worth of damage every year. More importantly, construction jobsites are classified among the most vulnerable environments to severe wind events. During hurricanes, unsecured and incomplete elements of construction sites, such as scaffoldings, plywoods, and metal rods, will become the potential wind-borne debris, causing cascading damages to the construction projects and the neighboring communities. Thus, it is no wonder that construction firms implement jobsite emergency plans to enforce preparedness responses before extreme weather events. However, relying on checklist-based emergency action plans to carry out a thorough hurricane preparedness is challenging in large-scale and complex site environments. For enabling systematic responses for hurricane preparedness, we have proposed a vision-based technique to identify and analyze the potential wind-borne debris in construction jobsites. Building on this, this paper demonstrates the fidelity of a new machine vision-based method to support construction site hurricane preparedness and further discuss its implications. The outcomes indicate that the convenience of visual data collection and the advantages of the machine vision-based frameworks enable rapid scene understanding and thus, provide critical heads up for practitioners to recognize and localize the potential wind-borne derbies in construction jobsites and effectively implement hurricane preparedness.

Recent evidence has shown the existence of a so-called double-descent and even triple-descent behavior for the generalization error of deep-learning models. This important phenomenon commonly appears in implemented neural network architectures, and also seems to emerge in epoch-wise curves during the training process. A recent line of research has highlighted that random matrix tools can be used to obtain precise analytical asymptotics of the generalization (and training) errors of the random feature model. In this contribution, we analyze the whole temporal behavior of the generalization and training errors under gradient flow for the random feature model. We show that in the asymptotic limit of large system size the full time-evolution path of both errors can be calculated analytically. This allows us to observe how the double and triple descents develop over time, if and when early stopping is an option, and also observe time-wise descent structures. Our techniques are based on Cauchy complex integral representations of the errors together with recent random matrix methods based on linear pencils.

British researchers say they have created an artificial intelligence (AI) model that is highly effective at predicting rainfall within the next 90 minutes. The model was built by scientists at Google-owned research company DeepMind in London. The team says tests of the system showed it produced more accurate predictions, or forecasts, for short-term rainfall than other existing systems. The paper recently appeared in the publication Nature. The scientists centered on a kind of weather prediction known as "precipitation nowcasting."

Akida NSoC and intellectual property enable a wide array of edge AI capabilities that include continuous learning and inference. BrainChip is offering two development kits both including the AKD1000 chip on a mini-PCI board: an X86 Shuttle PC development kit as well as an ARM-based Raspberry Pi development kit. "Offering development kits is not only a major step towards full commercialization, it's also an exciting opportunity to see how our partners and future customers will put Akida to work in environments and scenarios like consumer electronics, industrial applications, aerospace and defense systems, healthcare and medical devices, automotive technology, and more," said Anil Mankar, BrainChip co-founder and chief development officer. "We believe the AKD1000 silicon, or the licensing of Akida in a configurable IP format, will lead to major changes in industries using AI at the edge because of its performance, security, low power requirements, and mainly Akida's ability to perform AI training and learning on the device itself, without dependency on the cloud." Development kits for Akida-based applications and solutions evolving to production status are a step toward joining the neuromorphic revolution for edge AI applications.

NATO defence ministers have agreed upon a new master plan to defend against any potential Russian attack on multiple fronts, reaffirming the alliance's core goal of deterring Moscow despite a growing focus on China. The confidential strategy aims to prepare for any simultaneous attack in the Baltic and Black Sea regions that could include nuclear weapons, hacking of computer networks and assaults from space. "We continue to strengthen our alliance with better and modernised plans," NATO Secretary General Jens Stoltenberg said after the meeting on Thursday, which also agreed a $1bn fund to provide seed financing to develop new digital technologies. Officials stressed that they do not believe any Russian attack is imminent. Moscow has denied any aggressive intentions and said it is NATO that risks destabilising Europe with such preparations.

NATO will adopt its first strategy on artificial intelligence and launch an innovation fund this week with the aim of investing $1 billion to "futureproof" the 30-nation security pact, Secretary-General Jens Stoltenberg said Wednesday. "We see authoritarian regimes racing to develop new technologies, from artificial intelligence to autonomous systems," Stoltenberg said at a news conference at the alliance's Brussels headquarters. U.S. Defense Secretary Lloyd Austin will join his NATO member counterparts Thursday in Brussels to formally approve the plans during two days of talks. Stoltenberg said he expects the new NATO fund to invest in emerging and disruptive technologies. New headquarters and test centers will be set up in both Europe and North America to support the effort, he said.

Artificial intelligence is reorganizing the world, introducing innovations that will likely exceed those that came with the World Wide Web. And like the Web there were, and still are, security concerns. Today, trust in artificial intelligence is probably the single greatest risk to continuing AI innovation and adoption. A simple framework for the operational elements that need to be addressed for the responsible deployment of artificial intelligence must include'Fairness and Bias', 'Interpretability and Explainability', as well as the newest and equally important element of'Robustness and Security'. Note, that these are operational considerations, and privacy should be inherent in the design and implementation of responsible AI -- e.g., privacy must be foundational in every step of the process.

Nicolas Chaillan, who resigned as the Air Force chief software officer, warns of the'existential threat' China's technological advantages pose. The former chief software officer for the U.S. Air Force was "not surprised" by China's nuclear-capable rocket launch and warned that the U.S. "is running out of time" to catch up in the artificial intelligence race against China. "AI is so important to what's coming next in terms of innovation, but also in terms of weapons, quite honestly," Nicolas Chaillan told Fox News in an interview. China's expanding artificial intelligence and tech capabilities are "an existential threat to our kids and all our friendly nations' kids as well," he added. Chaillan served as the chief software officer in the Air Force for three years before announcing his resignation last month, criticizing the Pentagon's low prioritization of technological innovation on his way out.

Which languages and language-users are prioritized by digital platforms? Speakers of non-dominant languages are disproportionately subject to algorithmic harms.¹ They confront content moderation algorithms that "only work in certain languages"² on platforms that structurally omit non-Western nations from governance considerations. I call this tendency algolinguicism -- a matrix of automated processes that minoritize language-users outside the Global North and obstruct their access to political participation. This essay addresses digital platforms as sites of algolinguicism.

The symptoms and side effects of Covid-19 are scattered across a diagnostic spectrum. Some patients are asymptomatic or experience a mild immune response, while others report significant long-term illnesses, lasting complications, or suffer fatal outcomes. Three researchers from the Georgia Institute of Technology and one from Emory University are trying to help clinicians sort through these factors and spectrum of patient outcomes by equipping healthcare professionals with a new "decision prioritization tool." The team's new artificial intelligence-based tool helps clinicians understand and better predict which adverse effects their Covid-19 patients could experience, based on comorbidities and current side effects -- and, in turn, also helps suggest specific Food and Drug Administration-approved (FDA) drugs that could help treat the disease and improve patient health outcomes. The researcher's latest findings are the focus of a new study published October 21 in Scientific Reports.