Tens of thousands of stroke patients across the country are benefitting from quicker treatment and improved outcomes thanks to government investment in cutting edge artificial intelligence (AI) to diagnose and determine the best treatment for patients who suffer a stroke. Early-stage analysis of the technology, which received funding from the first round of the government's AI in Health and Care Awards, shows it can reduce the time between presenting with a stroke and treatment by more than 60 minutes, and is associated with a tripling in the number of stroke patients recovering with no or only slight disability - defined as achieving functional independence - from 16% to 48%. Trailblazing AI technologies are revolutionising the health and care system making it fit for the future. These ground-breaking diagnosis and treatment tools are not only helping improve patient outcomes, but freeing up valuable clinician time, supporting hard working NHS staff who are working tirelessly to tackle the COVID backlogs. AI has the potential to transform our NHS - delivering faster, more accurate diagnoses and making sure patients can get the treatment they need, when they need it.

Spry Squared is a Minority and Woman Owned Small Business headquartered in Denver, Colorado with offices across the United States of America. We are an experienced federal government and commercial service provider with security cleared personnel working on various projects across the USA and the globe. Spry Squared provides organizations with Best in Class Enterprise Solutions, Managed IT Services, Cybersecurity Solutions, IT Professional Services, Recruiting Services, Project/Program Management and technology products. We are your strategic partner and value-added reseller, solving complex business challenges by leveraging technology solutions that reduce costs, optimize productivity and minimize risk. The analyst will support the development and maintenance of reference data bases in support of critical kill-chain scenarios.

Artificial intelligence is creeping into every aspect of our lives. AI-powered software is triaging hospital patients to determine who gets which treatment, deciding whether an asylum seeker is lying or telling the truth in their application and even conjuring up weird conceits for sitcoms. Just lately, these kinds of tools have been helping killer robots select their targets in the war in Ukraine. AI systems have been proven to carry systemic biases again and again, but their increasing centrality to the way we live makes those debates even more urgent. In typical tech fashion, AI-driven tools are advancing much faster than the laws that could theoretically govern them.

Artificial intelligence has become an essential tool in our daily lives and has fundamentally altered the ways in which we communicate and work with one another. In recent years, the federal government has sought to advance AI technology development and adoption through a number of important initiatives, including the National AI Initiative Act, the AI in Government Act, and the National AI Advisory Committee, which advises the president on issues of U.S. competitiveness and enhancing AI opportunities across the... Artificial intelligence has become an essential tool in our daily lives and has fundamentally altered the ways in which we communicate and work with one another. In recent years, the federal government has sought to advance AI technology development and adoption through a number of important initiatives, including the National AI Initiative Act, the AI in Government Act, and the National AI Advisory Committee, which advises the president on issues of U.S. competitiveness and enhancing AI opportunities across the country. While these efforts underscore the government's commitment to AI research and innovation, federal leaders should pay special attention to policies and programs that bolster entrepreneurs. Startups and small businesses develop and introduce new AI-enabled solutions and accelerate the implementation of AI tools across the public and private sectors.

As everyone knows the US and China are the main rivals in the AI race. The majority of the world's largest and most well-financed AI start-ups are located in the US and China, and the pace of investment, business expansion, and adoption does not appear to be declining any time soon. The study reveals by outlining a terrible scenario in which China would surpass the US in technological advancement. In such a scenario, China gets an edge in the generative AI race and generates revenue through the invention of cutting-edge technologies that it later employs as a tool of international political influence. The potential of ChatGPT to facilitate intelligent dialogues has replaced message tools from Stable Artificial Intelligence and Open-AI as the new object of desire throughout businesses. Companies, scholars, and entrepreneurs are exploring methods to enter the generative AI in China, where the country's IT industry has historically closely followed the West's new advancements.

Russia is planning a protracted campaign of aerial bombardments and attacks using Iranian-made drones in a bid to "exhaust" Ukraine into submission, according to Ukrainian President Volodymyr Zelenskyy. In his nightly address to the nation on Monday, Zelenskyy said that Ukraine had received intelligence that Russia would increase its campaign of drone raids on the country. "We have information that Russia is planning a long-term attack using Shahed drones," Zelenskyy said. "It is probably banking on exhaustion. Exhausting our people, our anti-aircraft defences, our energy," he said.

A Digital Twin (DT) is a simulation of a physical system that provides information to make decisions that add economic, social or commercial value. The behaviour of a physical system changes over time, a DT must therefore be continually updated with data from the physical systems to reflect its changing behaviour. For resource-constrained systems, updating a DT is non-trivial because of challenges such as on-board learning and the off-board data transfer. This paper presents a framework for updating data-driven DTs of resource-constrained systems geared towards system health monitoring. The proposed solution consists of: (1) an on-board system running a light-weight DT allowing the prioritisation and parsimonious transfer of data generated by the physical system; and (2) off-board robust updating of the DT and detection of anomalous behaviours. Two case studies are considered using a production gas turbine engine system to demonstrate the digital representation accuracy for real-world, time-varying physical systems.

$\mathrm{\gamma}$-ray spectroscopy is a quantitative, non-destructive technique that may be utilized for the identification and quantitative isotopic estimation of radionuclides. Traditional methods of isotopic determination have various challenges that contribute to statistical and systematic uncertainties in the estimated isotopics. Furthermore, these methods typically require numerous pre-processing steps, and have only been rigorously tested in laboratory settings with limited shielding. In this work, we examine the application of a number of machine learning based regression algorithms as alternatives to conventional approaches for analyzing $\mathrm{\gamma}$-ray spectroscopy data in the Emergency Response arena. This approach not only eliminates many steps in the analysis procedure, and therefore offers potential to reduce this source of systematic uncertainty, but is also shown to offer comparable performance to conventional approaches in the Emergency Response Application.

HiClass is an open-source Python library for local hierarchical classification entirely compatible with scikit-learn. It contains implementations of the most common design patterns for hierarchical machine learning models found in the literature, that is, the local classifiers per node, per parent node and per level. Additionally, the package contains implementations of hierarchical metrics, which are more appropriate for evaluating classification performance on hierarchical data. The documentation includes installation and usage instructions, examples within tutorials and interactive notebooks, and a complete description of the API. HiClass is released under the simplified BSD license, encouraging its use in both academic and commercial environments.

Spear Phishing is a harmful cyber-attack facing business and individuals worldwide. Considerable research has been conducted recently into the use of Machine Learning (ML) techniques to detect spear-phishing emails. ML-based solutions may suffer from zero-day attacks; unseen attacks unaccounted for in the training data. As new attacks emerge, classifiers trained on older data are unable to detect these new varieties of attacks resulting in increasingly inaccurate predictions. Spear Phishing detection also faces scalability challenges due to the growth of the required features which is proportional to the number of the senders within a receiver mailbox. This differs from traditional phishing attacks which typically perform only a binary classification between phishing and benign emails. Therefore, we devise a possible solution to these problems, named RAIDER: Reinforcement AIded Spear Phishing DEtectoR. A reinforcement-learning based feature evaluation system that can automatically find the optimum features for detecting different types of attacks. By leveraging a reward and penalty system, RAIDER allows for autonomous features selection. RAIDER also keeps the number of features to a minimum by selecting only the significant features to represent phishing emails and detect spear-phishing attacks. After extensive evaluation of RAIDER over 11,000 emails and across 3 attack scenarios, our results suggest that using reinforcement learning to automatically identify the significant features could reduce the dimensions of the required features by 55% in comparison to existing ML-based systems. It also improves the accuracy of detecting spoofing attacks by 4% from 90% to 94%. In addition, RAIDER demonstrates reasonable detection accuracy even against a sophisticated attack named Known Sender in which spear-phishing emails greatly resemble those of the impersonated sender.