Addressing the challenge of low-resource information extraction remains an ongoing issue due to the inherent information scarcity within limited training examples. Existing data augmentation methods, considered potential solutions, struggle to strike a balance between weak augmentation (e.g., synonym augmentation) and drastic augmentation (e.g., conditional generation without proper guidance). This paper introduces a novel paradigm that employs targeted augmentation and back validation to produce augmented examples with enhanced diversity, polarity, accuracy, and coherence. Extensive experimental results demonstrate the effectiveness of the proposed paradigm. Furthermore, identified limitations are discussed, shedding light on areas for future improvement.

In safety-critical applications such as medical imaging and autonomous driving, where decisions have profound implications for patient health and road safety, it is imperative to maintain both high adversarial robustness to protect against potential adversarial attacks and reliable uncertainty quantification in decision-making. With extensive research focused on enhancing adversarial robustness through various forms of adversarial training (AT), a notable knowledge gap remains concerning the uncertainty inherent in adversarially trained models. To address this gap, this study investigates the uncertainty of deep learning models by examining the performance of conformal prediction (CP) in the context of standard adversarial attacks within the adversarial defense community. It is first unveiled that existing CP methods do not produce informative prediction sets under the commonly used $l_{\infty}$-norm bounded attack if the model is not adversarially trained, which underpins the importance of adversarial training for CP. Our paper next demonstrates that the prediction set size (PSS) of CP using adversarially trained models with AT variants is often worse than using standard AT, inspiring us to research into CP-efficient AT for improved PSS. We propose to optimize a Beta-weighting loss with an entropy minimization regularizer during AT to improve CP-efficiency, where the Beta-weighting loss is shown to be an upper bound of PSS at the population level by our theoretical analysis. Moreover, our empirical study on four image classification datasets across three popular AT baselines validates the effectiveness of the proposed Uncertainty-Reducing AT (AT-UR).

Many Machine Learning (ML) models are referred to as black box models, providing no real insights into why a prediction is made. Feature importance and explainability are important for increasing transparency and trust in ML models, particularly in settings such as healthcare and finance. With quantum computing's unique capabilities, such as leveraging quantum mechanical phenomena like superposition, which can be combined with ML techniques to create the field of Quantum Machine Learning (QML), and such techniques may be applied to QML models. This article explores feature importance and explainability insights in QML compared to Classical ML models. Utilizing the widely recognized Iris dataset, classical ML algorithms such as SVM and Random Forests, are compared against hybrid quantum counterparts, implemented via IBM's Qiskit platform: the Variational Quantum Classifier (VQC) and Quantum Support Vector Classifier (QSVC). This article aims to provide a comparison of the insights generated in ML by employing permutation and leave one out feature importance methods, alongside ALE (Accumulated Local Effects) and SHAP (SHapley Additive exPlanations) explainers.

Just one week before the world's second-ever global summit on artificial intelligence, protesters of a small but growing movement called "Pause AI" demanded that the world's governments regulate AI companies and freeze the development of new cutting edge artificial intelligence models. They say that the development of these models should only be allowed to continue if companies agree to let them be thoroughly evaluated to test their safety first. Protests took place across thirteen different countries, including the U.S., the U.K, Brazil, Germany, Australia, and Norway on Monday. In London, a group of 20 or so protesters stood outside of the U.K.'s Department of Science, Innovation and Technology chanting things like "stop the race, it's not safe" and "who's future? The protestors say their goal is to get governments to regulate the companies developing frontier AI models, including OpenAI's Chat GPT. They say that companies are not taking enough precautions to make sure their AI models are safe enough to be released into the world. "[AI companies] have proven time and time again… through the way that these companies' workers are treated, with the way that they treat other people's work by literally stealing it and throwing it into their models, They have proven that they cannot be trusted," said Gideon Futerman, an Oxford undergraduate student who gave a speech at the protest. One protester, Tara Steele, a freelance writer who works on blogs and SEO content, said that she had seen the technology impact her own livelihood. "I have noticed since ChatGPT came out, the demand for freelance work has reduced dramatically," she says. "I love writing personally… I've really loved it.

A bipartisan group of senators, including Majority Leader Charles E. Schumer, will unveil a long-awaited "road map" for regulating artificial intelligence this week, directing Congress to infuse billions of dollars into research and development of the technology while addressing its potential harms.

In February 2022, a meeting was set up between New York City mayor Eric Adams' team and an artificial intelligence gun-detection company called Evolv. An email thread from Evolv representatives included an accompanying brochure, which listed opportunities to partner together: in the Port Authority Bus Terminal, NYC schools, hospitals, and gathering places such as Times Square. One area conspicuously missing from the list, though, was the subway. After an in-person meeting a few days later, Evolv cofounder Anil Chitkara made another attempt to sell the company's technology--through name-dropping. "As I mentioned, Linda Reid, VP Security for Walt Disney World (Florida) has known us since 2014 and deployed many of our systems at the Parks and Disney Springs," Chitkara wrote in a February 7 email to the Mayor's Office, obtained by WIRED. "They've had success screening for weapons with Evolv Express … There may be some interesting parallels to how you are thinking about everyone's role in security."

The Conservatives are currently behind Labour in the polls and Mr Sunak faces an uphill struggle to turn thing around before the next general election, expected this year. In his 30 minute speech - which sounded like the opening salvo in an election campaign and included a personal attack on the Labour leader - Mr Sunak described a future that was both a time of danger but also of transformation. He warned of threats from an "axis of authoritarian powers" such as Russia, Iran, North Korea and China and spoke of the challenges to cyber security. However, he also argued there were reasons for optimism citing the potential of artificial intelligence to improve education and health. In contrast to his conference speech last autumn, which had the theme of change, he also sought to defend the Conservatives' time in government, while attempting to personify the future.

Fox News chief national security correspondent Jennifer Griffin discusses how the Genesis health system is impacting recruiting on'Special Report.' Forced to do more with less and learning from the war in Ukraine, U.S. special operations commanders are juggling how to add more high-tech experts to their teams while still cutting their overall forces by about 5,000 troops over the next five years. The conflicting pressures are forcing a broader restructuring of the commando teams, which are often deployed for high-risk counterterrorism missions and other sensitive operations around the world. The changes under consideration are being influenced by Russia's invasion of Ukraine. U.S. Army Special Operations Command, which bears the brunt of the personnel cuts, is eyeing plans to increase the size of its Green Beret teams -- usually about 12 members -- to bring in people with more specialized and technical abilities.

The age of the laser weapon is finally upon us. The United States Army has officially sent a pair of high-energy laser weapons overseas to defend American troops and US allies against enemy drones, the service recently revealed, marking the first publicly known deployment of a directed-energy system for air defense in military history. And, according to a top official, those weapons are actively blasting threats out of the sky. The weapon, known as the Palletized High Energy Laser (P-HEL) and developed by the American defense contractor BlueHalo based on the company's 20-kilowatt Locust Laser Weapon System, first arrived in an unspecified location overseas and "commenced operational employment" in November 2022, according to an April press release from the company. A second system arrived overseas "earlier this year."

The world may end up breaking into tech alliances as a guiding political issue in the years to come, according to a retired American serviceman-turned-novelist as detailed in his new book. "I think for us, particularly with regards to the technology that we're imagining and the incredible power it unleashes, it just becomes obvious that the real source of national power might not be military or even economic, but could quickly become technological power," Elliot Ackerman told Fox News Digital. "Whoever gets there first is going to so stratospherically outpace their rivals that they'll be able to dominate as a nation," he said. Ackerman served in the U.S. Marine Corps for eight years, working as both an infantry and special operations officer with tours in the Middle East and Central Asia. Following the conclusion of his service, he pursued a career as a novelist, drawing on his experience to write acclaimed fiction.