Don Beyer's car dealerships were among the first in the U.S. to set up a website. As a representative, the Virginia Democrat leads a bipartisan group focused on promoting fusion energy. He reads books about geometry for fun. So when questions about regulating artificial intelligence emerged, the 73-year-old Beyer took what for him seemed like an obvious step, enrolling at George Mason University to get a master's degree in machine learning. In an era when lawmakers and Supreme Court justices sometimes concede they don't understand emerging technology, Beyer's journey is an outlier, but it highlights a broader effort by members of Congress to educate themselves about artificial intelligence as they consider laws that would shape its development.

Since 2020, Donald Trump and his devotees have sown doubt over election results and spread dozens of election conspiracies. Some of these election deniers have become elected officials themselves, serving as governors and US senators. Others, like Mike Lindell, have spent millions of dollars in hopes of overturning the 2020 election. On WIRED.com and in our new podcast, the WIRED Politics team reported that many of these election deniers on both the national and grassroots level have been training others to challenge voter rolls and overwhelm election officials even before polls open in November. And they're using tech to do it.

In November 2019, the US government's National Security Commission on Artificial Intelligence (NSCAI), an influential body chaired by former Google CEO and executive chairman Eric Schmidt, warned that China was using artificial intelligence to "advance an autocratic agenda." Just two months earlier, Schmidt was also seeking potential personal connections to China's AI industry on a visit to Beijing, newly disclosed emails reveal. Separately, tax filings show that a nonprofit private foundation overseen by Schmidt and his wife contributed to a fund that feeds into a private equity firm that has made investments in numerous Chinese tech firms, including those in AI. When the NSCAI issued its full findings in 2021, Schmidt and the NSCAI's vice chairman said in a statement that "China's plans, resources, and progress should concern all Americans," and warned that "China's domestic use of AI is a chilling precedent for anyone around the world who cherishes individual liberty." The 2019 email communications, obtained through a Freedom of Information Act request by the Tech Transparency Project (TTP), a nonprofit research initiative that tracks tech industry influence, show staff at Schmidt Futures, a philanthropic venture, asking NSCAI employees to help identify "possible engagements [Schmidt] might have on AI, in a personal capacity."

A video has surfaced of a senior official at Israel's cyber intelligence agency, Unit 8200, talking last year about the use of machine learning "magic powder" to help identify Hamas targets in Gaza. The footage raises questions about the accuracy of a recent statement about use of artificial intelligence (AI) by the Israeli Defense Forces (IDF), which said it "does not use an artificial intelligence system that identifies terrorist operatives or tries to predict whether a person is a terrorist". However, in the video, the head of data science and AI at Unit 8200 – named only as "Colonel Yoav" – said he would reveal an "example of one of the tools we use" before describing how the intelligence division used machine learning techniques in Israel's May 2021 offensive in Gaza for "finding new terrorists". "Let's say we have some terrorists that form a group and we know only some of them," he said. "By practising our data science magic powder we are able to find the rest of them."

Ukraine needs military aid and air defence systems in the face of Russia's intensifying attacks, President Volodymyr Zelenskyy said, as he criticised his country's allies for engaging in "lengthy discussions" and "turning a blind eye". The Russian military launched attacks on five regions across Ukraine, killing at least seven people and damaging infrastructure including substations and power generation facilities, Ukrainian officials said on Thursday. Zelenskyy said Russia fired more than 40 missiles and about 40 attack drones overnight, many targeting energy infrastructure. The attacks show how "critical" air defence has become for Ukraine, he posted on X, adding that the Russian missiles and Iranian-designed one-way drones must not be allowed to hit Ukraine. In southern Odesa, Governor Oleh Kiper said on his Telegram channel on Wednesday night that Russian missile strikes killed four people, including a 10-year-old girl, and left several others in critical condition.

A year into Sudan's civil war, Iranian-made armed drones have helped the army turn the tide of the conflict, halting the progress of the rival paramilitary Rapid Support Force and regaining territory around the capital, a senior army source has said. Six Iranian sources, regional officials and diplomats -- who, like the army source, asked not to be identified because of the sensitivity of the information -- also said the military had acquired Iranian-made unmanned aerial vehicles (UAVs) over the past few months. The Sudanese Armed Forces (SAF) used some older UAVs in the first months of the war alongside artillery batteries and fighter jets, but had little success in rooting out RSF fighters embedded in heavily populated neighborhoods in Khartoum and other cities, more than a dozen Khartoum residents said.

Earth observation (EO) satellite missions have been providing detailed images about the state of the Earth and its land cover for over 50 years. Long term missions, such as NASA's Landsat, Terra, and Aqua satellites, and more recently, the ESA's Sentinel missions, record images of the entire world every few days. Although single images provide point-in-time data, repeated images of the same area, or satellite image time series (SITS) provide information about the changing state of vegetation and land use. These SITS are useful for modeling dynamic processes and seasonal changes such as plant phenology. They have potential benefits for many aspects of land and natural resource management, including applications in agricultural, forest, water, and disaster management, urban planning, and mining. However, the resulting satellite image time series (SITS) are complex, incorporating information from the temporal, spatial, and spectral dimensions. Therefore, deep learning methods are often deployed as they can analyze these complex relationships. This review presents a summary of the state-of-the-art methods of modelling environmental, agricultural, and other Earth observation variables from SITS data using deep learning methods. We aim to provide a resource for remote sensing experts interested in using deep learning techniques to enhance Earth observation models with temporal information.

There are a number of hypotheses underlying the existence of adversarial examples for classification problems. These include the high-dimensionality of the data, high codimension in the ambient space of the data manifolds of interest, and that the structure of machine learning models may encourage classifiers to develop decision boundaries close to data points. This article proposes a new framework for studying adversarial examples that does not depend directly on the distance to the decision boundary. Similarly to the smoothed classifier literature, we define a (natural or adversarial) data point to be $(\gamma,\sigma)$-stable if the probability of the same classification is at least $\gamma$ for points sampled in a Gaussian neighborhood of the point with a given standard deviation $\sigma$. We focus on studying the differences between persistence metrics along interpolants of natural and adversarial points. We show that adversarial examples have significantly lower persistence than natural examples for large neural networks in the context of the MNIST and ImageNet datasets. We connect this lack of persistence with decision boundary geometry by measuring angles of interpolants with respect to decision boundaries. Finally, we connect this approach with robustness by developing a manifold alignment gradient metric and demonstrating the increase in robustness that can be achieved when training with the addition of this metric.

Auditing of AI systems is a promising way to understand and manage ethical problems and societal risks associated with contemporary AI systems, as well as some anticipated future risks. Efforts to develop standards for auditing Artificial Intelligence (AI) systems have therefore understandably gained momentum. However, we argue that creating auditing standards is not just insufficient, but actively harmful by proliferating unheeded and inconsistent standards, especially in light of the rapid evolution and ethical and safety challenges of AI. Instead, the paper proposes the establishment of an AI Audit Standards Board, responsible for developing and updating auditing methods and standards in line with the evolving nature of AI technologies. Such a body would ensure that auditing practices remain relevant, robust, and responsive to the rapid advancements in AI. The paper argues that such a governance structure would also be helpful for maintaining public trust in AI and for promoting a culture of safety and ethical responsibility within the AI industry. Throughout the paper, we draw parallels with other industries, including safety-critical industries like aviation and nuclear energy, as well as more prosaic ones such as financial accounting and pharmaceuticals. AI auditing should emulate those fields, and extend beyond technical assessments to include ethical considerations and stakeholder engagement, but we explain that this is not enough; emulating other fields' governance mechanisms for these processes, and for audit standards creation, is a necessity. We also emphasize the importance of auditing the entire development process of AI systems, not just the final products...

This paper summarizes the most cogent advantages and risks associated with Artificial Intelligence from an in-depth review of the literature. Then the authors synthesize the salient risk-related models currently being used in AI, technology and business-related scenarios. Next, in view of an updated context of AI along with theories and models reviewed and expanded constructs, the writers propose a new framework called "The Transformation Risk-Benefit Model of Artificial Intelligence" to address the increasing fears and levels of AI risk. Using the model characteristics, the article emphasizes practical and innovative solutions where benefits outweigh risks and three use cases in healthcare, climate change/environment and cyber security to illustrate unique interplay of principles, dimensions and processes of this powerful AI transformational model.