"So this is good news for NHS workers and the police who will be freed from over a million hours of time spent doing admin, bereaved parents who will be supported to get the answers they deserve, and people who will be kept safer online thanks to new offences for deepfake abuse," DSIT said. But even though the Lords have decided they had made their point on AI, the argument has not gone away. Those who fought the battle have not changed their minds. Baroness Kidron, a film maker who led the charge for the amendment, told me the passing of the bill was "a pyrrhic victory at best" for the government, meaning it would lose more than it gains. That cost, she argues, is the giving away of UK assets, in the form of creative content, to largely US-based AI developers.

On 1 June, Ukraine stunned the world with an audacious attack against Russian airbases. Using cheap, small drones concealed inside trucks that had penetrated deep into Russian territory, Ukraine was able to hit dozens of nuclear-capable strategic bombers, taking out 7 billion of military hardware. The drone-smuggling attack, codenamed Operation Spiderweb, was an incredible feat of military planning – but it also highlighted a vulnerability that has defence chiefs around the world concerned that their assets could be hit next. "The risk potentials of small drone attacks to US or UK air bases right now are 100 per cent," says Robert Bunker at US consultancy firm C/O Futures. "You simply need a group with the intent and capability, which is a very low bar to overcome."

The flash crash is probably the most well-known example of the dangers raised by agents--automated systems that have the power to take actions in the real world, without human oversight. That power is the source of their value; the agents that supercharged the flash crash, for example, could trade far faster than any human. But it's also why they can cause so much mischief. "The great paradox of agents is that the very thing that makes them useful--that they're able to accomplish a range of tasks--involves giving away control," says Iason Gabriel, a senior staff research scientist at Google DeepMind who focuses on AI ethics. "If we continue on the current path … we are basically playing Russian roulette with humanity." Agents are already everywhere--and have been for many decades.

The site of the secret Manhattan Project in Oak Ridge, Tennessee has a new mission to help achieve an A.I. advantage over China. A new uranium enrichment facility in Oak Ridge will supply nuclear fuel to the reactors that power A.I. data centers. Over 80 years after scientists of the'Manhattan Project' harnessed the power of the atom to end World War II, the top-secret worksite has a new mission to help dominate AI before China does. The first phase of the United States' latest uranium enrichment facility opened in Oak Ridge, Tennessee in May. Uranium powers the nuclear reactors the AI data centers are turning to for reliable energy.

The United States ambassador to NATO, Matthew Whitaker, said the Ukrainian drone attack on Russian strategic bombers at their airbases earlier this month was "badass" but also "a little bit reckless, and a little bit dangerous". Ukrainian President Volodymyr Zelenskyy, addressing a conference of southeast European leaders in the Black Sea port of Odesa, said Russia was determined to destroy the south of his country as well as nearby Moldova and Romania, as he called for increased pressure on Moscow to prevent further military threats. It is the first time the leader has visited Ukraine during his 12 years in power. Finland's Ministry for Foreign Affairs said it had summoned a Russian diplomat over a suspected June 10 violation of Finnish airspace by Russian aircraft, the second such event in under three weeks. Slovakia will not back the European Union's 18th package of sanctions against Russia unless the European Commission provides a solution to the situation the country faces if the bloc phases out Russian energy as planned, the country's Prime Minister Robert Fico has said. Germany's imports of goods from Russia fell by 95 percent in the 2021-2024 period, while its exports of goods to Russia were cut by 72 percent, the country's statistics office Destatis has reported.

There is an increasing interest in analyzing the behavior of machine learning systems against adversarial attacks. However, most of the research in adversarial machine learning has focused on studying weaknesses against evasion or poisoning attacks to predictive models in classical setups, with the susceptibility of Bayesian predictive models to attacks remaining underexplored. This paper introduces a general methodology for designing optimal evasion attacks against such models. We investigate two adversarial objectives: perturbing specific point predictions and altering the entire posterior predictive distribution. For both scenarios, we propose novel gradient-based attacks and study their implementation and properties in various computational setups.

A central concern in classification is the vulnerability of machine learning models to adversarial attacks. Adversarial training is one of the most popular techniques for training robust classifiers, which involves minimizing an adversarial surrogate risk. Recent work characterized when a minimizing sequence of an adversarial surrogate risk is also a minimizing sequence of the adversarial classification risk for binary classification-- a property known as adversarial consistency . However, these results do not address the rate at which the adversarial classification risk converges to its optimal value for such a sequence of functions that minimize the adversarial surrogate. This paper provides surrogate risk bounds that quantify that convergence rate. Additionally, we derive distribution-dependent surrogate risk bounds in the standard (non-adversarial) learning setting, that may be of independent interest.

Having designed a VQVAE that maps digital radio waveforms into discrete latent space, and yields a perfectly classifiable reconstruction of the original data, we here analyze the attack suppressing properties of VQVAE when an adversarial attack is performed on high-SNR radio-frequency (RF) data-points. To target amplitude modulations from a subset of digitally modulated waveform classes, we first create adversarial attacks that preserve the phase between the in-phase and quadrature component whose values are adversarially changed. We compare them with adversarial attacks of the same intensity where phase is not preserved. We test the classification accuracy of such adversarial examples on a classifier trained to deliver 100% accuracy on the original data. To assess the ability of VQVAE to suppress the strength of the attack, we evaluate the classifier accuracy on the reconstructions by VQVAE of the adversarial datapoints and show that VQVAE substantially decreases the effectiveness of the attack. We also compare the I/Q plane diagram of the attacked data, their reconstructions and the original data. Finally, using multiple methods and metrics, we compare the probability distribution of the VQVAE latent space with and without attack. Varying the attack strength, we observe interesting properties of the discrete space, which may help detect the attacks.

Liquid democracy is a mechanism for the division of labor in decision-making through the transitive delegation of influence. In essence, all individuals possess the autonomy to determine the issues with which they will engage directly, while for other matters, they may appoint a representative of their choosing. So far, the literature has studied the delegation structures emerging in liquid democracy as static. As a result, transitivity defined as the capacity to transfer acquired authority to another entity, has been identified as a concern as it would be conducive to unrestrained accumulation of power. Focusing on the implementation of liquid democracy supported by the LiquidFeedback software, we propose a novel approach to assessing the influence of voting nodes in a transitive delegation graph, taking into account the process nature of real-world liquid democracy in which delegation and voting are distinct and increasingly independent activities. By introducing a novel model of delegations in liquid democracy, we show how transitivity may in fact contribute to an effective regulation of deliberation influence and decision-making power. While maintaining the one-person, one-vote paradigm for all votes cast, the anticipated influence of an agent, to the extent it is stemming from transitivity, experiences a precipitous decline following an exponential trajectory. In general, it is our objective to move the first steps towards a rigorous analysis of liquid democracy as an adaptive democratic representation process. The adaptivity aspect of liquid democracy has not yet been explored within the existing academic literature despite it being, we believe, one of its most important features. We therefore also outline a research agenda focusing on this aspect of liquid democracy.

The rapid advancement of Vehicle-to-Everything (V2X) communication is transforming Intelligent Transportation Systems (ITS), with 6G networks expected to provide ultra-reliable, low-latency, and high-capacity connectivity for Connected and Autonomous Vehicles (CAVs). Artificial Intelligence (AI) and Machine Learning (ML) have emerged as key enablers in optimizing V2X communication by enhancing network management, predictive analytics, security, and cooperative driving due to their outstanding performance across various domains, such as natural language processing and computer vision. This survey comprehensively reviews recent advances in AI and ML models applied to 6G-V2X communication. It focuses on state-of-the-art techniques, including Deep Learning (DL), Reinforcement Learning (RL), Generative Learning (GL), and Federated Learning (FL), with particular emphasis on developments from the past two years. Notably, AI, especially GL, has shown remarkable progress and emerging potential in enhancing the performance, adaptability, and intelligence of 6G-V2X systems. Despite these advances, a systematic summary of recent research efforts in this area remains lacking, which this survey aims to address. We analyze their roles in 6G-V2X applications, such as intelligent resource allocation, beamforming, intelligent traffic management, and security management. Furthermore, we explore the technical challenges, including computational complexity, data privacy, and real-time decision-making constraints, while identifying future research directions for AI-driven 6G-V2X development. This study aims to provide valuable insights for researchers, engineers, and policymakers working towards realizing intelligent, AI-powered V2X ecosystems in 6G communication.