Context. The security of critical infrastructure has been a pressing concern since the advent of computers and has become even more critical in today's era of cyber warfare. Protecting mission-critical systems (MCSs), essential for national security, requires swift and robust governance, yet recent events reveal the increasing difficulty of meeting these challenges. Aim. Building on prior research showcasing the potential of Generative AI (GAI), such as Large Language Models, in enhancing risk analysis, we aim to explore practitioners' views on integrating GAI into the governance of IT MCSs. Our goal is to provide actionable insights and recommendations for stakeholders, including researchers, practitioners, and policymakers. Method. We designed a survey to collect practical experiences, concerns, and expectations of practitioners who develop and implement security solutions in the context of MCSs. Conclusions and Future Works. Our findings highlight that the safe use of LLMs in MCS governance requires interdisciplinary collaboration. Researchers should focus on designing regulation-oriented models and focus on accountability; practitioners emphasize data protection and transparency, while policymakers must establish a unified AI framework with global benchmarks to ensure ethical and secure LLMs-based MCS governance.

The advancements in generative AI inevitably raise concerns about their risks and safety implications, which, in return, catalyzes significant progress in AI safety. However, as this field continues to evolve, a critical question arises: are our current efforts on AI safety aligned with the advancements of AI as well as the long-term goal of human civilization? This paper presents a blueprint for an advanced human society and leverages this vision to guide current AI safety efforts. It outlines a future where the Internet of Everything becomes reality, and creates a roadmap of significant technological advancements towards this envisioned future. For each stage of the advancements, this paper forecasts potential AI safety issues that humanity may face. By projecting current efforts against this blueprint, this paper examines the alignment between the current efforts and the long-term needs, and highlights unique challenges and missions that demand increasing attention from AI safety practitioners in the 2020s. This vision paper aims to offer a broader perspective on AI safety, emphasizing that our current efforts should not only address immediate concerns but also anticipate potential risks in the expanding AI landscape, thereby promoting a safe and sustainable future of AI and human civilization.

Multivariate time series forecasting is crucial for various applications, such as financial investment, energy management, weather forecasting, and traffic optimization. However, accurate forecasting is challenging due to two main factors. First, real-world time series often show heterogeneous temporal patterns caused by distribution shifts over time. Second, correlations among channels are complex and intertwined, making it hard to model the interactions among channels precisely and flexibly. In this study, we address these challenges by proposing a general framework called DUET, which introduces dual clustering on the temporal and channel dimensions to enhance multivariate time series forecasting. First, we design a Temporal Clustering Module (TCM) that clusters time series into fine-grained distributions to handle heterogeneous temporal patterns. For different distribution clusters, we design various pattern extractors to capture their intrinsic temporal patterns, thus modeling the heterogeneity. Second, we introduce a novel Channel-Soft-Clustering strategy and design a Channel Clustering Module (CCM), which captures the relationships among channels in the frequency domain through metric learning and applies sparsification to mitigate the adverse effects of noisy channels. Finally, DUET combines TCM and CCM to incorporate both the temporal and channel dimensions. Extensive experiments on 25 real-world datasets from 10 application domains, demonstrate the state-of-the-art performance of DUET.

Presidents Clinton, George H.W. Bush, Obama, Biden and Trump all pay respect to Jimmy Carter at his state funeral in Washington, D.C.. During the 2024 campaign cycle, Americans witnessed what appeared to be no love lost between President-elect Donald Trump and former President Barack Obama. However, at former President Jimmy Carter's funeral the two recent presidents appeared to be enjoying each other's company and largely ignored other dignitaries arriving around them, including Vice President Kamala Harris and President Biden. Susan Constantine, a communication and body language expert, said Harris came off "as cool as could be." When she was walking she was very robotic.

Now Bores hopes to revive the battle. The main provisions in the RAISE Act include requiring AI companies to develop safety plans for the development and deployment of their models. The bill also provides protections for whistleblowers at AI companies. It forbids retaliation against an employee who shares information about an AI model in the belief that it may cause "critical harm"; such whistleblowers can report the information to the New York attorney general. One way the bill defines critical harm is the use of an AI model to create a chemical, biological, radiological, or nuclear weapon that results in the death or serious injury of 100 or more people.

The rules would allow most European countries, Japan and other close U.S. allies to make unfettered purchases of A.I. chips, while blocking two dozen adversaries, like China and Russia, from buying them. More than 100 other countries would face different quotas on the amount of A.I. chips they could receive from U.S. companies. The regulations would also make it easier for A.I. chips to be sent to trusted American companies that run data centers, like Google and Microsoft, than to their foreign competitors. The rules would establish security procedures that data centers would have to follow to keep A.I. systems safe from cybertheft. The Biden administration's plan has prompted swift pushback from American tech companies, which say global regulations could slow their businesses and create costly compliance requirements.

The H-1B visa program was intended to bring in specialized talent from abroad, but instead it has become a tool for employers to hire lower-cost labor for ordinary jobs. The result is a distorted job market, where highly skilled workers are being squeezed out of the H-1B visa program by spam applications for ordinary workers who then take entry-level positions that are already in short supply. This misuse of H-1B visas has a negative synergy with growing impact of AI on the job market and is part of a larger problem that urgently needs attention. The impact of this visa-farming problem is particularly acute among young people and recent college graduates, who face a bleak job market despite moderate overall unemployment rates. According to government data, the ratio of unemployment for college grads under 25 to those over 25 has hit an all-time high of more than four to one.

U.S. President Joe Biden's administration plans one additional round of restrictions on the export of artificial intelligence chips from the likes of Nvidia just days before leaving office, a final push in his effort to keep advanced technologies out of the hands of China and Russia. The U.S. wants to curb the sale of AI chips used in data centers on both a country and company basis, with the goal of concentrating AI development in friendly nations and getting businesses around the world to align with American standards, according to people familiar with the matter. The result would be an expansion of semiconductor caps to most of the world -- an attempt to control the spread of AI technology at a time of soaring demand. The regulations, which could be issued as soon as Friday, would create three tiers of chip trade restrictions, said the people, who asked not to be identified because the discussions are private.

Recent work has demonstrated that Bayesian neural networks (BNN's) trained with mean field variational inference (MFVI) can be implemented in analog hardware, promising orders of magnitude energy savings compared to the standard digital implementations. However, while Gaussians are typically used as the variational distribution in MFVI, it is difficult to precisely control the shape of the noise distributions produced by sampling analog devices. This paper introduces a method for MFVI training using real device noise as the variational distribution. Furthermore, we demonstrate empirically that the predictive distributions from BNN's with the same weight means and variances converge to the same distribution, regardless of the shape of the variational distribution. This result suggests that analog device designers do not need to consider the shape of the device noise distribution when hardware-implementing BNNs performing MFVI.

Correlations between input parameters play a crucial role in many scientific classification tasks, since these are often related to fundamental laws of nature. For example, in high energy physics, one of the common deep learning use-cases is the classification of signal and background processes in particle collisions. In many such cases, the fundamental principles of the correlations between observables are often better understood than the actual distributions of the observables themselves. In this work, we present a new adversarial attack algorithm called Random Distribution Shuffle Attack (RDSA), emphasizing the correlations between observables in the network rather than individual feature characteristics. Correct application of the proposed novel attack can result in a significant improvement in classification performance - particularly in the context of data augmentation - when using the generated adversaries within adversarial training. Given that correlations between input features are also crucial in many other disciplines. We demonstrate the RDSA effectiveness on six classification tasks, including two particle collision challenges (using CERN Open Data), hand-written digit recognition (MNIST784), human activity recognition (HAR), weather forecasting (Rain in Australia), and ICU patient mortality (MIMIC-IV), demonstrating a general use case beyond fundamental physics for this new type of adversarial attack algorithms.