Despite the widespread utilization of Gaussian process models for versatile nonparametric modeling, they exhibit limitations in effectively capturing abrupt changes in function smoothness and accommodating relationships with heteroscedastic errors. Addressing these shortcomings, the heteroscedastic Gaussian process (HeGP) regression seeks to introduce flexibility by acknowledging the variability of residual variances across covariates in the regression model. In this work, we extend the HeGP concept, expanding its scope beyond regression tasks to encompass classification and state-space models. To achieve this, we propose a novel framework where the Gaussian process is coupled with a covariate-induced precision matrix process, adopting a mixture formulation. This approach enables the modeling of heteroscedastic covariance functions across covariates. To mitigate the computational challenges posed by sampling, we employ variational inference to approximate the posterior and facilitate posterior predictive modeling. Additionally, our training process leverages an EM algorithm featuring closed-form M-step updates to efficiently evaluate the heteroscedastic covariance function. A notable feature of our model is its consistent performance on multivariate responses, accommodating various types (continuous or categorical) seamlessly. Through a combination of simulations and real-world applications in climatology, we illustrate the model's prowess and advantages. By overcoming the limitations of traditional Gaussian process models, our proposed framework offers a robust and versatile tool for a wide array of applications.

Although Ukraine has not said it carried out specific drone strikes, President Volodymyr Zelensky has previously said that attacks on Russian territory are an "inevitable, natural and absolutely fair process" as the war with Russia continues.

The UK's cybersecurity agency has warned that chatbots can be manipulated by hackers to cause scary real-world consequences. The National Cyber Security Centre (NCSC) has said there are growing cybersecurity risks of individuals manipulating the prompts through "prompt injection" attacks. This is where a user creates an input or a prompt that is designed to make a language model – the technology behind chatbots – behave in an unintended manner. A chatbot runs on artificial intelligence and is able to give answers to prompted questions by users. They mimic human-like conversations, which they have been trained to do through scraping large amounts of data.

The F-22 and F-35 are two of the most cutting-edge and capable war machines in America's arsenal. They also cost $143 million and $75 million a pop, respectively. Facing increasing pressure from China, which has accelerated its conventional weapon procurement efforts in recent months, the Pentagon announced Monday a program designed to build out America's drone production base in response. As part of that effort, the United States Air Force has requested nearly $6 billion in federal funding over the next five years to construct a fleet of XQ-58A Valkyrie uncrewed aircraft, each of which will cost a (comparatively) paltry $3 million. The Valkyrie comes from Kratos Defense & Security Solutions as part of the USAF's Low Cost Attritable Strike Demonstrator (LCASD) program.

Watermarking--a technique where you hide a signal in a piece of text or an image to identify it as AI-generated--has become one of the most popular ideas proposed to curb such harms. In July, the White House announced it had secured voluntary commitments from leading AI companies such as OpenAI, Google, and Meta to develop watermarking tools in an effort to combat misinformation and misuse of AI-generated content. At Google's annual conference I/O in May, CEO Sundar Pichai said the company is building its models to include watermarking and other techniques from the start. Google DeepMind is now the first Big Tech company to publicly launch such a tool. Traditionally images have been watermarked by adding a visible overlay onto them, or adding information into their metadata.

In May, Sputnik International, a state-owned Russian media outlet, posted a series of tweets lambasting US foreign policy and attacking the Biden administration. Each prompted a curt but well-crafted rebuttal from an account called CounterCloud, sometimes including a link to a relevant news or opinion article. It generated similar responses to tweets by the Russian embassy and Chinese news outlets criticizing the US. Russian criticism of the US is far from unusual, but CounterCloud's material pushing back was: The tweets, the articles, and even the journalists and news sites were crafted entirely by artificial intelligence algorithms, according to the person behind the project, who goes by the name Nea Paw and says it is designed to highlight the danger of mass-produced AI disinformation. Paw did not post the CounterCloud tweets and articles publicly but provided them to WIRED and also produced a video outlining the project.

Russian air defences shot down three Ukrainian drones flying over the Russian regions of Tula and Belgorod, the Ministry of Defence has reported, in the latest attempted attacks on targets inside Russian territory. Two drones "were destroyed" by air defences over the Tula region south of Moscow, the defence ministry said in a statement on the Telegram messaging app early on Tuesday morning. Another drone was "destroyed by air defence forces" over the Belgorod region, which borders Ukraine, at about 11pm Moscow time (20:00 GMT) on Monday, the ministry said in a separate statement. The ministry did not say whether there had been damage or casualties as a result of the drone raids. Moscow and other Russian regions have been hit by a barrage of Ukrainian drone attacks in recent weeks with Ukrainian President Volodymyr Zelenskyy saying late last month that the war would be returning to Russia.

The Pentagon plans to field thousands of drones and other high-tech military equipment within the next two years as the United States military turns to "autonomous systems" to counter China's numerical edge in terms of personnel and weaponry, a senior defence official said. US Deputy Secretary of Defense Kathleen Hicks told a military technology conference in Washington, DC on Monday that the "imperative to innovate" was crucial at a time of strategic competition with China, a rival who Hick described as being very different to the "relatively slow and lumbering" competitors the US faced during the Cold War. While US forces were engaged in fighting for 20 years in Iraq and Afghanistan, "the PRC [People's Republic of China] worked with focus and determination to build a modern military, carefully crafting it to blunt the operational advantages we've enjoyed for decades", Hicks said in a speech. In a candid address that highlighted Washington's view of the military threat posed by China and its ability to out-scale the US military, Hicks said the US maintained an advantage owing to its ability "to imagine, create and master the future character of warfare". Beijing's main military advantage is "mass: more ships, more missiles, more people", she said.

Commonly adopted in the manufacturing and aerospace sectors, digital twin (DT) platforms are increasingly seen as a promising paradigm to control, monitor, and analyze software-based, "open", communication systems. Notably, DT platforms provide a sandbox in which to test artificial intelligence (AI) solutions for communication systems, potentially reducing the need to collect data and test algorithms in the field, i.e., on the physical twin (PT). A key challenge in the deployment of DT systems is to ensure that virtual control optimization, monitoring, and analysis at the DT are safe and reliable, avoiding incorrect decisions caused by "model exploitation". To address this challenge, this paper presents a general Bayesian framework with the aim of quantifying and accounting for model uncertainty at the DT that is caused by limitations in the amount and quality of data available at the DT from the PT. In the proposed framework, the DT builds a Bayesian model of the communication system, which is leveraged to enable core DT functionalities such as control via multi-agent reinforcement learning (MARL), monitoring of the PT for anomaly detection, prediction, data-collection optimization, and counterfactual analysis. To exemplify the application of the proposed framework, we specifically investigate a case-study system encompassing multiple sensing devices that report to a common receiver. Experimental results validate the effectiveness of the proposed Bayesian framework as compared to standard frequentist model-based solutions.

A consensus report produced for the Air Force Research Laboratory by the National Academies of Sciences, Engineering, and Mathematics documented a prevalent and increasing desire to support human-Artificial Intelligence (AI) teaming across military service branches. Sonalysts has begun an internal initiative to explore the training of human-AI teams. The first step in this effort is to develop a Synthetic Task Environment (STE) that is capable of facilitating research on human-AI teams. We decided to use Joint All-Domain Command and Control (JADC2) as a focus point for developing the STE because the volume of sensor inputs and decision options within the JADC2 concept likely requires the use of AI systems to enable timely decisions. Given this focus, we engaged a number of Subject-Matter Experts (SMEs) with Command and Control experience to gain insight into developing a STE that embodied the teaming challenges associated with JADC2. This report documents our initial engagement with those stakeholders. The research team identified thirteen Sonalysts employees with military backgrounds and Command and Control experience, and invited them to participate. Twelve respondents completed the survey. The team then analyzed the responses to identify themes that emerged and topics that would benefit from further analysis. The results indicated that our SMEs were amenable to research using tasks that were analogous to those encountered in military environments, as long as they required teams to process a great deal of incoming data to arrive at complex decisions. The SMEs felt that the testbed should support 'teams of teams" that represented a matrixed organization, and that it should support a robust array to spoken, text-based, and face-to-face communications.