Western countries have been ramping up weapons and ammunition shipments to Ukraine as Kyiv fights off a Russian invasion, but arms trade experts warn some of the lethal assistance could end up falling into the wrong hands. Ukraine in particular has a history as a hub of the arms trade during the 1990s, setting off alarm bells for those who study illicit flows. "There are very significant risks associated to the proliferation of weapons in Ukraine at the moment, in particular regarding small arms and light weapons," said Nils Duquet, a researcher and director of the Flemish Peace Institute. Western nations, above all the US, have announced successive shipments of both light and heavy weapons for Kyiv's forces since Russian troops crossed the Ukrainian border on February 24. Washington alone has delivered or promised military gear including hundreds of Switchblade kamikaze drones, 7,000 assault rifles with 50 million rounds of ammunition, laser-guided missiles and radar systems to detect enemy drones and incoming artillery fire.

Nasa's Perseverance rover has reached a key moment in its search for evidence of past life on Mars. The car-sized robot, which landed on the Red Planet in February last year, will today (Tuesday) begin climbing up an ancient delta to look for sampling sites that might contain ancient microbes and organics. This ascent will be for reconnaissance, as Perseverance goes'walkabout' looking for rocks with the best chance of holding secrets about whether alien life once existed on Mars. As it makes its way back down, the rover will then collect some of these specimens from the Jezero Crater and leave the samples at the base of the delta to be retrieved by future missions. Nasa's Perseverance rover (pictured) has reached a key moment in its search for evidence of past life on Mars. The engineering cameras give detailed information in colour about the terrain the rover has to cross.

Accurate prediction of postoperative complications can inform shared decisions regarding prognosis, preoperative risk-reduction, and postoperative resource use. We hypothesized that multi-task deep learning models would outperform random forest models in predicting postoperative complications, and that integrating high-resolution intraoperative physiological time series would result in more granular and personalized health representations that would improve prognostication compared to preoperative predictions. In a longitudinal cohort study of 56,242 patients undergoing 67,481 inpatient surgical procedures at a university medical center, we compared deep learning models with random forests for predicting nine common postoperative complications using preoperative, intraoperative, and perioperative patient data. Our study indicated several significant results across experimental settings that suggest the utility of deep learning for capturing more precise representations of patient health for augmented surgical decision support. Multi-task learning improved efficiency by reducing computational resources without compromising predictive performance. Integrated gradients interpretability mechanisms identified potentially modifiable risk factors for each complication. Monte Carlo dropout methods provided a quantitative measure of prediction uncertainty that has the potential to enhance clinical trust. Multi-task learning, interpretability mechanisms, and uncertainty metrics demonstrated potential to facilitate effective clinical implementation.

As companies increasingly involve AI in their hiring processes, advocates, lawyers, and researchers have continued to sound the alarm. Algorithms have been found to automatically assign job candidates different scores based on arbitrary criteria like whether they wear glasses or a headscarf or have a bookshelf in the background. Hiring algorithms can penalize applicants for having a Black-sounding name, mentioning a women's college, and even submitting their résumé using certain file types. They can disadvantage people who stutter or have a physical disability that limits their ability to interact with a keyboard. All of this has gone widely unchecked.

Hosted by the Alan Turing Institute, AI UK was a two day conference that showcased AI research, development, and policy in the UK. The event took place on 22 and 23 March, and participants were treated to a variety of interesting talks, panel discussions, and conversations on a wide variety of topics. We covered some of the policy and strategy-related sessions in this article. The organisers have now made the recorded content from the conference available for everyone to watch. This can all be found on the Institute's YouTube channel.

Poisoning attacks are a category of adversarial machine learning threats in which an adversary attempts to subvert the outcome of the machine learning systems by injecting crafted data into training data set, thus increasing the machine learning model's test error. The adversary can tamper with the data feature space, data labels, or both, each leading to a different attack strategy with different strengths. Various detection approaches have recently emerged, each focusing on one attack strategy. The Achilles heel of many of these detection approaches is their dependence on having access to a clean, untampered data set. In this paper, we propose CAE, a Classification Auto-Encoder based detector against diverse poisoned data. CAE can detect all forms of poisoning attacks using a combination of reconstruction and classification errors without having any prior knowledge of the attack strategy. We show that an enhanced version of CAE (called CAE+) does not have to employ a clean data set to train the defense model. Our experimental results on three real datasets MNIST, Fashion-MNIST and CIFAR demonstrate that our proposed method can maintain its functionality under up to 30% contaminated data and help the defended SVM classifier to regain its best accuracy.

What kinds of arguments do people make, and what effect do they have on others? Normative constraints on argument-making are as old as philosophy itself, but little is known about the diversity of arguments made in practice. We use NLP tools to extract patterns of argument-making from the Reddit site "Change My View" (r/CMV). This reveals six distinct argument patterns: not just the familiar deductive and inductive forms, but also arguments about definitions, relevance, possibility and cause, and personal experience. Data from r/CMV also reveal differences in efficacy: personal experience and, to a lesser extent, arguments about causation and examples, are most likely to shift a person's view, while arguments about relevance are the least. Finally, our methods reveal a gradient of argument-making preferences among users: a two-axis model, of "personal--impersonal" and "concrete--abstract", can account for nearly 80% of the strategy variance between individuals.

We consider the One-Sided Matching problem, where n agents have preferences over n items, and these preferences are induced by underlying cardinal valuation functions. The goal is to match every agent to a single item so as to maximize the social welfare. Most of the related literature, however, assumes that the values of the agents are not a priori known, and only access to the ordinal preferences of the agents over the items is provided. Consequently, this incomplete information leads to loss of efficiency, which is measured by the notion of distortion. In this paper, we further assume that the agents can answer a small number of queries, allowing us partial access to their values. We study the interplay between elicited cardinal information (measured by the number of queries per agent) and distortion for One-Sided Matching, as well as a wide range of well-studied related problems. Qualitatively, our results show that with a limited number of queries, it is possible to obtain significant improvements over the classic setting, where only access to ordinal information is given.

Understanding, modelling and predicting human risky decision-making is challenging due to intrinsic individual differences and irrationality. Fuzzy trace theory (FTT) is a powerful paradigm that explains human decision-making by incorporating gists, i.e., fuzzy representations of information which capture only its quintessential meaning. Inspired by Broniatowski and Reyna's FTT cognitive model, we propose a computational framework which combines the effects of the underlying semantics and sentiments on text-based decision-making. In particular, we introduce Category-2-Vector to learn categorical gists and categorical sentiments, and demonstrate how our computational model can be optimised to predict risky decision-making in groups and individuals.

Employers have a responsibility to inspect artificial intelligence tools for disability bias and should have plans to provide reasonable accommodations, the Equal Employment Opportunity Commission and Justice Department said in guidance documents. The guidance released Thursday is the first from the federal government on the use of AI hiring tools that focuses on their impact on people with disabilities. The guidance also seeks to inform workers of their right to inquire about a company's use of AI and to request accommodations, the agencies said. "Today we are sounding an alarm regarding the dangers of blind reliance on AI and other technologies that are increasingly used by employers," Assistant Attorney General Kristen Clarke told reporters. The DOJ enforces disability discrimination laws with respect to state and local government employers, while the EEOC enforces such laws in the private sector and federal employers.