The study was a page-turner: Researchers at Google showed that an artificial intelligence system could predict acute kidney injury, a common killer of hospitalized patients, up to 48 hours in advance. The results were so promising that the Department of Veterans Affairs, which supplied de-identified patient data to help build the AI, said in 2019 that it would immediately start work to bring it to the bedside. But a new study shows how treacherous that journey can be. Researchers found that a replica of the AI system, trained on a predominantly male population of veterans, does not perform nearly as well on women. Their study, published recently in the journal Nature, reports that a model built to approximate Google's AI overestimated the risk for women in certain circumstances and was less accurate in predicting the condition for women overall. "If we have this problem, then half the population won't benefit," said Jie Cao, a Ph.D. student at the University of Michigan and the lead author of the paper.

The Underwriting Agencies Council (UAC) says annual gross written premium at Australian agencies is now around $7.5 billion, and technology-enabled firms are leading the way as the sector expands dramatically. Sydney-based GM William Legge says UAC now has more than 120 agency members, even as mergers and acquisitions created fewer, larger agencies and a build-up of "cluster groups" owning multiple specialist agency brands. As major carriers relinquish capacity in some lines, the agency market is filling gaps in coverage, Mr Legge says, and brokers have found agencies to be a trusted, reliable market that can provide responsive service, quick turn-around times, and bespoke, tailored products for hard-to-place risks. Insurance consulting firm Xceedance offers its MGA Agility Suite tailored platform to agencies, encompassing policy administration, underwriting, distribution, a broker portal and reporting functionality. Xceedance works with agencies and insurers to facilitate and support end-to-end insurance processes across claims, finance and accounting, insurance operations, catastrophe modelling, underwriting, actuarial and analytical services, policy services and data management.

On October 11, the Bank of England (BoE), the Prudential Regulation Authority (PRA), and the UK Financial Conduct Authority (FCA) (together, the Supervisory Authorities) published a discussion paper (DP5/22) on the safe and responsible adoption of artificial intelligence (AI) in financial services (Discussion Paper). The Discussion Paper forms part of the Supervisory Authorities' AI-related program of works, including the AI Public Private Forum and is being considered in light of the UK government's efforts towards regulating AI. The purpose of the Discussion Paper is to provide a platform for assessing the desirability of regulating AI technology adoption in UK financial services by safeguarding each of the Supervisory Authorities' own objectives. The BoE's objectives are to maintain financial stability and support the UK government's economic policy. The PRA focuses on the promotion of safety, soundness, and competition for services provided by PRA-authorized firms and insurance firms, while the FCA's strategic objective is to ensure market integrity, effective competition, and protection of consumers in the UK financial system. The Supervisory Authorities consider it useful to distinguish what constitutes AI by either (1) providing a more precise legal definition of what AI is (and what it is not); or (2) viewing AI as part of a wider spectrum of analytical techniques with a range of characteristics for mapping out AI.

Top European Union officials intend to complain loudly to their U.S. counterparts at a trade meeting on Monday about the bloc's electric vehicles being cut off from tax credits in U.S. President Joe Biden's signature climate law. The U.S.-EU Trade and Technology Council (TTC), a year-old transatlantic forum for dialogue, focused in its first two meetings on regulatory co-operation and presenting a united front against China's non-market economic practices. But the 27-country bloc fears that the $430-billion Inflation Reduction Act with its generous tax credits of $7,500 for Tesla, Ford and other North American-made EVs, will significantly damage European automakers. The topic is on the agenda of the TTC meeting on the University of Maryland campus in College Park, Maryland, U.S. and EU officials said. Participants include U.S. Secretary of State Antony Blinken, Commerce Secretary Gina Raimondo, U.S. Trade Representative Katherine Tai and European Commission Executive Vice Presidents Valdis Dombrovskis and Margrethe Vestager.

Two of the main principles underlying the life cycle of an artificial intelligence (AI) module in communication networks are adaptation and monitoring. Adaptation refers to the need to adjust the operation of an AI module depending on the current conditions; while monitoring requires measures of the reliability of an AI module's decisions. Classical frequentist learning methods for the design of AI modules fall short on both counts of adaptation and monitoring, catering to one-off training and providing overconfident decisions. This paper proposes a solution to address both challenges by integrating meta-learning with Bayesian learning. As a specific use case, the problems of demodulation and equalization over a fading channel based on the availability of few pilots are studied. Meta-learning processes pilot information from multiple frames in order to extract useful shared properties of effective demodulators across frames. The resulting trained demodulators are demonstrated, via experiments, to offer better calibrated soft decisions, at the computational cost of running an ensemble of networks at run time. The capacity to quantify uncertainty in the model parameter space is further leveraged by extending Bayesian meta-learning to an active setting. In it, the designer can select in a sequential fashion channel conditions under which to generate data for meta-learning from a channel simulator. Bayesian active meta-learning is seen in experiments to significantly reduce the number of frames required to obtain efficient adaptation procedure for new frames.

Identifying anomalies has become one of the primary strategies towards security and protection procedures in computer networks. In this context, machine learning-based methods emerge as an elegant solution to identify such scenarios and learn irrelevant information so that a reduction in the identification time and possible gain in accuracy can be obtained. This paper proposes a novel feature selection approach called Finite Element Machines for Feature Selection (FEMa-FS), which uses the framework of finite elements to identify the most relevant information from a given dataset. Although FEMa-FS can be applied to any application domain, it has been evaluated in the context of anomaly detection in computer networks. The outcomes over two datasets showed promising results.

Knowledge about outcomes is critical for complex event understanding but is hard to acquire. We show that by pre-identifying a participant in a complex event, crowd workers are able to (1) infer the collective impact of salient events that make up the situation, (2) annotate the volitional engagement of participants in causing the situation, and (3) ground the outcome of the situation in state changes of the participants. By creating a multi-step interface and a careful quality control strategy, we collect a high quality annotated dataset of 8K short newswire narratives and ROCStories with high inter-annotator agreement (0.74-0.96 weighted Fleiss Kappa). Our dataset, POQue (Participant Outcome Questions), enables the exploration and development of models that address multiple aspects of semantic understanding. Experimentally, we show that current language models lag behind human performance in subtle ways through our task formulations that target abstract and specific comprehension of a complex event, its outcome, and a participant's influence over the event culmination.

Unmanned combat air vehicle (UCAV) combat is a challenging scenario with continuous action space. In this paper, we propose a general hierarchical framework to resolve the within-vision-range (WVR) air-to-air combat problem under 6 dimensions of degree (6-DOF) dynamics. The core idea is to divide the whole decision process into two loops and use reinforcement learning (RL) to solve them separately. The outer loop takes into account the current combat situation and decides the expected macro behavior of the aircraft according to a combat strategy. Then the inner loop tracks the macro behavior with a flight controller by calculating the actual input signals for the aircraft. We design the Markov decision process for both the outer loop strategy and inner loop controller, and train them by proximal policy optimization (PPO) algorithm. For the inner loop controller, we design an effective reward function to accurately track various macro behavior. For the outer loop strategy, we further adopt a fictitious self-play mechanism to improve the combat performance by constantly combating against the historical strategies. Experiment results show that the inner loop controller can achieve better tracking performance than fine-tuned PID controller, and the outer loop strategy can perform complex maneuvers to get higher and higher winning rate, with the generation evolves.

In recent years, deep-learning-based approaches have been introduced to solving time-series forecasting-related problems. These novel methods have demonstrated impressive performance in univariate and low-dimensional multivariate time-series forecasting tasks. However, when these novel methods are used to handle high-dimensional multivariate forecasting problems, their performance is highly restricted by a practical training time and a reasonable GPU memory configuration. In this paper, inspired by a change of basis in the Hilbert space, we propose a flexible data feature extraction technique that excels in high-dimensional multivariate forecasting tasks. Our approach was originally developed for the National Science Foundation (NSF) Algorithms for Threat Detection (ATD) 2022 Challenge. Implemented using the attention mechanism and Convolutional Neural Networks (CNN) architecture, our method demonstrates great performance and compatibility. Our models trained on the GDELT Dataset finished 1st and 2nd places in the ATD sprint series and hold promise for other datasets for time series forecasting.

In this paper we raise the research question of whether fake news and hate speech spreaders share common patterns in language. We compute a novel index, the ingroup vs outgroup index, in three different datasets and we show that both phenomena share an "us vs them" narrative.