As text generated by large language models proliferates, it becomes vital to understand how humans engage with such text, and whether or not they are able to detect when the text they are reading did not originate with a human writer. Prior work on human detection of generated text focuses on the case where an entire passage is either human-written or machine-generated. In this paper, we study a more realistic setting where text begins as human-written and transitions to being generated by state-of-the-art neural language models. We show that, while annotators often struggle at this task, there is substantial variance in annotator skill and that given proper incentives, annotators can improve at this task over time. Furthermore, we conduct a detailed comparison study and analyze how a variety of variables (model size, decoding strategy, fine-tuning, prompt genre, etc.) affect human detection performance. Finally, we collect error annotations from our participants and use them to show that certain textual genres influence models to make different types of errors and that certain sentence-level features correlate highly with annotator selection. We release the RoFT dataset: a collection of over 21,000 human annotations paired with error classifications to encourage future work in human detection and evaluation of generated text.

Then, an unsupervised learning Recently, cell-free massive multiple-input multiple-output based approach is proposed to tackle the EE maximization (MIMO) has emerged as a promising technology to effectively problem. Specifically, we model a two-stage deep neural alleviate inter-cell interference [1]. In this system, a large number network (DNN) and design a reasonable loss function, and of distributed access points (APs) are linked to the central train this DNN in an unsupervised manner to learn the optimal processing unit (CPU) through the backhaul link and provide beamforming and phase shifts. At last, simulation results show services to all the users without cell boundaries. However, the that compared with the traditional genetic algorithm (GA) and large-scale deployment of APs will bring some problems, such the proposed iterative optimization algorithm, the unsupervised as high deployment cost and power consumption. To address learning based approach can achieve better EE performance these difficulties, a promising technique called intelligent with extremely low running time.

Vehicle-to-Everything (V2X) communication has been proposed as a potential solution to improve the robustness and safety of autonomous vehicles by improving coordination and removing the barrier of non-line-of-sight sensing. Cooperative Vehicle Safety (CVS) applications are tightly dependent on the reliability of the underneath data system, which can suffer from loss of information due to the inherent issues of their different components, such as sensors failures or the poor performance of V2X technologies under dense communication channel load. Particularly, information loss affects the target classification module and, subsequently, the safety application performance. To enable reliable and robust CVS systems that mitigate the effect of information loss, we proposed a Context-Aware Target Classification (CA-TC) module coupled with a hybrid learning-based predictive modeling technique for CVS systems. The CA-TC consists of two modules: A Context-Aware Map (CAM), and a Hybrid Gaussian Process (HGP) prediction system. Consequently, the vehicle safety applications use the information from the CA-TC, making them more robust and reliable. The CAM leverages vehicles path history, road geometry, tracking, and prediction; and the HGP is utilized to provide accurate vehicles' trajectory predictions to compensate for data loss (due to communication congestion) or sensor measurements' inaccuracies. Based on offline real-world data, we learn a finite bank of driver models that represent the joint dynamics of the vehicle and the drivers' behavior. We combine offline training and online model updates with on-the-fly forecasting to account for new possible driver behaviors. Finally, our framework is validated using simulation and realistic driving scenarios to confirm its potential in enhancing the robustness and reliability of CVS systems.

While flying cars have long been a vision of science fiction movies, many companies, including NASA, have started turning them into a reality. However, the US space agency have left one'destroyed beyond expectations' after crashing it into the ground on purpose. This test was completed to see how the electric vertical takeoff and landing vehicle (eVTOL) would respond to such an event. Simulating a'severe crash', NASA engineers dropped a mock eVTOL containing six crash test dummies from a height. NASA has completed a crash test of its electric vertical takeoff and landing vehicle to test its response to such an event.

There is an increase in the adoption of insurance analytics in large and medium-sized organizations to prevent internal frauds, rate evasions, and underwriting and cybersecurity fraud committed by applicants, policyholders, third-party claimants, and professionals. This currently represents one of the major factors strengthening the market growth around the world. In line with this, the expanding number of cyber-attacks and anonymous security threats is bolstering the growth of the market. Moreover, the rising occurrence of insurance frauds like insurance padding, inflated claims, staged accidents, and submission of inaccurate information evidence across numerous industries is favoring the market growth. Along with this, due to a considerable rise in data generation in the insurance industry, there is an increase in the adoption of analytic solutions worldwide.

Around 60 years ago, the U.S. Department of Justice Civil Rights Division was established for prohibiting discrimination based on protected attributes. Over these 60 years, they established a set of policies and guidelines to identify and penalize those who discriminate1. The widespread use of machine learning (ML) models in routine life has prompted researchers to begin studying the extent to which these models are discriminatory. However, some researcher are unaware that the legal system already has well established procedures for describing and proving discrimination in law. In this series of blog posts, we'll try to bridge this gap.

England's BBC Channel 4 is going have an AI robot named Ameca provide the alternative Christmas message to King Charles' official royal remarks. A dystopian humanoid cyborg is set to give us the seasons' greetings this year on Channel 4. According to a report from Deadline, the AI robot, whose name is Ameca will be delivering alternate remarks to King Charles III's annual Royal Christmas message which will broadcast on its usual home on Channel 1. The robot was developed by Engineered Arts, a developing firm in Cornwall, England. The AI for Ameca is apparently set to deliver remarks which seek to calm the nation and the world at large, by reassuring us that 2022 was a "learning opportunity, a chance to change the way we think about the world and a reminder to help those in need whenever we can." This sounds suspiciously like some kind of terrifying cyborg threat, especially without hearing the accompanying Apple Maps voice delivering the statement, but Channel 4 assures us the robot supports the human race and loves a good laugh when times get tough.

Over the last few years, more and more AI & ML use cases have moved beyond the proof-of-concept stage and are becoming mainstream. Be it making processes more efficient, gaining new insights, or boosting service levels, new use cases are continuously developed and implemented both in the public and private sectors. Traditionally the private sector has been a front runner, though governments and public organisations have also picked up pace in the recent past. Despite facing budgetary constraints, the need for improved service to citizens and stakeholders in the public sector is now a part of the roadmap of any digital transformation. One of the key barriers to a broader AI & ML adoption has usually been the lack of the required infrastructure.

Machine learning (ML) on graph-structured data has recently received deepened interest in the context of intrusion detection in the cybersecurity domain. Due to the increasing amounts of data generated by monitoring tools as well as more and more sophisticated attacks, these ML methods are gaining traction. Knowledge graphs and their corresponding learning techniques such as Graph Neural Networks (GNNs) with their ability to seamlessly integrate data from multiple domains using human-understandable vocabularies, are finding application in the cybersecurity domain. However, similar to other connectionist models, GNNs are lacking transparency in their decision making. This is especially important as there tend to be a high number of false positive alerts in the cybersecurity domain, such that triage needs to be done by domain experts, requiring a lot of man power. Therefore, we are addressing Explainable AI (XAI) for GNNs to enhance trust management by exploring combining symbolic and sub-symbolic methods in the area of cybersecurity that incorporate domain knowledge. We experimented with this approach by generating explanations in an industrial demonstrator system. The proposed method is shown to produce intuitive explanations for alerts for a diverse range of scenarios. Not only do the explanations provide deeper insights into the alerts, but they also lead to a reduction of false positive alerts by 66% and by 93% when including the fidelity metric.

Computing systems are tightly integrated today into our professional, social, and private lives. An important consequence of this growing ubiquity of computing is that it can have significant ethical implications of which computing professionals should take account. In most real-world scenarios, it is not immediately obvious how particular technical choices during the design and use of computing systems could be viewed from an ethical perspective. This article provides a perspective on the ethical challenges within semiconductor chip design, IoT applications, and the increasing use of artificial intelligence in the design processes, tools, and hardware-software stacks of these systems.