When the original Microsoft Flight simulator was released almost 40 years ago, it was very much for enthusiasts only. Early home computers could barely cope with drawing cockpit instrument panels, let alone scenery – so what you saw as you fought with the controls was a lot of dials and numbers, usually followed by an on-screen message politely informing you that you had crashed during take-off. This is not the experience you will have with Microsoft Flight Simulator 2020. Developed by French studio Asobo using accurate geographic data culled from Bing Maps, a global cloud computing network, and real-time weather information, this is as much a visual spectacle as it is a simulator. And you will want to see as much as you can, because at 10,000 feet, the world looks spectacular (especially on the Ultra graphical settings, where it's almost photorealistic).

The future of a wide variety of industries is being shaped by the advent of Artificial Intelligence (AI) in technological processes that previously took massive amounts of manpower and attention to detail. The automation of these processes through AI techniques like Machine Learning is a big win for cybersecurity, where they can help catch and prevent all kinds of cyberattacks. While Machine Learning isn't quite the same as AI as it is often understood, both systems allow computer processes to learn as a human would in order to compute complex processes at a scale no human could achieve. Arguably nowhere is this more important than in the ever-growing world of cybersecurity. Here, we explore what machine learning is and how it stands to benefit cybersecurity practices.

… the only way to do that is to use machine learning instead of placing every object by hand — you’ll still find plenty of oddness in the world, too.

Government is ushering in a new era of work, using automation and artificial intelligence to help the federal workforce achieve higher levels of productivity and decision-making. Over the past two years, agencies have focused on shifting the workforce to "high-value" work -- a key goal of the President's Management Agenda -- by taking advantage of robotic process automation and other technologies to reduce error, improve compliance and eliminate repetitive administrative tasks. Although RPA is a useful IT capability that allows agencies to eliminate low-value, mundane, transactional work, it can only make simple decisions. By adding AI to the equation, agencies can accelerate the ability of RPA to complete a multitude of tasks at once. This can be particularly helpful when analyzing large swaths of data, enabling decision-makers to meet goals more efficiently and effectively.

The University of Florida has pioneered a method that uses artificial intelligence to find a disease early so growers who produce summer squash can keep it under control. Early detection gives farmers a fighting chance at a better crop. Summer and winter squash are grown commercially throughout the US state, particularly in southeast and southwest Florida. In 2019, Florida growers harvested 7,700 acres of squash, with a production value of US$35.4 million, according to the USDA National Agricultural Statistics Service. But powdery mildew disease, common throughout the world, can decrease yields.

Improving and optimizing oceanographic sampling is a crucial task for marine science and maritime resource management. Faced with limited resources in understanding processes in the water-column, the combination of statistics and autonomous systems provide new opportunities for experimental design. In this work we develop efficient spatial sampling methods for characterizing regions defined by simultaneous exceedances above prescribed thresholds of several responses, with an application focus on mapping coastal ocean phenomena based on temperature and salinity measurements. Specifically, we define a design criterion based on uncertainty in the excursions of vector-valued Gaussian random fields, and derive tractable expressions for the expected integrated Bernoulli variance reduction in such a framework. We demonstrate how this criterion can be used to prioritize sampling efforts at locations that are ambiguous, making exploration more effective. We use simulations to study and compare properties of the considered approaches, followed by results from field deployments with an autonomous underwater vehicle as part of a study mapping the boundary of a river plume. The results demonstrate the potential of combining statistical methods and robotic platforms to effectively inform and execute data-driven environmental sampling.

Artificial intelligence (AI) has become prevalent in our everyday technologies and impacts both individuals and communities. The explainable AI (XAI) scholarship has explored the philosophical nature of explanation and technical explanations, which are usually driven by experts in lab settings and can be challenging for laypersons to understand. In addition, existing XAI research tends to focus on the individual level. Little is known about how people understand and explain AI-led decisions in the community context. Drawing from XAI and activity theory, a foundational HCI theory, we theorize how explanation is situated in a community's shared values, norms, knowledge, and practices, and how situated explanation mediates community-AI interaction. We then present a case study of AI-led moderation, where community members collectively develop explanations of AI-led decisions, most of which are automated punishments. Lastly, we discuss the implications of this framework at the intersection of CSCW, HCI, and XAI.

Wikidata and Wikipedia have been proven useful for reason-ing in natural language applications, like question answering or entitylinking. Yet, no existing work has studied the potential of Wikidata for commonsense reasoning. This paper investigates whether Wikidata con-tains commonsense knowledge which is complementary to existing commonsense sources. Starting from a definition of common sense, we devise three guiding principles, and apply them to generate a commonsense subgraph of Wikidata (Wikidata-CS). Within our approach, we map the relations of Wikidata to ConceptNet, which we also leverage to integrate Wikidata-CS into an existing consolidated commonsense graph. Our experiments reveal that: 1) albeit Wikidata-CS represents a small portion of Wikidata, it is an indicator that Wikidata contains relevant commonsense knowledge, which can be mapped to 15 ConceptNet relations; 2) the overlap between Wikidata-CS and other commonsense sources is low, motivating the value of knowledge integration; 3) Wikidata-CS has been evolving over time at a slightly slower rate compared to the overall Wikidata, indicating a possible lack of focus on commonsense knowledge. Based on these findings, we propose three recommended actions to improve the coverage and quality of Wikidata-CS further.

Predictive business process monitoring (PBPM) is a class of techniques designed to predict behaviour, such as next activities, in running traces. PBPM techniques aim to improve process performance by providing predictions to process analysts, supporting them in their decision making. However, the PBPM techniques' limited predictive quality was considered as the essential obstacle for establishing such techniques in practice. With the use of deep neural networks (DNNs), the techniques' predictive quality could be improved for tasks like the next activity prediction. While DNNs achieve a promising predictive quality, they still lack comprehensibility due to their hierarchical approach of learning representations. Nevertheless, process analysts need to comprehend the cause of a prediction to identify intervention mechanisms that might affect the decision making to secure process performance. In this paper, we propose XNAP, the first explainable, DNN-based PBPM technique for the next activity prediction. XNAP integrates a layer-wise relevance propagation method from the field of explainable artificial intelligence to make predictions of a long short-term memory DNN explainable by providing relevance values for activities. We show the benefit of our approach through two real-life event logs.

But they are a few problems relating to using a traditional R CNN. Mainly that processing an image takes a lot of time. As the model is extracting 2000 regions to check. Prediction with the model takes around 40 seconds. A better version of R CNN was created by the same person.