Microsoft will release the latest edition of its famous Flight Simulator software for PC later this month with ultra lifelike graphics - but it will require a massive 150GB of free storage to install it. The software will make use of satellite maps from Bing as well as live readings from weather stations and airports around the world to create'the most realistic' version of the game ever developed. Users simply upload their desired destination and use the'realistic' training system to navigate the area – the simulator is set to be released for PC on August 18, Xbox shortly after and VR later this year. The simulator lets'pilots' sit in a realistic cockpit, allowing them to learn the ins and outs of a real airplane and travel from or to more than 40,000 real-world airports and visit sites between them. The latest version of the 38 year old software includes 37 thousand airports, 1.5 billion buildings and two trillion trees, mountains, roads and rivers. According to Microsoft it will include live traffic, real time weather and moving animals to reflect the fact Earth is a'living world'.

Johannesburg-based producer HYMR's debut album, Artificial Intelligence is dripping in cinematic glory but for a handful of tracks that, while sounding good, don't add any weight to the piece. Fillers aside the record paints an intriguing portrait of the cyber-dystopia we are so rapidly heading towards where robots have the power to kill us and the environment has been tortured to within an inch of its life. 'Artificial Intelligence', a remix of a track that features, later on, sounds like the intro to a dystopian film. A young protagonist stands on the roof of a high-rise building dreaming of a better world as they look out on a 21st-century Hell-scape defined by monotonous grey buildings and never-ending rain. 'Cosmic Dreamer', an instrumental number that brings a world of tension to the album, continues this cinematic idea before'Polluted Planet' takes things in a more EDM-based direction.

Recent research shows that sublevel sets of the loss surfaces of overparameterized networks are connected, exactly or approximately. We describe and compare experimentally a panel of methods used to connect two low-loss points by a low-loss curve on this surface. Our methods vary in accuracy and complexity. Most of our methods are based on "macroscopic" distributional assumptions, and some are insensitive to the detailed properties of the points to be connected. Some methods require a prior training of a "global connection model" which can then be applied to any pair of points. The accuracy of the method generally correlates with its complexity and sensitivity to the endpoint detail.

Natural language processing (NLP) research predominantly focuses on developing methods that work well for English despite the many positive benefits of working on other languages. These benefits range from an outsized societal impact to modelling a wealth of linguistic features to avoiding overfitting as well as interesting challenges for machine learning (ML). There are around 7,000 languages spoken around the world. The map above (see the interactive version at Langscape) gives an overview of languages spoken around the world, with each green circle representing a native language. Most of the world's languages are spoken in Asia, Africa, the Pacific region and the Americas.

Intel has recently partnered with Accenture and the Sulubaaï Environmental Foundation to create an AI-driven data collection platform aimed at analyzing and protecting vulnerable marine habitats, habitats like coral reefs. A combination of climate change, pollution, and overfishing have been damaging the world's oceans, particularly coral reefs. Coral reefs around the world are experiencing mass die-offs and problems like coral bleaching. Scientists and conservationists are looking for ways to protect coral reefs and help them recover. Designing plans to support coral reefs requires data, and as Engadget reported, Intel has partnered with two environmental foundations to create the CORaiL platform.

Up Market Research (UMR) offers a detailed report on Global Artificial Intelligence Market. The report is a comprehensive research study that provides the scope of Artificial Intelligence market size, industry growth opportunities and challenges, current market trends, potential players, and expected performance of the market in regions for the forecast period from 2020 to 2027. This report highlights key insights on the market focusing on the possible requirements of the clients and assisting them to make right decision about their business investment plans and strategies. The Artificial Intelligence market report also covers an overview of the segments and sub-segmentation's including the product types, applications, companies and regions. This report further includes the impact of COVID-19 on the market and explains dynamics of the market, future business impact, competition landscape of the companies, and the flow of the global supply and consumption.

Being overweight puts you at greater risk of serious illness or death from Covid-19, experts say - and now new anti-obesity strategies have been launched around the UK. In Bradford, community schemes to promote healthy lifestyles offers a novel approach to the problem. Dr John Wright of the city's Royal Infirmary explains why radical thinking is necessary. Our complete concentration on Covid-19 has concealed another global pandemic that has been more insidious but much more harmful: obesity. Early in the pandemic, we spotted common patterns in our sickest Covid-19 patients - they were more likely to have diabetes and heart disease and, in particular, to be obese.

Negotiation is the complex social process by which multiple parties come to mutual agreement over a series of issues. As such, it has proven to be a key challenge problem for designing adequately social AIs that can effectively navigate this space. Artificial AI agents that are capable of negotiating must be capable of realizing policies and strategies that govern offer acceptances, offer generation, preference elicitation, and more. But the next generation of agents must also adapt to reflect their users’ experiences.      The best human negotiators tend to have honed their craft through hours of practice and experience. But, not all negotiators agree on which strategic tactics to use, and endorsement of deceptive tactics in particular is a controversial topic for many negotiators. We examine the ways in which deceptive tactics are used and endorsed in non-repeated human negotiation and show that prior experience plays a key role in governing what tactics are seen as acceptable or useful in negotiation. Previous work has indicated that people that negotiate through artificial agent representatives may be more inclined to fairness than those people that negotiate directly. We present a series of three user studies that challenge this initial assumption and expand on this picture by examining the role of past experience.      This work constructs a new scale for measuring endorsement of manipulative negotiation tactics and introduces its use to artificial intelligence research. It continues by presenting the results of a series of three studies that examine how negotiating experience can change what negotiation tactics and strategies human endorse. Study #1 looks at human endorsement of deceptive techniques based on prior negotiating experience as well as representative effects. Study #2 further characterizes the negativity of prior experience in relation to endorsement of deceptive techniques. Finally, in Study #3, we show that the lessons learned from the empirical observations in Study #1 and #2 can in fact be induced—by designing agents that provide a specific type of negative experience, human endorsement of deception can be predictably manipulated.

Understanding consumer behavior is an important task, not only for developing marketing strategies but also for the management of economic policies. Detecting consumption patterns, however, is a high-dimensional problem in which various factors that would affect consumers' behavior need to be considered, such as consumers' demographics, circadian rhythm, seasonal cycles, etc. Here, we develop a method to extract multi-timescale expenditure patterns of consumers from a large dataset of scanned receipts. We use a non-negative tensor factorization (NTF) to detect intra- and inter-week consumption patterns at one time. The proposed method allows us to characterize consumers based on their consumption patterns that are correlated over different timescales.

For some time now, the United Arab Emirates (UAE) has been adopting artificial intelligence (AI) in the public and business sectors. This is part of the Gulf country's economic diversification strategy, aimed at transforming the UAE away from an oil-dependent economy to a knowledge-based one. AI is generally conceived as human intelligence processes which are simulated by computer systems, including learning, reasoning, problem-solving, planning, predictive analytics, and advanced robotics. Like other Arab states, the UAE has advanced a public discourse based on a dominant narrative of nationalism which is meant to solidify its image while reinforcing the Emirati rulers' power and legitimacy. Its foundational theme is made up of different frames such as diversity, tolerance, moderation, international cooperation, humanitarianism, and modernity. State leaders use narratives not only to persuade and influence a national and international audience of its image and self-perception, but also as a means to determine its understanding of its place and purpose in the international system.