Events are occurrences in specific locations, time, and semantics that nontrivially impact either our society or the nature, such as civil unrest, system failures, and epidemics. It is highly desirable to be able to anticipate the occurrence of such events in advance in order to reduce the potential social upheaval and damage caused. Event prediction, which has traditionally been prohibitively challenging, is now becoming a viable option in the big data era and is thus experiencing rapid growth. There is a large amount of existing work that focuses on addressing the challenges involved, including heterogeneous multi-faceted outputs, complex dependencies, and streaming data feeds. Most existing event prediction methods were initially designed to deal with specific application domains, though the techniques and evaluation procedures utilized are usually generalizable across different domains. However, it is imperative yet difficult to cross-reference the techniques across different domains, given the absence of a comprehensive literature survey for event prediction. This paper aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction in the big data era. First, systematic categorization and summary of existing techniques are presented, which facilitate domain experts' searches for suitable techniques and help model developers consolidate their research at the frontiers. Then, comprehensive categorization and summary of major application domains are provided. Evaluation metrics and procedures are summarized and standardized to unify the understanding of model performance among stakeholders, model developers, and domain experts in various application domains. Finally, open problems and future directions for this promising and important domain are elucidated and discussed.

Parking is an inevitable issue in the fast-growing developing countries. Increasing number of vehicles require more and more urban land to be allocated for parking. However, a little attention has been conferred to the parking issues in developing countries like India. This study proposes a model for analysing the influence of car users' socioeconomic and travel characteristics on parking duration. Specifically, artificial neural networks (ANNs) is deployed to capture the interrelationship between driver characteristics and parking duration. ANNs are highly efficient in learning and recognizing connections between parameters for best prediction of an outcome. Since, utility of ANNs has been critically limited due to its Black Box nature, the study involves the use of Garson algorithm and Local interpretable model-agnostic explanations (LIME) for model interpretations. LIME shows the prediction for any classification, by approximating it locally with the developed interpretable model. This study is based on microdata collected on-site through interview surveys considering two land-uses: office-business and market/shopping. Results revealed the higher probability of prediction through LIME and therefore, the methodology can be adopted ubiquitously. Further, the policy implications are discussed based on the results for both land-uses. This unique study could lead to enhanced parking policy and management to achieve the sustainability goals.

Machine learning models differ in terms of accuracy, computational/memory complexity, training time, and adaptability among other characteristics. For example, neural networks (NNs) are well-known for their high accuracy due to the quality of their automatic feature extraction while brain-inspired hyperdimensional (HD) learning models are famous for their quick training, computational efficiency, and adaptability. This work presents a hybrid, synergic machine learning model that excels at all the said characteristics and is suitable for incremental, on-line learning on a chip. The proposed model comprises an NN and a classifier. The NN acts as a feature extractor and is specifically trained to work well with the classifier that employs the HD computing framework. This work also presents a parameterized hardware implementation of the said feature extraction and classification components while introducing a compiler that maps any arbitrary NN and/or classifier to the aforementioned hardware. The proposed hybrid machine learning model has the same level of accuracy (i.e. $\pm$1%) as NNs while achieving at least 10% improvement in accuracy compared to HD learning models. Additionally, the end-to-end hardware realization of the hybrid model improves power efficiency by 1.60x compared to state-of-the-art, high-performance HD learning implementations while improving latency by 2.13x. These results have profound implications for the application of such synergic models in challenging cognitive tasks.

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.