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'.

Total conference attendance at the 2020 Design Automation Conference (DAC), the industry's premier event dedicated to the design and design automation of electronic circuits and systems, leapt by 52% compared to DAC 2019, according to the 57th DAC Executive Committee (EC). The intense engagement at the 57th DAC, held for the first time virtually due to the recent pandemic, reflected a voracious appetite among engineers for information and insights to propel design innovation. Submissions to DAC's research track increased by 20% in the past two years, and the Designer, IP and Embedded Tracks submissions increased by 15% compared to 2019, continuing a steady three-year rise. The global reach of DAC, July 19 - 24, soared at the 2020 virtual event with attendance from the following regions: 24% Asia Pac, 11% Europe, 52% United States and 13% a combination of Canada, South America and Middle East. Despite the economic and social disruption caused by the pandemic, design innovation never sleeps," said Zhuo Li, General Chair of the 57th DAC. "We had record attendance viewing each of the four Keynotes, plus attendees globally were able to view the recorded technical sessions at their leisure in their respected time-zones.

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.

MLOps is a relatively new concept in the AI (Artificial Intelligence) world and stands for "machine learning operations." Its about how to best manage data scientists and operations people to allow for the effective development, deployment and monitoring of models. "MLOps is the natural progression of DevOps in the context of AI," said Samir Tout, who is a Professor of Cybersecurity at the Eastern Michigan University's School of Information Security & Applied Computing (SISAC). "While it leverages DevOps' focus on security, compliance, and management of IT resources, MLOps' real emphasis is on the consistent and smooth development of models and their scalability." The origins of MLOps goes back to 2015 from a paper entitled "Hidden Technical Debt in Machine Learning Systems."

Emergency vehicle (EMV) service is a key function of cities and is exceedingly challenging due to urban traffic congestion. A main reason behind EMV service delay is the lack of communication and cooperation between vehicles blocking EMVs. In this paper, we study the improvement of EMV service under V2I connectivity. We consider the establishment of dynamic queue jump lanes (DQJLs) based on real-time coordination of connected vehicles. We develop a novel Markov decision process formulation for the DQJL problem, which explicitly accounts for the uncertainty of drivers' reaction to approaching EMVs. We propose a deep neural network-based reinforcement learning algorithm that efficiently computes the optimal coordination instructions. We also validate our approach on a micro-simulation testbed using Simulation of Urban Mobility (SUMO). Validation results show that with our proposed methodology, the centralized control system saves approximately 15\% EMV passing time than the benchmark system.

Several models have been developed to predict how the COVID-19 pandemic spreads, and how it could be contained with non-pharmaceutical interventions (NPIs) such as social distancing restrictions and school and business closures. This paper demonstrates how evolutionary AI could be used to facilitate the next step, i.e. determining most effective intervention strategies automatically. Through evolutionary surrogate-assisted prescription (ESP), it is possible to generate a large number of candidate strategies and evaluate them with predictive models. In principle, strategies can be customized for different countries and locales, and balance the need to contain the pandemic and the need to minimize their economic impact. While still limited by available data, early experiments suggest that workplace and school restrictions are the most important and need to be designed carefully. It also demonstrates that results of lifting restrictions can be unreliable, and suggests creative ways in which restrictions can be implemented softly, e.g. by alternating them over time. As more data becomes available, the approach can be increasingly useful in dealing with COVID-19 as well as possible future pandemics.

This paper describes the SemEval-2020 shared task "Assessing Humor in Edited News Headlines." The task's dataset contains news headlines in which short edits were applied to make them funny, and the funniness of these edited headlines was rated using crowdsourcing. This task includes two subtasks, the first of which is to estimate the funniness of headlines on a humor scale in the interval 0-3. The second subtask is to predict, for a pair of edited versions of the same original headline, which is the funnier version. To date, this task is the most popular shared computational humor task, attracting 48 teams for the first subtask and 31 teams for the second.

Overparameterized networks trained to convergence have shown impressive performance in domains such as computer vision and natural language processing. Pushing state of the art on salient tasks within these domains corresponds to these models becoming larger and more difficult for machine learning practitioners to use given the increasing memory and storage requirements, not to mention the larger carbon footprint. Thus, in recent years there has been a resurgence in model compression techniques, particularly for deep convolutional neural networks and self-attention based networks such as the Transformer. Hence, this paper provides a timely overview of both old and current compression techniques for deep neural networks, including pruning, quantization, tensor decomposition, knowledge distillation and combinations thereof. We assume a basic familiarity with deep learning architectures\footnote{For an introduction to deep learning, see ~\citet{goodfellow2016deep}}, namely, Recurrent Neural Networks~\citep[(RNNs)][]{rumelhart1985learning,hochreiter1997long}, Convolutional Neural Networks~\citep{fukushima1980neocognitron}~\footnote{For an up to date overview see~\citet{khan2019survey}} and Self-Attention based networks~\citep{vaswani2017attention}\footnote{For a general overview of self-attention networks, see ~\citet{chaudhari2019attentive}.},\footnote{For more detail and their use in natural language processing, see~\citet{hu2019introductory}}. Most of the papers discussed are proposed in the context of at least one of these DNN architectures.

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COVID-19 Outbreak-Global Artificial Intelligence (AI) in Healthcare Industry Market Report-Development Trends, Threats, Opportunities and Competitive Landscape in 2020 is latest research study released by HTF MI evaluating the market, highlighting opportunities, risk side analysis, and leveraged with strategic and tactical decision-making support. The study provides information on market trends and development, drivers, capacities, technologies, and on the changing investment structure of the COVID-19 Outbreak-Global Artificial Intelligence (AI) in Healthcare Market. Some of the key players profiled in the study are Zephyr Health, Inc., Atomwise, Inc, Enlitic, Inc., Nvidia Corporation, Welltok, Inc., General Vision, Inc., Microsoft Corporation, Sentirian, IBM Corporation, Next IT Corporation, Intel Corporation, Google Inc. & Siemens Healthineers GmbH. If you are involved in the COVID-19 Outbreak- Artificial Intelligence (AI) in Healthcare industry or intend to be, then this study will provide you comprehensive outlook. It's vital you keep your market knowledge up to date segmented by Patient Data and Risk Analysis, Medical Imaging and Diagnosis, Lifestyle Management and Monitoring, Virtual Assistant, Precision Medicine, In-Patient Care and Hospital Management, Drug Discovery, Wearables & Research,, Deep Learning, Querying Method, NLP & Context Aware Processing and major players.