Both weather and climate models have improved drastically in recent years, as advances in one field have tended to benefit the other. But there is still significant uncertainty in model outputs that are not quantified accurately. Thats because the processes that drive climate and weather are chaotic, complex, and interconnected in ways that researchers have yet to describe in the complex equations that power numerical models.

Historically, researchers have used approximations called parameterizations to model the relationships underlying small-scale atmospheric processes and their interactions with large-scale atmospheric processes. Stochastic parameterizations have become increasingly common for representing the uncertainty in subgrid-scale processes, and they are capable of producing fairly accurate weather forecasts and climate projections. But its still a mathematically challenging method. Now researchers are turning to machine learning to provide more efficiency to mathematical models.

Here Gagne et al. evaluate the use of a class of machine learning networks known as generative adversarial networks (GANs) with a toy model of the extratropical atmospherea model first presented by Edward Lorenz in 1996 and thus known as the L96 system that has been frequently used as a test bed for stochastic parameterization schemes. The researchers trained 20 GANs, with varied noise magnitudes, and identified a set that outperformed a hand-tuned parameterization in L96. The authors found that the success of the GANs in providing accurate weather forecasts was predictive of their performance in climate simulations: The GANs that provided the most accurate weather forecasts also performed best for climate simulations, but they did not perform as well in offline evaluations.

The study provides one of the first practically relevant evaluations for machine learning for uncertain parameterizations. The authors conclude that GANs are a promising approach for the parameterization of small-scale but uncertain processes in weather and climate models. (Journal of Advances in Modeling Earth Systems (JAMES), https://doi.org/10.1029/2019MS001896, 2020)

Kate Wheeling, Science Writer

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Machine Learning Improves Weather and Climate Models - Eos

Machine Learning in Tax and Accounting:

This report studies the Machine Learning in Tax and Accounting market with many aspects of the industry like the market size, market status, market trends and forecast, the report also provides brief information of the competitors and the specific growth opportunities with key market drivers. Find the complete Machine Learning in Tax and Accounting market analysis segmented by companies, region, type and applications in the report.

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The final report will add the analysis of the Impact of Covid-19 in this report Machine Learning in Tax and Accounting Industry.

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Machine Learning in Tax and Accounting Marketcontinues to evolve and expand in terms of the number of companies, products, and applications that illustrates the growth perspectives. The report also covers the list of Product range and Applications with SWOT analysis, CAGR value, further adding the essential business analytics.Machine Learning in Tax and Accounting Marketresearch analysis identifies the latest trends and primary factors responsible for market growth enabling the Organizations to flourish with much exposure to the markets.

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TheMachine Learning in Tax and Accounting Marketresearch report completely covers the vital statistics of the capacity, production, value, cost/profit, supply/demand import/export, further divided by company and country, and by application/type for best possible updated data representation in the figures, tables, pie chart, and graphs. These data representations provide predictive data regarding the future estimations for convincing market growth. The detailed and comprehensive knowledge about our publishers makes us out of the box in case of market analysis.

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Global Machine Learning in Tax and Accounting Market: Competitive Landscape

This section of the report identifies various key manufacturers of the market. It helps the reader understand the strategies and collaborations that players are focusing on combat competition in the market. The comprehensive report provides a significant microscopic look at the market. The reader can identify the footprints of the manufacturers by knowing about the global revenue of manufacturers, the global price of manufacturers, and production by manufacturers during the forecast period of 2015 to 2019.

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2020-2026 Machine Learning in Tax and Accounting Market Status and Forecast, By Players, Types and Applications - Science In Me

Parasoft C/C++test is honored for its leading technology to increase software engineer productivity and achieve safety compliance

MONROVIA, Calif., April 7, 2020 /PRNewswire/ --Parasoft, a global software testing automation leader for over 30 years, received the VDC Research Embedded Award for 2020. The technology research and consulting firm yearly recognizes cutting-edge Software and Hardware Technologies in the embedded industry. This year, Parasoft C/C++test, aunified development testing solution forsafety and securityof embedded C and C++ applications, was recognized for its new, innovative approach that expedites the adoption of software code analysis, increasing developer productivity and simplifying compliance with industry standards such as CERT C/C++, MISRA C 2012 and AUTOSAR C++14. To learn more about Parasoft C/C++test, please visit: https://www.parasoft.com/products/ctest.

Parasoft C/C++test is honored for its leading technology to increase software engineer productivity and achieve safety compliance

"Parasoft has continued its investment in the embedded market, adding new products and personnel to boost its market presence. In addition to highlighting expanded partnerships and coding-standard support, the company announced the integration of AI capabilities into its static analysis engine. While defect prioritization systems have been part of static analysis solutions for well over ten years, Parasoft's solution takes the idea a step further. Their solution now effectively learns from past interactions with identified defects and the codebase to better help users triage new findings," states Chris Rommel, EVP, VDC Research Group.

Parasoft's latest innovation applies AI/Machine Learning to the process of reviewing static analysis findings. Static analysis is a foundational part of the quality process, especially in safety-critical development (e.g., ISO26262, IEC61508), and is an effective first step to establish secure development practices. A common challenge when deploying static analysis tools is dealing with the multitude of reported findings. Scans can produce tens of thousands of findings, and teams of highly qualified resources need to go through a time-consuming process of reviewing and identifying high-priority findings. This process leads to finding and reviewing critical issues late in the cycle, delaying the delivery, and worse, allowing insecure/unsafe code to become embedded into the codebase.

Parasoft leaps forwardbeyond the rest of the competitive market by having AI/ML take into account the context of both historical interactions with the code base and prior static analysis findings to predict relevance and prioritize new findings. This innovation helps organizations achieve compliance with industry standards and offers a unique application of AI/ML in helping organizations with the adoption of Static Analysis. This innovative technology builds on Parasoft's previous AI/ML innovations in the areas of Web UI, API, and Unit testing - https://blog.parasoft.com/what-is-artificial-intelligence-in-software-testing.

"We are extremely honored to have received this award, particularly in light of the competition, VDC's expertise and knowledge of the embedded market," said Mark Lambert, VP of Products at Parasoft. "We have always been committed to innovation led by listening to our customers and leveraging capabilities that will help drive them forward. This creativity has always driven Parasoft's development and is something that has been in the company's DNA from its founding."

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About Parasoft (www.parasoft.com):Parasoft, the global leader in software testing automation, has been reducing the time, effort, and cost of delivering high-quality software to the market for the last 30+ years. Parasoft's tools support the entire software development process, from when the developer writes the first line of code all the way through unit and functional testing, to performance and security testing, leveraging simulated test environments along the way. Parasoft's unique analytics platform aggregates data from across all testing practices, providing insights up and down the testing pyramid to enable organizations to succeed in today's most strategic development initiatives, including Agile/DevOps, Continuous Testing, and the complexities of IoT.

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Parasoft wins 2020 VDC Research Embeddy Award for Its Artificial Intelligence (AI) and Machine Learning (ML) Innovation - Yahoo Finance

One of the advantages we have today in the fight against coronavirus that wasnt as sophisticated in the SARS outbreak of 2003 is big data and the high level of technology available. China tapped into big data, machine learning, and other digital tools as the virus spread through the nation in order to track and contain the outbreak. The lessons learned there have continued to spread across the world as other countries fight the spread of the virus and use digital technology to develop real-time forecasts and arm healthcare professionals and government decision-makers with intel they can use to predict the impact of the coronavirus.

The Vital Role Of Big Data In The Fight Against Coronavirus

Chinas Surveillance Infrastructure Used to Track Exposed People

Chinas surveillance culture became useful in the countrys response to COVID-19. Thermal scanners were installed in train stations to detect elevated body temperaturesa potential sign of infection. If a high temperature was detected, then the person was detained by health officials to undergo coronavirus testing. If the coronavirus test came back positive, authorities would alert every other passenger who may have been exposed to the virus so they could quarantine themselves. This notification was enabled because of the country's transportation rules that require every passenger who travels on public transport to use their real names and government-issued ID cards.

China has millions of security cameras that are used to track citizens movements in addition to spotting crimes. This helped authorities discover people who werent compliant with quarantine orders. If a person was supposed to be in quarantine, but cameras tracked them outside their homes, authorities would be called. Mobile phone data was also used to track movements.

The Chinese government also rolled out a Close Contact Detector app that alerted users if they were in contact with someone who had the virus. Travel verification reports produced by telecom providers could list all the cities visited by a user in the last 14 days to determine if quarantine was recommended based on their locations. By integrating the data collected by Chinas surveillance system, the country was able to find ways to fight the spread of the coronavirus.

Mobile App for Contact Tracing

In Europe and America, privacy considerations for citizens are of bigger concern than they are in China, yet medical researchers and bioethics experts understand the power of technology to support contact tracing in a pandemic. Oxford Universitys Big Data Institute worked with government officials to explain the benefits of a mobile app that could provide valuable data for an integrated coronavirus control strategy. Since nearly half of all coronavirus transmissions occur before symptoms occur, speed and effectiveness to alert people that may have been exposed are paramount during a pandemic such as coronavirus. A mobile app that harnesses 21st-century technology can accelerate the notification process while maintaining ethics to slow the rate of contagion.

Tech innovators had already worked on solutions to effectively monitor and track the spread of flu. FluPhone was introduced in 2011, but the app wasn't highly adopted, which limited its usefulness. Other app solutions are in the works from a variety of organizations that aim to give people a tool to self-identify their health status and symptoms. Along with all the challenges coronavirus has us facing, it's also providing essential learning experiences for data science in healthcare.

In the United States, the government is in conversation with tech giants such as Facebook, Google, and others to determine what's possibleand ethicalin terms of using location data from Americans' smartphones to track movements and understand patterns.

Official Dashboards Track the Virus and Outbreak Analytics

Another tool that has been helpful for private citizens, government policy-makers and healthcare professionals to see the progression of contagion and to inform models of how invasive this virus will be are dashboards from entities such as the World Health Organization that provide real-time stats. The dashboard I have been watching is this one. These dashboards pull in data from around the world to show confirmed cases and deaths from coronavirus and locations. This comprehensive data set can then be used to create models and predict hotspots for the disease so that decisions can be made about stay-at-home orders and to help healthcare systems prepare for a surge of cases.

Outbreak analytics takes all available data, including the number of confirmed cases, deaths, tracing contacts of infected people, population densities, maps, traveler flow, and more, and then processes it through machine learning to create models of the disease. These models represent the best predictions regarding peak infection rates and outcomes.

Big Data Analytics and Successes in Taiwan

As coronavirus spread in China, it was assumed that Taiwan would be heavily hit in part because of its proximity to China, the regular flights that went from the island to China each day, and how many Taiwanese citizens work in China. However, Taiwan used technology and a robust pandemic plan created after the 2003 SARS outbreak to minimize the virus impact on its land.

Part of their strategy integrated the national health insurance database with data from its immigration and customs database. By centralizing the data in this way, when faced with coronavirus, they were able to get real-time alerts regarding who might be infected based on symptoms and travel history. In addition to this, they had QR code scanning and online reporting of travel and health symptoms that helped them classify travelers infection risks and a toll-free hotline for citizens to report suspicious symptoms. Officials took immediate action from the minute WHO broadcast information about a pneumonia of unknown cause in China on Dec. 31, 2019. This was the first reported case of coronavirus, and Taiwan's quick response and use of technology are the likely reasons they have a lower rate of infection than others despite their proximity to China.

Technology is vital in the fight against coronavirus and future pandemics. In addition to being able to support modeling efforts and predicting the flow of a pandemic, big data, machine learning, and other technology can quickly and effectively analyze data to help humans on the frontlines figure out the best preparation and response to this and future pandemics.

For more on AI and technology trends, see Bernard Marrs bookArtificial Intelligence in Practice: How 50 Companies Used AI and Machine Learning To Solve Problemsand his forthcoming bookTech Trends in Practice: The 25 Technologies That Are Driving The 4ThIndustrial Revolution, which is available to pre-order now.

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The Vital Role Of Big Data In The Fight Against Coronavirus - Forbes

Market Research Inc has included analytical data ofMachine Learning in Healthcaremarket to its massive database. The report comprises of various verticals of the businesses. The report is aggregated on the basis of different dynamic aspects of the market study. The statistical report is compiled by means of primary and secondary research methodologies. A comprehensive overview of Porters five analysis and SWOT analysis is used to examine the strength, weaknesses, threats and opportunities of the market.

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The study further also presents the details on financial attributes such as pricing structures, shares and profit margins. In a distinctive feature the report, it includes a summary of top notch companies such asMachine Learning in Healthcare.The competitive landscape of theMachine Learning in Healthcaremarket is presented by analyzing various successful and startup industries. The economic aspects of the businesses are provided by using facts and figures.

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The market study covers the lucrative market scope ofNorth America, Latin America, Asia-Pacific, Europe and Africaon the basis of productivity, thus focusing on the leading countries from the global regions. The report also highlights the pricing structure including cost of raw material and cost of manpower.

The report further also offers a clear picture of the various factors that demonstrate as significant business stimulants of theMachine Learning in Healthcaremarket. This market study also analyzes and presents more accurate data which helps to gauge the overall framework of the businesses. Technological advancements in globalMachine Learning in Healthcaresector is accurately examined by experts.

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Study of the annual revenues and market developments of the major players that supply Machine Learning in Healthcare Analysis of the demand for Machine Learning in Healthcare by component Assessment of future trends and growth of architecture in the Machine Learning in Healthcare market Assessment of the Machine Learning in Healthcare market with respect to the type of application Study of the market trends in various regions and countries, by component, of the Machine Learning in Healthcare market Study of contracts and developments related to the Machine Learning in Healthcare market by key players across different regions Finalization of overall market sizes by triangulating the supply-side data, which includes product developments, supply chain, and annual revenues of companies supplying Machine Learning in Healthcare across the globe.

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In this study, the years considered to estimate the size of Machine Learning in Healthcare are as follows:

History Year: 2013-2019

Base Year: 2019

Estimated Year: 2020

Forecast Year 2020 to 2026.

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Machine Learning in Healthcare Market to Witness Tremendous Growth in Forecasted Period 2020-2027 - Bandera County Courier

This Machine Learning as a Service report comprises of a deep knowledge and information on what the markets definition, classifications, applications, and engagements and also explains the drivers and restraints of the market which is derived from SWOT analysis. An analytical assessment of the competitors confers clear idea of the most important challenges faced by them in the present market and in upcoming years. Besides, the identity of respondents is also kept undisclosed and no promotional approach is made to them while analyzing the data. Global Machine Learning as a Service market research document covers major manufacturers, suppliers, distributors, traders, customers, investors and major types, major applications.

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Geographically, the globalMachine Learning as a Servicemarket has been fragmented across several regions such asNorth America, Latin America, Asia-Pacific, Africa, and Europe. The study enlists various market key players in order to present a clear idea about different strategies undertaken by top-notch companies. Inclusive of in-depth analysis of market dynamics such as drivers, restraints and global opportunities, the study provides a cogent study about the fluctuating highs and lows of the businesses. Several market parameters are also stated while curating the research report, these include investors, share market and budget of the companies.

Top Key Players in the Global Machine Learning as a Service Market Research Report:

Microsoft (Washington,US), Amazon Web Services (Washington, US), Hewlett Packard Enterprises (California, US), Google, Inc

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In order to understand the competitive business environment, the report studies various market analysis methodologies such as Porters five analysis and SWOT analysis. Several market dynamics have been scrutinized which are responsible for driving or hampering the progress of theMachine Learning as a Servicemarket. Additionally, the study underlines recent technological advancements and tools referred by several industries. Furthermore, it draws attention to several effective sales methodologies which help to increase number of customers rapidly. Insightful case studies from different industry experts also form an inclusive part of the report. The bargaining power of several vendors and buyers also form a salient feature of the report.

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Machine Learning as a Service Market: Indoor Applications Projected to be the Most Attractive Segment during 2020-2027 - Bandera County Courier

When Machines Design: Artificial Intelligence and the Future of Aesthetics

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Are machines capable of design? Though a persistent question, it is one that increasingly accompanies discussions on architecture and the future of artificial intelligence. But what exactly is AI today? As we discover more about machine learning and generative design, we begin to see that these forms of "intelligence" extend beyond repetitive tasks and simulated operations. They've come to encompass cultural production, and in turn, design itself.

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When artificial intelligence was envisioned during thethe 1950s-60s, thegoal was to teach a computer to perform a range of cognitive tasks and operations, similar to a human mind. Fast forward half a century, andAIis shaping our aesthetic choices, with automated algorithms suggesting what we should see, read, and listen to. It helps us make aesthetic decisions when we create media, from movie trailers and music albums to product and web designs. We have already felt some of the cultural effects of AI adoption, even if we aren't aware of it.

As educator and theorist Lev Manovich has explained, computers perform endless intelligent operations. "Your smartphones keyboard gradually adapts to your typing style. Your phone may also monitor your usage of apps and adjust their work in the background to save battery. Your map app automatically calculates the fastest route, taking into account traffic conditions. There are thousands of intelligent, but not very glamorous, operations at work in phones, computers, web servers, and other parts of the IT universe."More broadly, it's useful to turn the discussion towards aesthetics and how these advancements relate to art, beauty and taste.

Usually defined as a set of "principles concerned with the nature and appreciation of beauty, aesthetics depend on who you are talking to. In 2018, Marcus Endicott described how, from the perspective of engineering, the traditional definition of aesthetics in computing could be termed "structural, such as an elegant proof, or beautiful diagram." A broader definition may include more abstract qualities of form and symmetry that "enhance pleasure and creative expression." In turn, as machine learning is gradually becoming more widely adopted, it is leading to what Marcus Endicott termed a neural aesthetic. This can be seen in recent artistic hacks, such as Deepdream, NeuralTalk, and Stylenet.

Beyond these adaptive processes, there are other ways AI shapes cultural creation. Artificial intelligence hasrecently made rapid advances in the computation of art, music, poetry, and lifestyle. Manovich explains that AIhas given us the option to automate our aesthetic choices (via recommendation engines), as well as assist in certain areas of aesthetic production such as consumer photography and automate experiences like the ads we see online. "Its use of helping to design fashion items, logos, music, TV commercials, and works in other areas of culture is already growing." But, as he concludes, human experts usually make the final decisions based on ideas and media generated by AI. And yes, the human vs. robot debate rages on.

According to The Economist, 47% of the work done by humans will have been replaced by robots by 2037, even those traditionally associated with university education. The World Economic Forum estimated that between 2015 and 2020, 7.1 million jobs will be lost around the world, as "artificial intelligence, robotics, nanotechnology and other socio-economic factors replace the need for human employees." Artificial intelligence is already changing the way architecture is practiced, whether or not we believe it may replace us. As AI is augmenting design, architects are working to explore the future of aesthetics and how we can improve the design process.

In a tech report on artificial intelligence, Building Design + Construction explored how Arup had applied a neural network to a light rail design and reduced the number of utility clashes by over 90%, saving nearly 800 hours of engineering. In the same vein, the areas of site and social research that utilize artificial intelligence have been extensively covered, and examples are generated almost daily. We know that machine-driven procedures can dramatically improve the efficiency of construction and operations, like by increasing energy performance and decreasing fabrication time and costs. The neural network application from Arup extends to this design decision-making. But the central question comes back to aesthetics and style.

Designer and Fulbright fellow Stanislas Chaillou recently created a project at Harvard utilizing machine learning to explore the future of generative design, bias and architectural style. While studying AI and its potential integration into architectural practice, Chaillou built an entire generation methodology using Generative Adversarial Neural Networks (GANs). Chaillou's project investigates the future of AI through architectural style learning, and his work illustrates the profound impact of style on the composition of floor plans.

As Chaillou summarizes, architectural styles carry implicit mechanics of space, and there are spatial consequences to choosing a given style over another. In his words, style is not an ancillary, superficial or decorative addendum; it is at the core of the composition.

Artificial intelligence and machine learningare becomingincreasingly more important as they shape our future. If machines can begin to understand and affect our perceptions of beauty, we should work to find better ways to implement these tools and processes in the design process.

Architect and researcher Valentin Soana once stated that the digital in architectural design enables new systems where architectural processes can emerge through "close collaboration between humans and machines; where technologies are used to extend capabilities and augment design and construction processes." As machines learn to design, we should work with AI to enrich our practices through aesthetic and creative ideation.More than productivity gains, we can rethink the way we live, and in turn, how to shape the built environment.

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When Machines Design: Artificial Intelligence and the Future of Aesthetics - ArchDaily

Awarded to recognize Google Cloud partners who have made a positive impact on the world

Quantiphi, an award-winning applied artificial intelligence and data science software and services company, announced today that it has been named 2019 Social Impact Partner of the Year by Google Cloud. Quantiphi was recognized for its achievements for working with nonprofits, research institutions, and healthcare providers, to leverage AI for Social Good.

We are believers in the power of human acumen and technology to solve the worlds toughest challenges. This award is a recognition of our mission driven culture and our passion to apply AI for social good, said Asif Hasan, Co-founder, Quantiphi. Partnering with Google Cloud has given us the opportunity to work with the worlds leading nonprofit, healthcare and research institutions and we are truly humbled by this recognition.

Recommended AI News:Opinion: Young Jamaicans Invention Could Help Tackle Spread of Viruses Like COVID-19

Were delighted to recognize Quantiphis commitment to social impact, said Carolee Gearhart, Vice President, Worldwide Channel Sales at Google Cloud. By applying its capabilities in AI and ML to important causes, Quantiphi has demonstrated how Google Cloud partners are contributing to positive change in the world.

A few initiatives that helped Quantiphi earn this recognition:

Recommended AI News:Automation Provides A Content Lifeline For Remote Work

Quantiphi previously earned the Google Cloud Machine Learning Partner of the Year twice in a row for 2018 and 2017 and is a premier partner for Google Cloud and holds Specializations in machine learning, data analytics and marketing analytics.

Recommended AI News:Identity Theft is Booming; Your SSN Sells for Less than $4 on Darknet

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Quantiphi Wins Google Cloud Social Impact Partner of the Year Award - AiThority