2020 Research Report on Global Data Science and Machine-Learning Platforms Market is a professional and comprehensive report on the Data Science and Machine-Learning Platforms industry.

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The key players covered in this study- SAS- Alteryx- IBM- RapidMiner- KNIME- Microsoft- Dataiku- Databricks- TIBCO Software- MathWorks- H20.ai- Anaconda- SAP- Google- Domino Data Lab- Angoss- Lexalytics- Rapid Insight

The report pinpoints on the leading market competitors with explaining Data Science and Machine-Learning Platforms company profile depends on SWOT analysis to illustrate the competitive nature of the Data Science and Machine-Learning Platforms market globally. Even more, the report consists of company recent Data Science and Machine-Learning Platforms market evolution, market shares, associations and level of investments with other Data Science and Machine-Learning Platforms leading companies, monetary settlements impacting the Data Science and Machine-Learning Platforms market in recent years are analyzed.

Development policies and plans are discussed as well as manufacturing processes and cost structures are also analyzed. This report also states import/export consmption, supply and demand Figures, cost, price, revenue and gross margins.

The report focuses on global major leading Data Science and Machine-Learning Platforms Industry players providing information such as company profiles, product picture and specification, capacity, production, price, cost, revenue and contact information. Upstream raw materials and equipment and downstream demand analysis is also carried out.

The Data Science and Machine-Learning Platforms industry development trends and marketing channels are analyzed. Finally the feasibility of new investment projects are assessed and overall research conclusions offered.

Geographically, this report is categorized into various main regions, including sales, proceeds, market share and expansion Rate (percent) of Data Science and Machine-Learning Platforms in the following areas, North America, Asia-Pacific, South America, Europe, Asia-Pacific, The Middle East and Africa.

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Major Points from Table of Contents

Chapter 1 - Data Science and Machine-Learning Platforms Market Overview

Chapter 2 - Global Data Science and Machine-Learning Platforms Competition by Players/Suppliers, Type and Application

Chapter 3 - United States Data Science and Machine-Learning Platforms (Volume, Value and Sales Price)

Chapter 4 - China Data Science and Machine-Learning Platforms (Volume, Value and Sales Price)

Chapter 5- Europe Data Science and Machine-Learning Platforms (Volume, Value and Sales Price)

Chapter 6 - Japan Data Science and Machine-Learning Platforms (Volume, Value and Sales Price)

Chapter 7 - Southeast Asia Data Science and Machine-Learning Platforms (Volume, Value and Sales Price)

Chapter 8 - India Data Science and Machine-Learning Platforms (Volume, Value and Sales Price)

Chapter 9 - Global Data Science and Machine-Learning Platforms Players/Suppliers Profiles and Sales Data

Chapter 10 - Data Science and Machine-Learning Platforms Maufacturing Cost Analysis

Chapter 11 - Industrial Chain, Sourcing Strategy and Downstream Buyers

Chapter 12 - Marketing Strategy Analysis, Distributors/Traders

Chapter 13 - Market Effect Factors Analysis

Chapter 14 - Global Data Science and Machine-Learning Platforms Market Forecast (2020-2026)

Chapter 15 - Research Findings and Conclusion

Chapter 16 - Appendix

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In the end, the Global Data Science and Machine-Learning Platforms Market report's conclusion part notes the estimation of the industry veterans.

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Data Science and Machine-Learning Platforms Market discussed in a new research report - WhaTech Technology and Markets News

Cape Analytics is announcing that Kin Insurance a fully licensed home insurance technology company that provides easy, affordable coverage to homeowners in catastrophe-prone regions has expanded its partnership with Cape Analytics. Kin is using Cape Analytics geospatial property intelligence to inform its homeowner insurance offering and provide customers the best possible coverage at the lowest price with the least hassle. By utilizing Cape Analytics for remote risk assessment, Kin is continuing to write policies and serve customers, while maintaining social distancing rules that keep customers and employees safe.

We are thrilled to have a growing partnership with an innovative, data-first carrier like Kin where we can enable them to expand usage in alignment with their rapid growth as an upstart insurer

Cape Analytics is providing Kin with the most comprehensive, timely, and accurate property information available, by leveraging geospatial imagery, computer vision, and machine learning. The integration of Cape Analytics data allows Kin to provide customers with policies tailored to individual property and coverage needs. Cape Analytics automatically provides information such as roof condition, roof type, tree coverage, and presence of a swimming pool, allowing Kin customers to get the right coverage faster.

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Kin is using this new form of instant property intelligence in innovative ways by leveraging property attributes that are related to geo-specific risks. For example, in a wind-prone state like Florida, Kin can access Capes wind-related property attributes such as roof type and the presence of pool enclosures. In states with higher risk of wildfire, Kin may automatically retrieve Cape information regarding vegetation coverage surrounding a structure. In precipitation-heavy areas, Capes loss-predictive Roof Condition Rating can allow Kin to better understand the potential of a property experiencing water damage from a leaking roof.

In a recent study of Hurricane Irma, Cape Analytics found that Florida homes with roofs in poor or severe condition were far more vulnerable and had a 45 percent higher chance of suffering major damage. In addition, 65 percent of homes affected by the hurricane took more than six months to repair. Kin is leveraging these and other insights to decrease customer risk while improving their experience.

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Our platform is built from the ground up to seamlessly integrate industry-leading sources of data, which is exactly what Cape Analytics provides. As a result, we can leverage our machine learning prediction framework to instantly assess risk and customize coverage and prices through our super simple online experience, said Blake Konrardy, VP of Product at Kin.

We are thrilled to have a growing partnership with an innovative, data-first carrier like Kin where we can enable them to expand usage in alignment with their rapid growth as an upstart insurer, said Busy Cummings, VP of Sales at Cape Analytics.

Both companies have received outstanding recognition in recent months: Fast Company named Kin one of the most innovative finance companies of 2020, while Insurance Insider shortlisted Cape Analytics as 2020 InsurTech of the Year.

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Kin Insurance Partners with Cape Analytics to Improve Insurance Experience - AiThority

The following was written byHarald Collet, CEO at Alkymi andHugues Chabanis, Product Portfolio Manager,Alternative Investments at SimCorp

Institutional investors are buckling under the operational constraint of processing hundreds of data streams from unstructured data sources such as email, PDF documents, and spreadsheets. These data formats bury employees in low-value copy-paste workflows andblockfirms from capturing valuable data. Here, we explore how Machine Learning(ML)paired with a better operational workflow, can enable firms to more quickly extract insights for informed decision-making, and help governthe value of data.

According to McKinsey, the average professional spends 28% of the workday reading and answering an average of 120 emails on top ofthe19% spent on searching and processing data.The issue is even more pronouncedininformation-intensive industries such as financial services,asvaluable employees are also required to spendneedlesshoursevery dayprocessing and synthesizing unstructured data. Transformational change, however,is finally on the horizon. Gartner research estimates thatby 2022, one in five workers engaged in mostly non-routine tasks will rely on artificial intelligence (AI) to do their jobs. And embracing ML will be a necessity for digital transformation demanded both by the market and the changing expectations of the workforce.

For institutional investors that are operating in an environment of ongoing volatility, tighter competition, and economic uncertainty, using ML to transform operations and back-office processes offers a unique opportunity. In fact, institutional investors can capture up to 15-30% efficiency gains by applying ML and intelligent process automation (Boston Consulting Group, 2019)inoperations,which in turn creates operational alpha withimproved customer service and redesigning agile processes front-to-back.

Operationalizingmachine learningworkflows

ML has finally reached the point of maturity where it can deliver on these promises. In fact, AI has flourished for decades, but the deep learning breakthroughs of the last decade has played a major role in the current AI boom. When it comes to understanding and processing unstructured data, deep learning solutions provide much higher levels of potential automation than traditional machine learning or rule-based solutions. Rapid advances in open source ML frameworks and tools including natural language processing (NLP) and computer vision have made ML solutions more widely available for data extraction.

Asset class deep-dive: Machine learning applied toAlternative investments

In a 2019 industry survey conducted byInvestOps, data collection (46%) and efficient processing of unstructured data (41%) were cited as the top two challenges European investment firms faced when supportingAlternatives.

This is no surprise as Alternatives assets present an acute data management challenge and are costly, difficult, and complex to manage, largely due to the unstructured nature ofAlternatives data. This data is typically received by investment managers in the form of email with a variety of PDF documents or Excel templates that require significant operational effort and human understanding to interpret, capture,and utilize. For example, transaction data istypicallyreceived by investment managers as a PDF document via email oran online portal. In order to make use of this mission critical data, the investment firm has to manually retrieve, interpret, and process documents in a multi-level workflow involving 3-5 employees on average.

The exceptionally low straight-through-processing (STP) rates already suffered by investment managers working with alternative investments is a problem that will further deteriorate asAlternatives investments become an increasingly important asset class,predictedbyPrequinto rise to $14 trillion AUM by 2023 from $10 trillion today.

Specific challenges faced by investment managers dealing with manual Alternatives workflows are:

WithintheAlternatives industry, variousattempts have been madeto use templatesorstandardize the exchange ofdata. However,these attempts have so far failed,or are progressing very slowly.

Applying ML to process the unstructured data will enable workflow automation and real-time insights for institutional investment managers today, without needing to wait for a wholesale industry adoption of a standardized document type like the ILPA template.

To date, the lack of straight-through-processing (STP) in Alternatives has either resulted in investment firms putting in significant operational effort to build out an internal data processing function,or reluctantly going down the path of adopting an outsourcing workaround.

However, applyinga digital approach,more specificallyML, to workflows in the front, middle and back office can drive a number of improved outcomes for investment managers, including:

Trust and control are critical when automating critical data processingworkflows.This is achieved witha human-in-the-loopdesign that puts the employee squarely in the drivers seat with features such as confidence scoring thresholds, randomized sampling of the output, and second-line verification of all STP data extractions. Validation rules on every data element can ensure that high quality output data is generated and normalized to a specific data taxonomy, making data immediately available for action. In addition, processing documents with computer vision can allow all extracted data to be traced to the exact source location in the document (such as a footnote in a long quarterly report).

Reverse outsourcing to govern the value of your data

Big data is often considered the new oil or super power, and there are, of course, many third-party service providers standing at the ready, offering to help institutional investors extract and organize the ever-increasing amount of unstructured, big data which is not easily accessible, either because of the format (emails, PDFs, etc.) or location (web traffic, satellite images, etc.). To overcome this, some turn to outsourcing, but while this removes the heavy manual burden of data processing for investment firms, it generates other challenges, including governance and lack of control.

Embracing ML and unleashing its potential

Investment managers should think of ML as an in-house co-pilot that can help its employees in various ways: First, it is fast, documents are processed instantly and when confidence levels are high, processed data only requires minimum review. Second, ML is used as an initial set of eyes, to initiate proper workflows based on documents that have been received. Third, instead of just collecting the minimum data required, ML can collect everything, providing users with options to further gather and reconcile data, that may have been ignored and lost due to a lack of resources. Finally, ML will not forget the format of any historical document from yesterday or 10 years ago safeguarding institutional knowledge that is commonly lost during cyclical employee turnover.

ML has reached the maturity where it can be applied to automate narrow and well-defined cognitive tasks and can help transform how employees workin financial services. However many early adopters have paid a price for focusing too much on the ML technology and not enough on the end-to-end business process and workflow.

The critical gap has been in planning for how to operationalize ML for specific workflows. ML solutions should be designed collaboratively with business owners and target narrow and well-defined use cases that can successfully be put into production.

Alternatives assets are costly, difficult, and complex to manage, largely due to the unstructured nature of Alternatives data. Processing unstructured data with ML is a use case that generates high levels of STP through the automation of manual data extraction and data processing tasks in operations.

Using ML to automatically process unstructured data for institutional investors will generate operational alpha; a level of automation necessary to make data-driven decisions, reduce costs, and become more agile.

The views represented in this commentary are those of its author and do not reflect the opinion of Traders Magazine, Markets Media Group or its staff. Traders Magazine welcomes reader feedback on this column and on all issues relevant to the institutional trading community.

Read more:
Machine Learning: Making Sense of Unstructured Data and Automation in Alt Investments - Traders Magazine

The legal profession, the technology industry and the relationship between the two are in a state of transition. Computer processing power has doubled every year for decades, leading to an explosion in corporate data and increasing pressure on lawyers entrusted with reviewing all of this information.

Now, the legal industry is undergoing significant change, with the advent of machine learning technology fundamentally reshaping the way lawyers conduct their day-to-day practice. Indeed, whilst technological gains might once have had lawyers sighing at the ever-increasing stack of documents in the review pile, technology is now helping where it once hindered. For the first time ever, advanced algorithms allow lawyers to review entire document sets at a glance, releasing them from wading through documents and other repetitive tasks. This means legal professionals can conduct their legal review with more insight and speed than ever before, allowing them to return to the higher-value, more enjoyable aspect of their job: providing counsel to their clients.

In this article, we take a look at how this has been made possible.

Practicing law has always been a document and paper-heavy task, but manually reading huge volumes of documentation is no longer feasible, or even sustainable, for advisors. Even conservatively, it is estimated that we create 2.5 quintillion bytes of data every day, propelled by the usage of computers, the growth of the Internet of Things (IoT) and the digitalisation of documents. Many lawyers have had no choice but resort to sampling only 10 per cent of documents, or, alternatively, rely on third-party outsourcing to meet tight deadlines and resource constraints. Whilst this was the most practical response to tackle these pressures, these methods risked jeopardising the quality of legal advice lawyers could give to their clients.

Legal technology was first developed in the early 1970s to take some of the pressure off lawyers. Most commonly, these platforms were grounded on Boolean search technology, requiring months and even years building the complex sets of rules. As well as being expensive and time-intensive, these systems were also unable to cope with the unpredictable, complex and ever-changing nature of the profession, requiring significant time investment and bespoke configuration for every new challenge that arose. Not only did this mean lawyers were investing a lot of valuable time and resources training a machine, but the rigidity of these systems limited the advice they could give to their clients. For instance, trying to configure these systems to recognise bespoke clauses or subtle discrepancies in language was a near impossibility.

Today, machine learning has become advanced enough that it has many practical applications, a key one being legal document review.

Machine learning can be broadly categorised into two types: supervised and unsupervised machine learning. Supervised machine learning occurs when a human interacts with the system in the case of the legal profession, this might be tagging a document, or categorising certain types of documents, for example. The machine then builds its understanding to generate insights to the user based on this human interaction.

Unsupervised machine learning is where the technology forms an understanding of a certain subject without any input from a human. For legal document review, the unsupervised machine learning will cluster similar documents and clauses, along with clear outliers from those standards. Because the machine requires no a priori knowledge of what the user is looking for, the system may indicate anomalies or unknown unknowns- data which no one had set out to identify because they didnt know what to look for. This allows lawyers to uncover critical hidden risks in real time.

It is the interplay between supervised and unsupervised machine learning that makes technology like Luminance so powerful. Whilst the unsupervised part can provide lawyers with an immediate insight into huge document sets, these insights only increase with every further interaction, with the technology becoming increasingly bespoke to the nuances and specialities of a firm.

This goes far beyond more simplistic contract review platforms. Machine learning algorithms, such as those developed by Luminance, are able to identify patterns and anomalies in a matter of minutes and can form an understanding of documents both on a singular level and in their relationship to each another. Gone are the days of implicit bias being built into search criteria, since the machine surfaces all relevant information, it remains the responsibility of the lawyer to draw the all-important conclusions. But crucially, by using machine learning technology, lawyers are able to make decisions fully appraised of what is contained within their document sets; they no longer need to rely on methods such as sampling, where critical risk can lay undetected. Indeed, this technology is designed to complement the lawyers natural patterns of working, for example, providing results to a clause search within the document set rather than simply extracting lists of clauses out of context. This allows lawyers to deliver faster and more informed results to their clients, but crucially, the lawyer is still the one driving the review.

With the right technology, lawyers can cut out the lower-value, repetitive work and focus on complex, higher-value analysis to solve their clients legal and business problems, resulting in time-savings of at least 50 per cent from day one of the technology being deployed. This redefines the scope of what lawyers and firms can achieve, allowing them to take on cases which would have been too time-consuming or too expensive for the client if they were conducted manually.

Machine learning is offering lawyers more insight, control and speed in their day-to-day legal work than ever before, surfacing key patterns and outliers in huge volumes of data which would normally be impossible for a single lawyer to review. Whether it be for a due diligence review, a regulatory compliance review, a contract negotiation or an eDiscovery exercise, machine learning can relieve lawyers from the burdens of time-consuming, lower value tasks and instead frees them to spend more time solving the problems they have been extensively trained to do.

In the years to come, we predict a real shift in these processes, with the latest machine learning technology advancing and growing exponentially, and lawyers spending more time providing valuable advice and building client relationships. Machine learning is bringing lawyers back to the purpose of their jobs, the reason they came into the profession and the reason their clients value their advice.

James Loxam, CTO, Luminance

See original here:
The impact of machine learning on the legal industry - ITProPortal

Machine learning is by no means a new phenomenon. It has been used in various forms for decades, but it is very much a technology of the present due to the massive increase in the data upon which it thrives. It has been widely adopted by businesses, reducing the time and improving the value of the insight they can distil from large volumes of customer data.

However, in the public sector there is a different story. Despite being championed by some in government, machine learning has often faced a reaction of concern and confusion. This is not intended as general criticism and in many cases it reflects the greater value that civil servants place on being ethical and fair, than do some commercial sectors.

One fear is that, if the technology is used in place of humans, unfair judgements might not be noticed or costly mistakes in the process might occur. Furthermore, as many decisions being made by government can dramatically affect peoples lives and livelihood then often decisions become highly subjective and discretionary judgment is required. There are also those still scarred by films such as iRobot, but thats a discussion for another time.

Fear of the unknown is human nature, so fear of unfamiliar technology is thus common. But fears are often unfounded and providing an understanding of what the technology does is an essential first step in overcoming this wariness. So for successful digital transformation not only do the civil servants who are considering such technologies need to become comfortable with its use but the general public need to be reassured that the technology is there to assist, not replace, human decisions affecting their future health and well-being.

Theres a strong case to be made for greater adoption of machine learning across a diverse range of activities. The basic premise of machine learning is that a computer can derive a formula from looking at lots of historical data that enables the prediction of certain things the data describes. This formula is often termed an algorithm or a model. We use this algorithm with new data to make decisions for a specific task, or we use the additional insight that the algorithm provides to enrich our understanding and drive better decisions.

For example, machine learning can analyse patients interactions in the healthcare system and highlight which combinations of therapies in what sequence offer the highest success rates for patients; and maybe how this regime is different for different age ranges. When combined with some decisioning logic that incorporates resources (availability, effectiveness, budget, etc.) its possible to use the computers to model how scarce resources could be deployed with maximum efficiency to get the best tailored regime for patients.

When we then automate some of this, machine learning can even identify areas for improvement in real time and far faster than humans and it can do so without bias, ulterior motives or fatigue-driven error. So, rather than being a threat, it should perhaps be viewed as a reinforcement for human effort in creating fairer and more consistent service delivery.

Machine learning is an iterative process; as the machine is exposed to new data and information, it adapts through a continuous feedback loop, which in turn provides continuous improvement. As a result, it produces more reliable results over time and evermore finely tuned and improved decision-making. Ultimately, its a tool for driving better outcomes.

The opportunities for AI to enhance service delivery are many. Another example in healthcare is Computer Vision (another branch of AI), which is being used in cancer screening and diagnosis. Were already at the stage where AI, trained from huge libraries of images of cancerous growths, is better at detecting cancer than human radiologists. This application of AI has numerous examples, such as work being done at Amsterdam UMC to increase the speed and accuracy of tumour evaluations.

But lets not get this picture wrong. Here, the true value is in giving the clinician more accurate insight or a second opinion that informs their diagnosis and, ultimately, the patients final decision regarding treatment. A machine is there to do the legwork, but the human decision to start a programme for cancer treatment, remains with the humans.

Acting with this enhanced insight enables doctors to become more efficient as well as effective. Combining the results of CT scans with advanced genomics using analytics, the technology can assess how patients will respond to certain treatments. This means clinicians avoid the stress, side effects and cost of putting patients through procedures with limited efficacy, while reducing waiting times for those patients whose condition would respond well. Yet, full-scale automation could run the risk of creating a lot more VOMIT.

Victims Of Modern Imaging Technology (VOMIT) is a new phenomenon where a condition such as a malignant tumour is detected by imaging and thus at first glance it would seem wise to remove it. However, medical procedures to remove it carry a morbidity risk which may be greater than the risk the tumour presents during the patients likely lifespan. Here, ignorance could be bliss for the patient and doctors would examine the patient holistically, including mental health, emotional state, family support and many other factors that remain well beyond the grasp of AI to assimilate into an ethical decision.

All decisions like these have a direct impact on peoples health and wellbeing. With cancer, the faster and more accurate these decisions are, the better. However, whenever cost and effectiveness are combined there is an imperative for ethical judgement rather than financial arithmetic.

Healthcare is a rich seam for AI but its application is far wider. For instance, machine learning could also support policymakers in planning housebuilding and social housing allocation initiatives, where they could both reduce the time for the decision but also make it more robust. Using AI in infrastructural departments could allow road surface inspections to be continuously updated via cheap sensors or cameras in all council vehicles (or cloud-sourced in some way). The AI could not only optimise repair work (human or robot) but also potentially identify causes and then determine where strengthened roadways would cost less in whole-life costs versus regular repairs or perhaps a different road layout would reduce wear.

In the US, government researchers are already using machine learning to help officials make quick and informed policy decisions on housing. Using analytics, they analyse the impact of housing programmes on millions of lower-income citizens, drilling down into factors such as quality of life, education, health and employment. This instantly generates insightful, accessible reports for the government officials making the decisions. Now they can enact policy decisions as soon as possible for the benefit of residents.

While some of the fears about AI are fanciful, there is a genuine cause for concern about the ethical deployment of such technology. In our healthcare example, allocation of resources based on gender, sexuality, race or income wouldnt be appropriate unless these specifically had an impact on the prescribed treatment or its potential side-effects. This is self-evident to a human, but a machine would need this to be explicitly defined. Logically, a machine would likely display bias to those groups whose historical data gave better resultant outcomes, thus perpetuating any human equality gap present in the training data.

The recent review by the Committee on Standards in Public Life into AI and its ethical use by government and other public bodies concluded that there are serious deficiencies in regulation relating to the issue, although it stopped short of recommending the establishment of a new regulator.

The review was chaired by crossbench peer Lord Jonathan Evans, who commented:

Explaining AI decisions will be the key to accountability but many have warned of the prevalence of Black Box AI. However our review found that explainable AI is a realistic and attainable goal for the public sector, so long as government and private companies prioritise public standards when designing and building AI systems.

Fears of machine learning replacing all human decision-making need to be debunked as myth: this is not the purpose of the technology. Instead, it must be used to augment human decision-making, unburdening them from the time-consuming job of managing and analysing huge volumes of data. Once its role can be made clear to all those with responsibility for implementing it, machine learning can be applied across the public sector, contributing to life-changing decisions in the process.

Find out more on the use of AI and machine learning in government.

Simon Dennis, Director of AI & Analytics Innovation, SAS UK

Excerpt from:
Machine learning: the not-so-secret way of boosting the public sector - ITProPortal

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

Originally posted here:
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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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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Top Key Players in the Global Machine Learning in Healthcare Market Research Report:

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.

Key Objectives of Machine Learning in Healthcare Market Report:

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