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Artificial intelligence for the general cardiologist

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The majority of experts and opinion leaders believe that artificial intelligence (AI) is going to revolutionise many industries, including healthcare [1]. In the short term, the power and potential of AI appear most suitable for complementing human expertise. In other words, machines will help humans do a better job. Consequently, it is anticipated that AI will help with repetitive tasks, in-depth quantification and classification of findings, improved patient and disease phenotyping and, ultimately, with better outcomes for patients, physicians, hospital administrators, insurance companies and governments [2]. This focus issue of the Netherlands Heart Journal aims to help general cardiologists explore the state of the art of AI in cardiology.


A Primer on Robotic Process Automation Best Practices

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This is essential reading for those interested in incorporating robotics into their organization! Jonathan Padgett, VP, UiPath, talks to ReadITQuik about the many fascinating aspects of Robotic Process Automation (RPA) and how RPA can improve processes and drive efficiencies. Learn about how to select the best RPA provider and RPA best practices in this geektastic interview on one of the latest IT trends. Robotic Process Automation (RPA) technology integrates with the workforce to not only improve execution but also to tackle routine business processes. Think of a UiPath bot as a teleworker or virtual employee to which your employees can delegate repetitive (although necessary) tasks to free up your most valuable resources (your people) to focus on more strategic, creative and interpersonal work.


Who Is Winning the AI Race: China, the EU or the United States?

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The United States Geological Survey confirmed with the Center for Data Innovation over email on July 22, 2019, that "1-meter DEMs are available or in progress for 45% of the country."


How data can predict which employees are about to quit: Rather than relying on exit interviews and their comparisons to occasional employee surveys to determine engagement, organizations can turn instead to big data and advanced analytics to identify those workers at greatest risk of quitting.

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Rather than relying on exit interviews and their comparisons to occasional employee surveys to determine engagement, organizations can turn instead to big data and advanced analytics to identify those workers at greatest risk of quitting. A new Harvard Business Review article outlines how applying machine learning algorithms to turnover data and employee information can provide a much more accurate picture of workplace satisfaction. This measure of "turnover propensity" comprised two main indicators: turnover shocks, which are organizational and personal events that cause workers to reconsider their jobs, and job embeddedness, which describes an employee's social ties in their workplace and interest in the work they do. Though achieving this kind of "proactive anticipation" will require a sizable investment of time and effort to develop the necessary data and algorithms, the payoff will likely be worth it: "Leaders can proactively engage valued employees at risk of leaving through interviews, to better understand how the firm can increase the odds that they stay," per HBR. More articles on leadership and management: Can your anesthesia department handle NORA?


Artificial Intelligence and the rise of related patent applications. - Steer & Co

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Following the World Intellectual Property Organization (WIPO) report in early 2019, a new report from the UK Intellectual Property Office (UKIPO) now identifies the growth in terms of published AI patent applications. This insight provides an overview of the UKIPO findings and considerations for technology businesses in this space. AI is the use of technology to perform tasks that would usually require some intelligence, if done by humans. A patent is a registered intellectual property right, which seeks to create a monopoly over the exploitation of an invention. Patents historically can take years to process from application, publication to grant.


The future of rescue robotics

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Current research is aligned with the need of rescue workers but robustness and ease of use remain significant barriers to adoption, NCCR Robotics researchers find after reviewing the field and consulting with field operators. Robots for search and rescue are developing at an impressive pace, but they must become more robust and easier to use in order to be widely adopted, and researchers in the field must devote more effort to these aspects in the future. This is one of the main findings by a group of NCCR Robotics researchers who focus on search-and-rescue applications. After reviewing the recent developments in technology and interviewing rescue workers, they have found that the work by the robotics research community is well aligned with the needs of those who work in the field. Consequently, although current adoption of state-of-the-art robotics in disaster response is still limited, it is expected to grow quickly in the future.


AutoML: A Survey of the State-of-the-Art

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Deep learning has penetrated all aspects of our lives and brought us great convenience. However, the process of building a high-quality deep learning system for a specific task is not only time-consuming but also requires lots of resources and relies on human expertise, which hinders the development of deep learning in both industry and academia. To alleviate this problem, a growing number of research projects focus on automated machine learning (AutoML). In this paper, we provide a comprehensive and up-to-date study on the state-of-the-art AutoML. First, we introduce the AutoML techniques in details according to the machine learning pipeline.


Artificial Intelligence in Insurtech Market 2019-26 growth by new innovation focuses on major key players Cognizant (U.S.),Next IT Corp. (U.S.),Kasisto (U.S.),Cape Analytics. – IT Technology News24

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The insurance industry, after the trade market, is another sector where it is hard to predict the next big paradigm shift. Given the tentative stability and natural catastrophes, insurance companies often stand on a trembling ground and confront massive challenges, even when i comes to adopting seamless and intuitive digital solutions such as Artificial Intelligence in Insurance. Research N Reports has added a report, titled "Global Artificial Intelligence in Insurtech Market Size, Status and Forecast 2025," which provides an overview of the various factors enabling growth in the market. The statistical report offers a prime wellspring of applicable information for global business progress. What will be the market size and growth rate in the forecast year?


Making Machine Learning Models Clinically Useful

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Recent advances in supervised machine learning have improved diagnostic accuracy and prediction of treatment outcomes, in some cases surpassing the performance of clinicians.1 In supervised machine learning, a mathematical function is constructed via automated analysis of training data, which consists of input features (such as retinal images) and output labels (such as the grade of macular edema). With large training data sets and minimal human guidance, a computer learns to generalize from the information contained in the training data. The result is a mathematical function, a model, that can be used to map a new record to the corresponding diagnosis, such as an image to grade macular edema. Although machine learning–based models for classification or for predicting a future health state are being developed for diverse clinical applications, evidence is lacking that deployment of these models has improved care and patient outcomes.2 One barrier to demonstrating such improvement is the basis used to assess the performance of a model.


When will lifelong learning come of age?

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Last month's announcement by Amazon that it plans to spend $700 million (£569 million) over six years to retrain a third of its US workforce was eye-catching for many reasons. One was the price tag: even for the world's second most valuable company, spending three-quarters of a billion dollars over half a decade to retrain 100,000 workers is a huge undertaking. Also noteworthy was the firm's reasoning. Amazon explicitly attributed its move to the rise of automation, machine learning and other technology: the so-called fourth industrial revolution. There was a sense that the pioneer of online retailing, famed for its use of automation, was merely an early accepter of an inescapable truth that all employers will soon have to face: that the skills of their existing workforces will no longer have any market value as their old roles are taken by machines and new roles are created. The company reportedly has 20,000 current vacancies.