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Tech Developments In Every Sector, And The Innovators Leading The Way

#artificialintelligence

Despite market disruptions and unprecedented global events, the evolution of technology in every sector will continue because leaders worldwide actively develop solutions, overcome obstacles, and create new products. As this rate of advancement accelerates, technology will continue to be an essential component of success on any terms. Moreover, the leaders spearheading growth in this area also exercise best-in-class corporate practices, create healthy cultures, and achieve record-breaking revenues, concludes Dr Lebene Soga of Henley Business School. As the interplay between humans and technology develops, the prevalence of Artificial Intelligence (AI), intuitive interfaces, and predictive capabilities also grow. This asynchronous development has the net impact of making life easier, businesses more profitable, and infrastructure more enduring.


Using AI And Machine Learning To Improve The Health Insurance Process

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Health insurance companies have been looking to artificial intelligence (AI) and machine learning to identify at-risk individuals and reduce rising costs in the healthcare sphere.


La veille de la cybersécurité

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Health insurance is a source of confusion, frustration and stress for many Americans. While the federal and state governments have taken measures to improve the health insurance system, many Americans still groan at the complexities and shortcomings that leave some 15% of adults ages 19-34 uninsured, and both uninsured and insured people say insurance is too expensive. Reforms to the nation's healthcare system are also insufficient for many. About 11% of uninsured people had income below the poverty level but were ineligible for Medicaid because their state did not expand the program. Even reforms to the health insurance system are not reaching most of those who still lack insurance.


Council Post: Using AI And Machine Learning To Improve The Health Insurance Process

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Albert Pomales is Co-Founder and CEO of KindHealth, bringing complex insurance solutions to the consumer. Health insurance is a source of confusion, frustration and stress for many Americans. While the federal and state governments have taken measures to improve the health insurance system, many Americans still groan at the complexities and shortcomings that leave some 15% of adults ages 19-34 uninsured, and both uninsured and insured people say insurance is too expensive. Reforms to the nation's healthcare system are also insufficient for many. About 11% of uninsured people had income below the poverty level but were ineligible for Medicaid because their state did not expand the program.


Fairness Score and Process Standardization: Framework for Fairness Certification in Artificial Intelligence Systems

arXiv.org Artificial Intelligence

Decisions made by various Artificial Intelligence (AI) systems greatly influence our day-to-day lives. With the increasing use of AI systems, it becomes crucial to know that they are fair, identify the underlying biases in their decision-making, and create a standardized framework to ascertain their fairness. In this paper, we propose a novel Fairness Score to measure the fairness of a data-driven AI system and a Standard Operating Procedure (SOP) for issuing Fairness Certification for such systems. Fairness Score and audit process standardization will ensure quality, reduce ambiguity, enable comparison and improve the trustworthiness of the AI systems. It will also provide a framework to operationalise the concept of fairness and facilitate the commercial deployment of such systems. Furthermore, a Fairness Certificate issued by a designated third-party auditing agency following the standardized process would boost the conviction of the organizations in the AI systems that they intend to deploy. The Bias Index proposed in this paper also reveals comparative bias amongst the various protected attributes within the dataset. To substantiate the proposed framework, we iteratively train a model on biased and unbiased data using multiple datasets and check that the Fairness Score and the proposed process correctly identify the biases and judge the fairness.


Top 5 Applications of Deep Learning in Healthcare

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Deep learning in healthcare can uncover the hidden opportunities and patterns in clinical data, helping doctors to treat their patients more efficiently. Artificial Intelligence, machine learning and deep learning have gained a lot of attention for quite some time now. These technologies are revolutionizing various industries such as retail, finance, travel, manufacturing, healthcare, and so on. Healthcare is an important industry that implements these technologies. As health is a priority, medical experts are continually trying to find ways to implement new technologies and provide impactful results.


Insurance risk managers must embrace technology disruption

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The accessibility of sophisticated artificial intelligence (AI) and machine learning (ML) algorithms, enabled by the high-performance capabilities of cloud-based data centres, mean insurers can more readily embrace a culture of innovation when it comes to their traditional approaches to business processes. Of course, the rising number of insurtechs, and evolving customer demands, have contributed to the creation of a disruptive environment that can capitalise on digitally driven solutions. Whether through partnering with insurtechs or looking at more effective ways of modernising their existing processes and systems, insurers are well-positioned to capitalise on global changes in the industry that have been accelerated with the onset of the COVID-19 pandemic. AI and ML introduce a level of automation in data analysis and the decision process that was not possible before. It is especially in underwriting, claim decisioning and product development that these technologies prove to be invaluable.


Director, Data Science

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The world's largest and fastest-growing companies such as Accenture, Adobe, DocuSign, and Salesforce rely on Demandbase to drive their Account-Based Marketing strategy and maximize their B2B marketing performance. We pioneered the ABM category nearly a decade ago, and today we lead the category as an indispensable part of the B2B MarTech stack. Our achievements and innovation would not be possible without the driven and collaborative teams here at Demandbase. As a company, we're as committed to growing careers as we are to building world-class technology. We invest heavily in people, our culture, and the community around us, and have continuously been recognized as one of the best places to work in the Bay Area.


Zephyr AI Launches its Big Data, Machine-Learning Approach to Aid Precision Medicine

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Technology investment company and incubator Red Cell Partners announced today the launch of Zephyr AI, a company that leverages large data sets to inform both clinical care and the development of new targeted precision therapies. The management team of the new company consists of CEO Yisroel Brumer, formerly of the office of the Secretary of Defense; Executive Chairman Grant Verstandig, who most recently served Chief Digital Officer at UnitedHealth Group; and Chief Technology Officer Jeff Sherman, who was the machine learning architect at Rally Health, which was acquired in 2017 by UnitedHealth's Optum unit. According to a press release announcing its launch, Zephyr AI will look to improve patient outcomes while lowering costs by integrating "artificial intelligence with extensive datasets to upend traditional'guess and test' drug development and personalized medicine processes to unearth novel therapeutics, new applications for existing therapeutics, and advanced biomarkers for individualized treatments." The potential new company gave a hint at its direction earlier in the year via the publication of two papers by the founders in the journal Oncogene that detailed the company's technology and it's performance. "These findings demonstrate that Zephyr AI can already identify novel-use cases for existing therapeutics in cancer," company CTO Sherman.


Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance

arXiv.org Artificial Intelligence

Individuals often make different decisions when faced with the same context, due to personal preferences and background. For instance, judges may vary in their leniency towards certain drug-related offenses, and doctors may vary in their preference for how to start treatment for certain types of patients. With these examples in mind, we present an algorithm for identifying types of contexts (e.g., types of cases or patients) with high inter-decision-maker disagreement. We formalize this as a causal inference problem, seeking a region where the assignment of decision-maker has a large causal effect on the decision. Our algorithm finds such a region by maximizing an empirical objective, and we give a generalization bound for its performance. In a semi-synthetic experiment, we show that our algorithm recovers the correct region of heterogeneity accurately compared to baselines. Finally, we apply our algorithm to real-world healthcare datasets, recovering variation that aligns with existing clinical knowledge.