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The Resiliency Remit: How to Enable the Hybrid Future of Work

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A recent IDC survey revealed that 62% of IT and business leaders believe their organizations will expand resiliency plans in 2021 and 2022 to support unique requirements of the pandemic. But what does that mean exactly? Traditionally, resiliency has been framed in terms of responding to business disruptions and restoring operations in a timely fashion. However, this definition of resiliency is no longer enough - it's not enough to simply respond or restore. Digital resiliency shifts the focus from responding reactively to adapting and moving forward proactively.


Use of Artificial Intelligence in Banking World today

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AI is evolving on fast pace. Financial organizations are already using AI technologies to identify fraud and unusual transactions, personalize customer service, help make decisions on creditworthiness, using natural language processing on text documents, and for cybersecurity and general risk management. Over the past decades, banks have been improving their methods of interacting with customers. They have tailored modern technology to the specific character of their work. As an example, in the 1960s, the first ATMs were installed, and ten years later, there were already cards for doing transactions and payment.


Artificial Intelligence In Genomics Market 2028 By Offering, Technology, Functionality, Application, End-User and Geography

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Your data will never be shared with third parties, however, we may send you information from time to time about our products that may be of interest to you. By submitting your details, you agree to be contacted by us. You may contact us at any time to opt-out. Artificial Intelligence in Retail Market to 2025 - Global Analysis and Forecasts by Retail Format (E-Commerce and, Brick & Mortar), Technology (Chat-B.. Artificial Lift System Market Forecast to 2028 - Covid-19 Impact and Global Analysis - by Type (Progressive Cavity Pumping (PCP), Rod Lift, Gas Lift,..


Demoting Outdated 'Truth' With Machine Learning

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Sometimes the truth has an expiry date. When a time-limited claim (such as'masks are obligatory on public transport') emerges in search engine rankings, its apparent'authoritative' solution can outstay its welcome even by many years, outranking later and more accurate content on the same topic. This is a by-product of search engine algorithms' determination to identify and promote'long-term' definitive solutions, and of their proclivity to prioritize well-linked content that maintains traffic over time – and of an increasingly circumspect attitude to newer content in the emerging age of fake news. Alternately, devaluing valuable web content simply because the timestamp associated with it has passed an arbitrary'validity window' risks that a generation of genuinely useful content will be automatically demoted in favor of subsequent material that may be of a lower standard. Towards redressing this syndrome, a new paper from researchers in Italy, Belgium and Denmark has used a variety of machine learning techniques to develop a methodology for time-aware evidence ranking.


Data Scientists Will be Extinct in 10 Years - KDnuggets

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Here are the results of the KDnuggets Poll inspired by this blog: Relax! As advances in AI continue to progress in leaps and bounds, accessibility to data science at a base level has become increasingly democratized. Traditional entry barriers to the field such as a lack of data and computing power have been swept aside with a continuous supply of new data startups popping up(some offering access for as little as a cup of coffee a day) and all powerful cloud computing removing the need for expensive onsite hardware. Rounding out the trinity of prerequisites, is the skill and know-how to implement, which has arguably become the most ubiquitous aspect of data science. One does not need to look far to find online tutorials touting taglines like "implement X model in seconds", "apply Z method to your data in just a few lines of code". In a digital world, instant gratification has become the name of the game.


Machine Learning Consensus Clustering of Hospitalized Patients with Admission Hyponatremia

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Background: The objective of this study was to characterize patients with hyponatremia at hospital admission into clusters using an unsupervised machine learning approach, and to evaluate the short- and long-term mortality risk among these distinct clusters. Methods: We performed consensus cluster analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 11,099 hospitalized adult hyponatremia patients with an admission serum sodium below 135 mEq/L. The standardized mean difference was utilized to identify each cluster’s key features. We assessed the association of each hyponatremia cluster with hospital and one-year mortality using logistic and Cox proportional hazard analysis, respectively. Results: There were three distinct clusters of hyponatremia patients: 2033 (18%) in cluster 1, 3064 (28%) in cluster 2, and 6002 (54%) in cluster 3. Among these three distinct clusters, clusters 3 patients were the youngest, had lowest comorbidity burden, and highest kidney function. Cluster 1 patients were more likely to be admitted for genitourinary disease, and have diabetes and end-stage kidney disease. Cluster 1 patients had the lowest kidney function, serum bicarbonate, and hemoglobin, but highest serum potassium and prevalence of acute kidney injury. In contrast, cluster 2 patients were the oldest and were more likely to be admitted for respiratory disease, have coronary artery disease, congestive heart failure, stroke, and chronic obstructive pulmonary disease. Cluster 2 patients had lowest serum sodium and serum chloride, but highest serum bicarbonate. Cluster 1 patients had the highest hospital mortality and one-year mortality, followed by cluster 2 and cluster 3, respectively. Conclusion: We identified three clinically distinct phenotypes with differing mortality risks in a heterogeneous cohort of hospitalized hyponatremic patients using an unsupervised machine learning approach.


Top Use Cases of Artificial Intelligence in Banks

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Already, financial institutions are using AI technology to detect fraud and other unusual transactions, personalize services, make credit decisions, and use natural language processing on text documents. Banks have improved their customer service over the years. Modern technology has been tailored to their specific work. In 1960, the first ATMs were built. Ten years later, payment and transaction cards were available.


Pinecone CEO on bringing vector similarity search to dev teams

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All the sessions from Transform 2021 are available on-demand now. The traditional way for a database to answer a query is with a list of rows that fit the criteria. If there's any sorting, it's done by one field at a time. Vector similarity search looks for matches by comparing the likeness of objects, as captured by machine learning models. Vector similarity search is particularly useful with real-world data because that data is often unstructured and contains similar yet not identical items.


Spxbot robo-advisory performance - spxbot blog

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I've always been reticent to publish the performance of the Position trading system: I've even dismissed the record table for the subscribers, without any complaint from them. It should be complex to explain why, it's something intuitive, but it is related to my distrust in backtesting. First, I've always tested forward, not behind. The model, or as I call it now: r.Virgeel has taken shape during four years and is performing well. It is still under development: new ideas are passing as clouds at the moment.


What is Artificial Intelligence? -- Suffixtree

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Artificial Intelligence (AI) is the field of computer science dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, problem solving, and pattern recognition. Artificial Intelligence, often abbreviated as "AI", may connote robotics or futuristic scenes, AI goes well beyond the automatons of science fiction, into the non-fiction of modern day advanced computer science. Professor Pedro Domingos, a prominent researcher in this field, describes "five tribes" of machine learning, comprised of symbolists, with origins in logic and philosophy; connectionists, stemming from neuroscience; evolutionaries, relating to evolutionary biology; Bayesians, engaged with statistics and probability; and analogizers with origins in psychology. Recently, advances in the efficiency of statistical computation have led to Bayesians being successful at furthering the field in a number of areas, under the name "machine learning". Similarly, advances in network computation have led to connectionists furthering a subfield under the name "deep learning".