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EDMONTON, Alberta, April 14, 2022 (GLOBE NEWSWIRE) -- Amii (the Alberta Machine Intelligence Institute) has announced the program for AI Week, May 24-27 in Edmonton, Canada. With more than 20 events taking place across four days throughout the city, the celebration of Albertas AI excellence will feature an academic keynote from Richard S. Sutton, leading expert in reinforcement learning, who will discuss future research directions in the field.

The jam-packed week also includes panels on AI career paths for kids, AI for competitive advantage and the ethics of AI; a career and talent mixer connecting AI career seekers with top companies; and a full-day academic symposium bringing together the brightest minds in AI. The celebrations are rounded out by a house party at a secret, soon-to-be-revealed location and the Amiiversary street party, marking 20 years of AI excellence in Alberta. Learn more about the program at http://www.ai-week.ca/program

Over the past 20 years, Alberta has emerged as one of the worlds top destinations for AI research and application, says Cam Linke, CEO of Amii. With AI Week, were putting a global spotlight on the province and welcoming the worlds AI community to experience what many in the field have known for a long time: that Alberta is at the forefront of the AI revolution. AI Week isnt just a celebration of 20 years of AI excellence its a launching point for the next 20 years of advancement.

AI Week has something for everyone including sessions, networking events and socials for a range of ages and familiarity with AI. Additional keynotes will be delivered by Alona Fyshe, speaking about what brains and AI can tell us about one another, and Martha White, who will present on innovative applications of reinforcement learning. A special AI in Health keynote will highlight the work of Dornoosh Zonoobi and Jacob Jaremko of Medo.ai, which uses machine learning in concert with ultrasound technology to screen infants for hip dysplasia.

Informal networking and social events will help forge connections between members of the research, industry and innovation communities as well as AI beginners and enthusiasts. Meanwhile, the Amiiversary street party, hosted on Rice Howard Way in Edmontons downtown core, will mark 20 years of AI excellence in Alberta. The party will be attended by the whos-who of Edmonton AI, technology and innovation scenes.

AI Week will be attended by the worlds AI community, with over 500 applicants for travel bursaries from more than 35 different countries. The successful applicants, emerging researchers and industry professionals alike, will have the opportunity to learn alongside leaders in the field at the AI Week Academic Symposium, which is being organized by Amiis Fellows from the University of Alberta, one of the worlds top academic institutions for AI research. The symposium will include talks and discussions among top experts in AI and machine learning as well as demos and lab showcases from the Amii community.

I chose to set up in Canada in 2003 because, at the time, Alberta was one of the few places investing in building a community of AI researchers, says Richard S. Sutton, Amiis Chief Scientific Advisor, who is also a Professor at the University of Alberta and a Distinguished Research Scientist at DeepMind. Nearly twenty years later, I am struck by how much we have achieved to advance the field of AI, not only locally but globally. AI Week is an opportunity to celebrate those achievements and showcase some of the brightest minds in AI.

The event is being put on by Amii, one of Canadas AI institutes in the Pan-Canadian AI Strategy and will feature event partners and community-led events from across Canadas AI ecosystem. AI Week is made possible in part by our event partners and talent bursary sponsors: AltaML, Applied Pharmaceutical Innovation, ATB, Attabotics, BDC, CBRE, CIFAR, DeepMind, DrugBank, Explore Edmonton, NeuroSoph, RBC Royal Bank, Samdesk, TELUS and the University of Alberta.

About Amii

One of Canadas three centres of AI excellence as part of the Pan-Canadian AI Strategy, Amii (the Alberta Machine Intelligence Institute) is an Alberta-based non-profit institute that supports world-leading research in artificial intelligence and machine learning and translates scientific advancement into industry adoption. Amii grows AI capabilities through advancing leading-edge research, delivering exceptional educational offerings and providing business advice all with the goal of building in-house AI capabilities. For more information, visit amii.ca.

Spencer MurrayCommunications & Public Relationst: 587.415.6100 ext. 109 | c: 780.991.7136spencer.murray@amii.ca

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AI Week brings together the world AI community - GlobeNewswire

Predicting Growth Scope: Predictive Analytics And Machine Learning MarketThe Predictive Analytics And Machine Learning Market report provides an in-depth look at service providers and how their business policies are implemented in the market. The Predictive Analytics And Machine Learning market research report examines market share, size, growth drivers, and major players in detail. In addition to evaluating the sectors financial position, the report provides an inclusive market and dealer climate. Input, market size, sales income, growth rate, revenue, demand, gross margin, technological innovation, supply, import, export, expense, and potential growth strategies are all covered in this report.

The goal of this worldwide Predictive Analytics And Machine Learning market study is to outline the industrys current state as well as its future prospects. It investigates new Predictive Analytics And Machine Learning competitors and changing customer behavior to help market participants make better judgments. The survey helps market participants choose which issues and regions are most important to them. It investigates the growth of present and emerging categories, as well as the revenue performance of the Predictive Analytics And Machine Learning industry.

Competition Spectrum:Schneider ElectricSAS Institue Inc.MakinaRocks Co., Ltd.Globe Telecom,Inc.QlikRapidMinerIBMAlteryxAlibaba GroupHuaweiBaidu4Paradigm

The research on the worldwide keyword market covers a wide range of qualitative and quantitative analytical results that reflect a variety of crucial characteristics that define the keyword markets current state. The qualitative data supports the categorised assessment of the critical growth inducing elements indicating important drivers driving the worldwide keyword markets growth, either influencing demand or generating income. The report uses current quantitative market share and size scales, as well as industry valuation using the above-mentioned qualitative elements, to arrive at an accurate projection of the worldwide keyword market.

We Have Recent Updates of Predictive Analytics And Machine Learning Market in Sample [emailprotected] https://www.orbisresearch.com/contacts/request-sample/5517146?utm_source=PoojaGIR1

The report highlights the nations that are growing in demand and also the nations where the demand for the Predictive Analytics And Machine Learning market products and services is contracted. It highlights the worlds largest producers of the Predictive Analytics And Machine Learning market products and the consumption of the products in million tons. The foreign and domestic demand for the products in mn tons is also given in the report. Moreover, the factors driving the increased demand in the selected nations are also studied. The challenges for the market participants including the cost competitiveness of the raw materials, competition from imports, and technology obsolescence are included in the report.

The market is roughly segregated into:

Analysis by Product Type:General AIDecision AI

Application Analysis:FinancialRetailManufactureMedical TreatmentEnergyInternet

The report investigates the Predictive Analytics And Machine Learning market in all industrial segments and identifies opportunities to modify the competitive climate. The research looks at end-user groups, analyses emerging applications, and analyses market participants methods for keeping ahead of the competition.

Segmentation by Region with details about Country-specific developments North America (U.S., Canada, Mexico) Europe (U.K., France, Germany, Spain, Italy, Central & Eastern Europe, CIS) Asia Pacific (China, Japan, South Korea, ASEAN, India, Rest of Asia Pacific) Latin America (Brazil, Rest of L.A.) Middle East and Africa (Turkey, GCC, Rest of Middle East)

Table of Contents Chapter One: Report Overview 1.1 Study Scope1.2 Key Market Segments1.3 Players Covered: Ranking by Predictive Analytics And Machine Learning Revenue1.4 Market Analysis by Type1.4.1 Predictive Analytics And Machine Learning Market Size Growth Rate by Type: 2020 VS 20281.5 Market by Application1.5.1 Predictive Analytics And Machine Learning Market Share by Application: 2020 VS 20281.6 Study Objectives1.7 Years Considered

Chapter Two: Growth Trends by Regions 2.1 Predictive Analytics And Machine Learning Market Perspective (2015-2028)2.2 Predictive Analytics And Machine Learning Growth Trends by Regions2.2.1 Predictive Analytics And Machine Learning Market Size by Regions: 2015 VS 2020 VS 20282.2.2 Predictive Analytics And Machine Learning Historic Market Share by Regions (2015-2020)2.2.3 Predictive Analytics And Machine Learning Forecasted Market Size by Regions (2021-2028)2.3 Industry Trends and Growth Strategy2.3.1 Market Top Trends2.3.2 Market Drivers2.3.3 Market Challenges2.3.4 Porters Five Forces Analysis2.3.5 Predictive Analytics And Machine Learning Market Growth Strategy2.3.6 Primary Interviews with Key Predictive Analytics And Machine Learning Players (Opinion Leaders)

Chapter Three: Competition Landscape by Key Players 3.1 Top Predictive Analytics And Machine Learning Players by Market Size3.1.1 Top Predictive Analytics And Machine Learning Players by Revenue (2015-2020)3.1.2 Predictive Analytics And Machine Learning Revenue Market Share by Players (2015-2020)3.1.3 Predictive Analytics And Machine Learning Market Share by Company Type (Tier 1, Tier Chapter Two: and Tier 3)3.2 Predictive Analytics And Machine Learning Market Concentration Ratio3.2.1 Predictive Analytics And Machine Learning Market Concentration Ratio (Chapter Five: and HHI)3.2.2 Top Chapter Ten: and Top 5 Companies by Predictive Analytics And Machine Learning Revenue in 20203.3 Predictive Analytics And Machine Learning Key Players Head office and Area Served3.4 Key Players Predictive Analytics And Machine Learning Product Solution and Service3.5 Date of Enter into Predictive Analytics And Machine Learning Market3.6 Mergers & Acquisitions, Expansion Plans

Do You Have Any Query or Specific Requirement? Ask Our Industry [emailprotected] https://www.orbisresearch.com/contacts/enquiry-before-buying/5517146?utm_source=PoojaGIR1

A critically emphasised section of the report that primarily focuses on the influence of COVID-19 on the worldwide keyword market follows a detailed examination of the multi-variable industry dynamics. The paper includes a concise and in-depth analysis of the major differences between the pre-pandemic and post-pandemic eras. The research considers the exact magnitudes of adversities on market share, general infrastructure, financial state, rate of demand, revenue incurred, supply chain, and production capacities of the keyword market on a worldwide scale. The paper focuses on the unique changes in market dynamics that characterise the pandemics short- and long-term consequences on the worldwide keyword market.

Key Pointers of the Predictive Analytics And Machine Learning Market Report: The study analyses the industrys leading firms and their market share. The research provides strategies that may improve market performance across the board. The study offers a variety of alternatives for benchmarking against the rest of the market as well as best practices for competing in the market. The paper examines the influence of changing megatrends on the operating environment, supply chain, and entire business. The study discusses the influence of new technologies on the worldwide Predictive Analytics And Machine Learning market as well as the impact of introducing new business models. The research highlights future potential for both new and incumbent companies.

About Us:Orbis Research (orbisresearch.com) is a single point aid for all your market research requirements. We have vast database of reports from the leading publishers and authors across the globe. We specialize in delivering customized reports as per the requirements of our clients. We have complete information about our publishers and hence are sure about the accuracy of the industries and verticals of their specialization. This helps our clients to map their needs and we produce the perfect required market research study for our clients.

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Predictive Analytics And Machine Learning Market Focus on End User, Application, Solution, Component, and Range: Schneider Electric, SAS Institue...

Health systems and payers eager to trim costs think the answer lies in a small group of patients who account for more spending than anyone else.

If they can catch these patients typically termed high utilizers or high cost, high need before their conditions worsen, providers and insurers can refer them to primary care or social programs like food services that could keep them out of the emergency department. A growing number also want to identify the patients at highest risk of being readmitted to the hospital, which can rack up more big bills. To find them, theyre whipping up their own algorithms that draw on previous claims information, prescription drug history, and demographic factors like age and gender.

A growing number of the providers he works with globally are piloting and using predictive technology for prevention, said Mutaz Shegewi, research director of market research firm IDCs global provider IT practice.

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Crafted precisely and accurately, these models could significantly reduce costs and also keep patients healthier, said Nigam Shah, a biomedical informatics professor at Stanford. We can use algorithms to do good, to find people who are likely to be expensive, and then subsequently identify those for whom we may be able to do something, he said.

But that requires a level of coordination and reliability that so far remains rare in the use of health care algorithms. Theres no guarantee that these models, often homegrown by insurers and health systems, work as theyre intended to. If they rely only on past spending as a predictor of future spending and medical need, they risk skipping over sick patients who havent historically had access to health care at all. And the predictions wont help at all if providers, payers, and social services arent actually adjusting their workflow to get those patients into preventive programs, experts warn.

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Theres very little organization, Shah said. Theres definitely a need for industry standardization both in terms of how you do it and what you do with the information.

The first issue, experts said, is that theres not an agreed-upon definition of what constitutes high utilization. As health systems and insurers develop new models, Shah said they will need to be very precise and transparent about whether their algorithms to identify potentially expensive patients are measuring medical spending, volume of visits compared to a baseline, or medical need based on clinical data.

Some models use cost as a proxy measure for medical need, but they often cant account for disparities in a persons ability to actually get care. In a widely cited 2019 paper examining an algorithm used by Optum, researchers concluded that the tool which used prior spending to predict patient need referred white patients for follow-up care more frequently than Black patients who were equally sick.

Predicting future high-cost patients can differ from predicting patients with high medical need because of confounding factors like insurance status, said Irene Chen, an MIT computer science researcher who co-authored a Health Affairs piecedescribing potential bias in health algorithms.

If a high-cost algorithm isnt accurate, or is exacerbating biases, it could be difficult to catch especially when models are developed by and implemented in individual health systems, with no outside oversight or auditing by government or industry. A group of Democratic lawmakers has floated a bill requiring organizations using AI to make decisions to assess them for bias and creating a public repository of these systems at the Federal Trade Commission, though its not yet clear if it will progress.

That puts the onus, for the time being, on health systems and insurers to ensure that their models are fair, accurate, and beneficial to all patients. Shah suggested that the developers of any cost prediction model especially payers outside the clinical system cross-check the data with providers to ensure that the targeted patients do also have the highest medical needs.

If were able to know who is going to get into trouble, medical trouble, fully understanding that cost is a proxy for thatwe can then engage human processes to attempt to prevent that, he said.

Another key question about the use of algorithms to identify high-cost patients is what, exactly, health systems and payers should do with that information.

Even if you might be able to predict that a human being next year is going to cost a lot more because this year they have colon cancer stage 3, you cant wish away their cancer, so that cost is not preventable, Shah said.

For now, the hard work of figuring out what to make of the predictions produced by algorithms has been left in the hands of the health systems making their own models. So, too, is the data collection to understand whether those interventions make a difference in patient outcomes or costs.

At UTHealth Harris County Psychiatric Center, a safety net center catering primarily to low-income individuals in Houston, researchers are using machine learning to better understand which patients have the highest need and bolster resources for those populations. In one study, researchers found that certain factors like dropping out of high school or being diagnosed with schizophrenia were linked to frequent and expensive visits. Another analysis suggestedthat lack of income was strongly linked to homelessness, which in turn has been linked to costly psychiatric hospitalizations.

Some of those findings might seem obvious, but quantifying the strength of those links helps hospital decision makers with limited staff and resources decide what social determinants of health to address first, according to study author Jane Hamilton, an assistant professor of psychiatry and behavioral sciences at the University of Texas Health Science Center at Houstons Medical School.

The homelessness study, for instance, led to more local intermediate interventions like residential step-down programs for psychiatric patients. What youd have to do is get all the social workers to really sell it to the social work department and the medical department to focus on one particular finding, Hamilton said.

The predictive technology isnt directly embedded in the health record system yet, so its not yet a part of clinical decision support. Instead, social workers, doctors, nurses, and executives are informed separately about the factors the algorithm identifies for readmission risk, so they can refer certain patients for interventions like short-term acute visits, said Lokesh Shahani, the hospitals chief medical officer and associate professor at UTHealths Department of Psychiatry and Behavioral Sciences. We rely on the profile the algorithm identifies and then kind of pass that information to our clinicians, Shahani said.

Its a little bit harder to put a complicated algorithm in the hospital EHR and change the workflow, Hamilton said, though Shahani said the psychiatric hospital plans to link the two systems so that risk factors are flagged in individual records over the next few months.

Part of changing hospital operations is identifying which visits can actually be avoided, and which are part of the normal course of care. Were really looking for malleable factors, Hamilton said. What could we be doing differently?

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Health systems are using machine learning to predict high-cost care. Will it help patients? - STAT

BOSTON, April 13, 2022 (GLOBE NEWSWIRE) -- VMRay, a provider of automated malware analysis and detection solutions, today announced the release of new Machine Learning-based capabilities for its flagship VMRay Platform, helping enterprise security teams detect and neutralize novel malware and phishing threats. Recognized as the gold standard for advanced threat detection and analysis, the high-fidelity threat data used by VMRay to train and evaluate its Machine Learning system is both highly accurate and relevant, allowing customers to detect threats such as zero-day malware which were previously thought to be undetectable.

To get the best out of AI, you need a carefully arranged combination of Machine Learning and other cutting-edge technologies. Because the value and efficacy of each ML utilization is dependent on how you train and evaluate the model: namely, the quality of the inputs and the expertise of the team, said Carsten Willems, co-founder and CEO of VMRay. The data that you use to train the model and evaluate the accuracy of its predictions must be accurate, noise-free, and relevant to the task at hand. This is why Machine Learning can only add value when its based on an already advanced technology platform with outstanding detection capabilities. Our approach is to use ML together with our best-of-breed technologies to enhance detection capabilities to perfection, by combining the best of two worlds.

Todays threat landscape is a dynamic one, evolving by the day with attacks growing in complexity, scale and stealth. Since late detection and response is among the most important problems that cause huge costs, its more critical than ever that security teams can rapidly identify and stop these threats at the initial point of entry, before a minor incident cascades into a full-blown data breach. Whereas conventional signature and rule-based heuristics are unable to detect unknown or sophisticated threats that use advanced evasive techniques, the VMRay Platform is able to detonate a malicious file or URL in a safe environment, observe and document the genuine behavior of the threat as the threat is unaware that its being observed.

Four of the top five global technology enterprises, three of the Big 4 accounting firms, and more than 50 government agencies across 17 countries today rely on VMRay to supplement their existing security solutions, automate security operations and thus, accelerate detection and response. Gartners Emerging Technologies: Tech Innovators in AI in Attack Detection report asserts that the critical requirements for an AI-based attack detection solution are improved attack detection and reduced false positives. This latest, ML-enhanced version of VMRay Platform addresses these two challenges with unmatched precision, delivering the following benefits to security teams and threat analysts:

Improved Threat Detection: Featuring a machine learning model that improves threat detection capabilities by recognizing additional patterns, the VMRay Platform brings advanced threat detection to customers existing security solutions and covers the blind spots. With this supplementary approach, VMRay minimizes security risks and maximizes the value that customers get from their security investment.

Reduced False Positives: False positives and alert fatigue continue to plague enterprise SOC teams, hampering their ability to quickly respond to genuine threats. VMRay Analyzer generates high-fidelity, noise-free reports that dramatically reduce false positives to keep teams efficient. Seamless integrations with all the major EDR, SIEM, SOAR, Email Security, and Threat Intelligence platforms enable full automation, empowering resource-strapped security teams to focus their energies on higher-value strategic initiatives.

To try VMRay Analyzer visit: https://www.vmray.com/try-vmray-products/

About VMRay

VMRay was founded with a mission to liberate the world from undetectable digital threats. Led by notable cyber security pioneers, VMRay develops best-of-breed technologies to detect unknown threats that others miss. Thus, we empower organizations to augment and automate security operations by providing the worlds best threat detection and analysis platform. We help organizations build and grow their products, services, operations, and relationships on secure ground that allows them to focus on what matters with ultimate peace of mind. This, for us, is the foundation stone of digital transformation.

Press ContactRobert NachbarKismet Communications206-427-0389rob@kismetcommunications.net

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VMRay Unveils Advanced Machine Learning Capabilities to Accelerate Threat Detection and Analysis - GlobeNewswire

In recent years, we have seen accelerated OLED materials development, aided by software tools based on machine learning and Artificial Intelligence. This is an excellent development which contributes to the continued improvement in OLED efficiency, brightness and lifetime.

Kyulux's Kyumatic AI material discover system

The promise of these new technologies is the ability to screen millions of possible molecules and systems quickly and efficiently. Materials scientists can then take the most promising candidates and perform real synthesis and experiments to confirm the operation in actual OLED devices.

The main drive behind the use of AI systems and mass simulations is to save the time that actual synthesis and testing of a single material can take - sometimes even months to complete the whole cycle. It is simply not viable to perform these experiments on a mass scale, even for large materials developers, let alone early stage startups.

In recent years we have seen several companies announcing that they have adopted such materials screening approaches. Cynora, for example, has an AI platform it calls GEM (Generative Exploration Model) which its materials experts use to develop new materials. Another company is US-based Kebotix, which has developed an AI-based molecular screening technology to identify novel blue OLED emitters, and it is now starting to test new emitters.

The first company to apply such an AI platform successfully was, to our knowledge, Japan-based Kyulux. Shortly after its establishment in 2015, the company licensed Harvard University's machine learning "Molecular Space Shuttle" system. The system has been assisting Kyulux's researchers to dramatically speed up their materials discovery process. The company reports that its development cycle has been reduced from many months to only 2 months, with higher process efficiencies as well.

Since 2016, Kyulux has been improving its AI platform, which is now called Kyumatic. Today, Kyumatic is a fully integrated materials informatics system that consists of a cloud-based quantum chemical calculation system, an AI-based prediction system, a device simulation system, and a data management system which includes experimental measurements and intellectual properties.

Kyulux is advancing fast with its TADF/HF material systems, and in October 2021 it announced that its green emitter system is getting close to commercialization and the company is now working closely with OLED makers, preparing for early adoption.

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How machine learning and AI help find next-generation OLED materials - OLED-Info