Archive for the ‘Machine Learning’ Category

A call for ethical use of AI in Earth system science | NCAR & UCAR News – University Corporation for Atmospheric Research

Apr 15, 2022 - by Laura Snider

Artificial intelligence holds vast potential to help solve a number of challenging problems in Earth system science, from improving prediction of severe weather events to increasing the efficiency of climate models. But as in all AI applications, the use of machine learning and other techniques in environmental science has the potential to introduce biases that could deepen inequities.

The authors of a new paper published in the journal Environmental Data Science argue that researchers must develop ethical, responsible, and trustworthy approaches to applying AI in Earth system science to ensure that unintentional consequences do not worsen environmental and climate injustice.

Its really exciting to see all the ways researchers are finding to creatively apply artificial intelligence in weather, climate, and other environmental science research, said David John Gagne, a scientist at the National Center for Atmospheric Research (NCAR) and a paper co-author. But we have a responsibility to ensure that we dont cause more harm than good.

The papers lead author is Amy McGovern of the University of Oklahoma. Other co-authors include Imme Ebert-Uphoff of Colorado State University and Ann Bostrom of the University of Washington.

A central bias that could be exacerbated by AI is related to where and how weather and climate data are collected. For example, hailstorms, tornadoes, and other severe weather events are more likely to be reported in areas with higher populations. Therefore, the severe weather datasets used to train machine learning models may not adequately represent the amount of severe weather that takes place in rural, sparsely populated parts of the country. The machine learning model, then, will also tend to underpredict the weather in those regions.

These relatively low-population areas may be home to communities that are already underserved by the weather community.

The authors list a range of other issues that can arise through the use of AI for environmental science, including the use of non-trustworthy models or applying a model to inappropriate situations.

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Top 10 Deep Learning Python Courses to Take Up in 2022 – Analytics Insight

Deep learning Python is trending in the global tech market to transform a business in 2022

Deep learning and Python are gaining huge popularity among aspiring techies as well as working professionals. Deep learning (DL) is known as a class of machine learning algorithms for feature extraction and transformation. Meanwhile, Python is one of the popular and trending programming languages across the world for developers. Thus, the combination, deep learning Python, is thriving in the global tech market in recent times with different best deep learning courses in Python. There are multiple courses on deep learning Python to gain a deep understanding of the concepts before entering a professional career. Machine learning in Python and programming language courses are available on multiple educational platforms in recent times. Machine learning in Python is becoming important to transform businesses with digital transformation. Thus, lets explore some of the top ten deep learning Python courses in 2022 to enroll.

Duration: 4 hours

Datacamp offers one of the top deep learning Python courses to learn the fundamentals of neural networks and build models with Keras 2.0. This course on deep learning Python consists of 17 videos and 50 exercises with hands-on knowledge through a cutting-edge library.

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Duration: 25 hours

One of the best deep learning Python courses is set to offer a deep knowledge of multiple features regarding debugging, software programmers, language skills, pattern designing, and many more. This course in deep learning Python is focused on providing optimizing a simple model in pure Theano, enhancing generalization with data augmentation, and many more with 20 hours of lab.

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Duration: 57 hours 17 mins

One of the udemy deep learning Python courses helps with an experimental scientific approach through architectures of feedforward and convolutional networks, calculus and code of gradient descent, fine-tuning deep network models, programming language Python, and many more. there are 265 lectures with 32 sections for students who have sufficient knowledge of a programming language.

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Duration: 15 hours 36 mins

This course on deep learning Python offers a complete hands-on machine learning tutorial with data science, artificial intelligence, and neural networks. The curriculum includes building artificial neural networks with TensorFlow and Keras, classifying data, programming languages such as Python, and many more. There are 115 lectures with 13 sections including machine learning with Python, neural networks, and so on.

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Duration: 14 hours 9 mins

Udemy offers a complete guide to TensorFlow for Deep Learning with Python to learn how to solve complex problems with cutting-edge techniques. Students can have a deep understanding of how neural networks work, and the process of building their own neural network from scratch with the programming language, Python, and many more. There are 96 lectures with 13 sections covering all the concepts and mechanisms with hands-on practical knowledge.

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Duration: 2 hours

Coursera offers one of the top deep learning Python courses to make students understand the concepts behind convolutional neural networks with TensorFlow 2.0. This is a project-based course with eight different tasks including building a model with a trending programming language, Python.

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Duration: 8 hours

Great Learning provides a course on deep learning Python with explanations and an introduction to the TensorFlow library of the Python programming language. This course includes the hands-on session on regression with TensorFlow and the Keras framework.

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Duration: 35 hours

This machine learning in Python course is known for focusing on techniques and methods of statistics. The course starts with the discussion of how machine learning is different from descriptive statistics, more advanced techniques, scikit learn predictive models and many more.

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Duration: 2-4 weeks

This machine learning in Python course provides hands-on Python tutorials with machine learning applications. This programming language is needed to build machine learning systems with hands-on tutorials including code and real-world datasets.

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Duration: 40 hours

The machine learning in Python training course is focused on covering the curriculum consisting of 16 modules. These modules include convolutional neural networks, reinforcement learning, programming language, training models, and many more.

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Top 10 Deep Learning Python Courses to Take Up in 2022 - Analytics Insight

AI Week brings together the world AI community – GlobeNewswire

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

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

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

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