Archive for the ‘Machine Learning’ Category

FDA Issues Advisory on Use of AI and Machine Learning for Large Vessel Occlusion in the Brain – Diagnostic Imaging

Suggesting that some radiologists may not be aware of the intended use of computer-aided triage and notification (CADt) devices, the Food and Drug Administration (FDA) has issued an advisory on the use of the imaging software for patients with suspected large vessel occlusion (LVO) in the brain.

Emphasizing proper use of CADt software, the FDA notes these devices are not intended to substitute for diagnostic assessment by radiologists. While CADt devices can help flag and prioritize brain imaging with findings that are suspicious for LVO, the advisory points out that an LVO, a common cause of acute ischemic strokes, may still be present even if it is not flagged by the CADt imaging software.

If there is any potential over-reliance on CADt software, Vivek Bansal, MD said it may stem from a team of health-care providers striving to do the right thing for the patient under tight time constraints. While interventionalists, neurosurgeons and neurologists all have strong knowledge of brain vessels, there may be different levels of experience, according to Dr. Bansal, the national subspecialty lead for neuroradiology at Radiology Partners. He added that while these specialists look closely at images they take in the operating suite, they may not look at the actual CT images to the same level.

In regard to the imaging, Dr. Bansal said one may be looking at tiny branching vessels that are diving up and down into different slices of the images, and you have to scroll up and down to really trace them out vessel by vessel. This can be challenging and particularly hard to do on a smartphone in a brightly lit room, pointed out Dr. Bansal.

The clock is ticking, and time is brain. We are trying to race against the clock because every minute we take to arrive at a diagnosis, more brain cells may be dying (if the patient has a clot). The quicker we can get them to a diagnosis and the patient gets to a cath lab, the better the outcomes for the patient. I think that is the biggest challenge: trying to do something that is very meticulous in a very small amount of time, explained Dr. Bansal.

The FDA advisory also maintained that it is important to have awareness of the design capabilities of different CADt devices, many of which have artificial intelligence (AI) or machine learning technology, For example, the FDA cautioned that LVO CADt devices may not assess all intracranial vessels. Dr. Bansal said this is an important distinction with AI tools.

While some AI tools are very good at looking at an M1 occlusion, which is the proximal part of the middle cerebral artery, the newer AI tools are capable of looking at M2 occlusions with proximal anterior cerebral artery (ACA) and posterior cerebral artery (PCA) occlusions. All of these things are important in terms of patient care, maintained Dr. Bansal, who is affiliated with the East Houston Pathology Group in Texas.

Dr. Bansal said the key is understanding the role of AI-enabled devices and their value in triaging cases.

At any given moment, I might have 40 stat exams on my list. Im cranking through them as fast as I can but if AI tools are saying 'Hey, look at this one next, whether it is a potential large vessel occlusion or brain bleed, that is very helpful, suggested Dr. Bansal. Where we are at right now, I think that the only way we can look at AI is to look at it as a triaging tool.

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FDA Issues Advisory on Use of AI and Machine Learning for Large Vessel Occlusion in the Brain - Diagnostic Imaging

AdTheorent Uses Machine Learning-Powered Predictive Advertising to Boost Donations and Drive Awareness for American Cancer Society – PR Newswire

AdTheorent's performance-first platform drove a 68% engagement rate and delivered a Return on Ad Spend that exceeded benchmark by 117%

NEW YORK, April 14, 2022 /PRNewswire/ -- AdTheorent Holding Company, Inc. ("AdTheorent" or the "Company") (Nasdaq: ADTH), a leading programmatic digital advertising company using advanced machine learning technology and privacy-forward solutions to deliver measurable value for advertisers and marketers, today announced campaign results from a recent digital fundraising campaign for American Cancer Society (ACS). The campaign goal was to drive cost-effective donations and positive Return on Ad Spend (RoAS), as well as raise awareness of ACS. The campaign drove strong donations revenue, yielding an overall campaign RoAS which was 2-times more efficient than the ACS target benchmark.

The Approach:

AdTheorent worked with Tombras, media agency of record for ACS, to drive efficient donations and achieve a strong RoAS, in addition to increasing awareness of the brand's core areas of focus, including: advocacy, discovery, and patient support. In order to achieve the dual-pronged objectives, AdTheorent leveraged a mix of cross-device rich media, interactive banners and display tactics, targeted using AdTheorent's advanced predictive advertising platform. AdTheorent developed custom machine learning models fueled by non-individualized statistics to identify and reach consumers with the highest likelihood of completing the required campaign actions. AdTheorent's programmatic performance optimizers utilized myriad signals in the custom predictive models such as ad position, publisher, geo-intelligence, non-individualized user device attributes, location DMA, time of day, connection signal and many others to find the most qualified users and reach ACS' target audience of prospective donors, current donors, and lapsed donors, with a national footprint. Additionally, AdTheorent utilized real-time contextual signals to identify and reach consumers engaging with content related to ACS or charitable donations. Through in-unit pixel placement, user engagement fueled targeting allowing AdTheorent to optimize in real-time and scale targeting to drive results for each targeting tactic.

"Every dollar raised helps the American Cancer Society improve the lives of people with cancer and their families as the only organization that integrates advocacy, discovery and direct patient support," said Ben Devore, Director, Media Strategy at ACS. "Every bit of our campaign spend needs to be optimized for the best possible performance, so our key advertising goal was to reach the most probable donors, and then engage them in a way that would drive donations. AdTheorent helped us outperform our KPIs, with a very efficient return on ad spend and an exceptionally high engagement rate of nearly 70% throughout the duration of the campaign which helps our organization achieve greater impact, overall."

The Results:

The campaign exceeded all benchmarks across all tactics:

AdTheorent's data driven-platform identified targeting variables which yielded conversion lift, providing valuable insights for future flights of the campaign.

"AdTheorent Predictive Advertising uses advanced machine learning and data science to drive real-world performance and advertiser ROI in the most privacy-forward and efficient manner," said James Lawson, CEO at AdTheorent. "We are honored to work with Tombras and ACS to further ACS's vital mission. And we are proud of the results we have helped produce, driving donation revenue at an efficiency rate 2X greater than ACS expectations."

About AdTheorent

AdTheorent uses advanced machine learning technology and privacy-forward solutions to deliver impactful advertising campaigns for marketers.AdTheorent's industry-leading machine learning platform powers its predictive targeting, geo-intelligence,audience extension solutions and in-house creative capability, Studio AT.Leveraging only non-sensitive data and focused on the predictive value of machine learning models, AdTheorent'sproduct suite and flexible transaction models allow advertisers to identify the most qualified potential consumers coupled with the optimal creative experience todeliver superior results, measured by each advertiser's real-world business goals.

AdTheorent is consistently recognized with numerous technology, product, growth and workplace awards. AdTheorent was awarded "Best AI-Based Advertising Solution" (AI Breakthrough Awards) and "Most Innovative Product" (B.I.G. Innovation Awards) for four consecutive years. Additionally, AdTheorent is the only six-time recipient of Frost & Sullivan's "Digital Advertising Leadership Award."AdTheorent is headquartered in New York, with fourteen offices across the United States and Canada. For more information, visit adtheorent.com.

About Tombras

Tombras is a 430+ person full service, independent advertising agency headquartered in Knoxville, Tennessee connecting data and creativity for business results. Named a FastCo Most Innovative Company, to the AdAge A-List and a Most Effective Independent Agency per Effie Worldwide. Tombras is one of the fastest growing full-service independent agencies with offices in New York, Atlanta, Washington, D.C., Charlotte, NC, and headquarters in Knoxville. Tombras works with notable brands including American Cancer Society, Big Lots, MoonPie, Mozilla Firefox, Orangetheory Fitness, Pernod Ricard and others. More information:tombras.com.

About American Cancer Society

The American Cancer Society is on a mission to free the world from cancer. We invest in lifesaving research, provide 24/7 information and support, and work to ensure that individuals in every community have access to cancer prevention, detection, and treatment. For more information, visit cancer.org.

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AdTheorent Uses Machine Learning-Powered Predictive Advertising to Boost Donations and Drive Awareness for American Cancer Society - PR Newswire

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.

Read the University of Oklahoma news release

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

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