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Automated deep learning-based paradigm for high-risk plaque detection in B-mode …

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Automated deep learning-based paradigm for high-risk plaque detection in B-mode common carotid ultrasound scans: an asymptomatic Japanese cohort study.


AI Researchers Estimate 97% Of EU Websites Fail GDPR Privacy Requirements- Especially User Profiling

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Researchers in the US have used machine learning techniques to study the GDPR privacy policies of over a thousand representative websites based in the EU. They found that 97% of the sites studied failed to comply with at least one requirement of the European Union's 2018 regulatory framework, and that they complied least of all with regulatory requirements around the practice of'user profiling'. '[Since] the privacy policy is the essential communication channel for users to understand and control their privacy, many companies updated their privacy policies after GDPR was enforced. However, most privacy policies are verbose, full of jargon, and vaguely describe companies' data practices and users' rights. Therefore, it is unclear if they comply with GDPR.' 'Our results show that even after GDPR went into effect, 97% of websites still fail to comply with at least one requirement of GDPR.'


Council Post: AI In Healthcare Presents Unique Challenges And Amazing Opportunities

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Artificial intelligence is a hot topic in almost every industry right now, and healthcare is no exception. The big data revolution has transformed manufacturing supply chains, retail advertising and customer service. However, transforming healthcare with AI is a very different and exponentially more difficult challenge. In this article, I'll explain a few reasons why AI in healthcare poses a steeper climb, as well as the potential opportunities that make it worth working toward. Designing and implementing AI tools in healthcare is fundamentally different from using machine learning or big data in other industries.


Predicting Protein Interactions With Artificial Intelligence

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UT Southwestern and University of Washington researchers led an international team that used artificial intelligence (AI) and evolutionary analysis to produce 3D models of eukaryotic protein interactions. The study, published in Science, identified more than 100 probable protein complexes for the first time and provided structural models for more than 700 previously uncharacterized ones. Insights into the ways pairs or groups of proteins fit together to carry out cellular processes could lead to a wealth of new drug targets. "Our results represent a significant advance in the new era in structural biology in which computation plays a fundamental role," said Qian Cong, Ph.D., Assistant Professor in the Eugene McDermott Center for Human Growth and Development with a secondary appointment in Biophysics. Dr. Cong led the study with David Baker, Ph.D., Professor of Biochemistry and Dr. Cong's postdoctoral mentor at the University of Washington prior to her recruitment to UT Southwestern.


Automated deep learning-based paradigm for high-risk plaque detection in B-mode common carotid ultrasound scans: an asymptomatic Japanese cohort study

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BACKGROUND: The death due to stroke is caused by embolism of the arteries which is due to the rupture of the atherosclerotic lesions in carotid arteries. The lesion formation is over time, and thus, early screening is recommended for asymptomatic and moderate-risk patients. The previous techniques adopted conventional methods or semi-automated and, more recently, machine learning solutions. A handful of studies have emerged based on solo deep learning (SDL) models such as UNet architecture. METHODS: The proposed research is the first to adopt hybrid deep learning (HDL) artificial intelligence models such as SegNet-UNet.


Machine Learning in Medicine -- Journal Club

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The use of machine learning techniques in biomedical research has exploded over the past few years, as exemplified by the dramatic increase in the number of journal articles indexed on PubMed by the term "machine learning", from 3,200 in 2015 to over 18,000 in 2020. While substantial scientific advancements have been made possible thanks to machine learning, the inner working of most machine learning algorithms remains foreign to many clinicians, most of whom are quite familiar with traditional statistical methods but have little formal training on advanced computer algorithms. Unfortunately, journal reviewers and editors are sometimes content with accepting machine learning as a black box operation and fail to analyze the results produced by machine learning models with the same level of scrutiny that is applied to other clinical and basic science research. The goal of this journal club is to help readers develop the knowledge and skills necessary to digest and critique biomedical journal articles involving the use of machine learning techniques. It is hard for a reviewer to know what questions to ask if he/she does not understand how these algorithms work.


Robots can be companions, caregivers, collaborators -- and social influencers

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Robot and artificial intelligence are poised to increase their influences within our every day lives. In the mid-1990s, there was research going on at Stanford University that would change the way we think about computers. The Media Equation experiments were simple: participants were asked to interact with a computer that acted socially for a few minutes after which, they were asked to give feedback about the interaction. Participants would provide this feedback either on the same computer (No. 1) they had just been working on or on another computer (No. 2) across the room. The study found that participants responding on computer No. 2 were far more critical of computer No. 1 than those responding on the same machine they'd worked on. People responding on the first computer seemed to not want to hurt the computer's feelings to its face, but had no problem talking about it behind its back.


6 famous analytics and AI disasters

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In 2017, The Economist declared that data, rather than oil, had become the world's most valuable resource. The refrain has been repeated ever since. Organizations across every industry have been and continue to invest heavily in data and analytics. But like oil, data and analytics have their dark side. According to IDG's State of the CIO 2021 report, 39% of IT leaders say that data analytics will drive the most IT investment at their organization this year, up from 37% in 2020. Insights gained from analytics and actions driven by machine learning algorithms can give organizations a competitive advantage, but mistakes can be costly in terms of reputation, revenue, or even lives.


Future Vision & Direction of AI Part II: Scaling AI Whilst Preventing a Big Brother World & Solving The Curse of the Modern Data Scientist

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Venture Capitalists are hoping to find the next superstar tech unicorn, AI startup founders dreaming of creating the next unicorn, and corporates adopting AI need to consider their data growth strategy in order to be able to scale their AI-enabled services or products. The past decade has been one of explosive growth in digital data and AI capabilities across the digital media and e-commerce space. And it is no accident that the strongest AI capabilities reside in the Tech majors. The author argues that there will be no AI winter in the 2020s as there was in 1974 and 1987 as the internet (social media and e-commerce) are so dependent upon AI capabilities and so too with being the Metaverse, and the era of 5G enabled Edge Computing with the Internet of Things (IoT). Furthermore, the following infographics illustrate how many people globally use social media and hence how central these channels have become to the everyday lives of people. Likewise, the size of the e-commerce market is vast. Although the era of standalone 5G networks may enable a window of opportunity for a new wave of consumer-facing applications in the business to consumer (B2C) in relation to e-commerce and perhaps even new digital media platforms that may challenge the current incumbents, after all the arrival of 4G provided a window for the likes of Airbnb, Uber, and leading social media platforms such as Facebook, Instagram, etc. to scale.


Segna Newsletter -- 25 November 2021

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Training a single AI model can emit as much carbon as five cars in their lifetimes MIT Tech Review Training an AI model has the equivalent carbon footprint as five American cars, including fuel usage, according to researchers at the University of Massachusetts, who performed life cycle assessments for training several large AI models. While that figure relates to a neural net with more than 200 million parameters, the study highlights the unbelievable efficiency of the human brain. The bigger question now is whether we will build machines that rival the brain for efficiency. To be energy-efficient, brains predict their perceptions Quanta Magazine Many neuroscientists view the brain as a "prediction machine" which, through predictive processing, uses knowledge of the world to make inferences or generate hypotheses about the causes of incoming information. Computational neuroscientists are building artificial neural networks that learn to make predictions about incoming information.