Google reveals 'Project Nightingale' after being accused of secretly gathering personal health records

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Google secretly gathered millions of patient records across 21 states on behalf of a health care provider, in an effort dubbed "Project Nightingale," reports The Wall Street Journal. Neither the provider's doctors nor patients were made aware of the effort, according to the report. The Wall Street Journal's Rob Copeland wrote that the data amassed in the program includes "lab results, doctor diagnoses and hospitalization records, among other categories, and amounts to a complete health history, complete with patient names and dates of birth," and that as many as 150 Google employees may have had access to the data. The New York Times corroborated much of the report later in the day, writing that "dozens of Google employees" may have access to sensitive patient data, and that there are concerns that some Google employees may have downloaded some of that data. But Google tells The Verge that despite the surprise, it's standard industry practice for a health care provider to share highly sensitive health records with tech workers under an agreement like the kind it signed -- one that narrowly allows Google to build tools for that health care provider by using the private medical data of its patients, and one that doesn't require patients to be notified, the company claims.


Google reveals 'Project Nightingale' after being accused of secretly gathering personal health records

#artificialintelligence

Google secretly gathered millions of patient records across 21 states on behalf of a health care provider, in an effort dubbed "Project Nightingale," reports The Wall Street Journal. Neither the provider's doctors nor patients were made aware of the effort, according to the report. The Wall Street Journal's Rob Copeland wrote that the data amassed in the program includes "lab results, doctor diagnoses and hospitalization records, among other categories, and amounts to a complete health history, complete with patient names and dates of birth," and that as many as 150 Google employees may have had access to the data. The New York Times corroborated much of the report later in the day, writing that "dozens of Google employees" may have access to sensitive patient data, and that there are concerns that some Google employees may have downloaded some of that data. But Google tells The Verge that despite the surprise, it's standard industry practice for a health care provider to share highly sensitive health records with tech workers under an agreement like the kind it signed -- one that narrowly allows Google to build tools for that health care provider by using the private medical data of its patients, and one that doesn't require patients to be notified, the company claims.


Want smarter investment management insights? See what machine learning can do - IBM Business Partners blog

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Artificial intelligence (AI) and machine learning were two of the key buzzwords of 2018-19, and interest in these areas is expected to continue into the new decade. They have moved out of the realms of theory and have taken their place in the corporate toolbox of forward-thinking CEOs. No strategy conversation today should be complete without a discussion of how these connected fields can be used to deliver an edge to business leaders, who face ever increasing rates of business change and the emergence of new – and often unexpected – competition. In fact, the term "machine learning" was coined by an IBM researcher in the 1950s after he had created one of the very first self-learning programs. But recent developments in the availability of compute power to run the algorithms essential for machine learning and the exponential growth of data available for analysis have brought about a tipping point for machine learning to bring a genuine competitive advantage.


Efficient Machine Learning

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NEW, 4.1 (12 ratings), Created by Usama Albaghdady, English If you're a machine learning specialist looking to make the transaction into the real-world AI applications. This comprehensive course will be your guide to learning how to scale-up your machine learning model to the optimal state possible, you'll be learning everything you need to move you machine learning model to the next stage. This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science! You'll learn the machine learning, AI, and data mining techniques real employers are looking for, including:


Watch how Tesla trains its neural networks for self-driving in 10 minutes - Electrek

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Andrej Karpathy, Tesla's head of AI and computer vision, gave an interesting talk to get into how Tesla trains its neural networks for self-driving. It results in an interesting overview of the concept in about 10 minutes. Karpathy obtained his PhD from Stanford University in Machine Learning with a focus on Deep Learning for Computer Vision and Natural Language Processing in 2016. In a short time, he made a name for himself in the space by teaching a new Stanford class on Convolutional Neural Networks for Visual Recognition that became very popular while he was doing his PhD. He was working for Elon Musk's new nonprofit AI research firm, OpenAI, since September 2016, and it looks like his research impressed Musk enough to hire him at Tesla to turn his neural net expertise into actual real-world applications.


Use Cases for Machine Learning in the SOC

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Micro Focus is a global software company with 40 years of experience in delivering and supporting enterprise software solutions that help customers innovate faster with lower risk. Our portfolio enables our 20,000 customers to build, operate, and secure the applications and IT systems that meet the challenges of change. We are a global software company, committed to enabling customers to both embrace the latest technologies and maximize the value of their existing IT investments.


Nvidia GPUs for data science, analytics, and distributed machine learning using Python with Dask ZDNet

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Nvidia has been more than a hardware company for a long time. As its GPUs are broadly used to run machine learning workloads, machine learning has become a key priority for Nvidia. In its GTC event this week, Nvidia made a number of related points, aiming to build on machine learning and extend to data science and analytics. Nvidia wants to "couple software and hardware to deliver the advances in computing power needed to transform data into insights and intelligence." Jensen Huang, Nvidia CEO, emphasized the collaborative aspect between chip architecture, systems, algorithms and applications.


Why AI is your friend when it comes to cloud migration

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But the drawbacks of not making the investment to rebuild your legacy apps for the cloud means technological debt, competitive disadvantages in agility and frustrated customers left suffering poor user experiences. Organisations need to decide which applications to move to the cloud and which to keep on-premise. Then, they must decide how to refactor those apps with cloud-native technologies or create a hybrid-cloud setup - it's a complicated process. Successful cloud migrations and transformation rely on automating continuous builds, integration and delivery as well as automating performance monitoring, root-cause analysis and remediation. Together with this'automate everything' approach is leveraging AI.


Convolutional Neural Networks: Briefly

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Neural networks, particularly convolutional neural networks, have become more and more popular in the field of computer vision. What are convolutional neural networks and what are they used for? Recall from my earlier blog that a computer sees an image as an ordered set of pixels. We recall the notorious RGB red, green, blue (which is NOT the Notorious R.B.G., nor the Notorious B.I.G., so please don't get confused). Where each pixel is represented by three numbers from 0 to 255, giving the intensity of red, green or blue.


Geisinger studies show AI deep learning model helping cardiologists detect AFib

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Artificial intelligence technology based on a deep learning model could help cardiologists predict irregular heart rhythms, known as atrial fibrillation, before it develops. WHY IT MATTERS That's the conclusion drawn from two studies to be presented at the American Heart Association Scientific Sessions 2019 and conducted by Geisinger researchers. A team of scientists trained a neural network to evaluate electrocardiograms to predict which patients were likely to develop an irregular heartbeat, using the AI model to analyze the results of 1.77 million ECGs and other records from almost 400,000 patients. Researchers trained deep neural networks using ECG results from across 30 years of archived medical records in Pennsylvania and New Jersey's Geisinger Health System, finding the AI was able to provide longer-term prognostication and more accurately identify at-risk patients. The model was also able to predict which patients would develop an irregular heartbeat, even when doctors interpreted the test results as normal, by analyzing 15 segments of data comprised of more than 30,000 data points for each ECG.