RSNA COVID-19 Imaging Data Sharing Survey

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The Radiological Society of North America (RSNA) has received numerous inquiries seeking access to COVID-19 related imaging data, both from radiology sites interested in sharing such data for use in research and education and from researchers. RSNA is committed to accelerating open source collaborative research on the uses of medical imaging in addressing the COVID-19 pandemic, including the use of new tools like artificial intelligence (AI). This form will enable institutions with COVID-19 data to express interest in participating in a planned open data repository for international COVID-19 imaging research and education efforts. Please complete this form if your institution has COVID-19 data that you may be willing and able to share for research purposes. Completing this brief survey does not represent a final commitment to collaborate with us or to share your data.


Machine Learning in GIS: Understand the Theory and Practice

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This course is designed to equip you with the theoretical and practical knowledge of Machine Learning as applied for geospatial analysis, namely Geographic Information Systems (GIS) and Remote Sensing. By the end of the course, you will feel confident and completely understand the Machine Learning applications in GIS technology and how to use Machine Learning algorithms for various geospatial tasks, such as land use and land cover mapping (classifications) and object-based image analysis (segmentation). This course will also prepare you for using GIS with open source and free software tools. In the course, you will be able to apply such Machine Learning algorithms as Random Forest, Support Vector Machines and Decision Trees (and others) for classification of satellite imagery. On top of that, you will practice GIS by completing an entire GIS project by exploring the power of Machine Learning, cloud computing and Big Data analysis using Google Erath Engine for any geographic area in the world.


Tool Finds Software Update Update Bugs In Hours, Not Days - aster.cloud

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It's a common frustration--software updates intended to make our applications run faster inadvertently end up doing just the opposite. These bugs, called performance regressions in the field of computer science, are time-consuming to fix because locating software errors normally requires substantial human intervention. To overcome this obstacle, researchers at Texas A&M University, in collaboration with computer scientists at Intel Labs, developed a completely automated way of identifying the source of the errors. Their algorithm, based on a specialized form of machine learning called deep learning, is not only turnkey, but also quick. It finds performance bugs in a matter of a few hours instead of days.


What Machine Learning Can Do In Fabs

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Semiconductor Engineering sat down to discuss the issues and challenges with machine learning in semiconductor manufacturing with Kurt Ronse, director of the advanced lithography program at Imec; Yudong Hao, senior director of marketing at Onto Innovation; Romain Roux, data scientist at Mycronic; and Aki Fujimura, chief executive of D2S. What follows are excerpts of that conversation. SE: Machine learning is a hot topic. This technology uses a neural network to crunch data and identify patterns, then matches certain patterns and learns which of those attributes are important. We also have more advanced forms called deep learning.


ThetaRay, Provider of Big Data and AI-enhanced Analytics Tools, Joins Microsoft's Partner Program to Offer AML Solution

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ThetaRay, a provider of Big Data and artificial intelligence (AI)-enhanced analytics tools, has joined Microsoft's (NASDAQ:MSFT) partner program, One Commercial Partner, which provides various cloud-powered solutions. ThetaRay's anti-money laundering (AML) solution for correspondent banking can be accessed through Microsoft's Azure Marketplace. A large US bank has reportedly signed an agreement to use the solution. "We are proud to join the One Commercial Partner program and offer Microsoft Azure customers access to our industry-leading AML for Correspondent Banking solution." "Global banks are increasingly de-risking or abandoning their correspondent banking relationships due to a lack of transparency and fears of money laundering and regulatory fines. Our solution provides banks with the … ability to reverse the trend and grow their business by allowing full visibility into all links of the cross-border payment chain, from originator to beneficiary."


Deltec Bank, Bahamas says, Artificial Intelligence Improves Data Analysis Processes in the Banking Sector

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According to Deltec Bank, Bahamas - 'The presence of AI produces several specific benefits that banks can use to generate new revenue streams through individualization.' We all understand that artificial intelligence and data analytics are excellent teammates. Over the next several years, the banking sector will use the power of this combination to create and deliver essential products and strategies that can help consumers and businesses grow their wealth through new and unique methods. The success of this process depends on where the industry focuses its energy, and AI enables institutions to concentrate its initiatives on crucial tasks instead of bureaucratic responsibilities. The banking sector already uses a standardized analytics report to understand the reasons why specific behaviors and actions happen.


PulseNetOne: Fast Unsupervised Pruning of Convolutional Neural Networks for Remote Sensing

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Scene classification is an important aspect of image/video understanding and segmentation. However, remote-sensing scene classification is a challenging image recognition task, partly due to the limited training data, which causes deep-learning Convolutional Neural Networks (CNNs) to overfit. Another difficulty is that images often have very different scales and orientation (viewing angle). Yet another is that the resulting networks may be very large, again making them prone to overfitting and unsuitable for deployment on memory- and energy-limited devices. We propose an efficient deep-learning approach to tackle these problems.


PyTorch for Deep Learning and Computer Vision

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PyTorch has rapidly become one of the most transformative frameworks in the field of Deep Learning. Since its release, PyTorch has completely changed the landscape in the field of deep learning due to its flexibility, and how easy it is to use when building Deep Learning models. Deep Learning jobs command some of the highest salaries in the development world. This course is meant to take you from the complete basics, to building state-of-the art Deep Learning and Computer Vision applications with PyTorch. With over 44000 students, Rayan is a highly rated and experienced instructor who has followed a "learn by doing" style to create this amazing course.


DarwinAI Unleashes COVID-Net

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COVID-19 has gripped the world for the past month as it brought global industries to a screeching halt and overwhelmed healthcare systems. From AI and big data to supercomputing, tech has been fighting back by leveraging complex data analysis to aid in diagnosis, epidemiology and treatment. Now, DarwinAI, a Canadian AI startup, has announced a new tool to fight back against the pandemic: COVID-Net. The news was announced in a blog post by Sheldon Fernandez, CEO of DarwinAI. "The global crisis brought on by COVID-19 has affected us all," Fernandez wrote.


Artificial intelligence translates thoughts into text using brain implant

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Scientists have developed an artificial intelligence system that can translate a person's thoughts into text by analysing their brain activity. Researchers at the University of California, San Francisco, developed the AI to decipher up to 250 words in real time from a set of between 30 and 50 sentences. The algorithm was trained using the neural signals of four women with electrodes implanted in their brains, which were already in place to monitor epileptic seizures. The volunteers repeatedly read sentences aloud while the researchers fed the brain data to the AI to unpick patterns that could be associated with individual words. The average word error rate across a repeated set was as low as 3 per cent.