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U.S. companies, labs rush to produce blood test for coronavirus immunity

The Japan Times

LOS ANGELES/CHICAGO/TORONTO – As the United States works overtime to screen thousands for the novel coronavirus, a new blood test offers the chance to find out who may have immunity -- a potential game-changer in the battle to contain infections and get the economy back on track. Several academic laboratories and medical companies are rushing to produce these blood tests, which can quickly identify disease-fighting antibodies in people who already have been infected but may have had mild symptoms or none at all. This is different from the current, sometimes hard-to-come-by diagnostic tests that draw on a nasal swab to confirm active infection. "Ultimately, this (antibody test) might help us figure out who can get the country back to normal," said Florian Krammer, a professor in vaccinology at Mount Sinai's Icahn School of Medicine. "People who are immune could be the first people to go back to normal life and start everything up again."


Coronavirus outbreak: China's use of Artificial Intelligence for COVID-19

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So far China is known to have more than 3200 fatal cases owing to the Covid-19 epidemic. Currently, this infection has engulfed almost the entire world. Although, this infection began at China, but it was a Canadian AI (Artificial Intelligence) start-up based at Toronto that had spotted this infection first. BlueDot is an AI-based infectious disease surveillance system. This platform searches the world 24 by 7, for any possible largescale disease, spread.


Augmented Training Scheme Fixes CNN Texture Bias

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A quirk in convolutional neural networks (CNNs) trained to recognize images is that they tend to over-rely on textural information at the expense of shape information during processing. Now, researchers from the University of Toronto, Mila, Nvidia and Google Brain have proposed a new training scheme that targets this bias by controlling and exposing textural information slowly through the training process. The associated paper, Curriculum By Texture, is currently under review by the 2020 International Conference on Machine Learning (ICML). CNNs have been very successful in computer vision (CV) tasks such as image classification and segmentation. To achieve their effectiveness, they show a necessary bias towards spatial equivariance, which enables them to study more detailed information.


AI Predicted to Take Over Privacy Tech

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More than 40% of privacy tech solutions aimed at ensuring legal compliance are predicted to rely on Artificial Intelligence (AI) over the course of the next three years, analysts from the business research and advisory firm Gartner Inc have found. The company--which is set to present these findings among others at the Gartner IT Symposium/Xpo 2020 in Toronto, Canada in May--has found that reliance on privacy tech to ensure compliance with various privacy laws is expected to increase by at least 700% between 2020 and 2023. This marks an increase from the 5% of privacy tech solutions that are AI driven today to the more than 40% that are predicted to become available within the next 36 months. This development comes as companies are increasingly exposed to the combined pressures of privacy legislations and data breach risks. An October 2019 study by Bitdefender, for example, found that nearly 60% of companies had experienced a data breach since the beginning of 2017, and that nearly a quarter of the companies surveyed had suffered such a breach within the first six months of 2019 alone.


Top Artificial Intelligence Influencers To Follow in 2020 MarkTechPost

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Yoshua Bengio: Yoshua Bengio OCFRSC (born 1964 in Paris, France) is a Canadian computer scientist, most noted for his work on artificial neural networks and deep learning.[1][2][3] He was a co-recipient of the 2018 ACM A.M. Turing Award for his work in deep learning.[4] He is a professor at the Department of Computer Science and Operations Research at the Université de Montréal and scientific director of the Montreal Institute for Learning Algorithms (MILA). Geoffrey Hinton: Geoffrey Everest HintonCCFRSFRSC[11] (born 6 December 1947) is an English Canadian cognitive psychologist and computer scientist, most noted for his work on artificial neural networks. Since 2013 he divides his time working for Google (Google Brain) and the University of Toronto.


AI, machine learning to deliver 'wave of discoveries'

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The past 20 years have seen remarkable advances in the mining industry, particularly in mineral exploration technologies with vast volumes of data generated from geologic, geophysical, geochemical, satellite and other surveying techniques. However, the abundance of data has not necessarily translated into the discovery of new deposits, according to Colin Barnett, co-founder of BW Mining, a Boulder, Colorado-based data mining and mineral exploration company. "One of the problems we're facing in exploration is the huge increase in the amounts of data we have to look at," said Barnett, in his presentation at the Managing and exploring big data through artificial intelligence and machine learning session at the recent PDAC 2020 convention in Toronto. "And although it's high-quality data, the sheer volume is becoming almost overwhelming for human interpreters, and so we need help in getting to the bottom of it." By integrating hundreds or even thousands of interdependent layers of data, with each layer making its own statistically determined contribution, machine learning offers a solution to the problem of tackling the massive amounts of data generated, and a powerful new tool in the search for mineral deposits.


Artificial intelligence, machine learning primed to deliver 'a wave of discoveries'

#artificialintelligence

The past 20 years have seen remarkable advances in the mining industry, particularly in mineral exploration technologies with vast volumes of data generated from geologic, geophysical, geochemical, satellite and other surveying techniques. However, the abundance of data has not necessarily translated into the discovery of new deposits, according to Colin Barnett, co-founder of BW Mining, a Boulder, Colorado-based data mining and mineral exploration company. "One of the problems we're facing in exploration is the huge increase in the amounts of data we have to look at," said Barnett, in his presentation at the Managing and exploring big data through artificial intelligence and machine learning session the recent PDAC 2020 convention in Toronto. "And although it's high-quality data, the sheer volume is becoming almost overwhelming for human interpreters, and so we need help in getting to the bottom of it." By integrating hundreds or even thousands of interdependent layers of data, with each layer making its own statistically determined contribution, machine learning offers a solution to the problem of tackling the massive amounts of data generated, and a powerful new tool in the search for mineral deposits.


AAAI 2020 A Turning Point for Deep Learning?

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This is an updated version. The Godfathers of AI and 2018 ACM Turing Award winners Geoffrey Hinton, Yann LeCun, and Yoshua Bengio shared a stage in New York on Sunday night at an event organized by the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020). The trio of researchers have made deep neural networks a critical component of computing, and in individual talks and a panel discussion they discussed their views on current challenges facing deep learning and where it should be heading. Introduced in the mid 1980s, deep learning gained traction in the AI community the early 2000s. The year 2012 saw the publication of the CVPR paper Multi-column Deep Neural Networks for Image Classification, which showed how max-pooling CNNs on GPUs could dramatically improve performance on many vision benchmarks; while a similar system introduced months later by Hinton and a University of Toronto team won the large-scale ImageNet competition by a significant margin over shallow machine learning methods.


U of T's Kamran Khan on how his startup used AI to spot the coronavirus before anyone else: CNBC

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Nine days before the World Health Organization alerted the world to the threat posed by COVID-19, an artificial intelligence-powered startup led by the University of Toronto's Kamran Khan had already spotted the first signs of an unusual outbreak. In an interview with CNBC, Khan explained how his company, BlueDot, was able to scour big data and spot the emergence of the novel coronavirus before anyone else. He said BlueDot uses machine learning and natural language processing to comb through masses of data, which are then reviewed by doctors and computer programmers who create threat reports. "We don't use artificial intelligence to replace human intelligence, we basically use it to find the needles in the haystack and present them to our team," said Khan, an associate professor at the Institute of Health Policy, Management and Evaluation at the Dalla Lana School of Public Health and an infectious disease physician at St. Michael's Hospital. He said his experience treating patients during the SARS outbreak in 2003 inspired him to start BlueDot.


How AI May Prevent The Next Coronavirus Outbreak

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AI can be used for the early detection of virus outbreaks that might result in a pandemic. AI detected the coronavirus long before the world's population really knew what it was. On December 31st, a Toronto-based startup called BlueDot identified the outbreak in Wuhan, several hours after the first cases were diagnosed by local authorities. The BlueDot team confirmed the info its system had relayed and informed their clients that very day, nearly a week before Chinese and international health organisations made official announcements. Thanks to the speed and scale of AI, BlueDot was able to get a head start over everyone else.