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Machine learning can help keep the global supply chain moving

#artificialintelligence

TechRepublic's Karen Roby spoke with Noel Calhoun, CTO of Interos, an artificial intelligence supply chain solution, about AI in the supply chain. The following is an edited transcript of their conversation. Karen Roby: Noel, we're going to talk a little bit about AI today in our supply chain. You spent many years in the public and the private sectors, working with the CIA. When we talk about our supply chain, I mean, never before has the light been put on it as much as it is right now.


Rapid antigen testing in COVID-19 responses

Science

The value of rapid antigen testing of people (with or without COVID-19 symptoms) to reduce transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been discussed extensively ([ 1 ][1]–[ 5 ][2]) but remains a topic of policy debates ([ 6 ][3], [ 7 ][4]). Lateral flow devices (LFDs) to test for SARS-CoV-2 antigen are inexpensive, provide results in minutes, and are highly specific ([ 2 ][5]–[ 4 ][6]), and although less sensitive than reverse transcriptase polymerase chain reaction (RT-PCR) tests to detect viral RNA, they detect most cases with high viral load ([ 2 ][5], [ 3 ][7], [ 8 ][8]), which are likely the most infectious ([ 8 ][8], [ 9 ][9]). Successful mass testing relies on public trust, the social and organizational factors that support uptake, contact tracing, and adherence to quarantine. On page 635 of this issue, Pavelka et al. ([ 10 ][10]) report the substantial reduction in transmission that population-wide rapid antigen testing had, in combination with other measures, in Slovakia. Slovakia ran mass testing interventions from the last week of October to the second week of November 2020, with 65% of the target populations taking rapid antigen tests. Testing started in the four counties with the highest rates of infection, continued with national mass testing, then was followed up with more testing in high-prevalence areas. Nasopharyngeal swabs for the LFDs were taken by clinical staff, not self-administered. Sample quality and test accuracy are higher with tests taken by health professionals ([ 3 ][7]). Although the specific impact of Slovakia's mass testing could not be disentangled from the contribution of other concurrent control measures (including closure of secondary schools and restrictions on hospitality and indoor leisure activities), statistical modelling by Pavelka et al. estimated a 70% reduction in the prevalence of COVID-19 cases compared with unmitigated growth. The UK piloted mass testing in Liverpool in November 2020 after the city experienced the highest COVID-19 prevalence in the country. Slovakia applied more pressure on its citizens to get tested than did Liverpool, by requiring anyone not participating in mass testing to quarantine. The Liverpool testing uptake was consequently lower than Slovakia's, involving 25% of the population in 4 weeks. Liverpool's public health service valued the testing as an additional control measure, but impacts were limited by lack of support for those in socioeconomically deprived areas facing income loss from quarantine after a positive test ([ 2 ][5]): Test positivity rates were highest and testing uptake lowest in the most deprived areas ([ 2 ][5], [ 11 ][11]). Similar socioeconomic barriers were reported for test uptake among care home staff ([ 12 ][12]). This highlights the importance of addressing public perceptions of testing and support for low-income workers to quarantine when implementing mass testing. ![Figure][13] Predictive value of testing changes with prevalence When testing 100,000 individuals with a lateral flow device with 80% sensitivity and 99.9% specificity, the proportion of false-positive and false-negative test results will vary according to the prevalence of infection. GRAPHIC: V. ALTOUNIAN/ SCIENCE The predictive value of testing varies with the population prevalence of infection and phase of the epidemic curve ([ 7 ][4]). As the prevalence of SARS-CoV-2 infections decreases, the proportion of false-positive test results increases, whereas the number of false-negative test results decreases. For example, with 99.9% specificity (proportion of noninfections that the test rejects) and 80% sensitivity (proportion of infections that the test detects), the positive predictive value (proportion of people with a positive test result who are infected) is 89% when the prevalence is 1%, and it drops to 44% at 0.1% prevalence (55 in 100 positive test results are false). In absolute terms, however, if testing 100,000 people, these scenarios would result in 99 false positives (out of 899 positive results) and 100 false positives (out of 180 positive results) for 1% and 0.1% prevalence, respectively (see the figure). Confirmatory RT-PCR tests after a positive LFD test result was recently reintroduced by Public Health England because of both the low positive predictive values of testing at low prevalence of infection and the utility of reusing PCR samples for viral genetic sequencing in variant surveillance ([ 13 ][14]). The pilot in Slovakia was conducted while the prevalence was still high (3.9% in areas with the highest rate of infection). Rapid antigen testing was used as an additional tool to identify a substantial proportion of asymptomatic SARS-CoV-2–infected individuals, who were required to quarantine. Additionally, those who did not agree to take part in testing were required to quarantine, thus reducing the chance of transmission among those who were permitted to mix. At higher prevalence, more SARS-CoV-2 infections can be identified, but the proportion of false-negative tests is also higher, so the reliance on other control measures is greater. No matter what the prevalence, mass testing regimes can only properly be considered amid other health protection measures. By the end of the mass testing program in Slovakia, rapid antigen tests had identified more than 50,000 people without COVID-19 symptoms who were likely contagious with SARS-CoV-2. UK mass testing pilots in Liverpool and also in Wales that started at a similar time as the pilot in Slovakia, but with fewer pressures to take part, identified more than 4000 asymptomatic cases in the Cheshire and Merseyside region around Liverpool ([ 14 ][15]) and more than 700 in Wales ([ 15 ][16]). Although the testing technology was equivalent across Slovakia, England, and Wales, the interventions were different, spanning a variety of population prevalence, phases of the epidemic curve, surges of new variants, periods of lockdown, periods of reopening of large-scale social mixing, and targeting of testing. For example, the Liverpool project shifted in public messaging from “Let's All Get Tested” to “Test Before You Go” to “Testing Our Front Line” (for anyone having to leave home to go to work in lockdown). In places with low SARS-CoV-2 prevalence, mindful of the cumulative harms from COVID-19 restrictions, the emphasis is on restarting social and economic activities while minimizing infections. As research continues to clarify the impact of vaccines on SARS-CoV-2 transmission, there is a need to use rapid antigen testing as a part of comprehensive public health measures that reduce the risk of the virus escaping vaccine or natural immunity through avoidable transmission—for example, testing to secure workplaces and large events as societies reopen after lockdowns. Successful implementation, however, depends on public participation in testing and adequate support to quarantine. 1. [↵][17]1. Z. Kmietowicz , BMJ 372, n81 (2021). 10.1136/bmj.n81 [OpenUrl][18][FREE Full Text][19] 2. [↵][20]1. I. Buchan et al ., Liverpool COVID-19 community testing pilot. Interim evaluation report. 2020 (University of Liverpool, 2020); [www.gov.uk/government/publications/liverpool-covid-19-community-testing-pilot-interim-evaluation-report-summary][21]. 3. [↵][22]1. T. Peto et al ., medRxiv 10.1101/2021.01.13.21249563 (2021). 4. [↵][23]1. A. Crozier, 2. S. Rajan, 3. I. Buchan, 4. M. McKee , BMJ 372, 208 (2021). [OpenUrl][24] 5. [↵][25]1. M. J. Mina, 2. T. E. Peto, 3. M. García-Fiñana, 4. M. G. Semple, 5. I. E. Buchan , Lancet 397, 1425 (2021). [OpenUrl][26] 6. [↵][27]1. L. Y. W. Lee et al ., medRxiv 10.1101/2021.03.31.21254687 (2021). 7. [↵][28]1. R. W. Peeling, 2. P. Olliaro , Lancet 10.1016/S1473-3099(21)00152-3 (2021). 8. [↵][29]1. L. Y. W. Lee et al ., medRxiv 10.1101/2021.03.31.21254687 (2021). 9. [↵][30]1. M. Marks et al ., Lancet Infect. Dis. (2021). 10.1016/S1473-3099(20)30985-3 10. [↵][31]1. M. Pavelka et al ., Science 372, 635 (2021). [OpenUrl][32][Abstract/FREE Full Text][33] 11. [↵][34]1. M. A. Green et al ., medRxiv 10.1101/2021.02.10.21251256 (2021). 12. [↵][35]1. J. Tulloch et al ., SSRN 10.2139/ssrn.3822257 (2021). 13. [↵][36]1. S. Hopkins , Gov.UK 30 March 2021); . 14. [↵][37]NHS Cheshire and Merseyside, Combined Intelligence for Population Health Action (2021): [www.cipha.nhs.uk][38]. 15. [↵][39]1. K. Nnoaham , Evaluation of the lateral flow device testing pilot for COVID-19 in Merthyr Tydfil and the lower Cynon Valley (2021); [https://cwmtafmorgannwg.wales/Docs/Publications/FINAL\_V2\_Whole%20Area%20Testing%20Evaluation%20Full%20Report%2020210325.pdf][40]. Acknowledgments: I. E.B. and M.G.-F. received grant funding from the UK Department of Health and Social Care to evaluate the Liverpool community testing pilot. I.E.B. reports fees from AstraZeneca as chief data scientist adviser through Liverpool University and a senior investigator grant from the National Institute for Health Research (NIHR) outside the submitted work. 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AI, RPA, and Machine Learning - How are they Similar & Different?

#artificialintelligence

AI, RPA, and machine learning, you must have heard these words echoing in the tech industry. Be it blogs, websites, videos, or even product descriptions, disruptive technologies have made their presence bold. The fact that we all have AI-powered devices in our homes is a sign that the technology has come so far. If you are under the impression that AI, robotic process automation, and machine learning have nothing in common, then here's what you need to know, they are all related concepts. Oftentimes, people use these names interchangeably and incorrectly which causes confusion among businesses that are looking for the latest technological solutions.


Machine-learning project takes aim at disinformation

#artificialintelligence

What is new is how quickly malicious actors can spread disinformation when the world is tightly connected across social networks and internet news sites. We can give up on the problem and rely on the platforms themselves to fact-check stories or posts and screen out disinformation--or we can build new tools to help people identify disinformation as soon as it crosses their screens. Preslav Nakov is a computer scientist at the Qatar Computing Research Institute in Doha specializing in speech and language processing. He leads a project using machine learning to assess the reliability of media sources. That allows his team to gather news articles alongside signals about their trustworthiness and political biases, all in a Google News-like format. "You cannot possibly fact-check every single claim in the world," Nakov explains. Instead, focus on the source. "I like to say that you can fact-check the fake news before it was even written." His team's tool, called the Tanbih News Aggregator, is available in Arabic and English and gathers articles in areas such as business, politics, sports, science and technology, and covid-19. Business Lab is hosted by Laurel Ruma, editorial director of Insights, the custom publishing division of MIT Technology Review. The show is a production of MIT Technology Review, with production help from Collective Next. This podcast was produced in partnership with the Qatar Foundation. "Even the best AI for spotting fake news is still terrible," MIT Technology Review, October 3, 2018 Laurel Ruma: From MIT Technology Review, I'm Laurel Ruma, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.


SoftBank's Newest AI Unicorns Are After More Than Amazon And The Weeknd

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Coming off its best quarter ever, SoftBank is on the hunt for its next billion dollar IPO. Having funded 29 of the 657 unicorns in the world, according to CB Insights, the Japanese telecom giant has been on a shopping spree, looking for promising new AI startups to bet big on. At Collision's tech conference held online last month, I had a chance to talk with the CEOs of SoftBank's newest portfolio companies, Standard Cognition and Forward. Here's how the two San Francisco startups are leveraging artificial intelligence to help gain market dominance in the post-pandemic world. In 2017, a group of machine learning engineers at the SEC became obsessed with computers that could see better than humans and ditched their jobs to join Y Combinator to build the computer vision company of their dreams.


Artificial Intelligence and the COVID-19 Pandemic - Future of Privacy Forum

#artificialintelligence

Machine learning-based technologies are playing a substantial role in the response to the COVID-19 pandemic. Experts are using machine learning to study the virus, test potential treatments, diagnose individuals, analyze the public health impacts, and more. Below, we describe some of the leading efforts and identify data protection and ethical issues related to machine learning and COVID-19, with a particular focus on apps directed to health care professionals that leverage audio-visual data, text analysis, chatbots, and sensors. "Machine Intelligence (MI) is rapidly becoming an important approach across biomedical discovery, clinical research, medical diagnostics/devices, and precision medicine. Such tools can uncover new possibilities for researchers, physicians, and patients, allowing them to make more informed decisions and achieve better outcomes. When deployed in healthcare settings, these approaches have the potential to enhance efficiency and effectiveness of the health research and care ecosystem, and ultimately improve quality of patient care." Now – with the development of the pandemic resulting from the spread of the coronavirus (COVID-19), medical providers, institutions, and commercial developers are all considering whether and how to apply machine learning to confront the threat of this current crisis.


Ethics of AI: Benefits and risks of artificial intelligence

ZDNet

In 1949, at the dawn of the computer age, the French philosopher Gabriel Marcel warned of the danger of naively applying technology to solve life's problems. Life, Marcel wrote in Being and Having, cannot be fixed the way you fix a flat tire. Any fix, any technique, is itself a product of that same problematic world, and is therefore problematic, and compromised. Marcel's admonition is often summarized in a single memorable phrase: "Life is not a problem to be solved, but a mystery to be lived." Despite that warning, seventy years later, artificial intelligence is the most powerful expression yet of humans' urge to solve or improve upon human life with computers. But what are these computer systems? As Marcel would have urged, one must ask where they come from, whether they embody the very problems they would purport to solve. Ethics in AI is essentially questioning, constantly investigating, and never taking for granted the technologies that are being rapidly imposed upon human life. That questioning is made all the more urgent because of scale. AI systems are reaching tremendous size in terms of the compute power they require, and the data they consume. And their prevalence in society, both in the scale of their deployment and the level of responsibility they assume, dwarfs the presence of computing in the PC and Internet eras. At the same time, increasing scale means many aspects of the technology, especially in its deep learning form, escape the comprehension of even the most experienced practitioners. Ethical concerns range from the esoteric, such as who is the author of an AI-created work of art; to the very real and very disturbing matter of surveillance in the hands of military authorities who can use the tools with impunity to capture and kill their fellow citizens. Somewhere in the questioning is a sliver of hope that with the right guidance, AI can help solve some of the world's biggest problems. The same technology that may propel bias can reveal bias in hiring decisions. The same technology that is a power hog can potentially contribute answers to slow or even reverse global warming. The risks of AI at the present moment arguably outweigh the benefits, but the potential benefits are large and worth pursuing. As Margaret Mitchell, formerly co-lead of Ethical AI at Google, has elegantly encapsulated, the key question is, "what could AI do to bring about a better society?" Mitchell's question would be interesting on any given day, but it comes within a context that has added urgency to the discussion. Mitchell's words come from a letter she wrote and posted on Google Drive following the departure of her co-lead, Timnit Gebru, in December.


Ethics of AI: Benefits and risks of artificial intelligence

#artificialintelligence

In 1949, at the dawn of the computer age, the French philosopher Gabriel Marcel warned of the danger of naively applying technology to solve life's problems. Life, Marcel wrote in Being and Having, cannot be fixed the way you fix a flat tire. Any fix, any technique, is itself a product of that same problematic world, and is therefore problematic, and compromised. Marcel's admonition is often summarized in a single memorable phrase: "Life is not a problem to be solved, but a mystery to be lived." Despite that warning, seventy years later, artificial intelligence is the most powerful expression yet of humans' urge to solve or improve upon human life with computers. But what are these computer systems? As Marcel would have urged, one must ask where they come from, whether they embody the very problems they would purport to solve. Ethics in AI is essentially questioning, constantly investigating, and never taking for granted the technologies that are being rapidly imposed upon human life. That questioning is made all the more urgent because of scale. AI systems are reaching tremendous size in terms of the compute power they require, and the data they consume. And their prevalence in society, both in the scale of their deployment and the level of responsibility they assume, dwarfs the presence of computing in the PC and Internet eras. At the same time, increasing scale means many aspects of the technology, especially in its deep learning form, escape the comprehension of even the most experienced practitioners. Ethical concerns range from the esoteric, such as who is the author of an AI-created work of art; to the very real and very disturbing matter of surveillance in the hands of military authorities who can use the tools with impunity to capture and kill their fellow citizens. Somewhere in the questioning is a sliver of hope that with the right guidance, AI can help solve some of the world's biggest problems. The same technology that may propel bias can reveal bias in hiring decisions. The same technology that is a power hog can potentially contribute answers to slow or even reverse global warming. The risks of AI at the present moment arguably outweigh the benefits, but the potential benefits are large and worth pursuing. As Margaret Mitchell, formerly co-lead of Ethical AI at Google, has elegantly encapsulated, the key question is, "what could AI do to bring about a better society?" Mitchell's question would be interesting on any given day, but it comes within a context that has added urgency to the discussion. Mitchell's words come from a letter she wrote and posted on Google Drive following the departure of her co-lead, Timnit Gebru, in December.


AI empowers environmental regulators

#artificialintelligence

Like superheroes capable of seeing through obstacles, environmental regulators may soon wield the power of all-seeing eyes that can identify violators anywhere at any time, according to a new Stanford University-led study. The paper, published the week of April 19 in Proceedings of the National Academy of Sciences (PNAS), demonstrates how artificial intelligence combined with satellite imagery can provide a low-cost, scalable method for locating and monitoring otherwise hard-to-regulate industries. Go to the web site to view the video. Brick production, a major industry in South Asia, is a source of pollution that threatens health. Regulating brick kilns is difficult because there is no database of kiln locations.


Data Sciences – Top 100 DataSets – Data Visualization – Data Analytics – Big Data – Data Lakes – IT – Engineering – Cloud – Finance

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Sensitive health data files available are from the public data portal after a supplemental agreement is signed. HRS restricted data files require a detailed application process, and are available only through remote virtual desktop or encrypted physical media.