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Facebook develops A.I. to predict likelihood of worsening Covid symptoms

#artificialintelligence

Artificial intelligence researchers at Facebook claim they have developed software that can predict the likelihood of a Covid patient deteriorating or needing oxygen based on their chest X-rays. Facebook, which worked with academics at NYU Langone Health's predictive analytics unit and department of radiology on the research, says that the software could help doctors avoid sending at-risk patients home too early, while also helping hospitals plan for oxygen demand. The 10 researchers involved in the study -- five from Facebook AI Research and five from the NYU School of Medicine -- said they have developed three machine-learning "models" in total, that are all slightly different. One tries to predict patient deterioration based on a single chest X-ray, another does the same with a sequence of X-rays, and a third uses a single X-ray to predict how much supplemental oxygen (if any) a patient might need. "Our model using sequential chest X-rays can predict up to four days (96 hours) in advance if a patient may need more intensive care solutions, generally outperforming predictions by human experts," the authors said in a blog post published Friday.


Global Military Artificial Intelligence (AI) And Cybernetics Market 2020-2026

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The Global Military Artificial Intelligence (AI) And Cybernetics Market report provides information by Key Players, Geography, End users, Applications, Competitor analysis, Sales, Revenue, Price, Gross Margin, Market Share, Import-Export, Trends and Forecast. Initially, the report provides a basic overview of the industry including definitions, classifications, applications and industry chain structure. The Military Artificial Intelligence (AI) And Cybernetics market analysis is provided for the international markets including development trends, competitive landscape analysis, and key regions development status. Effect of COVID-19: Military Artificial Intelligence (AI) And Cybernetics Market report investigate the effect of Coronavirus (COVID-19) on the Military Artificial Intelligence (AI) And Cybernetics industry. Since December 2020, the COVID-19 infection spread to practically 180 nations around the world with the World Health Organization pronouncing it a general wellbeing crisis.


Trustworthy AI data governance around Covid-19 could help unlock innovation

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A major CDEI poll has found that the public believe digital technology has a role to play in tackling the pandemic, but that its potential is not yet being fully realised. Public support for greater use of digital technology depends on trust in how it is governed. According to the poll, the single biggest predictor for supporting greater use of digital technology was an individual believing that'the right rules and regulations are in place'. This was deemed more important than demographic factors such as age. Trend analysis of the use of AI and data-driven technologies in the same period has revealed that conventional data analysis has been more widely used in the Covid-19 response than AI.


Column: Have half of L.A. County residents had COVID-19? It depends whose estimate you trust

Los Angeles Times

I've grown accustomed to conflicting views when it comes to the pandemic. We can gather in the library, but our kids can't go to school. I can finally get my hair done, but a facial is not allowed. You shouldn't wear a mask, you have to wear a mask, you really should be wearing two masks. This virus is so new that all of us -- from CDC scientists to supermarket cashiers -- are still trying to navigate a steep learning curve. And I like to think that nothing surprises me anymore.


Apps, drone deliveries and AI: How technology stepped up its game to fight COVID-19

ZDNet

For the past few months, an independent board of technology experts has been closely tracking the new ways that AI and data have been used to counter and mitigate the effects of the COVID-19 pandemic in the UK; and now, they are lifting the veil on the good, the bad and the ugly of the past year in digital tech. The Center for Data Ethics and Innovation (CDEI) has released a new report diving deep into the 118 individual use-cases for AI and data-driven technologies that have been added to the organization's COVID-19 repository since last November. Spanning vastly different sectors and locations, the examples collated in the document provide a unique vision of the ways that technology can help in a time of crisis. From piloting drones to delivering medical supplies, to monitoring the behavior of residents in public transport during the easing of lockdown restrictions: if there is one observation that all experts will agree on, it is certainly that technology has been a central pillar in the support of the response to the pandemic. "While public attention largely centred on high-profile applications aimed at either suppressing the virus or coping with its effects, our research highlights the breadth of applications beyond these two use-cases," says the report.


The Martechno Beat: Decoding Martech!: Delivering delightful, personalized shopping experiences like India's leading fashion e-commerce brand, Myntra on Apple Podcasts

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The last few years saw customers shift their preference from buying clothes at retail outlets to conveniently buying online on their gadgets. This change in customer behavior was further accelerated by COVID-19 as customers had to depend on buying online for most of their shopping needs. Despite COVID-19 beginning to ease off slowly, customers believe that shopping online is a lot more convenient than having to go from store to store to get what they're looking for. E-commerce brands are thus doing their best to keep their hard-earned customers engaged on their platform by delivering personalized user experiences. We caught up with Mohit Panjwani, Associate Director- Revenue Growth at Myntra to understand how e-commerce fashion brands like Myntra are leveraging the power of customer data and marketing analytics to craft memorable and personalized shopping experiences.


Machine learning for COVID-19--asking the right questions

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Douglas PS, De Bruyne B, Pontone G, et al. 1-year outcomes of FFRCT-guided care in patients with suspected coronary disease: the PLATFORM study. Douglas PS, De Bruyne B, Pontone G, et al. 1-year outcomes of FFRCT-guided care in patients with suspected coronary disease: the PLATFORM study. COVID-19 Resource Centre Access the latest 2019 novel coronavirus disease (COVID-19) content from across The Lancet journals as it is published.


AI System Can Sniff Out Disease as Well as Dogs Do

#artificialintelligence

Most people consider smell their least important sense, surveys suggest. Dogs, however, feel their way through the world with their noses. Humans already employ the animals' olfactory acuity for contraband and explosives detection. More recently it has also proved uncannily good at sensing cancers, diabetes--and even COVID-19. Exactly how dogs detect diseases is a mystery, but that has not stopped researchers from mimicking this prowess with an artificial-intelligence-based noninvasive diagnostic tool.


McKinsey: Winning Companies Are Increasing Their Investment In AI During Covid-19. What Do They Know That You Don't?

#artificialintelligence

As a board member, I've enjoyed learning from those companies that perform at the highest levels in the area of AI. When I think about how to guide my company's strategy, I look at what the winners in various industries are doing to see what I can learn from them. McKinsey presented us with answers to what the highest performing companies were doing in The state of AI in 2020. I will summarize some of the essential findings and conclusions from their work. Companies typically adopt AI to reduce costs or increase revenues.


Potential neutralizing antibodies discovered for novel corona virus using machine learning

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The fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody. The recent outbreak of COVID-19 infected and killed thousands of people in the world. Rapid methods in finding peptides or antibody sequences that can inhibit the viral epitopes of SARS-CoV-2 will save the life of thousands. To predict neutralizing antibodies for SARS-CoV-2 in a high-throughput manner, in this paper, we use different machine learning (ML) model to predict the possible inhibitory synthetic antibodies for SARS-CoV-2. We collected 1933 virus-antibody sequences and their clinical patient neutralization response and trained an ML model to predict the antibody response. Using graph featurization with variety of ML methods, like XGBoost, Random Forest, Multilayered Perceptron, Support Vector Machine and Logistic Regression, we screened thousands of hypothetical antibody sequences and found nine stable antibodies that potentially inhibit SARS-CoV-2. We combined bioinformatics, structural biology, and Molecular Dynamics (MD) simulations to verify the stability of the candidate antibodies that can inhibit SARS-CoV-2.