Category Archives: Deep Mind
”Like Free-Riding In Deep Snow – Inside The Mind Of A Red Bull Wingsuit Flyer – The Sportsman
For the average human, plummeting to earth involves tripping over in the Tesco carpark. For professional skydiver Marco Waltenspiel, 35, one of the four man team who jump with the Red Bull Sky Dive team, plummeting to earth means stepping off the edge of a cliff wearing a wingsuit and utilising the time spent between cliff-top and ground performing what Buzz Lightyear would describe as falling with style.
Wingsuit flying began at the end of the 90s and, like all adrenaline sports, has been pretty much perfected by energy drink specialists, Red Bull, over the decades. As one of their flyers, Marcos hugely popular videos are essentially due to the fact hes happy to do what most of us are not: risk everything for the perfect shot. And it works. A short Instagram video on The Sportsmans Instagram page of Marco dropping face first down a mountain pulled in such rapid numbers that we thought it only right we got in touch with the daredevil to find out what makes him tick.
Its something that takes a fair bit of practice, Marco laughs when we speak to him. Ive been skydiving since 2001, and base jumping since 2008. My father was jumping for as long as I can remember and he was a big influence on me for sure.
Is it an expensive sport to get into?
Yes its kind of an expensive sport when you start. I put all my money into this sport and I got started because for me its the nearest you can get to flying like a bird, so it was always my goal to jump in a wingsuit.
Credit: Red Bull Content Pool
Have you ever jumped off a mountain and immediately had second thoughts?
Ive never jumped and thought sh*t! The most important thing is to say No, Im not going to jump when you think the conditions arent right. Its only dangerous when you don't use your brain and do jumps in bad conditions.
Credit: Red Bull Content Pool
For us land-lovers, describe how it feels to fly through the sky
Its hard to describe, but it feels like free-riding in deep snow, its just that you always have the first line in the sky. The best place to fly is at home; I love the Alps and the landscape in middle Europe! My highest jump was in the Czech Republic from 6200m out of a sky van (purpose built skydiving plane).
Credit: Red Bull Content Pool
Finally, it looks so simple, but how much work goes into a jump before you even step off the edge?
I have a lot of equipment: 6 wing suits, 3 base rigs, 3 skydive rigs, 4 canopies so yes, theres a lot of work, especially as my team and I are professional skydivers and we jump for a living. We do all the organisation on our own, which means organising events, shows, projects and working with our sponsors. So theres quite a lot of work behind the scenes for every jump.
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''Like Free-Riding In Deep Snow - Inside The Mind Of A Red Bull Wingsuit Flyer - The Sportsman
Apple, Google have teamed up to build system-level COVID-19 contact tracing, interoperable APIs for iOS, Android – MobiHealthNews
Two of the biggest names in technology have formed an unprecedented partnership to introducehealth data-sharing and COVID-19 contact-tracing technologies to the lion's share of the smartphone market.
In dual news releases published this afternoon, Apple and Google announced plans to launch APIs that will enable interoperability between iOS and Android products by way of official apps from public health authorities. The companies said these apps will be available for consumers to download from the App Store and the Google Play Store starting in May.
In the longer term, the two companies have committed to building a Bluetooth-based contac-tracing functionality into their underlying operating systems. The companies said that this strategy will be designed as an opt-in functionality, but would open the door for more participants and deeper dataintegration with health apps and governments' public health initiatives.
"Privacy, transparency, and consent are of utmost importance in this effort, and we look forward to building this functionality in consultation with interested stakeholders," the companies wrote in their announcements. "We will openly publish information about our work for others to analyze."
To this end, the companies have releaseda slew of draft documentsregarding Bluetooth specifications, cryptography specifications and the framework API.
WHY IT MATTERS
Traditional contact-tracing is a labor-intensive process where public health groups interview confirmed cases to identify any other individuals they might have infected. As COVID-19 sweeps across the world, many health authorities are straining to keep up with new cases which opensthe door for an automated, device-driven approach to the practice.
In order for the U.S. to really contain this epidemic, we need to have a much more proactive approach that allows us to trace more widely contacts for confirmed cases," Dr. Louise Ivers, executive director of the Massachusetts General Hospital Center for Global Health,recently saidon the subject of Bluetooth-based contact tracing technologies.
Bluetooth in particular has been highlighted as a more privacy-friendly approach to contact tracing thanGPS data collection or other intrusive methods although it can be said that the data-privacy record of big tech is hardly flawless.
By baking these capabilities into the operating systems of the two biggest smartphone platforms, the companies have an opportunity to engage as many people as possible insuch a program. However, not everyone has responded to the initiative with enthusiasm former Federal Trade Commission Chief Technologist Ashkan Soltani, for instance,took to Twitterto discuss his concerns regarding digital contact tracing, which include the combination of potential false positives and system overreliance by policy makers.
Not to be lost in the announcement is the push toward greater health-data interoperability. Both the initial APIs and system-level features are being built to provide health organizations a consistent stream of public health data, no matter the platform. The COVID-19 pandemic may be the catalyst, but it will be worth keeping an eye on whether or not these interoperability efforts bleed into each company's other health technology projects down the line.
THE LARGER TREND
The Bluetooth-based contact-tracing functionality is very similar to an app-based project underway at MIT, which combines pings from smartphones with a cloud-based server. The system allows users to periodically check if their device has been within range of a positive case's smartphone, while it maintainsanonymity between all parties.
While Apple and Google's new collaboration is something of a first for the tech giants, it does follow individual efforts to address the spread and management of COVID-19.
Last week Google releasedan open online resourcethat aggregates anonymized location-tracking data from mobile devices to share large-scale mobility and behavior trends. Prior to this, its sister company Verilylaunched a websitethrough which California residents could be referred to a nearby mobile COVID-19 testing site (although this project's initial launch fell short of the scalepromisedby President Donald Trump). Other efforts from Alphabet companies include the promotion of World Health Organization educational initiatives across its platforms, the removal of misinformation from its websites and services, donations and grants through its philanthropic arm, and therelease of open-source researchfrom its artificial intelligence subsidiary DeepMind.
Apple, meanwhile, has repeatedly positioned its platforms as ainformational resources for worried users. In late March, the companyupdated its Siri voice assistantto provide symptom-based guidance and telehealth-app download links. A few days after, it worked with the CDC, FEMA and the White House Coronavirus Task Forceto release a COVID-19 website and corresponding appfor disease screening and information dissemination.
ON THE RECORD
"All of us at Apple and Google believe there has never been a more important moment to work together to solve one of the worlds most pressing problems," the companies wrote. "Through close cooperation and collaboration with developers, governments and public health providers, we hope to harness the power of technology to help countries around the world slow the spread of COVID-19 and accelerate the return of everyday life."
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Apple, Google have teamed up to build system-level COVID-19 contact tracing, interoperable APIs for iOS, Android - MobiHealthNews
AUDIOBOOKS: Words, books and politics | – theberkshireedge.com
Words and the love of books are at the heart of two audiobooks this week, along with a deep delve into White House politics. All titles are available for sale or download at local libraries and bookshops.
FearBob Woodward; read by Robert PetkoffSimon & Schuster Audioworks, 10 CDs, 12 hours and 20 minutes, $39.99/www.audible.com download, $27.99
Depending on their political leanings, listeners are going to love or hate this audiobook, but Woodward is a consummate reporter who details the chaos and shenanigans that are now a daily part of the White House. What one most takes away from Woodwards reporting is some insight into Trumps mind and his inability to act presidential, responsible or fair. Even if taken with a grain of salt, Woodwards reporting should, in fact, frighten even the most dedicated adherents of Trumpism. Petkoff reads with gravitas as needed, but underscores the entire production with energy. He comes across as approachable and conversational, which helps to somewhat tamp down the panic you may feel while listening to his words. This contains profanity.Grade: A
The Library BookSusan Orlean; read by the authorSimon & Schuster Audioworks, 10 CDs, 12 hours and nine minutes, $39.99/www.audible.com download, $29.95
The fire that burned the Los Angeles Public Library in 1986 is at the heart of this delightful audiobook that is part memoir and part tribute to libraries and books. Orlean delves into the life of the charming drifter suspected of arson, but comes to her own conclusions about the spark that burned approximately 400,000 books. The best bits, however, are her chats with librarians and other book lovers. Sometimes she is a bit out there the section in which she burns a book seemed a tad puerile but she generally captures us with fine writing and subtle wit. Sounding very Midwestern and very young, Orlean takes some getting used to as a narrator, but her timing is impeccable and she does grow on one.Grade: A-minus
Word by Word: The Secret Life of DictionariesKory Stamper; read by the authorRandom House Audio, nine hours and 48 minutes, $24.50, http://www.audible.comdownload
You do not have to be a word nerd to enjoy this audiobook, though it helps. Stamper writes and reads about the life of a lexicographer at the offices of theMerriam-Webster Company in Massachusetts. Though she has moved on to greener pastures, Stampers recollections of her 20 years writing dictionary entries and dealing with the public is funny, charming, occasionally profane, and educational. She does a commendable job for a first-time narrator, capturing her wonky humor as well as a sincere and deep love of words. Expect to learn the correct usage and weird, obscure meanings of words as well as getting into the minds of those who really, really care about such things.Grade: A-minus
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AUDIOBOOKS: Words, books and politics | - theberkshireedge.com
West Virginia peak now expected today, but Marsh says that doesn’t mean back to normal right away – WV MetroNews – West Virginia MetroNews
Today Easter a prominent model anticipates a coronavirus peak in West Virginia.
The states coronavirus response coordinator isnt suggesting a letdown in response, though.
Clay Marsh says social distancing guidelines are working. And when its time to transition from current practices, life wont go back to the way we knew it right away.
Many people are viewing this as When do we get back to the world that we left before the pandemic? Marsh said Saturday on MetroNews Talkline.
For me, Im already moving past that and understanding that were moving to a new place. The pressure made our whole system start to move with a great energy that is starting to shift us into a different place. And while I cant tell you what the different place is, I would say in my own mind and perspective that if we think were getting back there, its not true. I dont know exactly what were getting back to.
Marsh expanded on the next steps in dealing with coronavirus in a blog post published Saturday evening.
Across the country, leaders are talking about three steps to start emerging from the current stay-home guidelines that apply to all but essential workers.
The first is ramping uptestingto identify people who are infected.
The second is identifying people with whom they have interacted by going over their recent contacts on an enormous, unprecedented scale.
Third, restrictions would focus more narrowly on people who are infected and their contacts. That way, the rest would not have to remain in unceasing lockdown.
The American Enterprise Institute envisions this reopening happening state-by-state with schools and business resuming but with physical distancing measures and limitations on gatherings in place.
Individual states can move to Phase II when they are able to safely diagnose, treat, and isolate COVID-19 cases and their contacts, according to the public policy think tank based in Washington, D.C.
Deep cleanings on shared spaces would be more common, and the use of nonmedical fabric masks would remain initial practice.
Altogether, the next steps would involve a huge escalation of testing and questions about how willing Americans will be to submit to individual scrutiny.
I think Phase 2 is how do we get out successfully from where we are today to a more normal,' Marsh said.
Part of that relates in many experts minds on seeing a consistent reduction in the number of new cases. So when we know weve knocked the virus down through our physical distancing and home stay then well see that the reproduction rate go below one, and then we should see a consistent decline in new cases.
In his blog post, Marsh suggested that decline would appear consistently over two weeks before it could be considered a turning point.
When we see this consistent decline in new cases, we will likely askour citizens to wear masks or face coverings and stay physically distanced as much as possible, he wrote. We will still recommend limiting gatherings of people to 25 or less.
TheInstitute for Health Metrics and Evaluation at the University of Washington recently updated its modeling, showing a more promising outlook for West Virginia.
At one point, the model showed a peak of spread in early May and deaths of about 500 people in West Virginia.
But it takes into account new information and the social distancing measures West Virginia has put into place such as school building closures on March 14 and a stay-home order on March 25.
Those measures have decreased the predicted affects of coronavirus in the state.
The model now shows peak use of resources in West Virginia today and total deaths of 74.
And the model projects five deaths a day, today through the next few days before starting to go lower.
The model assumes social distancing will continue through May.
And it also provides a range of possibilities. Its anticipated range of deaths could actually be from just a few all the way up to a higher possibility of 352.
On Sunday morning, Dr. Christopher Murray, who oversees the model, warned that if states lift social distancing guidelines too soon then there could be tragic ramifications.
Some states, its possible in May but in other states its going to be very unlikely that that would not lead to an immediate resurgence, Murray said on Face the Nation.
Last week, health officer Terrance Reidy expressed concern that such models might encourage people to behave too casually.
To me, if the peak is delayed, thats wonderful. It means weve slowed the spread, said Reidy, the health officer for Morgan, Berkeley and Jefferson counties.
Its sort of like 20 inches of rain in one day or 20 inches of rain in two months are different things.
He described limited success in social distancing.
Not nearly successful enough, Reidy said.
Here, the stores are packed, even with executive orders people arent heeding that. The main way to stop the crowds is for people to stop the crowding.
With some people carrying the virus and not showing symptoms, Reidy said the best approach is to just stay away from each other
If youre within a crowd, you should look at every other person as if they have a concealed weapon or a bomb on them, he said.
Figures released Sunday morning by the state Department of Health and Human Resources showed 593 confirmed cases of coronavirus in West Virginia, although state health experts have acknowledged that there are probably many more cases that have not been confirmed.
West Virginia has recorded six deaths.
There have been 16,124 West Virginians tested for the virus.
State health leaders have pointed toward the rate of positive tests vs. overall testing still holding steady at 3.68 percent to encourage people to continue social distancing.
Please, please just stay the course, Gov. Jim Justice said during a Friday news briefing, encouraging state residents to continue social distancing efforts during the holiday weekend and beyond.
If In fact Easter is the peak, were on the downhill slide here.
Justice said of those who have heeded social distancing guidelines, Your good work has led to saving lots and lots and lots of lives
Marsh acknowledged that the overall number of new cases has continued to grow in West Virginia, but he said the continued emphasis on social distancing measures has helped. Without that, he said, the results could be far more severe.
When you look at this pandemic and look at the first phase, which is really this acute phase that weve been in, we can see countries where they didnt take these same sorts of precautions Italy is an example saw a 12 percent mortality rate, Marsh said.
So we know this has serious consequences. We also know that across the world the mortality rate is 5.5 percent. Here in the United States its now about 3.5 percent. So thats a lot of people dying from the covid pandemic.
Not much will get easier, Marsh said.
Although West Virginia might in the next few weeks get through the first difficult period of dealing with coronavirus, more adjustments are ahead.
Determining how far to go with precautions and gaining consensus among individuals will be big parts of the challenge.
So my belief is, as we look at repatriation and starting to open back up again, were going to have to do that like a dance back and forth and theres going to be things that open that do OK and there will be some things that open that dont do very well.
A lot of people say the second phase of this could be worse than the first phase. Everybodys rallied for this. But the second phase is going to be harder.
DeepMinds AI models transition of glass from a liquid to a solid – VentureBeat
In a paper published in the journal Nature Physics, DeepMind researchers describe an AI system that can predict the movement of glass molecules as they transition between liquid and solid states. The techniques and trained models, which have been made available in open source, could be used to predict other qualities of interest in glass, DeepMind says.
Beyond glass, the researchers assert the work yields insights into general substance and biological transitions, and that it could lead to advances in industries like manufacturing and medicine. Machine learning is well placed to investigate the nature of fundamental problems in a range of fields, a DeepMind spokesperson told VentureBeat. We will apply some of the learnings and techniques proven and developed through modeling glassy dynamics to other central questions in science, with the aim of revealing new things about the world around us.
Glass is produced by cooling a mixture of high-temperature melted sand and minerals. It acts like a solid once cooled past its crystallization point, resisting tension from pulling or stretching. But the molecules structurally resemble that of an amorphous liquid at the microscopic level.
Solving glass physical mysteries motivated an annual conference by the Simons Foundation, which last year hosted a group of 92 researchers from the U.S., Europe, Japan, Brazil, and India in New York. In the three years since the inaugural meeting, theyve managed breakthroughs like supercooled liquid simulation algorithms, but theyve yet to develop a complete description of the glass transition and predictive theory of glass dynamics.
Thats because there are countless unknowns about the nature of the glass formation process, like whether it corresponds to a structural phase transition (akin to water freezing) and why viscosity during cooling increases by a factor of a trillion. Its well-understood that modeling the glass transition is a worthwhile pursuit the physics behind it underlie behavior modeling, drug delivery methods, materials science, and food processing. But the complexities involved make it a hard nut to crack.
Fortunately, there exist structural markers that help identify and classify phase transitions of matter, and glasses are relatively easy to simulate and input into particle-based models. As it happens, glasses can be modeled as particles interacting via a short-range repulsive potential, and this potential is relational (because only pairs of particles interact) and local (because only nearby particles interact with each other).
The DeepMind team leveraged this to train a graph neural network a type of AI model that directly operates on a graph, a non-linear data structure consisting of nodes (vertices) and edges (lines or arcs that connect any two nodes) to predict glassy dynamics. They first created an input graph where the nodes and edges represented particles and interactions between particles, respectively, such that a particle was connected to its neighboring particles within a certain radius. Two encoder models then embedded the labels (i.e., translated them to mathematical objects the AI system could understand). Next, the edge embeddings were iteratively updated, at first based on their previous embeddings and the embeddings of the two nodes to which they were connected.
After all of the graphs edges were updated in parallel using the same model, another model refreshed the nodes based on the sum of their neighboring edge embeddings and their previous embeddings. This process repeated several times to allow local information to propagate through the graph, after which a decoder model extracted mobilities measures of how much a particle typically moves for each particle from the final embeddings of the corresponding node.
The team validated their model by constructing several data sets corresponding to mobilities predictions on different time horizons for different temperatures. After applying graph networks to the simulated 3D glasses, they found that the system strongly outperformed both existing physics-inspired baselines and state-of-the-art AI models.
They say that network was extremely good on short times and remained well matched up to the relaxation time of the glass (which would be up to thousands of years for actual glass), achieving a 96% correlation with the ground truth for short times and a 64% correlation for relaxation time of the glass. In the latter case, thats an improvement of 40% compared with the previous state of the art.
In a separate experiment, to better understand the graph model, the team explored which factors were important to its success. They measured the sensitivity of the prediction for the central particle when another particle was modified, enabling them to judge how large of an area the network used to extract its prediction. This provided an estimate of the distance over which particles influenced each other in the system.
They report theres compelling evidence that growing spatial correlations are present upon approaching the glass transition, and that the network learned to extract them. These findings are consistent with a physical picture where a correlation length grows upon approaching the glass transition, wrote DeepMind in a blog post. The definition and study of correlation lengths is a cornerstone of the study of phase transition in physics.
DeepMind claims the insights gleaned could be useful in predicting the other qualities of glass; as alluded to earlier, the glass transition phenomenon manifests in more than window (silica) glasses. The related jamming transition can be found in ice cream (acolloidal suspension), piles of sand (granular materials), and cell migration during embryonic development, as well as social behaviors such as traffic jams.
Glasses are archetypal of these kinds of complex systems, which operate under constraints where the position of elements inhibits the motion of others. Its believed that a better understanding of them will have implications across many research areas. For instance, imagine a new type of stable yet dissolvable glass structure that could be used for drug delivery and building renewable polymers.
Graph networks may not only help us make better predictions for a range of systems, wrote DeepMind, but indicate what physical correlates are important for modeling them that machine learning systems might be able to eventually assist researchers in deriving fundamental physical theories, ultimately helping to augment, rather than replace, human understanding.
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DeepMinds AI models transition of glass from a liquid to a solid - VentureBeat
AI and the coronavirus fight: How artificial intelligence is taking on COVID-19 – ZDNet
As the COVID-19 coronavirus outbreak continues to spread across the globe, companies and researchers are looking to use artificial intelligence as a way of addressing the challenges of the virus. Here are just some of the projects using AI to address the coronavirus outbreak.
Using AI to find drugs that target the virus
A number of research projects are using AI to identify drugs that were developed to fight other diseases but which could now be repurposed to take on coronavirus. By studying the molecular setup of existing drugs with AI, companies want to identify which ones might disrupt the way COVID-19 works.
BenevolentAI, a London-based drug-discovery company, began turning its attentions towards the coronavirus problem in late January. The company's AI-powered knowledge graph can digest large volumes of scientific literature and biomedical research to find links between the genetic and biological properties of diseases and the composition and action of drugs.
EE: How to implement AI and machine learning (ZDNet special report) | Download the report as a PDF (TechRepublic)
The company had previously been focused on chronic disease, rather than infections, but was able to retool the system to work on COVID-19 by feeding it the latest research on the virus. "Because of the amount of data that's being produced about COVID-19 and the capabilities we have in being able to machine-read large amounts of documents at scale, we were able to adapt [the knowledge graph] so to take into account the kinds of concepts that are more important in biology, as well as the latest information about COVID-19 itself," says Olly Oechsle, lead software engineer at BenevolentAI.
While a large body of biomedical research has built up around chronic diseases over decades, COVID-19 only has a few months' worth of studies attached to it. But researchers can use the information that they have to track down other viruses with similar elements, see how they function, and then work out which drugs could be used to inhibit the virus.
"The infection process of COVID-19 was identified relatively early on. It was found that the virus binds to a particular protein on the surface of cells called ACE2. And what we could with do with our knowledge graph is to look at the processes surrounding that ingestion of the virus and its replication, rather than anything specific in COVID-19 itself. That allows us to look back a lot more at the literature that concerns different coronaviruses, including SARS, etc. and all of the kinds of biology that goes on in that process of viruses being ingested in cells," Oechsle says.
The system suggested a number of compounds that could potentially have an effect on COVID-19 including, most promisingly, a drug called Baricitinib. The drug is already licensed to treat rheumatoid arthritis. As Baricitinib works to damp down the inflammatory processes that can cause the symptoms of rheumatoid arthritis, it can play a similar role in COVID-19, which can cause an acute inflammatory reaction that lands patients in ICU.
Shedding light on the structure of COVID-19
DeepMind, the AI arm of Google's parent company Alphabet, is using data on genomes to predict organisms' protein structure, potentially shedding light on which drugs could work against COVID-19.
DeepMind has released a deep-learning library calledAlphaFold, which uses neural networks to predict how the proteins that make up an organism curve or crinkle, based on their genome. Protein structures determine the shape of receptors in an organism's cells. Once you know what shape the receptor is, it becomes possible to work out which drugs could bind to them and disrupt vital processes within the cells: in the case of COVID-19, disrupting how it binds to human cells or slowing the rate it reproduces, for example.
Aftertraining up AlphaFold on large genomic datasets, which demonstrate the links between an organism's genome and how its proteins are shaped, DeepMind set AlphaFold to work on COVID-19's genome.
"We emphasise that these structure predictions have not been experimentally verified, but hope they may contribute to the scientific community's interrogation of how the virus functions, and serve as a hypothesis generation platform for future experimental work in developing therapeutics," DeepMind said. Or, to put it another way, DeepMind hasn't tested out AlphaFold's predictions outside of a computer, but it's putting the results out there in case researchers can use them to develop treatments for COVID-19.
Detecting the outbreak and spread of new diseases
Artificial-intelligence systems were thought to be among the first to detect that the coronavirus outbreak, back when it was still localised to the Chinese city of Wuhan, could become a full-on global pandemic.
It's thought that AI-driven HealthMap, which is affiliated with the Boston Children's Hospital,picked up the growing clusterof unexplained pneumonia cases shortly before human researchers, although it only ranked the outbreak's seriousness as 'medium'.
"We identified the earliest signs of the outbreak by mining in Chinese language and local news media -- WeChat, Weibo -- to highlight the fact that you could use these tools to basically uncover what's happening in a population," John Brownstein, professor of Harvard Medical School and chief innovation officer at Boston Children's Hospital, told the Stanford Institute for Human-Centered Artificial Intelligence's COVID-19 and AI virtual conference.
Human epidemiologists at ProMed, an infectious-disease-reporting group, published their own alert just half an hour after HealthMap, and Brownstein also acknowledged the importance of human virologists in studying the spread of the outbreak.
"What we quickly realised was that as much it's easy to scrape the web to create a really detailed line list of cases around the world, you need an army of people, it can't just be done through machine learning and webscraping," he said. HealthMap also drew on the expertise of researchers from universities across the world, using "official and unofficial sources" to feed into theline list.
The data generated by HealthMap has been made public, to be combed through by scientists and researchers looking for links between the disease and certain populations, as well as containment measures. The data has already been combined with data on human movements, gleaned from Baidu,to see how population mobility and control measuresaffected the spread of the virus in China.
HealthMap has continued to track the spread of coronavirus throughout the outbreak, visualising itsspread across the world by time and location.
Spotting signs of a COVID-19 infection in medical images
Canadian startup DarwinAI has developed a neural network that can screen X-rays for signs of COVID-19 infection. While using swabs from patients is the default for testing for coronavirus, analysing chest X-rays could offer an alternative to hospitals that don't have enough staff or testing kits to process all their patients quickly.
DarwinAI released COVID-Net as an open-source system, and "the response has just been overwhelming", says DarwinAI CEO Sheldon Fernandez. More datasets of X-rays were contributed to train the system, which has now learnt from over 17,000 images, while researchers from Indonesia, Turkey, India and other countries are all now working on COVID-19. "Once you put it out there, you have 100 eyes on it very quickly, and they'll very quickly give you some low-hanging fruit on ways to make it better," Fernandez said.
The company is now working on turning COVID-Net from a technical implementation to a system that can be used by healthcare workers. It's also now developing a neural network for risk-stratifying patients that have contracted COVID-19 as a way of separating those with the virus who might be better suited to recovering at home in self-isolation, and those who would be better coming into hospital.
Monitoring how the virus and lockdown is affecting mental health
Johannes Eichstaedt, assistant professor in Stanford University's department of psychology, has been examining Twitter posts to estimate how COVID-19, and the changes that it's brought to the way we live our lives, is affecting our mental health.
Using AI-driven text analysis, Eichstaedt queried over two million tweets hashtagged with COVID-related terms during February and March, and combined it with other datasets on relevant factors including the number of cases, deaths, demographics and more, to illuminate the virus' effects on mental health.
The analysis showed that much of the COVID-19-related chat in urban areas was centred on adapting to living with, and preventing the spread of, the infection. Rural areas discussed adapting far less, which the psychologist attributed to the relative prevalence of the disease in urban areas compared to rural, meaning those in the country have had less exposure to the disease and its consequences.
SEE:Coronavirus: Business and technology in a pandemic
There are also differences in how the young and old are discussing COVID-19. "In older counties across the US, there's talk about Trump and the economic impact, whereas in young counties, it's much more problem-focused coping; the one language cluster that stand out there is that in counties that are younger, people talk about washing their hands," Eichstaedt said.
"We really need to measure the wellbeing impact of COVID-19, and we very quickly need to think about scalable mental healthcare and now is the time to mobilise resources to make that happen," Eichstaedt told the Stanford virtual conference.
Forecasting how coronavirus cases and deaths will spread across cities and why
Google-owned machine-learning community Kaggle is setting a number of COVID-19-related challenges to its members, includingforecasting the number of cases and fatalities by cityas a way of identifying exactly why some places are hit worse than others.
"The goal here isn't to build another epidemiological model there are lots of good epidemiological models out there. Actually, the reason we have launched this challenge is to encourage our community to play with the data and try and pick apart the factors that are driving difference in transmission rates across cities," Kaggle's CEO Anthony Goldbloom told the Stanford conference.
Currently, the community is working on a dataset of infections in 163 countries from two months of this year to develop models and interrogate the data for factors that predict spread.
Most of the community's models have been producing feature-importance plots to show which elements may be contributing to the differences in cases and fatalities. So far, said Goldbloom, latitude and longitude are showing up as having a bearing on COVID-19 spread. The next generation of machine-learning-driven feature-importance plots will tease out the real reasons for geographical variances.
"It's not the country that is the reason that transmission rates are different in different countries; rather, it's the policies in that country, or it's the cultural norms around hugging and kissing, or it's the temperature. We expect that as people iterate on their models, they'll bring in more granular datasets and we'll start to see these variable-importance plots becoming much more interesting and starting to pick apart the most important factors driving differences in transmission rates across different cities. This is one to watch," Goldbloom added.
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AI and the coronavirus fight: How artificial intelligence is taking on COVID-19 - ZDNet
Step away from the news and breathe – Waukon Standard
by Jill Fleming, MS, RD/LD
The newest strain of the Coronavirus has created a huge wave of panic amongst our community. Everyone is worried about loved ones getting sick. It may seem like negative thoughts are running through your head on endless repeat over the past few weeks. You might feel like youre spiraling out of control, going crazy or about to burn out under the weight of all this anxiety. This is not good, as excess anxiety or worrying can weaken your immune system.
The good news is that there are steps you can take right now to interrupt all those anxious thoughts and give yourself a time out from your worrying. So, step away from the news and (in your mind) set down your big bag of worries. Pick one of the following ways to reconnect with your body and get out of your head for a few minutes today.
Get up and get moving. Exercise is a natural and effective anti-anxiety treatment because it releases endorphins which relieve tension and stress, boost energy, and enhance your sense of well-being. Even more importantly, by really focusing on how your body feels as you move, you can interrupt the constant flow of worries running through your head. Pay attention to the sensation of your feet hitting the ground as you walk, run, or dance, for example, or the rhythm of your breathing or the feeling of the sun or wind on your skin.
Get outside. Nature is the antidote to stress. Step outside and take a few deep breaths. The fresh air, sunshine, birds and breeze will help you reconnect with your spiritual side. Have a few silent minutes in nature in the morning and evening. Just listen to the sounds and feel the air fill your lungs and touch your skin.
Do an online yoga or tai chi session. By focusing your mind on your movements and breathing, doing yoga or tai chi keeps your attention on the present moment, helping to clear your mind and lead to a relaxed state.
Meditate. Meditation works by switching your focus from worrying about the future or dwelling on the past to whats happening right now. By being fully engaged in the present moment, you can interrupt the endless loop of negative thoughts and worries. And you dont need to sit cross-legged, light candles, or chant. Simply find a quiet, comfortable place and choose one of the many free meditation videos online that can guide you through the meditation process.
Practice progressive muscle relaxation. This can help you break the endless loop of worrying by focusing your mind on your body instead of your thoughts. By alternately tensing and then releasing different muscle groups in your body, you release muscle tension in your body. And as your body relaxes, your mind will follow.
Try deep breathing. When you worry, you become anxious and breathe faster, often leading to further anxiety. But by practicing deep breathing exercises, you can calm your mind and quiet negative thoughts.
Yes, you do need to be aware of how to best protect yourself and your loved ones. Keep washing your hands, practice social distancing, eat healthy foods daily and follow the general recommendations for dealing with the Coronavirus. In addition, plan to get out of your head at least once each day to limit your anxiety, stress and worry.
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Step away from the news and breathe - Waukon Standard
Companies are bracing for the toughest phase in business continuity: Karan Bajwa – Livemint
Karan Bajwa is settling into his new role as the managing director (MD) of Google Cloud in India after his appointment in March this year. An industry veteran, prior to this, Bajwa served as the MD of IBM, India and South Asia, before which he led Microsofts India operations. In a Mint interview, Bajwa shares his top priorities, growth of the cloud business, and the impact of covid-19, among others. Edited excerpts:
As you take over the new role, what are your top priorities?
As MD for Google Cloud in India, I am responsible for driving all revenue and go-to-market operations for our extensive solution portfolio that includes Google Cloud Platform and G Suite for Google Cloud in India. This includes managing our field sales, partner and customer engineering organisations in India, along with Google Clouds continued work with the local developer ecosystem and India-based Global System Integrators (GSIs). Given the challenges businesses are facing in India today with covid-19, modernising their technology infrastructure for business continuity is on the agenda for almost every enterprise CIO and CEO. My team and I are focused on helping Indian businesses of all sizes solve their most complex business with cloud technology so they can serve their staff and their own customers. This includes ensuring their teams can adapt to virtual work, that their business processes are scalable and resilient, and that the demands on their infrastructure are sustainable.
How has the impact of covid-19 been so far and what are you doing differently?
In addition to the serious implications on peoples health and healthcare services, the covid-19 pandemic is having a significant impact on businesses and the global economy. Companies have been hit hard and are bracing for what many are referring to as the toughest phase in business continuity. We are having daily conversations with businesses around how we can help them serve their staff and their customers in these challenging times. We have taken initiatives like making premium Google Meet features available to customers for free; helping governments build rapid response apps and virtual agents to ensure citizen preparedness; ensuring healthcare providers have the collaboration tools and infrastructure support that they need to provide enhanced healthcare services; and discussing how retailers can pursue omni-channel strategies and better predict product demand.
How has Google Clouds growth in India been and where does India stand?
We dont break down into regional numbers but as per Alphabets Q4 FY19 earnings call, Google Cloud has hit an annualised run rate over $10 billion, a 53% increase year-on-year. We are hiring aggressively in all major markets worldwide including India and are looking to triple the size of our customer-facing employees (sales, service, and support) globally over the next few years. We are also invested and committed to the Indian market. We launched our first GCP (Google Cloud Platform) region in Mumbai in 2017, and last month, we announced plans to launch a second GCP region in Delhi in 2021.
Which are the fastest growing verticals in terms of adoption of Google Cloud?
Both digital natives and incumbents across industries are choosing Google Cloud to run their critical workloads. Globally and in India, we are focused on six top industries: financial services, telecommunications, media & entertainment, retail, healthcare & life sciences, manufacturing & industrial, and public sector and are aligning our field sales organisation to them. This will allow us to have a deep understanding of the needs of each vertical, and partner with our customers to solve their most pressing business problems effectively.
How are you leveraging artificial intelligence (AI) and machine learning (ML) in Google Cloud?
AI is built into everything that Google does and it is a competitive differentiator for Google Cloud. One of the unique aspects of our Cloud AI group is that we are doing research and building products within the same organisation. This gives us an opportunity to tie our innovation and customer insights into the same feedback loop to build the best possible AI products. We also have the opportunity to work cross-functionally with research teams like Brain and DeepMind, as well as product teams across Google.
Many products within Google Cloud fit into established spaces that our customers already know. AI is still so nascent, and customers want to use it but need help understanding how. There are fewer than a million data scientists and just thousands of deep learning researchers in the world, but there are over 21 million developers. We want to empower that large developer base with products they can use to build AI into their technology stack, especially with an offering like AutoML.
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Companies are bracing for the toughest phase in business continuity: Karan Bajwa - Livemint
Applying Artificial Intelligence in the Fight Against The Coronavirus – HIT Consultant
Dr. Ulrik Kristensen, Senior Market Analyst at Signify Research
Drug discovery is a notoriously long, complex and expensive process requiring the concerted efforts of the worlds brightest minds. The complexity in understanding human physiology and molecular mechanisms is increasing with every new research paper published and for every new compound tested. As the world is facing a new challenge in trying to both adapt to and defend itself against the coronavirus, artificial intelligence is offering new hope that a cure might be developed faster than ever before.
In this article, we will present some of the technologies being developed and applied in todays drug discovery process, working side-by-side with scientists tracking new findings, and assisting in the creation of new compounds and potential vaccines. In addition, we will examine how the industry is applying AI in the fight against the coronavirus.
Start-ups focusing on the use of artificial intelligence in drug development and clinical trials have seen significant investment in recent years, and vendors focusing specifically on drug design and discovery received the majority of the total $5.2B funding observed between 2012 and 2019
Information EnginesInformation Engines are fundamental machines behind applications in both drug discovery and clinical trials, serving as the basic information aggregator and synthesizer layer, on which the other applications can draw their insights, conclusions and prescriptive functions. The information available to scientists today is increasing exponentially, so the purpose of information engines being developed today is to help scientists update and aggregate all this information and pull out the data most likely to be relevant for a specific study.
The types of information going into these engines vary broadly. An advanced information engine integrates information from multiple sources such as scientific research publications, medical records, doctors journals, biomedical information such as known drug targets, ligand information and disease-specific information, historical clinical trial data, patent information from molecules currently being investigated at global pharma companies, proprietary enterprise data from internal research studies at the individual pharma client, genomic sequencing data, radiology imaging data, cohort data and even other real-world evidence such as society and environmental data.
In a recentanalyst insight, we discussed how these information engines are being applied in clinical trials to enhance success rates and reduce associated trial costs. When it comes to the upstream processes relating to drug discovery, their purpose is to synthesize and analyze these vast amounts of information to help the scientist understand disease mechanisms and select the most promising targets, drug candidates or biomarkers; or as we will see in the next section, to assist the drug design application in creating the molecular designs or optimize a compound with desired properties. Information is typically presented via a knowledge graph that visualizes the relationships between diseases, genes, drugs and other data points, which the researcher then uses for target identification, biomarker discovery or other research areas.
Drug DesignAI-based drug design applications are involved directly with the molecular structure of the drugs. They draw data and insights from information engines to help generate novel drug candidates, to validate or optimize drug candidates, or to repurpose existing drugs for new therapeutic areas.
For target identification, machine learning is used to predict potential disease targets, and an AI triage then typically orders targets based on chemical opportunity, safety and druggability and presents them ranked with most promising targets. This information is then fed into the drug design application which optimizes the compounds with desired properties before they are selected for synthesis. Experimental data from the selected compounds can then be fed back into the model to generate additional data for optimization.
For drug repurposing, existing drugs approved for specific therapeutic areas are compared against possible similar pathways and targets in alternative diseases, which creates an opportunity for additional revenue from already developed pharmaceuticals. It also gives potential relief for rare disease areas where developing a new compound wouldnt be profitable. Additionally, keeping repurposing in mind during the development of a new drug as opposed to having a disease-specific mindset, may result in more profitable multi-purpose pharmaceuticals entering the market in the coming years.
Recent substantial investment in AI for drug development has meant the start-ups have had the manpower and resources to develop their technologies. Compared to AI in medical imaging the total investment has been more than four-fold, even though the number of funded start-ups is equivalent between the two industries. This makes the average deal size for AI in drug development 3.5 times bigger than in medical imaging. The funding has been spent on significantly expanding and building capacity, as the total number of employees across these AI start-ups is now close to 10,000 globally.
A strong focus for start-up vendors is to create tight partnerships with the pharma industry. For many still in the early product development stages, this gives them the ability to test and optimize their solutions and to create proof-of-concept as a basis for additional deals.
For the more established start-ups, partnerships with the pharmaceutical industry turn the initial investments into revenue in the form of subscription or consulting charges, and potential milestone payments for new drug candidates, preparing the company for further investments, IPO, acquisition or continued success as a separate company. Pharmaceutical companies with high numbers of publicly announced AI partnerships include AstraZeneca, GSK, Sanofi, Merck, Janssen, and Pfizer, but many more are actively pursuing such opportunities today.
Many AI start-ups are therefore in the phase where they have a solution ready and are either looking for further partnerships or would like to showcase their solution and capabilities. The COVID-19 pandemic has, therefore, come as an important test for many of these vendors, where they can demonstrate the value of their technologies and hopefully help the world get through this crisis faster.
Understanding the protein structures on the coronavirus capsule can form the basis of a drug or vaccine. Google Deepmind have been using their artificial intelligence engine to quickly predict the structure of six proteins linked to the coronavirus, and although they have not been experimentally verified, they may still contribute to the research ultimately leading to therapeutics.
Hong Kong-based Insilico Medicine took the next step in finding possible treatments, using their AI algorithms to design new molecules that could potentially limit the viruss ability to replicate. Using existing data on the similar virus which caused the SARS outbreak in 2003, they published structures of six new molecules that could potentially treat COVID-19. Also, Germany-based Innoplexus has used its drug discovery information engine to design a novel molecule candidate with a high binding affinity to a target protein on the coronavirus while maintaining drug-likeness criteria such as bioavailability, absorption, toxicity, etc. Other AI players following similar strategies to identify new targets and molecules include Pepticom, Micar Innovation, Acellera, MAbSilico, InveniAI and Iktos, and further initiatives are announced daily.
It is important to remember that even if AI helps researchers identify targets and design new molecules faster, clinical testing and regulatory approval will still take about a year. So, while waiting for a vaccine or a new drug to be developed, other teams are looking at existing drugs on the market that could be repurposed to treat COVID-19. BenevolentAI used their machine learning-based information engine to search for already approved drugs that could block the infection process. After analyzing chemical properties, medical data and scientific literature they identified Baricitinib, typically used to treat moderate and severe rheumatoid arthritis, as a potential candidate to treat COVID-19. The theory is that the drug would prevent the virus from entering the cells by inhibiting endocytosis, and thereby in combination with antiviral drugs reduce viral infectivity and replication and prevent the inflammatory response which causes some of the COVID-19 symptoms.
But although a lot is happening in the industry right now and there are many suggestions as to what might work as a therapy for COVID-19, both from existing drugs already on the market and from new molecules being designed by the AI drug developers, the scientific and medical community, as well as regulators, will not neglect the scientific method. Suggestions and new ideas are essential for progress, but so is rigor in testing and validation of hypotheses. A systematic approach, fuelled by accelerated findings using AI and bright minds in collaboration, will lead to a better outcome.
About Dr. Ulrik Kristensen
Dr. Ulrik Kristensen is a Senior Market Analyst atSignify Research, an independent supplier of market intelligence and consultancy to the global healthcare technology industry. Ulrik is part of the Healthcare IT team and leads the research covering Drug Development, Oncology, and Genomics. Ulrik holds an MSc in Molecular Biology from Aarhus University and a Ph.D. from the University of Strasbourg.
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Applying Artificial Intelligence in the Fight Against The Coronavirus - HIT Consultant
The Coronavirus and the Conservative Mind – The New York Times
So what has happened? Well, several different things. From the Wuhan outbreak through somewhere in mid-February, the responses to the coronavirus did seem to correspond very roughly to theories of conservative and liberal psychology. Along with infectious-disease specialists, the people who seemed most alarmed by the virus included the inhabitants of Weird Right-Wing Twitter (a collection of mordant, mostly anonymous accounts interested in civilizational decline), various Silicon Valley eccentrics, plus original-MAGA figures like Mike Cernovich and Steve Bannon. (The radio host Michael Savage, often considered the most extreme of the rights talkers, was also an early alarmist.)
Meanwhile, liberal officialdom and its media appendages were more likely to play down the threat, out of fear of giving aid and comfort to sinophobia or populism. This period was the high-water mark of its just the flu reassurances in liberal outlets, of pious critiques of Donald Trumps travel restrictions, of deceptive public-health propaganda about how masks dont work, of lectures from the head of the World Health Organization about how the greatest enemy we face is not the virus itself; its the stigma that turns us against each other.
But then, somewhere in February, the dynamic shifted. As the disease spread and the debate went mainstream, liberal opinion mostly abandoned its anti-quarantine posture and swung toward a reasonable panic, while conservative opinion divided, with a large portion of the right following the lead of Trump himself, who spent crucial weeks trying to wish the crisis away. Where figures like Bannon and Cernovich manifested a conservatism attuned to external perils, figures like Rush Limbaugh and Sean Hannity manifested a conservatism of tribal denial, owning the libs by minimizing the coronavirus threat.
Now we are in a third phase, where Trump is (more or less, depending on the day) on board with a robust response and most conservatives have joined most liberals in alarm. Polls show a minimal partisan divide in support for social distancing and lockdowns, and some of that minimal divide is explained by the fact that rural areas are thus far less likely to face outbreaks. (You dont need a complicated theory of the ideological mind to explain why New Yorkers are more freaked out than Nebraskans.)
But even now, there remains a current of conservative opinion that wants to believe that all of this is overblown, that the experts are wrong about the likely death toll, that Trump should reopen everything as soon as possible, that the liberal media just wants to crash the American economy to take his presidency down.
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The Coronavirus and the Conservative Mind - The New York Times