What if we find we rather like this working from home thing? – ComputerWeekly.com

As current events are showing, a lot more of us can work much more of the time from home than we actually do. We are even getting the occasional head-scratch as people realise how much more they are able to get done without having to go to internal meetings. Plus the inevitable bean-counters are wondering whether they need to be paying for quite this many acres of cubicle space. All of which brings to the surface of the mind the thought: well, why didnt we do this sooner?

The answer, according to economists, is the coordination problem. The insight hey, much of economics is really just giving fancy names to what we notice people do is that how you or I do things depends to a large extent on how everyone else does them. There is no point in my inscribing this on vellum if the editor wants an email if the world works on left-thread screws, then little point in setting up as a right-thread hole maker.

The classic example here is which side of the road to drive on. There is no particular reason for left or right and one-third of the world does it the other way to two-thirds. But within any area, it is important that everyone adopts the same rule. It is indeed a joke that we are going to change, with the lorries starting a week before the cars.

Its even true that, as time passes, places have changed over. Sweden did in 1969 international travel between the Nordic frozen wastelands was growing sufficiently to make the Swedes habit of driving on the left inconvenient. And that they generally used left-hand drive cars to do so wasnt a good idea either, so they changed (on 3 September, for completists).

Patterns of work, both practices and hours, are an area where the eggheads have noted that coordination problems matter. Factory working does depend on everyone being there at the same time, so the regimentation of working time was imposed as the industrial society grew in importance. It is very much less true of office work that we are dependent on the output of the person in the next cubicle for our own ability to start our task. Nothing here is entirely so, of course this is tendencies we are talking about, not absolutes.

As our society has become very much more services-oriented thats now some 80% of GDP, while manufacturing is a rump of perhaps 10% of it the idea of nine to five should have become very much less important. True, there are continuing experiments with flexi-working, while more of us are shuffling into the commuter train only three or four days a week. We have also had that burst of technologies that make the process easier. Broadband, the PC itself, cheap telecoms they are the basics. There are layers of tech on top of that, Skype, Zoom, Slack, and so on, that aid.

Yet a dispassionate observer might say that given the capabilities here, we are using them much less than we could be. Much more of the economy could be done at more of a distance with less travelling and personal presence, than is. The reason why is that coordination problem. Partly we are simply set in our ways, this is how we do things, partly this is how everyone else does them and weve got to fit in, got to coordinate with them.

The proof that more use could be made of home, or distance, working is all around us these very weeks. The economy is smaller than it was before lockdown started, certainly it is. But its not as much smaller as it would be if there hadnt been some substitution of remote working for that in the collective location.

There has been some research on how far we are from what might be optimal not-office working. One piece claims that 37% of all jobs can plausibly be done at home. It is actually much more than that, because that doesnt include any of the work done in the home, or about the home, but which is unpaid. But still, we are obviously a long way from 37% of all jobs, those that can be done at home, being done at home.

So, why is this so? One answer is that what can be done at home is not necessarily optimally done there and this is, of course, true. It is true in part at least. But also it is true that some portion of this is not done domestically simply because we have not done it that way so far. Our coordination solution doesnt break that way. And, as above, we a're entirely certain that our technological ability to be doing this out of the office has increased in recent years, more than the practice itself has spread.

This brings us to what coronavirus might do for us. By smashing the previous coordination solution, we enable the growth of a new one. One perhaps more in tune with our technological capabilities and without having the problem of coordinating everyone over from the old to the new. We dont, as the Swedes did, have to plan every road and route, rather the absence of the old solution allows the growth of a new one informed by what we have been doing this past few weeks.

This has, after all, happened before. True slavery died in England with the Norman Conquest of the Saxons. Their feudal system of villeinage met its final end with the Black Death, after which the shortage of labour led to the demand for wages and the freedom of contract. It is important to note that the new system is not necessarily an improvement. Exactly the same stimuli, the passing of one-third of the working population, led in Central and Eastern Europe for reasons still argued about - to the imposition of the much stricter system of serfdom rather than money wage labour.

The coordination of the actions of millions of people is difficult. When a stasis has been achieved, it is difficult to change and, as historical observation tells us, a crisis that disrupts that previous system can allow the growth of a new one more in tune with technological and societal capabilities.

I would expect there to be, in the post-Covid-19 economy, much more home working, perhaps part time and part in the office, than we had at the beginning of this year. Not because our technology has suddenly leapt forward, but because we have now had a taste of how the new way might work and, who knows, we might even like it.

As to who will really benefit from this, my bet would be on the chimney pot pub. We humans are, after all, primarily social beings and once we are not getting our gossip at the water cooler, something will have to take its place. Why not that ancient grand hub of a British community, the pub on the corner?

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What if we find we rather like this working from home thing? - ComputerWeekly.com

COVID-19 and Welfare Queens – Boston Review

Linda Taylor in 1944. Image: Washington State Archive, Puget Sound.

Conservatives have long been sounding the alarm about undeserving people receiving public assistance. These fears have deep ties to racism and the policing of black womens bodies.

The Queen: The Forgotten Life Behind an American Myth

Josh Levin

In the original Republican version of the CARES Act, the $2 trillion dollar stimulus package that will give most Americans $1,200, poor families were supposed to get just half the financial assistance that middle-class families received. The act that passed did not contain this provision, but the fact that it was proposed at all is telling. Many Republicans believe that low-income individuals are undeserving of aidand so resistant to work that financial assistance in the midst of a pandemic might incentivize them to stay unemployed. And, as legal scholar Mehrsa Baradaran has pointed out, the final version of the act still deems many people undeserving: those who work in the adult entertainment industry, for instance, are ineligible for stimulus money, and individuals with felony convictions cannot receive small business loans. None of this is terribly surprising. Indeed, Donald Trump has been trying to gut social programs for years. Just months ago, he proposed a budget that included staggering cuts to food stamps, Medicaid, and housing assistance. A federal judge recently put a stop to a Trump administration plan to kick 700,000 Americans off of food stamps in the midst of the pandemic.

Many Republicans believe that low-income individuals are undeserving of aidand so resistant to work that financial assistance in the midst of a pandemic might incentivize them to stay unemployed.

Motivating this antipathy toward welfare is Trumps (and conservatives) belief in a number of stereotypes about those who receive public assistance. Trump has suggested that welfare discourage[s] able-bodied adults from working, invoking the longstanding myth that people are impoverished because of lack of motivation and merit, and that they rely on public assistance out of laziness. This myth is rich with racial antipathy, drawing on stereotypes linking black people and laziness that date back to slavery, and which were later also connected with poor immigrants. In each generation, these stereotypes have taken on an incarnation suiting the Zeitgeist. In the 1930s, the Chicago Tribune decried unscrupulous parasites profiting from Depression relief, and likely had in mind both black Americans and southern European immigrants, whose status as white was not yet secure. In 1976 it was articulated memorably by Ronald Reagan, who claimed welfare fraud was so rampant that one woman in Chicago had fifteen telephone numbers, thirty addresses, and eighty names, which she used to bilk the system for $150,000 a year. The media at the time dubbed this womana black woman named Linda Taylorthe welfare queen, and the anecdote subsequently became a crowd favorite for Reagan, his go-to justification for his efforts to decimate public assistance.

The process by which public sentiment turned against welfare correlates with how the public imagined the stereotypical welfare recipient. When the program was created in the 1930s, amidst economic catastrophe, it was praised. Historian Premilla Nadasen has noted that it remained broadly popular until the 1960s, when a greater proportion of black women started receiving benefits. By the early 1970s, as journalist Josh Levin has written, news coverage and government reports framed public aid as an explicitly racial issue. By the mid-70s, Levin continues, almost 85percent of Americans believed that too many people on welfare cheat. This summation is not only racist and inaccurate, but misunderstands the structural forces that result in the exclusion of so many people from the labor market, effectively forcing them to rely on public assistance.

Scholars have shown that the welfare queen label is overwhelming used to indicate black women, and that the use of the term leads members of the public to support cuts to welfare. Yet contrary to the pernicious welfare queen stereotype, black people have never been a majority of welfare recipients, and welfare fraud is exceedingly uncommon. In fact, the welfare system has always been more likely to illegally deny benefits to those who fit the criteria than benefits-seekers are to commit fraud.

Taylor was first arrested not for welfare fraud, but because authorities suspected her of being sexually promiscuous.

Given how pervasive the trope of the welfare queen remains, its worth examining its origin in the public shaming of Linda Taylor. Levins The Queen: The Forgotten Life Behind an American Myth, is the first full-scale effort to present Taylors biography. Levin found that Taylor was indeed a welfare cheatbut also a serial scam artist, a kidnapper, and perhaps even a murderer. She really did drive a Cadillac, use multiple identities, and drape herself in fur coats. But, as all this reveals, she was also about as far from a typical welfare recipient as it is possible to be, a fact that Reagan elided and that Levin takes pains to emphasize.

Levin seeks to contextualize Taylors life, in an attempt to make some sense of her often bizarre, sometimes violent, constantly mendacious behavior. The daughter of a white mother and black father, she was born in 1926 in Golddust, Tennessee, where her parentage constituted a crime. Christened Martha Louise White, her impoverished white family excluded her because she could not pass for white, and she was expelled from her first school at the age of six. By fourteen, shed given birth to her first child. While still a teenager, she fled the Jim Crow South and followed the wartime labor boom to the West Coast. For the rest of her life, she moved continuously, changed her name constantly, and committed a truly astounding number of crimesfrom stealing babies from hospitals to possibly hiring a hitman to kill one of her husbands.

In the fall of 1974, the Chicago Tribune ran a story about welfare fraud, citing Taylor as an extreme example. Thousands of newspapers across the country reprinted versions of this story, many focusing on Taylor herself, with some branding her a welfare queen. She was indicted for welfare fraud, though for just $7,608 worth of payments; Cook County spent far more securing a conviction than shed allegedly stolen. Her case led Illinois to aggressively pursue, prosecute, and punish welfare cheats, and, in 1976, it led Reagana longtime foe of welfareto tell a stunned crowd about this woman from Chicago.

Levins book, widely praised, is impressive in its dedication to nuance and proper context. That said, The Queen misses a critical contributor to Taylors decades of social exclusion: she was a victim of something called the American Plan. Understanding the American Plan and its legacy not only adds critical context to Taylors life, but also helps explains the lives of so many other midcentury women, particularly women of color, who were excluded from the labor market and forced to rely on public assistance and underground economies.

As Levin recounts, Taylor was first arrested not for welfare fraud, but because authorities suspected her of being sexually promiscuous. In January 1943, when she was sixteen or seventeen years old, Taylor was arrested for disorderly conduct in Seattle; in October 1944, she was arrested a few miles west of Seattle for vagrancy; and the next month, the Seattle police again booked her for disorderly conduct. Levin notes that the formal charges in all three arrests were euphemisms, part of an expansive statute used to detain those suspected of lewd or undesirable behavior, passed by authorities seeking to control VD [venereal disease] by controlling women. In the weeks following her first 1943 arrest, Taylor was denied bail and forced to report to the health department for a VD test. Taylor was forced to undergo more VD tests after her other arrests. In April 1946, she was again arrested on vague morals charges, largely because the police suspected she might be infected with a venereal disease, this time in Oakland, California.

While Levin correctly describes how this carceral regime was widespread in and around Seattle and Oakland during the mid-1940s, he does not connect it to a nationwide system of social control. As I wrote in my book, from the 1910s through the 1950s (even into the 1970s in some areas), government officials across the United States detained tens of thousands of women (and virtually no men) on suspicion of promiscuity. These women were forcibly examined for VD, and then incarcerated if found to be infected. They were locked in what some called concentration campsfilthy, claustrophobic penal institutions where they were treated for VD with poisonous mercury- and arsenic-based drugs. This set of loosely connected local, state, and federal statutes, which reached all the way to the U.S. territories of Hawaii, Puerto Rico, the Philippines, and the Dominican Republic, were referred to by officials at the time as the American Plan. It was already in place decades before Taylor was first arrested, and it continued for decades after.

The welfare queen label is overwhelming used to indicate black women, and use of the term leads the public to support cuts to welfare.

Women detained under the American Planand women arrested for morals offenses more broadly, including disorderly conduct, vagrancy, waywardness, and prostitutionoften had remarkable difficulty getting a job. Community members and potential employers well understood the meaning of their arrests and shunned them. This exclusion from the labor market often pushed women into more dangerous jobs, such as selling sex or defying prohibition and anti-gambling laws. Perversely, authorities often required women to hold down jobs as a condition of their release, so many women who could not do so were forced to flee or hide from parole officers, making it even more difficult to find legal work. Other penal institutions paroled women (especially black women) into domestic positions, where wealthy white women would police their conduct and behavior, a situation so stifling that many fled and sought livelihoods in underground economies instead.

Levin does point out that, in spite of the thriving war industry job market in Oakland, Taylor didnt derive much benefit from the Bay Areas wartime economic boom. But he attributes this to her lack of formal schooling and to anti-black racism. Both of these factors may have prevented Taylor from securing certain jobs, but its worth noting that a dearth of academic qualifications hardly mattered for the vast majority of war industry positions, and, as Levin notes, no official records issued in California labeled her a Negro, instead identifying Taylor as both white and Mexican. But exclusion from the labor market was a quintessential result of detention under the American Plan.

Understanding this gendered system of policing, then, is crucial for making sense of how Taylor came to be the stereotypical welfare queen. While her choices were uniquely her own, the circumstances that pushed Taylor into the illicit economy were sadly far from unique. They are the same reasons that hundreds of thousands of Americans cannot secure stable employmentand have nothing to do with motivation or ability.

While the laws that led to Taylors early incarcerations are largely gone, the U.S. labor market unfortunately continues to sadistically penalize people for their incarceration historya practice that disproportionately harms people of color. More than a dozen states still allow employers to consider job applicants criminal history. This is to say nothing of other factors that might exacerbate the struggle to find employment, such as immigration status and the decimation of unions. And this will all, of course, be worsened by the COVID-19 pandemic, which will put many out-of-work low-wage workers in competition both with each other and with more conventionally qualified candidates. A history of incarceration, a challenge in the best of times, may now be in effect an immediate disqualification for finding employment.

A full accounting of the story of Linda Taylorand the welfare queen mythmust make clear that so many individuals are forced to rely on public assistance because of inequities baked into U.S. policing and capitalism. Even a true, one-in-a-million scam artist like Taylor was, to a large extent, forced into such a life by forces beyond her control.

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COVID-19 and Welfare Queens - Boston Review

Google Engineers ‘Mutate’ AI to Make It Evolve Systems Faster Than We Can Code Them – ScienceAlert

Much of the work undertaken by artificial intelligence involves a training process known as machine learning, where AI gets better at a task such as recognising a cat or mapping a route the more it does it. Now that same technique is being use to create new AI systems, without any human intervention.

For years, engineers at Google have been working on a freakishly smart machine learning system known as theAutoML system(or automatic machine learning system), which is already capable of creating AI that outperforms anything we've made.

Now, researchers have tweaked it to incorporate concepts of Darwinian evolution and shown it can build AI programs that continue to improve upon themselves faster than they would if humans were doing the coding.

The new system is called AutoML-Zero, and although it may sound a little alarming, it could lead to the rapid development of smarter systems - for example, neural networked designed to more accurately mimic the human brain with multiple layers and weightings, something human coders have struggled with.

"It is possible today to automatically discover complete machine learning algorithms just using basic mathematical operations as building blocks," write the researchers in their pre-print paper. "We demonstrate this by introducing a novel framework that significantly reduces human bias through a generic search space."

The original AutoML system is intended to make it easier for apps to leverage machine learning, and already includes plenty of automated features itself, but AutoML-Zero takes the required amount of human input way down.

Using a simple three-step process - setup, predict and learn - it can be thought of as machine learning from scratch.

The system starts off with a selection of 100 algorithms made by randomly combining simple mathematical operations. A sophisticated trial-and-error process then identifies the best performers, which are retained - with some tweaks - for another round of trials. In other words, the neural network is mutating as it goes.

When new code is produced, it's tested on AI tasks - like spotting the difference between a picture of a truck and a picture of a dog - and the best-performing algorithms are then kept for future iteration. Like survival of the fittest.

And it's fast too: the researchers reckon up to 10,000 possible algorithms can be searched through per second per processor (the more computer processors available for the task, the quicker it can work).

Eventually, this should see artificial intelligence systems become more widely used, and easier to access for programmers with no AI expertise. It might even help us eradicate human bias from AI, because humans are barely involved.

Work to improve AutoML-Zero continues, with the hope that it'll eventually be able to spit out algorithms that mere human programmers would never have thought of. Right now it's only capable of producing simple AI systems, but the researchers think the complexity can be scaled up rather rapidly.

"While most people were taking baby steps, [the researchers] took a giant leap into the unknown," computer scientist Risto Miikkulainen from the University of Texas, Austin, who was not involved in the work, told Edd Gent at Science. "This is one of those papers that could launch a lot of future research."

The research paper has yet to be published in a peer-reviewed journal, but can be viewed online at arXiv.org.

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Google Engineers 'Mutate' AI to Make It Evolve Systems Faster Than We Can Code Them - ScienceAlert

Artificial Intelligence That Can Evolve on Its Own Is Being Tested by Google Scientists – Newsweek

Computer scientists working for a high-tech division of Google are testing how machine learning algorithms can be created from scratch, then evolve naturally, based on simple math.

Experts behind Google's AutoML suite of artificial intelligence tools have now showcased fresh research which suggests the existing software could potentially be updated to "automatically discover" completely unknown algorithms while also reducing human bias during the data input process.

Read more

According to ScienceMag, the software, known as AutoML-Zero, resembles the process of evolution, with code improving every generation with little human interaction.

Machine learning tools are "trained" to find patterns in vast amounts of data while automating such processes and constantly being refined based on past experience.

But researchers say this comes with drawbacks that AutoML-Zero aims to fix. Namely, the introduction of bias.

"Human-designed components bias the search results in favor of human-designed algorithms, possibly reducing the innovation potential of AutoML," their team's paper states. "Innovation is also limited by having fewer options: you cannot discover what you cannot search for."

The analysis, which was published last month on arXiv, is titled "Evolving Machine Learning Algorithms From Scratch" and is credited to a team working for Google Brain division.

"The nice thing about this kind of AI is that it can be left to its own devices without any pre-defined parameters, and is able to plug away 24/7 working on developing new algorithms," Ray Walsh, a computer expert and digital researcher at ProPrivacy, told Newsweek.

As noted by ScienceMag, AutoML-Zero is designed to create a population of 100 "candidate algorithms" by combining basic random math, then testing the results on simple tasks such as image differentiation. The best performing algorithms then "evolve" by randomly changing their code.

The resultswhich will be variants of the most successful algorithmsthen get added to the general population, as older and less successful algorithms get left behind, and the process continues to repeat. The network grows significantly, in turn giving the system more natural algorithms to work with.

Haran Jackson, the chief technology officer (CTO) at Techspert, who has a PhD in Computing from the University of Cambridge, told Newsweek that AutoML tools are typically used to "identify and extract" the most useful features from datasetsand this approach is a welcome development.

"As exciting as AutoML is, it is restricted to finding top-performing algorithms out of the, admittedly large, assortment of algorithms that we already know of," he said.

"There is a sense amongst many members of the community that the most impressive feats of artificial intelligence will only be achieved with the invention of new algorithms that are fundamentally different to those that we as a species have so far devised.

"This is what makes the aforementioned paper so interesting. It presents a method by which we can automatically construct and test completely novel machine learning algorithms."

Jackson, too, said the approach taken was similar to the facts of evolution first proposed by Charles Darwin, noting how the Google team was able to induce "mutations" into the set of algorithms.

"The mutated algorithms that did a better job of solving real-world problems were kept alive, with the poorly-performing ones being discarded," he elaborated.

"This was done repeatedly, until a set of high-performing algorithms was found. One intriguing aspect of the study is that this process 'rediscovered' some of the neural network algorithms that we already know and use. It's extremely exciting to see if it can turn up any algorithms that we haven't even thought of yet, the impact of which to our daily lives may be enormous." Google has been contacted for comment.

The development of AutoML was previously praised by Alphabet's CEO Sundar Pichai, who said it had been used to improve an algorithm that could detect the spread of breast cancer to adjacent lymph nodes. "It's inspiring to see how AI is starting to bear fruit," he wrote in a 2018 blog post.

The Google Brain team members who collaborated on the paper said the concepts in the most recent research were a solid starting point, but stressed that the project is far from over.

"Starting from empty component functions and using only basic mathematical operations, we evolved linear regressors, neural networks, gradient descent... multiplicative interactions. These results are promising, but there is still much work to be done," the scientists' preprint paper noted.

Walsh told Newsweek: "The developers of AutoML-Zero believe they have produced a system that has the ability to output algorithms human developers may never have thought of.

"According to the developers, due to its lack of human intervention AutoML-Zero has the potential to produce algorithms that are more free from human biases. This theoretically could result in cutting-edge algorithms that businesses could rely on to improve their efficiency.

"However, it is worth bearing in mind that for the time being the AI is still proof of concept and it will be some time before it is able to output the complex kinds of algorithms currently in use. On the other hand, the research [demonstrates how] the future of AI may be algorithms produced by other machines."

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Artificial Intelligence That Can Evolve on Its Own Is Being Tested by Google Scientists - Newsweek

Teslas acquisition of DeepScale starts to pay off with new IP in machine learning – Electrek

Teslas acquisition of machine-learning startup DeepScale is starting to pay off, with the team hired through the acquisition starting to deliver new IP for the automaker.

Late last year, it was revealed that Tesla acquired DeepScale, a Bay Area-based startup that focuses on Deep Neural Network (DNN) for self-driving vehicles, for an undisclosed amount.

They specialized in computing power-efficient deep learning systems, which is also an area of focus for Tesla, who decided to design its own computer chip to power its self-driving software.

There was speculation that Tesla acquired the small startup team in order to accelerate its machine learning development.

Now we are seeing some of that teams work, thanks to a new patent application.

Just days after Tesla acquired the startup in October 2019, the automaker applied for a new patent with three members of DeepScale listed as inventors: Matthew Cooper, Paras Jain, and Harsimran Singh Sidhu.

The patent application called Systems and Methods for Training Machine Models with Augmented Data was published yesterday.

Tesla writes about it in the application:

Systems and methods for training machine models with augmented data. An example method includes identifying a set of images captured by a set of cameras while affixed to one or more image collection systems. For each image in the set of images, a training output for the image is identified. For one or more images in the set of images, an augmented image for a set of augmented images is generated. Generating an augmented image includes modifying the image with an image manipulation function that maintains camera properties of the image. The augmented training image is associated with the training output of the image. A set of parameters of the predictive computer model are trained to predict the training output based on an image training set including the images and the set of augmented images.

The system that the DeepScale team, now working under Tesla, is trying to patent here is related to training a neural net using data from several different sensors observing scenes, like the eight cameras in Teslas Autopilot sensor array.

They write about the difficulties of such a situation in the patent application:

In typical machine learning applications, data may be augmented in various ways to avoid overfitting the model to the characteristics of the capture equipment used to obtain the training data. For example, in typical sets of images used for training computer models, the images may represent objects captured with many different capture environments having varying sensor characteristics with respect to the objects being captured. For example, such images may be captured by various sensor characteristics, such as various scales (e.g., significantly different distances within the image), with various focal lengths, by various lens types, with various pre- or post-processing, different software environments, sensor array hardware, and so forth. These sensors may also differ with respect to different extrinsic parameters, such as the position and orientation of the imaging sensors with respect to the environment as the image is captured. All of these different types of sensor characteristics can cause the captured images to present differently and variously throughout the different images in the image set and make it more difficult to properly train a computer model.

Here they summarize their solution to the problem:

One embodiment is a method for training a set of parameters of a predictive computer model. This embodiment may include: identifying a set of images captured by a set of cameras while affixed to one or more image collection systems; for each image in the set of images, identifying a training output for the image; for one or more images in the set of images, generating an augmented image for a set of augmented images by: generating an augmented image for a set of augmented images by modifying the image with an image manipulation function that maintains camera properties of the image, and associating the augmented training image with the training output of the image; training the set of parameters of the predictive computer model to predict the training output based on an image training set including the images and the set of augmented images.

An additional embodiment may include a system having one or more processors and non-transitory computer storage media storing instructions that when executed by the one or more processors, cause the processors to perform operations comprising: identifying a set of images captured by a set of cameras while affixed to one or more image collection systems; for each image in the set of images, identifying a training output for the image; for one or more images in the set of images, generating an augmented image for a set of augmented images by: generating an augmented image for a set of augmented images by modifying the image with an image manipulation function that maintains camera properties of the image, and associating the augmented training image with the training output of the image; training the set of parameters of the predictive computer model to predict the training output based on an image training set including the images and the set of augmented images.

Another embodiment may include a non-transitory computer-readable medium having instructions for execution by a processor, the instructions when executed by the processor causing the processor to: identify a set of images captured by a set of cameras while affixed to one or more image collection systems; for each image in the set of images, identify a training output for the image; for one or more images in the set of images, generate an augmented image for a set of augmented images by: generate an augmented image for a set of augmented images by modifying the image with an image manipulation function that maintains camera properties of the image, and associate the augmented training image with the training output of the image; train the computer model to learn to predict the training output based on an image training set including the images and the set of augmented images.

As we previously reported, Tesla is going through a significant foundational rewrite in the Tesla Autopilot. As part of the rewrite, CEO Elon Musk says that the neural net is absorbing more and more of the problem.

It will also include a more in-depth labeling system.

Musk described 3D labeling as a game-changer:

Its where the car goes into a scene with eight cameras, and kind of paint a path, and then you can label that path in 3D.

This new way to train machine learning systems with multiple cameras, like Teslas Autopilot, with augmented data could be part of this new Autopilot update.

Here are some drawings from the patent application:

Heres Teslas patent application in full:

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Teslas acquisition of DeepScale starts to pay off with new IP in machine learning - Electrek

New AI improves itself through Darwinian-style evolution – Big Think

Machine learning has fundamentally changed how we engage with technology. Today, it's able to curate social media feeds, recognize complex images, drive cars down the interstate, and even diagnose medical conditions, to name a few tasks.

But while machine learning technology can do some things automatically, it still requires a lot of input from human engineers to set it up, and point it in the right direction. Inevitably, that means human biases and limitations are baked into the technology.

So, what if scientists could minimize their influence on the process by creating a system that generates its own machine-learning algorithms? Could it discover new solutions that humans never considered?

To answer these questions, a team of computer scientists at Google developed a project called AutoML-Zero, which is described in a preprint paper published on arXiv.

"Human-designed components bias the search results in favor of human-designed algorithms, possibly reducing the innovation potential of AutoML," the paper states. "Innovation is also limited by having fewer options: you cannot discover what you cannot search for."

Automatic machine learning (AutoML) is a fast-growing area of deep learning. In simple terms, AutoML seeks to automate the end-to-end process of applying machine learning to real-world problems. Unlike other machine-learning techniques, AutoML requires relatively little human effort, which means companies might soon be able to utilize it without having to hire a team of data scientists.

AutoML-Zero is unique because it uses simple mathematical concepts to generate algorithms "from scratch," as the paper states. Then, it selects the best ones, and mutates them through a process that's similar to Darwinian evolution.

AutoML-Zero first randomly generates 100 candidate algorithms, each of which then performs a task, like recognizing an image. The performance of these algorithms is compared to hand-designed algorithms. AutoML-Zero then selects the top-performing algorithm to be the "parent."

"This parent is then copied and mutated to produce a child algorithm that is added to the population, while the oldest algorithm in the population is removed," the paper states.

The system can create thousands of populations at once, which are mutated through random procedures. Over enough cycles, these self-generated algorithms get better at performing tasks.

"The nice thing about this kind of AI is that it can be left to its own devices without any pre-defined parameters, and is able to plug away 24/7 working on developing new algorithms," Ray Walsh, a computer expert and digital researcher at ProPrivacy, told Newsweek.

If computer scientists can scale up this kind of automated machine-learning to complete more complex tasks, it could usher in a new era of machine learning where systems are designed by machines instead of humans. This would likely make it much cheaper to reap the benefits of deep learning, while also leading to novel solutions to real-world problems.

Still, the recent paper was a small-scale proof of concept, and the researchers note that much more research is needed.

"Starting from empty component functions and using only basic mathematical operations, we evolved linear regressors, neural networks, gradient descent... multiplicative interactions. These results are promising, but there is still much work to be done," the scientists' preprint paper noted.

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New AI improves itself through Darwinian-style evolution - Big Think

Research Team Uses Machine Learning to Track COVID-19 Spread in Communities and Predict Patient Outcomes – The Ritz Herald

The COVID-19 pandemic is raising critical questions regarding the dynamics of the disease, its risk factors, and the best approach to address it in healthcare systems. MIT Sloan School of Management Prof. Dimitris Bertsimas and nearly two dozen doctoral students are using machine learning and optimization to find answers. Their effort is summarized in the COVIDanalytics platform where their models are generating accurate real-time insight into the pandemic. The group is focusing on four main directions; predicting disease progression, optimizing resource allocation, uncovering clinically important insights, and assisting in the development of COVID-19 testing.

The backbone for each of these analytics projects is data, which weve extracted from public registries, clinical Electronic Health Records, as well as over 120 research papers that we compiled in a new database. Were testing our models against incoming data to determine if it makes good predictions, and we continue to add new data and use machine-learning to make the models more accurate, says Bertsimas.

The first project addresses dilemmas at the front line, such as the need for more supplies and equipment. Protective gear must go to healthcare workers and ventilators to critically ill patients. The researchers developed an epidemiological model to track the progression of COVID-19 in a community, so hospitals can predict surges and determine how to allocate resources.

The team quickly realized that the dynamics of the pandemic differ from one state to another, creating opportunities to mitigate shortages by pooling some of the ventilator supply across states. Thus, they employed optimization to see how ventilators could be shared among the states and created an interactive application that can help both the federal and state governments.

Different regions will hit their peak number of cases at different times, meaning their need for supplies will fluctuate over the course of weeks. This model could be helpful in shaping future public policy, notes Bertsimas.

Recently, the researchers connected with long-time collaborators at Hartford HealthCare to deploy the model, helping the network of seven campuses to assess their needs. Coupling county level data with the patient records, they are rethinking the way resources are allocated across the different clinics to minimize potential shortages.

The third project focuses on building a mortality and disease progression calculator to predict whether someone has the virus, and whether they need hospitalization or even more intensive care. He points out that current advice for patients is at best based on age, and perhaps some symptoms. As data about individual patients is limited, their model uses machine learning based on symptoms, demographics, comorbidities, lab test results as well as a simulation model to generate patient data. Data from new studies is continually added to the model as it becomes available.

We started with data published in Wuhan, Italy, and the U.S., including infection and death rate as well as data coming from patients in the ICU and the effects of social isolation. We enriched them with clinical records from a major hospital in Lombardy which was severely impacted by the spread of the virus. Through that process, we created a new model that is quite accurate. Its power comes from its ability to learn from the data, says Bertsimas.

By probing the severity of the disease in a patient, it can actually guide clinicians in congested areas in a much better way, says Bertsimas.

Their fourth project involves creating a convenient test for COVID-19. Using data from about 100 samples from Morocco, the group is using machine-learning to augment a test previously designed at the Mohammed VI Polytechnic University to come up with more precise results. The model can accurately detect the virus in patients around 90% of the time, while false positives are low.

The team is currently working on expanding the epidemiological model to a global scale, creating more accurate and informed clinical risk calculators, and identifying potential ways that would allow us to go back to normality.

We have released all our source code and made the public database available for other people too. We will continue to do our own analysis, but if other people have better ideas, we welcome them, says Bertsimas.

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Research Team Uses Machine Learning to Track COVID-19 Spread in Communities and Predict Patient Outcomes - The Ritz Herald

Model quantifies the impact of quarantine measures on Covid-19’s spread – MIT News

The research described in this article has been published on a preprint server but has not yet been peer-reviewed by scientific or medical experts.

Every day for the past few weeks, charts and graphs plotting the projected apex of Covid-19 infections have been splashed across newspapers and cable news. Many of these models have been built using data from studies on previous outbreaks like SARS or MERS. Now, a team of engineers at MIT has developed a model that uses data from the Covid-19 pandemic in conjunction with a neural network to determine the efficacy of quarantine measures and better predict the spread of the virus.

Our model is the first which uses data from the coronavirus itself and integrates two fields: machine learning and standard epidemiology, explains Raj Dandekar, a PhD candidate studying civil and environmental engineering. Together with George Barbastathis, professor of mechanical engineering, Dandekar has spent the past few months developing the model as part of the final project in class 2.168 (Learning Machines).

Most models used to predict the spread of a disease follow what is known as the SEIR model, which groups people into susceptible, exposed, infected, and recovered. Dandekar and Barbastathis enhanced the SEIR model by training a neural network to capture the number of infected individuals who are under quarantine, and therefore no longer spreading the infection to others.

The model finds that in places like South Korea, where there was immediate government intervention in implementing strong quarantine measures, the virus spread plateaued more quickly. In places that were slower to implement government interventions, like Italy and the United States, the effective reproduction number of Covid-19 remains greater than one, meaning the virus has continued to spread exponentially.

The machine learning algorithm shows that with the current quarantine measures in place, the plateau for both Italy and the United States will arrive somewhere between April 15-20. This prediction is similar to other projections like that of the Institute for Health Metrics and Evaluation.

Our model shows that quarantine restrictions are successful in getting the effective reproduction number from larger than one to smaller than one, says Barbastathis. That corresponds to the point where we can flatten the curve and start seeing fewer infections.

Quantifying the impact of quarantine

In early February, as news of the virus troubling infection rate started dominating headlines, Barbastathis proposed a project to students in class 2.168. At the end of each semester, students in the class are tasked with developing a physical model for a problem in the real world and developing a machine learning algorithm to address it. He proposed that a team of students work on mapping the spread of what was then simply known as the coronavirus.

Students jumped at the opportunity to work on the coronavirus, immediately wanting to tackle a topical problem in typical MIT fashion, adds Barbastathis.

One of those students was Dandekar. The project really interested me because I got to apply this new field of scientific machine learning to a very pressing problem, he says.

As Covid-19 started to spread across the globe, the scope of the project expanded. What had originally started as a project looking just at spread within Wuhan, China grew to also include the spread in Italy, South Korea, and the United States.

The duo started modeling the spread of the virus in each of these four regions after the 500th case was recorded. That milestone marked a clear delineation in how different governments implemented quarantine orders.

Armed with precise data from each of these countries, the research team took the standard SEIR model and augmented it with a neural network that learns how infected individuals under quarantine impact the rate of infection. They trained the neural network through 500 iterations so it could then teach itself how to predict patterns in the infection spread.

Using this model, the research team was able to draw a direct correlation between quarantine measures and a reduction in the effective reproduction number of the virus.

The neural network is learning what we are calling the quarantine control strength function, explains Dandekar. In South Korea, where strong measures were implemented quickly, the quarantine control strength function has been effective in reducing the number of new infections. In the United States, where quarantine measures have been slowly rolled out since mid-March, it has been more difficult to stop the spread of the virus.

Predicting the plateau

As the number of cases in a particular country decreases, the forecasting model transitions from an exponential regime to a linear one. Italy began entering this linear regime in early April, with the U.S. not far behind it.

The machine learning algorithm Dandekar and Barbastathis have developed predictedthat the United States will start to shift from an exponential regime to a linear regime in the first week of April, with a stagnation in the infected case count likely betweenApril 15 and April20. It also suggests that the infection count will reach 600,000 in the United States before the rate of infection starts to stagnate.

This is a really crucial moment of time. If we relax quarantine measures, it could lead to disaster, says Barbastathis.

According to Barbastathis, one only has to look to Singapore to see the dangers that could stem from relaxing quarantine measures too quickly. While the team didnt study Singapores Covid-19 cases in their research, the second wave of infection this country is currently experiencing reflects their models finding about the correlation between quarantine measures and infection rate.

If the U.S. were to follow the same policy of relaxing quarantine measures too soon, we have predicted that the consequences would be far more catastrophic, Barbastathis adds.

The team plans to share the model with other researchers in the hopes that it can help inform Covid-19 quarantine strategies that can successfully slow the rate of infection.

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Model quantifies the impact of quarantine measures on Covid-19's spread - MIT News

Windows 10 news recap: Halo 2 Anniversary beta invites being sent out, machine learning utilised to identify security bugs, and more – OnMSFT

Welcome back to our Windows 10 news recap, where we go over the top stories of the past week in the world of Microsofts flagship operating system.

Microsoft to introduce PowerToys launcher for Windows 10 in May

A new report suggests that a new update for PowerToys is being prepared that includes a Mac OS style Spotlight launcher, making it easier find apps and files on a Windows 10 PC.

concept design for PowerToys Launcher UX

Microsoft starts sending invites for first Halo 2 Anniversary beta on PC

Invites for the Halo 2 Anniversary beta on PC have started to be sent out this week. Members of the Halo Insider program who have opted into PC flighting will receive an email with the invite.

Microsoft is using machine learning to identify security bugs during software development

In order to help Microsoft identify security bugs and resolve them before public release of software, the company is employing machine learning to find security bugs.

Thats it for this week. Well be back next week with more Windows 10 news.

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Windows 10 news recap: Halo 2 Anniversary beta invites being sent out, machine learning utilised to identify security bugs, and more - OnMSFT

Create Symbiotic Relationships with AI in Business – ReadWrite

Knowingly or unknowingly we are all using artificial intelligence or AI. There is a combination of always-on devices, cloud and edge computing, and APIs in our everyday lives and business practices bringing AI into practice. Here is how to create symbiotic relationships with AI in business.

Even though the relationship between humans and machines is growing ever closer, its much too early to describe many of these collaborations as symbiotic.

When humans have specific types of problems, weve built and trained machines to solve those problems.

Examples include machine learning or ML. The ML algorithms that can identify cancer in brain images. The algorithms can also determine the best placements or designs for online ads, and there are deep learning systems that can predict customer churn in business.

At the moment, we can only imagine how much more productive we will become as we form symbiotic relationships with AI. Routine tasks that currently take hours or days could be abbreviated to 10 or 15 minutes with the aid of a digital partner.

From simple exercises like finding a new restaurant to more expert tasks such as cancer detection, we will increasingly rely on machines for everyday tasks. Dependence on machines might begin as a second pair of eyes or a second opinion, but our commitment to machines (and AI) will evolve into full-on digital collaborators.

Machine learning could bring about a revolution in how we solve problems to which the principle of optimal stopping applies.

Research in mathematics and computer science regarding these problems has shown that the optimal time to stop searching and make a decision is after37% of the time has been spent, options have been reviewed, and parking spaces have been passed.

Examples of these sorts of traditions problems include hiring the right person, making the right amount of R&D investment, and buying or selling a home. Humans tend to stop searching and considering data at about 31% well before they could have found the best option.

Forming symbiotic relationships with machines will free up time for us to focus on honing soft skills such as empathy, management, and strategy. It is not unreasonable to conclude that this symbiotic relationship will even present a new factor in the simple ability to enjoy life outside of work.

Very soon, AI could help us review enough options to find the right homebuyer, apartment tenant, job applicant, and perhaps even the right spouse.

For businesses and organizations with knowledge work as their output employees will benefit in several ways by applying machine learning to their advantage. Employees will use applications that cut across a variety of industries.

Some industry-agnostic roles such as a project manager will be able to offload routine tasks.

Tech will benefit substantially. Similar to how content creators benefit from writing agents such as Grammarly, software developers will benefit from a pair programming agent. The agent will suggest not only the right code syntax, but also the most appropriate framework, library, or API.

These agents will also have the opportunity to improve code quality and user experience drastically.

For industries like construction, AI could take advantage of the increased digitization of blueprints. AI will automate tasks that are routine but critical as project estimation. Depending on the size of the project, a human estimator can take up to four weeks to estimate a project.

Effortlessly, a digital agent could determine the materials needed for the project and set the number of workers necessary to staff the project.

More dramatic still, the AI digital agent could be connected to a supply store and incorporate real-time pricing into the final quote.

Medicine is another prime exampleof an industry ripe for disruption through human-AI symbiosis.

Pharmaceutical companies are leveraging machine learning to determine the optimal levels of research and development, using factors such as projected market size, revenue, and lifetime value of potential drugs.

Many doctors and hospitals have begun to incorporate AI recommendations into their processes. Increasing successes are seen, with 35% of doctors in a 2019 survey stating they use AI in their practices.

Some approaches in medicine have leveraged AI to provide potential options to doctors. Other choices analyze a doctors recommendation to predict the probability of success.

The dynamic symbiotic relationship between doctors and AI will also likely alter how malpractice riskis assessed for insurance.

As AI becomesmore commonplace in healthcareand is proven to improve outcomes for patients and decrease costs for hospitals, malpractice insurance will evolve to see AI as a way to reduce overall risk.

Similarly, doctors and hospitals that invest in AI solutions will see an improved return on investment in the form of lower insurance costs, improved outcomes, and increased efficiency.

Organizations that want to embrace the advances in AI and ML to produce symbiotic relationships between machines and themselves can take these steps.

The first step is to assess how artificial intelligence stands to impact your business as well as your industry and value chain. Examine whether you can add AI to your services.

Will AI change your product entirely, or can AI open new possibilities for entirely new products and services?

Once you complete your assessment and identify your options, break down your potential financial value to the organization. The assessment will uncover both potential risks you could incur and opportunities for new revenue streams you could open once you achieve AI-human symbiosis.

Every organization needs to learn where its data is stored and used. Proactively make this data available across the organization for experimentation, proofs of concepts, and other innovation projects.

Gain a firm understanding of what data you have and who owns it and share the information across the organization safely and democratically. The open network and feeling you are creating with this action are crucial to enabling machines to work for you, and sowing the seeds of innovation.

Assess your workforce to determine the roles that will most likely benefit from AI and machine learning solutions. The assessments can be divided into varying styles across individual employees or teams. These assessments include:

Data-driven thinkers versus big-picture focus thinkers.

Strengths in strategy versus problem-solving strengths.

Skill sets in software development versus the risk assessment skill set.

Is the talent expertise contained in surgery versus the expertise in research and development?

Machines are forging new opportunities for human work throughout the value chain as humans and machines collaborate to create more meaningful human jobs.

An organization must align its approach to building symbiotic relationships with its overarching purpose and that begins with leadership.

Leaders must excite their workforces about the ultimate goal of integrating AI, provide a clear vision for the organizations goals, and assure their workers that machines will enhance and alter (but not replace) their roles.

Its important to create near and long-term plans and then share those timelines across the organization, and connect those benchmarks to your greater purpose.

Organizations wont be able to take advantage of the value of these symbiotic relationships without carefully appraising the opportunities and risks.

Businesses must get their data houses in order and encourage innovation that enhances their talent and their organizations purpose. Only then will humans use AI to its full potential.

Image Credit: franck-v, Unsplash

Daniel Williams is a principal with Pariveda Solutions, specializing in digital strategy, implementation, and analytics. With B.S. and M.S. degrees in Computer Science and Technology Management, he has become an expert in digital transformation and AI/ML.

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Create Symbiotic Relationships with AI in Business - ReadWrite

Podcast of the Week: TWIML AI Podcast – 9to5Mac

During the COVID19 pandemic, I decided that I wanted to use the time at home to invest in myself. One of the things I was challenged by in a recent episode of Business Casual was when Mark Cuban discussed the role of Artificial Intelligence in the future and recommended some tools to learn more. He mentioned some Coursera courses, so I am currently working my way through some of their AI training, but he also mentioned an AI-focused podcast called theTWIMLAI Podcast that I added to my podcast subscription list.

9to5Macs Podcast of the Week is a weekly recommendation of a podcast you should add to your subscription list.

TWIML (This Week in Machine Learning and AI) is a perfect way to hear from industry experts about how Machine Learning and AI will change our world. I plan to work through the back catalog soon, but the newest episodes have been informative. I particularly enjoyed this episode with Cathy Wu, Gilbert W. Winslow Career Development Assistant Professor in the Department of Civil and Environmental Engineering at MIT where they discussed simulating the future of traffic.

Machine learning and artificial intelligence are dramatically changing the way businesses operate and people live. By sharing and amplifying the voices of a broad and diverse spectrum of machine learning and AI researchers, practitioners, and innovators, our programs help make ML and AI more accessible, and enhance the lives of our audience and their communities.

TWIML has its origins in This Week in Machine Learning & AI, a podcast Sam launched in mid2016 to a small but enthusiastic reception. Fast forward three years, and the TWIML AI Podcast is now a leading voice in the field, with over five million downloads and a large and engaged community following. Our offerings now include online meetups and study groups, conferences, and a variety of educational content.

Subscribe to the TWIML AI Podcast on Apple Podcasts, Spotify, Castro, Overcast, Pocket Casts, and RSS.

Dont forget about the great lineup of podcasts on the 9to5 Network.

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Podcast of the Week: TWIML AI Podcast - 9to5Mac

Machine Learning as a Service (MLaaS) Market | Outlook and Opportunities in Grooming Regions with Forecast to 2029 – Jewish Life News

Documenting the Industry Development of Machine Learning as a Service (MLaaS) Market concentrating on the industry that holds a massive market share 2020 both concerning volume and value With top countries data, Manufacturers, Suppliers, In-depth research on market dynamics, export research report and forecast to 2029

As per the report, the Machine Learning as a Service (MLaaS) Market is anticipated to gain substantial returns while registering a profitable annual growth rate during the predicted time period.The global machine learning as a service (mlaas) market research report takes a chapter-wise approach in explaining the dynamics and trends in the machine learning as a service (mlaas) industry.The report also provides the industry growth with CAGR in the forecast to 2029.

A deep analysis of microeconomic and macroeconomic factors affecting the growth of the market are also discussed in this report. The report includes information related to On-going demand and supply forecast. It gives a wide stage offering numerous open doors for different businesses, firms, associations, and start-ups and also contains authenticate estimations to grow universally by contending among themselves and giving better and agreeable administrations to the clients. In-depth future innovations of machine learning as a service (mlaas) Market with SWOT analysis on the basis Of type, application, region to understand the Strength, Weaknesses, Opportunities, and threats in front of the businesses.

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***[Note: Our Complimentary Sample Report Accommodate a Brief Introduction To The Synopsis, TOC, List of Tables and Figures, Competitive Landscape and Geographic Segmentation, Innovation and Future Developments Based on Research Methodology are also Included]

An Evaluation of the Machine Learning as a Service (MLaaS) Market:

The report is a detailed competitive outlook including the Machine Learning as a Service (MLaaS) Market updates, future growth, business prospects, forthcoming developments and future investments by forecast to 2029. The region-wise analysis of machine learning as a service (mlaas) market is done in the report that covers revenue, volume, size, value, and such valuable data. The report mentions a brief overview of the manufacturer base of this industry, which is comprised of companies such as- Google, IBM Corporation, Microsoft Corporation, Amazon Web Services, BigML, FICO, Yottamine Analytics, Ersatz Labs, Predictron Labs, H2O.ai, AT and T, Sift Science.

Segmentation Overview:

Product Type Segmentation :

Software Tools, Cloud and Web-based Application Programming Interface (APIs), Other

Application Segmentation :

Manufacturing, Retail, Healthcare and Life Sciences, Telecom, BFSI, Other (Energy and Utilities, Education, Government)

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Key Highlights of the Machine Learning as a Service (MLaaS) Market:

The fundamental details related to Machine Learning as a Service (MLaaS) industry like the product definition, product segmentation, price, a variety of statements, demand and supply statistics are covered in this article.

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Other points comprised in the Machine Learning as a Service (MLaaS) report are driving factors, limiting factors, new upcoming opportunities, encountered challenges, technological advancements, flourishing segments, and major trends of the market.

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AQR’s former machine-learning head says quant funds should start ‘nowcasting’ to react to real-time data instead of trying to predict the future – One…

MagnusRT @rjparkerjr09: "Quants were too reliant on models and forecasts. They need to begin practicing nowcasting reacting to real-time data13 hours ago

Mansoor Fayyaz MianAQR's former machine-learning head says its time for quants to 'pay less attention to crystal balls' and react to re https://t.co/WrhikvFdjM17 hours ago

Jerry Parker"Quants were too reliant on models and forecasts. They need to begin practicing nowcasting reacting to real-time https://t.co/ozQlfldTdI22 hours ago

Truth 2 PowerAQR's former machine-learning head says it's time for quants to 'pay less attention to crystal balls' and react to https://t.co/i0jGvPVwBz1 day ago

JoseWorksAQR's former machine-learning head says its time for quants to 'pay less attention to crystal bal... https://t.co/PGaMlHXBS22 days ago

Manpreet SinghRT @businessinsider: AQR's former machine-learning head says its time for quants to 'pay less attention to crystal balls' and react to real2 days ago

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Nanotechnology Market 2020 Break Down by Top Companies, Applications, Challenges, Opportunities and Forecast 2026 Cole Reports – Cole of Duty

10x Technology 3M 3rd Millennium 3rdTech Bayer Material Science and Cortex

Nanotechnology Market: Competitive Landscape

The last chapter of the Nanotechnology market research report focuses exclusively on the competitive landscape. It examines the main market players. In addition to a brief overview of the business, analysts provide information on their assessment and development. The list of important products in preparation is also mentioned. The competitive landscape is analyzed by understanding the companies strategies and the initiatives they have taken in recent years to overcome intense competition.

Nanotechnology Market: Drivers and Restraints

The report explains the drivers of the future of the Nanotechnology market. It assesses the different forces which should have a positive impact on the whole market. Analysts have looked at investments in research and development for products and technologies, which should give players a significant boost. In addition, the researchers undertook an analysis of the evolution of consumer behavior which should have an impact on the cycles of supply and demand in the Nanotechnology market. In this research report, changes in per capita income, improvement in the economic situation and emerging trends were examined.

The research report also explains the potential restrictions on the Nanotechnology market. The aspects assessed are likely to hamper market growth in the near future. In addition to this assessment, it offers a list of opportunities that could prove lucrative for the entire market. Analysts offer solutions to turn threats and restrictions into successful opportunities in the years to come.

Nanotechnology Market: Regional Segmentation

In the following chapters, analysts have examined the regional segments of the Nanotechnology market. This gives readers a deeper insight into the global market and allows for a closer look at the elements that could determine its evolution. Countless regional aspects, such as the effects of culture, environment and government policies, which affect regional markets are highlighted.

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Market Dynamics: The report contains important information on influencing factors, market drivers, challenges, opportunities and market trends as part of the market dynamics.

Global Market Forecast: Readers receive production and sales forecasts for the Nanotechnology market, production and consumption forecasts for regional markets, production, sales and price forecasts for the Nanotechnology market by type and consumption forecasts for the Nanotechnology market per application.

Regional Market Analysis: It can be divided into two different sections: one for the analysis of regional production and one for the analysis of regional consumption. Here, analysts share gross margin, prices, sales, production, CAGR, and other factors that indicate the growth of all regional markets examined in the report.

Market Competition: In this section, the report provides information on the situation and trends of competition, including mergers and acquisitions and expansion, the market shares of the three or five main players and the concentration of the market. Readers could also get the production, revenue, and average price shares of manufacturers.

Key Players: The report provides company profiles for a decent number of leading players in the Nanotechnology market. It shows your current and future market growth taking into account price, gross margin, income, production, service areas, production locations and other factors.

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Cardiol Announces Study Demonstrating Its Proprietary Nanotechnology Targets Fibrous Tissue in the Heart | INN – Investing News Network

Results showed a greater than 100-fold increase in uptake of Cardiols nanoparticles in heart failure hearts compared with control hearts

Cardiol Therapeutics Inc. (TSX:CRDL) (OTCQX:CRTPF) (Cardiol or the Company), a leader in the production of pharmaceutical cannabidiol (CBD) and the development of innovative cannabidiol products for heart diseases, is pleased to announce that data describing Cardiols nanotechnology approach to drug delivery were submitted by the Companys international research collaborators and accepted for presentation at the American College of Cardiologys (ACC) 69th Annual Scientific Session & Expo together with the World Congress of Cardiology, held virtually from March 28-30.

Results from this study, conducted at the Houston Methodist DeBakey Heart & Vascular Center, showed that there was a greater than 100-fold increase in uptake of Cardiols nanoparticles in heart failure hearts compared with control hearts in a pre-clinical model of non-ischemic heart failure. The nanoparticles localized within the diseased hearts, predominantly in areas of fibrosis. Fibrosis is an important component of the pathology of heart failure and is primarily responsible for the stiffening and reduced function of the heart muscle. Moreover, the nanoparticles accumulated within the cytoplasm of the cultured fibroblasts. This evidence of Cardiols nanoparticles preferentially accumulating intracellularly in fibroblasts shows potential for the successful delivery of anti-fibrotic drugs, such as cannabidiol, to the diseased region of the heart.

These results are exciting and provide new insights into how nanotherapeutics may be utilized to target the anti-fibrotic properties of cannabidiol to fibrous tissue in the failing heart, said Dr. Arvind Bhimaraj, MD, MPH, Interim Division Chief of the Division of Heart Failure and Co-director of the Heart Failure Translational Research Laboratory at Houston Methodist DeBakey Heart & Vascular Center and a member of the Clinical Steering Committee for Cardiols planned international clinical trial in acute myocarditis. The specific targeting of the fibrotic tissue in the heart offers the potential to utilize drugs more effectively to prevent the progression of heart failure.

Cardiols proprietary nanotechnology is designed to enable the distribution of water insoluble drugs within the blood (aqueous) circulation, improve pharmacokinetics, and facilitate drug accumulation in the failing heart. Cardiols nanoparticles are based on a patented family of biocompatible and biodegradable amphiphilic block co-polymers made from polyethylene glycol (PEG) and polycaprolactone (PCL). Both PEG and PCL have a long history of safe use in humans.

About Cardiol Therapeutics

Cardiol Therapeutics Inc. (TSX: CRDL) (OTCQX: CRTPF) is focused on producing pharmaceutical cannabidiol (CBD) products and developing innovative therapies for heart diseases, including acute myocarditis and other causes of heart failure. The Companys lead product, CardiolRx, is formulated to be the most consistent cannabidiol formulation on the market. CardiolRx is pharmaceutically produced, manufactured under cGMP, and is THC free (<5 ppm). The Company also plans to commercialize CardiolRx in the billion-dollar market for medicinal cannabinoids in Canada and is pursuing distribution opportunities in Europe and Latin America.

In heart failure, Cardiol is planning an international clinical study of CardiolRx in acute myocarditis, a condition caused by inflammation in heart tissue, which remains the most common cause of sudden cardiac death in people less than 35 years of age. The Company is also developing proprietary nanotechnology to uniquely deliver pharmaceutical cannabidiol and other anti-inflammatory drugs directly to sites of inflammation in the heart associated with heart failure. Heart failure is the leading cause of death and hospitalization in North America with associated annual healthcare costs in the U.S. alone exceeding $30 billion. For further information about Cardiol Therapeutics, please visitcardiolrx.com.

For further information, please contact:

David Elsley, President & CEO +1-289-910-0850david.elsley@cardiolrx.com

Trevor Burns, Investor Relations +1-289-910-0855trevor.burns@cardiolrx.com

Cautionary statement regarding forward-looking information:

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Her father’s delirium was a first sign of coronavirus. He’s not the only one. – NBC News

Nicole Hutcherson first noticed something was wrong with her father normally a spry early-riser who enjoyed yard work and home renovation projects earlier this month, when he wasn't getting out of bed until nearly midday.

Her dad, Frank M. Carter, 82, of Goodlettsville, Tennessee, insisted he felt fine, despite some nausea and vomiting. Hutcherson suspected he was dehydrated, so she went to his house to give him intravenous fluids. Hutcherson is a nurse, and had supplies on hand.

Full coverage of the coronavirus outbreak

That was when she noticed her father, who had shown no previous signs of dementia, was largely unaware of what was happening around him.

"He looked distant," Hutcherson recalled. "He just had this weird look in his eye, like his mental status had changed."

Carter didn't react at all when his daughter put the IV needle in his arm. "It was like he was sedated," she said.

Hutcherson believes that the delirium she noted in her father was one of the first signs that he had been infected with the coronavirus. Carter died within a week.

There is growing evidence to suggest that COVID-19, the illness caused by the coronavirus, can affect not only the lungs, but the brain, too.

A recent study of 214 patients in Wuhan, China, where the pandemic started, found more than a third had neurologic manifestations of the disease, including loss of consciousness and stroke.

Physicians in the U.S. have noted the same.

"We're seeing a significant increase in the number of patients with large strokes," Dr. Johanna Fifi, associate director of the cerebrovascular center at the Mount Sinai Health System in New York City, said.

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Many are patients in their 30s and 40s. Over a recent two-week period, Fifi told NBC News she had five COVID-19 patients under age 49, all with strokes resulting from a blockage in one of the major blood vessels leading to the brain.

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Two of those patients had what Fifi described as mild coronavirus conditions before the stroke. The other three had no symptoms at all.

How the virus might lead to a stroke or other neurological impairment remains unclear. Fifi said it's possible that inflammation in the body could damage blood vessels in the brain, or that the viral infection leads to increased clotting.

"I don't think we know right now which one it is," she said.

Dr. E. Wesley Ely, a professor of medicine and critical care at Vanderbilt University Medical Center, has another theory: that the virus is "probably invading the brain."

Ely explained that symptoms such as loss of smell and taste reported among some coronavirus patients are neurologic in nature.

"This virus goes into your nose, and says, 'I'm just going to attack the first thing I see.' That's the respiratory tract," Ely said. But because humans have no immunity to this new virus, it's possible that it can attack any part of the body, including the brain.

"That's something that still needs to be teased apart and figured out," Dr. Felicia Chow, an assistant professor of neurology at the University of California, San Francisco, said.

But the issues surrounding loss of taste and smell "make us highly suspicious that ... the cranial nerves may be affected by the virus," she said. "We just don't have any direct proof at this point."

To fill that void, Ely and colleagues with the Critical Illness, Brain Dysfunction and Survivorship Center, in partnership with Vanderbilt and the Nashville VA, have launched a study of post-mortem brain tissue to look for signs of COVID-19 in the brain. The National Institutes of Health is funding the research.

The team will examine the brains' neurons for damage, measure various brain regions to see if parts have become unusually small, and analyze the hippocampus, which plays a large role in memory. They'll also look for evidence of amyloid and tau, two proteins linked to dementia and Alzheimer's disease.

"Anything we find is important because we're trying to understand the pathophysiology of this disease," Ely said.

The first brain donated to the project was Frank M. Carter's.

"My father would have wanted to do this because he was selfless," Hutcherson said. "He would have wanted to help others."

Hutcherson urged others to watch for unusual cognitive changes in family members, including lapses in consciousness and unexplained confusion. It is unknown whether Carter had suffered a COVID-19-related stroke.

Chow added that awareness of other stroke symptoms is also critical, including "drooping of the face, weakness of the arm or leg, especially on one side, and difficulties either understanding or producing speech."

"Those are definitely symptoms of a potential stroke and a reason to immediately call 911," whether they're related to COVID-19 or not, Chow said.

Delaying care can have devastating consequences. "One of our patients waited over a day at home, getting progressively weaker," Fifi, of Mount Sinai Health System, said. The patient told physicians she'd been afraid to go to the hospital because of the coronavirus outbreak.

"If you're having symptoms, it's safer to be in the hospital," Fifi said.

"If you don't re-establish blood flow quickly, the brain becomes irreversibly damaged. The sooner you do it, the better."

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Her father's delirium was a first sign of coronavirus. He's not the only one. - NBC News

It’s not just lungs: Covid-19 may damage the heart, brain, and kidneys – The Daily Briefing

It's widely known that the new coronavirus attacks patients' lungs, but clinicians and researchers around the world are reporting that the virus is damaging other organs, as wella discovery that could have implications for the way doctors treat Covid-19, the disease caused by the virus, and for how patients recover.

Your top resources for Covid-19 readiness

In addition to the widely reported lung injuries associated with Covid-19, clinicians around the world are reporting that the disease also could be causing cardiac injuries in patients that sometimes lead to cardiac arrest.

According to the Washington Post, health care workers in China and New York have reported seeing myocarditis, an inflammation of the heart muscle, as well as irregular heart rhythms in Covid-19 patientseven in patients with no pre-existing heart conditions.

At first, the patients "seem to be doing really well as far as respiratory status goes," said Mitchell Elkind, a neurologist at Columbia University and president-elect of the American Heart Association (AHA). But the patients "suddenly develop a cardiac issue that seems out of proportion to their respiratory issues," Elkind said.

Elkind noted that one review found about 40% of seriously ill Covid-19 patients in China experienced arrhythmias and 20% experienced other cardiac injuries. "There is some concern that some of it may be due to direct influence of the virus," Elkind said.

A separate study of 416 hospitalized Covid-19 patients in China found that 19% showed signs of heart damage, and those patients were more likely to die. According to the study, 51% of patients with heart damage died, compared with 4.5% of patients who showed no signs of cardiac injury.

Doctors are trying to determine whether the damage to patients' heart muscles is caused by the new coronavirus, itself, or if the damage occurs as a result of other symptoms of Covid-19, such as pneumonia and inflammation, Kaiser Health News (KHN) reports.

"It's extremely important to answer [that] question," said Ulrich Jorde, head of heart failure, cardiac transplantation, and mechanical circulatory support for Montefiore Health System. "This may save many lives in the end."

Doctors also are reporting a growing number of Covid-19 patients with symptoms of neurological damage, including brain inflammation, seizures, and hallucinations, the Wall Street Journal reports.

A group of Chinese doctors in a study published last week in JAMA Neurology found that more than one-third of 214 hospitalized Covid-19 patients in Wuhan had neurologic symptoms, the most common of which were dizziness, headaches, impaired consciousness, loss of taste and smell, and skeletal-muscle injuries. More serious but less commonly reported symptoms included seizures and stroke, according to the study.

The findings have prompted doctors to begin performing simple neurological exams on Covid-19 patients, the Journal reports.

Further, while health experts originally were telling patients to avoid seeking care at hospitals unless they had common Covid-19 symptoms such as a fever, cough, or trouble breathing, neurologists are hoping the new data will add neurological symptomssuch as confusion, numbness, or trouble speakingto that list. "This article should open up everyone's eyes that this disorder affects the brain as well." said S. Andrew Josephson, chair of neurology at the University of California-San Francisco.

Kidney damage also is becoming a commonly reported issue among Covid-19 patients.

Alan Kliger, a nephrologist at the Yale School of Medicine, said early data showed 14%to 30%of ICU Covid-19 patients in New York and Wuhan, China, lost kidney function and later required dialysis. Similarly, a study published last week in the journal Kidney International found that nine of 26 people who died of Covid-19 in Wuhan had acute kidney injuries, and seven had units of the new coronavirus in their kidneys.

The findings suggest it's "very possible that the virus attaches to the kidney cells and attacks them," Kliger said.

The new coronavirus also appears to produce blood clots that can travel from patients' veins to their lungs, causing a pulmonary embolism, and other organs.

According to STAT News, Chinese researchers in one report said they found small blood clots in about 70% of the patients who died of Covid-19 and were included in the study. In comparison, the researchers found similar blood clots in fewer than one in 100 patients who survived the disease. In a separate peer-reviewed study of 81 patients in Wuhan that was published last week in the Journal of Thrombosis and Hemostasis, researchers wrote that 20 patients experienced pulmonary embolism and eight died from the condition.

Based on what they've seen so far, doctors said the blood clots in Covid-19 patients are smaller but cause more damage than blood clots typically seen in patients with other conditions, STAT News reports.

Sanjum Sethi, an interventional cardiologist and assistant professor of medicine at Columbia University's Irving Medical Center, said doctors have been using blood thinners to treat the clots in Covid-19 patients, hoping that relieving the clots will allow the patients' immune systems to focus on fighting off the coronavirus.

While Clyde Yancy, chief of cardiology at Northwestern University Feinberg School of Medicine, said it's too early to "declare anything definitively," he added, "[W]e know from the best available data that about one-third of patients who have Covid-19 infections do in fact have evidence of thrombotic disease."

Doctors said it is still unclear why the clots develop in Covid-19 patients, according to STAT News.

While doctors' reports of different types of organ damage in Covid-19 patients are increasing, clinicians and researchers have yet to determine whether the new coronavirus is directly attacking those organs, or whether the injuries are caused by the patients' immune responses to the infection. Doctors said researchers also should investigate whether the organ damage and failure is being caused by medication, respiratory distress, fevers, the stress of hospitalization, and so-called "cytokine storms."

Regardless of the cause, the organ damage is threatening patients' lives. "It's not necessarily the virus killing people, it's the organ failure that happens as a result of the viral infection," said Christopher Barrett, a senior surgical resident at Beth Israel Deaconess Medical Center.

But results indicating that the virus is directly attacking patients' organs could impact the way doctors treat and evaluate Covid-19 patients, especially in the early stages of infection, KHN reports.

"This is a real-time learning experience," Yancy said (Bernstein et al., Washington Post, 4/15; Hawryluk, Kaiser Health News, 4/6; Hernandez, Wall Street Journal, 4/14; Cooney, STAT News, 4/16; Owermohle/Eisenberg, Politico, 4/15).

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It's not just lungs: Covid-19 may damage the heart, brain, and kidneys - The Daily Briefing

COVID-19 may harm both the brain and the lungs – PhillyVoice.com

As doctors gain a better understanding of how the coronavirus attacks the human body, the list of possible symptoms caused by an infection continues to grow. Itnow appears to include neurological damage.

Reportsfrom China, where the pandemic originated, and other coronavirus hotspots, including the United States, suggest that the coronavirus can spread to the brain, potentially leading to a seizure, stroke or encephalitis.

In response,theU.S. Centers for Disease Control and Preventionhas added "new confusion or inability to arouse" to the list of emergency warning signs for COVID-19.

The most common symptoms are cough, fever, fatigue and difficulty breathing. OtherCOVID-19 patients have experienced headache, vomiting, nausea and loss of sense of smell and taste.

But there is growing concern about the coronavirus's ability to harm the brain.

According to Dr. Lin Mei, director of the Cleveland and Brain Health Initiative, the coronavirus can travel to the brain from the nasal cavity, through the bloodstream or by attaching itself to nerve terminals.

More research is needed to determine whether the coronavirus directly causes neurological symptoms by breaching the blood-brain barrier or if those symptoms are a side effect of the virus attacking other systems in the body.

For instance, does the coronavirusdirectly cause a stroke or does the infection lead to a spike in blood pressure, which then triggers a stroke?

Henry Ford Health System doctors recently reported a case of encephalitis in a 58-year-old Detroit woman who tested positive for COVID-19. She developed acute necrotizing encephalitis, a central nervous system infection more commonly seen in young children.

Her symptoms began with just a fever, cough and muscle aches. But a few days later, she started experiencing confusion and disorientation. She was rushed to the emergency department by ambulance and was tested for the flu and COVID-19. The flu test came back negative, the rapid COVID-19 test positive.

Her care team suspected she had encephalitis and ordered imaging scans. The MRI scan showed abnormal lesions inboth the thalami and temporal lobes of the brain, which regulate consciousness, sensation and memory function.

"This is significant for all providers to be aware of and looking out for in patients who present with an altered level of consciousness,"Dr. Elissa Fory, a Henry Ford neurologist saidin a statement."We need to be thinking of how we're going to incorporate patients with severe neurological disease into our treatment paradigm.This complication is as devastating as severe lung disease."

Frank Carter, a 82-year-old man in Tennessee, also experienced neurological symptoms related to COVID-19, NBC Newsreported. Besides some nausea and vomiting, the first indicator of the infection was delirium, according to his daughter, who is a nurse. He died within a week.

There have been neurological symptoms in COVID-19 patients in China as well.

At the Union Hospital of Huazhong University of Science and Technology in Wuhan, 36.4% of COVID-19 patients developed neurological issues,according to a study published in the journalJAMA Neurology.For some, the neurological symptoms even showed up before the cough and fever.

"We've been telling people that the major complications of this new disease are pulmonary, but it appears there are a fair number of neurological complications that patients and their physicians should be aware of," Dr. Andrew Josephson, editor of JAMA Neurology, wrote in a commentary to the study.

Dr. E. Wesley Ely, a professor of medicine and critical care at Vanderbilt University Medical Center is collaborating with the Critical Illness, Brain Dysfunction and Survivorship Center in studying post-mortem brain tissue to better understand how COVID-19 affects the neurological system.

The researchers will measure different regions of the brain to see whether they have shrunk. They also will look for damage to neurons and evidence of the proteins associated with dementia and Alzheimer's disease. Carter's brain was the first to be donated to the project.

Health officials say that it is important for people to watch for sudden cognitive changes in family members so they can more quickly get the help they might need.

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COVID-19 may harm both the brain and the lungs - PhillyVoice.com

What We Know About Multiple Sclerosis and COVID-19 – Medscape

Find the latest COVID-19 news and guidance in Medscape's Coronavirus Resource Center.

This transcript has been edited for clarity.

Hello. I'm Dr Steven Krieger, a neurologist at the Corinne Goldsmith Dickinson Center for Multiple Sclerosis (MS) at Mount Sinai in New York, reporting for Medscape on the care of MS patients amidst the COVID-19 pandemic.

COVID-19 has impacted every one of us. Of course, we've been counseling our patients to observe stringent precautions: sheltering in place, maintaining effective home quarantine, practicing hand hygiene, wearing masks outside the home per the Centers for Disease Control and Prevention's recommendation, and encouraging social connectedness to prevent isolation during this time of physical distancing. But what else can we do specifically for our patients with MS?

Mount Sinai and many other institutions treating patients with MS have migrated all patient care to virtual and telehealth to try to minimize their exposure to the health system during this time. We're all developing clever ways to perform neurologic exams via video visits and have patients collect and report their own data. If you have any tips on how you're doing this in your own practice, please do add them in the comments section so everyone can benefit from your experience.

Our psychologists have also been holding video support groups for our patients to try to provide remote counseling and address the anxiety that everyone's feeling. We're trying to keep patients with MS from emergency department or urgent care exposure unless it's truly necessary.

In this new setting of COVID-19, we've established a higher threshold for treating MS relapses with steroids. If steroids must be used for a particularly debilitating relapse, we're trying to use oral preparations1250 mg of prednisone a day for 3-5 days with gastrointestinal prophylaxisso that patients can administer them at home and avoid intravenous treatment and nurse visits whenever possible.

There is yet no evidence that MS makes patients more susceptible to this infection. We're all incredibly susceptible to it. There's also no evidence that people with MS have a more severe experience of COVID-19; however, of course, disability is an important comorbidity.

Thankfully, there is also little evidence to date of increased infection susceptibility or risk for patients treated with disease-modifying therapies (DMTs). Whenever possible, our practice has been to not interrupt medication out of concern for COVID-19 and to counsel patients as such. We have to remain vigilant to the risk for disease rebound when DMTs are stopped, particularly the sphingosine 1-phosphate (S1P) modulators or natalizumab.

Sometimes we are choosing to delay infusions to minimize patients' risk of being exposed to the virus at a treatment center. Remember that natalizumab infusions can be safely delayed typically by about 1-2 weeks for extended-interval dosing. For patients with relapsing-remitting MS, we feel that ocrelizumab can probably be safely delayed by perhaps a month or 2 months without real risk for recurrent disease activity. Patients with primary progressive MS on ocrelizumab may be older, with more comorbidities and disability. Therefore, we've typically been trying to delay those infusions until the worst of the COVID-19 crisis, at least in this region, has begun to pass.

Many infusion centers may have limited resources as their nurses and other staff are redeployed for COVID-directed care. As such, trying to decrease the burden on patient centers during this time may be appropriate.

In recent months, we've really fielded two different waves of phone calls and inquiries from our patients with MS. In February and March, we had innumerable calls from people asking what they should do in advance out of concern for this disease. In April, those calls have since changed to ask what they should do now that they have COVID-19. Those are challenging conversations, because COVID-19 affects people in such a heterogeneous way, both in terms of symptoms and severity.

If one of our patients with MS develops COVID-19, we've begun counseling them that they can, for example, hold their interferon injections to avoid additional flu-like symptoms during the acute infection. We could counsel patients to hold S1P modulators like fingolimod during a prolonged episode of high fever, but hopefully not beyond the 14-day period, after which new first-dose observation would be needed again. Again, this strategy aims to prevent an extended amount of exposure in the medical system.

We are recommending that patients hold their infusion therapies at least until a week or so after their primary COVID-19 symptoms and fever have resolved. As previously noted, extended-interval dosing for natalizumab is likely both safe and effective.

And, of course, for any patient with symptoms of COVID-19, it's crucial to counsel them to seek urgent care if they develop difficulty breathing or significant shortness of breath.

There is a very nice review published this April in Neurology by Brownlee and colleagues looking at the implications of using DMTs in people with COVID-19. There's also research looking at the potential for S1P modulators like fingolimod to prevent acute respiratory distress syndrome in aggressively worsening COVID-19. The hope there is that this immunomodulatory strategy might prevent the potentially devastating influx of lymphocytes into the pulmonary compartment.

Clinical data on people with MS contracting COVID-19 are also now being collected by several different research consortia around the world. In pulling together this information, they are hoping to provide crucial information that we can use to guide our treatment decisions.

Comi and colleagues presented data via the National Multiple Sclerosis Society (NMSS), I believe with a publication forthcoming, from three Italian centers at the beginning of this crisis. They looked at 150 patients with MS and COVID-19, 90% of whom remained at home. Only a small handful required intensive care unit admission and critical care. There was no trend for worse outcomes for MS patients on individual DMTs. As with the general population, however, older patients had a more severe course of COVID-19.

The International Women in Multiple Sclerosis group has been gathering the latest data on their website, listing best practices for MS patients in the era of COVID-19. And a joint effort from the NMSS and the Consortium of Multiple Sclerosis Centers, called COViMS (COVID-19 Infections in MS & Related Diseases), is going to aggregate data for MS patients with COVID that we can all learn something from in the weeks and months to come.

As we await these forthcoming data and continue to provide care for patients with neurologic disease and with MS in particular, I'd like to offer a little reminder of the two essential tools we have at our disposal: effective hand hygiene and masks to prevent transmission of this disease. We need to protect ourselves as we protect our patients. Stay safe, everyone.

Reporting from New York City for Medscape, I'm Steven Krieger.

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What We Know About Multiple Sclerosis and COVID-19 - Medscape

Some COVID-19 patients forget where they are, what year it is – ARY NEWS

WASHINGTON: A pattern is emerging among COVID-19 patients arriving at hospitals in New York: Beyond fever, cough and shortness of breath, some are deeply disoriented to the point of not knowing where they are or what year it is.

At times this is linked to low oxygen levels in their blood, but in certain patients the confusion appears disproportionate to how their lungs are faring.

Jennifer Frontera, a neurologist at NYU Langone Brooklyn hospital seeing these patients, told AFP the findings were raising concerns about the impact of the coronavirus on the brain and nervous system.

By now, most people are familiar with the respiratory hallmarks of the COVID-19 disease that has infected more than 2.2 million people around the world.

But more unusual signs are surfacing in new reports from the frontlines.

A study published in the Journal of the American Medical Association last week found 36.4 percent of 214 Chinese patients had neurological symptoms ranging from loss of smell and nerve pain, to seizures and strokes.

A paper in the New England Journal of Medicine this week examining 58 patients in Strasbourg, France found that more than half were confused or agitated, with brain imaging suggesting inflammation.

Youve been hearing that this is a breathing problem, but it also affects what we most care about, the brain, S Andrew Josephson, chair of the neurology department at the University of California, San Francisco told AFP.

If you become confused, if youre having problems thinking, those are reasons to seek medical attention, he added.

The old mantra of Dont come in unless youre short of breath probably doesnt apply anymore.

Viruses and the brain

It isnt completely surprising to scientists that SARS-CoV-2 might impact the brain and nervous system, since this has been documented in other viruses, including HIV, which can cause cognitive decline if untreated.

Viruses affect the brain in one of two main ways, explained Michel Toledano, a neurologist at Mayo Clinic in Minnesota.

One is by triggering an abnormal immune response known as a cytokine storm that causes inflammation of the brain called autoimmune encephalitis.

The second is direct infection of the brain, called viral encephalitis.

How might this happen?

The brain is protected by something called the blood-brain-barrier, which blocks foreign substances but could be breached if compromised.

However, since loss of smell is a common symptom of the coronavirus, some have hypothesized the nose might be the pathway to the brain.

This remains unproven and the theory is somewhat undermined by the fact that many patients experiencing anosmia dont go on to have severe neurological symptoms.

In the case of the novel coronavirus, doctors believe based on the current evidence the neurological impacts are more likely the result of overactive immune response rather than brain invasion.

To prove the latter even happens, the virus must be detected in cerebrospinal fluid.

This has been documented once, in a 24-year-old Japanese man whose case was published in the International Journal of Infectious Disease.

The man developed confusion and seizures, and imaging showed his brain was inflamed. But since this is the only known case so far, and the virus test hasnt yet been validated for spinal fluid, scientists remain cautious.

More research needed

All of this emphasizes the need for more research.

Frontera, who is also a professor at NYU School of Medicine, is part of an international collaborative research project to standardize data collection.

Her team is documenting striking cases including seizures in COVID-19 patients with no prior history of the episodes, and unique new patterns of tiny brain hemorrhages.

One startling finding concerns the case of a man in his fifties whose white matter the parts of the brain that connect brain cells to each other was so severely damaged it would basically render him in a state of profound brain damage, she said.

The doctors are stumped and want to tap his spinal fluid for a sample.

Brain imaging and spinal taps are difficult to perform on patients on ventilators, and since most die, the full extent of neurologic injury isnt yet known.

But neurologists are being called out for the minority of patients who survive being on a ventilator.

Were seeing a lot of consults of patients presenting in confusional states, Rohan Arora, a neurologist at the Long Island Jewish Forest Hills hospital told AFP, saying that describes more than 40 percent of recovered virus patients.

Its not yet known whether the impairment is long term, and being in the ICU itself can be a disorienting experience as a result of factors including strong medications.

But returning to normal appears to be taking longer than for people who suffer heart failure or stroke, added Arora.

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Some COVID-19 patients forget where they are, what year it is - ARY NEWS