Quantum Computing Is Bigger Than Donald Trump – WIRED

Just this week the Senate had a hearing, ostensibly about speech on internet platforms. But what the hearing was really about was our continuing inability to figure out what to do with a technological infrastructure that gives every single person on the planet the ability to broadcast their thoughts, whether illuminating or poisonous. We know that solutions are elusive, especially in the context of our current electoral issues. But this is actually one of the less vexing conundrums that technology has dropped on our lap. What are we going to do about Crispr? How are we going to handle artificial intelligence, before it handles us? A not-encouraging sign of our ability to deal with change: While we werent looking, smart phones have made us cyborgs.

Heres another example of a change that might later look more significant than our current focus: Late last year, Google announced it had achieved Quantum Supremacy, This means that it solved a problem with its experimental quantum computer that couldnt be solved with a conventional one, or even a supercomputer.

Its a forgone conclusion that quantum computing is going to happen. When it does, what we thought was a speed limit will evaporate. Nobodynobody!has an idea of what can come from this. I bet it might even be bigger than whatever Donald Trump will do in a second (or third or fourth) term, or the civil disorder that might erupt if he isnt returned to the Peoples House.

A few days after the election, on that same West Coast trip, I had a random street encounter with one of the most important leaders in technology. We spoke informally for maybe 15 or 20 minutes about what had happened. He seemed shattered by the outcome, but no more than pretty much everyone I knew. He told me that he asked himself, should I have done more? Like all of the top people in the industry, he has since had to make his accommodations with the Trump administration. But as with all his peers, he has not relented on his drive to create new technology that will continue the remarkable and worrisome transformation of humanity.

The kind of people who work for him will keep doing what they do. Maybe they will no longer want to work for a company thats overly concerned about winning the favoror avoiding the disfavorof a president who they think is racist, a president who despises immigrants (wife and in-laws excepted), a president who encourages dictators and casts doubts on voting. If things get bad in this country, a lot of those engineers and scientists will leave, and a lot of other countries will welcome them. The adventure will continue. Even if the United States as we know it does not last another generation, scientists will continue advancing artificial intelligence, brain-machine interfaces, and, of course, quantum computing. And thats what our time will be known for.

Yes, a thousand years from now, historians will study the Donald Trump phenomenon and what it meant for our gutsy little experiment in democracy, as well as for the world at large. I am still confident, however, that historians will find more importance in learning about the moments in our lifetimes when science changed everything.

What I am not confident about is predicting how those future historians will do their work, and to what extent people of our time would regard those historians as human beings, or some exotic quantum Crispr-ed cyborgs. Thats something that Donald Trump will have no hand in. And why its so important, even as politics intrude on our everyday existence, to do the work of chronicling this great and fearsome adventure.

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Quantum Computing Is Bigger Than Donald Trump - WIRED

Quantum Computing Expert Warns Governments May Be First to Crack Algorithms Keeping Bitcoin and the Internet Secure – The Daily Hodl

Applied mathematician Peter Shor says government agencies could be the first to figure out a way to enable quantum computers to break algorithms that keep Bitcoin and the internet secure.

In an interview with Nature Magazine, the MIT professor of applied mathematics talks about the looming possibility that quantum computers can crack encryption keys, called RSA, that keep the internet and cryptocurrencies safe from security threats. Shor says that if theres anyone who can break the RSA first, it will be government bodies such as the National Security Agency (NSA).

The first people who break RSA either are going to be NSA or some other big organization. At first, these computers will be slow. If you have a computer that can only break, say, one RSA key per hour, anything thats not a high priority or a national-security risk is not going to be broken. The NSA has much more important things to use their quantum computer on than reading your e-mail theyll be reading the Chinese ambassadors e-mail.

Crypto enthusiasts are keeping close tabs on developments in the quantum computing space as the technology threatens to break the cryptographic algorithms that keep cryptocurrencies like Bitcoin secure. The World Economic Forum describes how quantum computing machines can crack the existing standards of encryption.

The sheer calculating ability of a sufficiently powerful and error-corrected quantum computer means that public-key cryptography is destined to fail, and would put the technology used to protect many of todays fundamental digital systems and activities at risk.

Recently, industrial powerhouse Honeywell announced that it built the System Model H1 quantum computer, which the company touts generates the highest quantum volume in the entire industry.

As to whether quantum computing poses an existential threat to the crypto industry, Ripple CTO David Schwartz says it could become powerful enough to break cryptographic algorithms within a decade.

I think we have at least eight years. I have very high confidence that its at least a decade before quantum computing presents a threat, but you never know when there could be a breakthrough. Im a cautious and concerned observer, I would say.

Featured Image: Shutterstock/archy13

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Quantum Computing Expert Warns Governments May Be First to Crack Algorithms Keeping Bitcoin and the Internet Secure - The Daily Hodl

Will Quantum Mechanics Produce the True Thinking Computer? – Walter Bradley Center for Natural and Artificial Intelligence

Some hope that quantum mechanics can explain human consciousness.

Maybe we are all quantum computers but dont know it? Maybe quantum computers could think like people?

There is an odd relationship between the human mind and quantum mechanics, the science of entities like electrons that are too small to be governed by ordinary physics.

Some aspects of consciousness appear to be mediated by such elementary particles. Science writer Philip Ball explains,

Nobody understands what consciousness is or how it works. Nobody understands quantum mechanics either. Could that be more than coincidence?

Quantum mechanics is the best theory we have for describing the world at the nuts-and-bolts level of atoms and subatomic particles. Perhaps the most renowned of its mysteries is the fact that the outcome of a quantum experiment can change depending on whether or not we choose to measure some property of the particles involved

To this day, physicists do not agree on the best way to interpret these quantum experiments, and to some extent what you make of them is (at the moment) up to you. But one way or another, it is hard to avoid the implication that consciousness and quantum mechanics are somehow linked.

This might, of course, be at least one part of the reason that consciousness remains a mystery.

But now, is a quantum computer smarter than the conventional machine that just computes numbers?

In Gaming AI, tech philosopher George Gilder notes that the resourceful AI geniuses believe that they can effect an astronomical speedup by changing the ordinary 1 or 0 bit to the quantum bit, or qubit:

The qubit is one of the most enigmatic tangles of matter and ghost in the entire armament of physics. Like a binary digit, it can register 0 or 1; what makes it quantum is that it can also register a nonbinary superposition of 0 and 1.

But before we get carried away by the possibilities, Gilder goes on to say that theres a hitch. An endless superposition works fine for Schrodingers cat. But, to be useful in the real world, the quantum computer must settle on either 0 or 1. If the needed number is your paycheck, to be cashed, it must be a number, not an infinite debate.

In any event, quantum computers come with real world problems that conventional computers dont have:

the chip can no longer function as a determinist logical device. For example, today the key problem in microchips is to avoid spontaneous quantum tunneling, where electrons can find themselves on the other side of a barrier that by the laws of classical physics would have been insurmountable and impenetrable. In digital memory chips or processors, spontaneous tunneling can mean leakage and loss.

Quantum computing has advantages and disadvantages. In any event, consciousness is still a mystery and its not clear at this point how quantum computers help us understand much. But stay tuned!

Note: You can download Gaming AI for free here.

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Quantum supremacy isnt the Big Fix. If human thought is Turings halting oracle, as seems likely, then even quantum computing will not allow us to replicate human intelligence (Eric Holloway)

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Will Quantum Mechanics Produce the True Thinking Computer? - Walter Bradley Center for Natural and Artificial Intelligence

Strategic Partnership will aid smooth work in the event of regional crisis: Australia High Commissioner – The Hindu

Artificial Intelligence, 5G, rare earth products, ground station tracking facilities to support Gaganyaan are among the areas covered, says Barry OFarell

Australian High Commissioner to India Barry OFarrell took charge a month before the COVID-19 pandemic struck in India, yet his time here has seen a steady uptick in the momentum of bilateral cooperation including a Prime Ministerial summit in June and, more recently, Australias inclusion in the Malabar naval exercises. He speaks toNarayan Lakshman about a range of cooperative initiatives on the anvil.

It will demonstrate the ability of our navy to work through exercises, warfare serials and like with the navies of India, Australia, the U.S. and Japan. That is important because, were there to be a regional crisis, like a natural or humanitarian disaster, the ability to work smoothly with partners is critical. It builds particularly on the maritime agreement that was one of the agreements underneath the CSP, but also to the mutual logistic support arrangement, which is designed to improve the collaboration between our armed forces. This reflects the commitment that Quad partners have to a free, open, and prosperous Indo Pacific. It demonstrates the commitment that Australia and India have to what Prime Minister Modi described at the June summit as a sacred duty to provide the neighbourhood with the environment where people could prosper, where there could be stability upon which to build your lives, and where you could live freely. It reiterates that.

It also comes off the back of ongoing interactions between our armed forces. To some extent, Malabar was a fixation that we are delighted to be part of, but it was a fixation because it ignored the fact that the AusIndex exercise last year was the largest naval engagement Australia had ever been a part of, and most complex involving submarine serials and P-8 Poseidon maritime patrols across the Bay of Bengal. Equally, the recent passage exercise again demonstrated our ability to work together, including practising warfare serials on water. All these things increase the level of cooperation, increase the significance of the relationship, but practically ensure that should they be called upon, our navies could work more closely together, effectively, in support of a peaceful, stable and prosperous Indo Pacific.

Also read: India-Australia friendship based on trust, respect: Scott Morrison

Certainly, the COVID-19 pandemic has damaged economies. It has accelerated geostrategic competition, and it has obviously disrupted our way of life. It has highlighted the importance, to countries like India and Australia, of ensuring a safe, secure and prosperous future for our citizens. Thats why, as part of the CSP, there were agreements in relation to critical technologies such as Artificial Intelligence, quantum computing and 5G because we recognise the opportunities they present to people, to businesses, to the broader economy, and the fact that they should be guarded by international standards to ensure they do not present risks, to security or prosperity. The Australia-India framework Arrangements on Cyber and Cyber Enabled Critical Technology cooperation, abbreviated as the Arrangement, will enhance bilateral cooperation. Under the agreement, we are going to cooperate together to promote and preserve that open, free, safe and secure Internet by working around those international norms and rules that we talk about. It sets out practical ways to promote and enhance digital trade, harness critical technologies, and address cyber security challenges. It provides a programme of 66 crore over four years for an Australia-India cyber and critical technology partnership to support research by institutions in both Australia and between institutions in Australia and India. We also signed an MoU on critical minerals between both countries because they are the essential inputs into these critical and emerging technologies, which cover areas like high tech electronics, telecommunications, clean energy, transport and defence. Critical minerals are essential if India wants to achieve its energy mission goal in the battery industry, storage industry and electric vehicle industry.

Editorial | A new dimension: On India-U.S.-Australia-Japan Quadrilateral

If you want to build batteries or electric vehicles, lithium, amongst other items, is required. We know that your northern neighbour is your most significant supplier of these critical minerals. We know that India is seeking to become more self-reliant. We know that imports from China are reducing. Australia potentially sees an opportunity for us to provide elements into Indias efforts to improve its manufacturing, defence and electric vehicle and energy mission projects. We have Indian companies who are currently owning or significant investors in Australian critical minerals and rare earths companies. We have just released a new prospectus on critical minerals and rare earths which lists over 200 projects capable of attracting more investment into India.

I know theres concern in some parts of the community that self-reliance means protectionism. Well, we believe, firstly, that that is not the case, and that there will always be markets in India for elements that can be used by India to grow economies, grow businesses and provide more jobs and more wealth into society. But secondly, if you were concerned about the protectionist angle, the fact is that there is nothing stopping you coming to Australia to buy a mine to put those resources, those elements, into your own businesses, in the same way as is happening with coalfield in Queensland.

Also read: Malabar 2020: the coming together of the Quad in the seas

Firstly, Australia is already contributing to Indias national quantum mission by facilitating partnerships with universities, research institutions and businesses. That includes one of the best relationships we have with India, which is the Australian India Strategic Research Fund, which has been going for over 20 years. Since 2013, one of our Australians of the Year, Professor Michelle Simmons, has led a team of researchers at New South Wales Universitys (UNSW) Centre for Quantum Computation and Communication Technology, seeking to build the first quantum computer in silicon.

For quantum computers to be successful with their calculations, they have to be 100% accurate, but electrical interference called charge noise gets in the way. To tackle this problem, the UNSW has used a Research Fund from that Australia India Strategic Research Fund to collaborate with the Indian Institute of Science Bangalore, to combine Australias state of the art fabrication facilities, and Indias ultra-sensitive noise measurement apparatus. This has helped identify how and where the fabrication process should be adjusted. Earlier this year, the UNSW team was able to achieve a 99.99% accuracy in their atomic level silicon prototype. They believe it is only a matter of time before theyre able to demonstrate 100% reliability, and produce a 10 qubit prototype quantum integrated processor, hopefully by 2023. This has the potential to revolutionise virtually every industry, solving problems and processing information that would take a conventional computer millions of years to calculate in seconds. This is practical cooperation between the UNSW and the Institute in Bangalore, going on right now ready to hopefully come to practical fruition in 2023. Equally, in the upcoming Bengaluru Tech Summit we will host an exclusive session providing an overview of our innovative ecosystem, our cyber and critical technology capabilities, growing space ambitions, and the applications of computing, and quantum computing. Professor Simmons will be one of the keynote speakers. We recommend tuning into 11 a.m. on Friday November 20 for the session From Cyberspace to Outer Space: Innovating with Australia in a Post-COVID World. The bottom line is that India and Australia, through two respected institutions, are close to cracking something nowhere else in the world has been cracked, and it is likely to be ready within the next three years.

Firstly, we have a space sector going back to back to 1967. We launched our first rocket in South Australia and Woomera in 1967. But we were also critical to NASA throughout, regarding the use of space as part of NASAs global space infrastructure. We received those pictures from the first moon landing and broadcast them to the world. The U.S.s two systems failed and ours didnt fail on camera, and thats why we had pictures of Neil Armstrong walking on the moon. We have facilitated communication with deep space probes and also the landing craft on Mars.

Australia and India have been cooperating together as countries since 1987, when we inked our first MoU, and there is a strong engagement between ISRO and Australian agencies. We have undertaken data collaboration on Indian remote satellites. Since 2013, we have been doing laser ranging for Indian regional navigational satellite systems. We launched an Australian satellite by an Australian company and of course, we look forward to your manned space mission in 2022. We are exploring how we can place temporary ground station tracking facilities in Australia to support that Gaganyaan Mission. That is something that is practically under way as we speak. But we have been impressed by Indias capabilities and ambitions in space. You have the record for the most number of satellites released by a single rocket ever. It was more than 100 in 2017.

A lot of the universities are using the online option. As someone whos been coming to India for 10 years, initially I did notice a resistance to online education. Like the other technologies that were finally using during COVID, that resistance has been broken down. I confirmed that with the Director of the Indian Institute of Technology, IIT Madras. But we recognise that it is face-to-face learning, like face-to-face working, is still what most people want. A number of Australian States are starting pilot programmes to demonstrate that students can be picked up and returned to Australia into campuses safely given the COVID spread. And my Education Minister Dan Tehan made the point two weeks ago that the Australian government is keen for that to happen as soon as possible. The latest part to be announced was one from South Australia that will fly students out of Singapore into Australia. There was an early one announced by the Northern Territory. On the back of those, there is a hope that we will be able to return students to Australia for Day One, Term One, next year. But it will depend on those State trials. It is a bit like our approach to opening up bubbles with other countries: we would like to see things being done in situ, in practice, in real time to show that it can succeed. If the trials are successful, I remain confident about next year.

The challenge at the present time is that both countries have international flight bans. The only flights operating between both countries are repatriation flights. Malaysia and Singapore, which were the two countries in pre-COVID times where passengers could transit to get to Australia or to come to India, are not accepting Indian citizens. But that in no way undermines Australias desire to resume whatever is going to be business as usual, in relation to tertiary education.

Australian State governments and our education institutions themselves have put a lot of effort into looking after those Indian students who were stranded in Australia due to the COVID-19 crisis. Some of them are people that have had to wait a month or two until the Vande Bharat planes started. Having graduated mid-year, they have now hopefully most of them flying home, while others are still continuing their studies. Whilst, like many places at the start of COVID-19, there were a few teething problems, Im delighted to say a combination of State and federal governments and the universities and the Indian community there have been supportive of Indian students in Australia.

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Strategic Partnership will aid smooth work in the event of regional crisis: Australia High Commissioner - The Hindu

Quantum Computing Technologies Market : Information, Figures and Analytical Insights 2020-2025 – Eurowire

The research report focuses on target groups of customers to help players to effectively market their products and achieve strong sales in the global Quantum Computing Technologies Market. It segregates useful and relevant market information as per the business needs of players. Readers are provided with validated and revalidated market forecast figures such as CAGR, Quantum Computing Technologies market revenue, production, consumption, and market share. Our accurate market data equips players to plan powerful strategies ahead of time. The Quantum Computing Technologies report offers deep geographical analysis where key regional and country level markets are brought to light. The vendor landscape is also analysed in depth to reveal current and future market challenges and Quantum Computing Technologies business tactics adopted by leading companies to tackle them.

Market dynamics including drivers, restraints, Quantum Computing Technologies market challenges, opportunities, influence factors, and trends are especially focused upon to give a clear understanding of the global Quantum Computing Technologies market. The research study includes segmental analysis where important type, application, and regional segments are studied in quite some detail. It also includes Quantum Computing Technologies market channel, distributor, and customer analysis, manufacturing cost analysis, company profiles, market analysis by application, production, revenue, and price trend analysis by type, production and consumption analysis by region, and various other market studies. Our researchers have used top-of-the-line primary and secondary research techniques to prepare the Quantum Computing Technologies report.

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Competitive Landscape

Key players of the global Quantum Computing Technologies market are profiled on the basis of various factors, which include recent developments, business strategies, financial strength, weaknesses, and main business. The Quantum Computing Technologies report offers a special assessment of top strategic moves of leading players such as merger and acquisition, collaboration, new product launch, and partnership.

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Table of Contents

Market Overview: In this section, the authors of the report provide an overview of products offered in the global Quantum Computing Technologies market, market scope, consumption comparison by application, production growth rate comparison by type, highlights of geographical analysis in Quantum Computing Technologies market, and a glimpse of market sizing forecast.

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Quantum Computing Technologies Consumption by Region: Consumption of all regional markets studied in the Quantum Computing Technologies report is analysed here. The review period considered is 2014-2019.

Quantum Computing Technologies Production by Region: It includes gross margin, production, price, production growth rate, and revenue of all regional markets between 2014 and 2019.

Competition by Manufacturer: It includes production share, revenue share, and average price by manufacturers. Quantum Computing Technologies market analysts have also discussed the products, areas served, and production sites of manufacturers and current as well as future competitive situations and trends.

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Quantum Computing Technologies Market : Information, Figures and Analytical Insights 2020-2025 - Eurowire

Why Aren’t We Talking More About Nutrition Amid COVID-19? – Anti Aging News

We recently came across this article on Mind BodyGreen that was written by their senior health editor, Kristine Thomason, that we thought was well worth the share as it ties in with similar articles that we have published.

By now, you're very familiar with the daily COVID-prevention checklist: Wash your hands, don't touch your face, wear your mask in public, and socially distance from others. And repeat. Each of these precautions aligns with guidelines the Centers for Disease Control and Prevention (CDC) released early on in the COVID-19 pandemic, to help mitigate viral transmission.

What the CDC (or any of the powers that be, for that matter) doesn't address quite so clearlymuch to many experts' dismayis the fact that nutrition is also a non-negotiable in the fight against COVID-19.

As for the CDC guidelines, there is a mention tucked into their "Food and Coronavirus" guidelines, where they advise: Reduce pandemic-related stress through good nutrition; incorporate vitamins C and D, plus zinc, into your diet for possible immune system support; read labels on any canned foods you buy, and seek out the healthiest options; and prioritize fruits, vegetables, lean protein, and whole grains. They also point toward resources at the USDA Nutrition Assistance Program if you need help securing nutritious foods.

Of course, all that information is important and usefulas are the other COVID-19 guidelines the CDC has laid out. But, unfortunately, there's not a single mention of nutrition as a preventive measureit's entirely left out of the conversation on their "Prevent Getting Sick" section. The way we see it, leaving nutrition as a side note is a huge miss. After all, we've had nutrition top of mind since day one of the pandemicwhether it's featuring an immunologist's COVID dietary advice or discussing top immune-supporting nutrients with a longevity expert.

One expert who has been particularly outspoken about this topic is preventive medicine specialist David Katz, M.D. He already gave a compelling COVID reality check on the mindbodygreen podcast, and now, he's sharing his thoughts on the importance of nutrition as a tool to keep you healthynow and always. But especially now.

Why nutrition needs to be a priority, not an afterthought.

"The greatest single influence of whether you develop a bad chronic disease or die prematurely is your diet quality," says Katz. "Diet is constantly, universally important. Literature showing that it is the single leading predictor of all-cause mortality is incontrovertible."

So, why exactly don't we hear more about diet in relation to disease prevention? To start, other factors that affect health and mortality are often much more straightforward. For example: You're either a smoker or a nonsmoker; you either do physical activity or you don't; your blood pressure is either high or normal. "But diet is an infinite array of intermingled variables," says Katz. "There are many ways to get it right. There are many more ways to get it wrong."

There are also numerous other factors at play (think cultural, socioeconomic, the list goes on) that can interfere with your access and understanding of optimal nutrition. Not to mention, as survival-driven humans, our instincts are programmed to be more attuned to immediate threats rather than long-term ones, Katz explains. "One of the reasons we neglect our diet is it doesn't fly at the speed of a bullet," he says. "If I eat a doughnut today, it won't affect me tomorrow. The cause and effect are separated by time, so it's hard to see. We are pretty blas about the massive association between diet and adverse health outcomes in general." That is, until we're faced with a pressing threat. Enter: COVID-19.

Why focusing on diet amid COVID-19 is both a necessity and an opportunity.

It's no secret that individuals with underlying health conditions like heart disease, asthma, diabetes, and chronic lung disease are at a higher risk of adverse COVID outcomes. "To ignore that is absurd, and to ignore that diet is the greatest single driver of all of that is also absurd," says Katz.

For that reason, Katz sees the current COVID-19 climate not only as a reason to prioritize diet more than ever but also an ideal time for people to make lasting change.

"It's a massive opportunity to address the acute and the chronic," he says. "We should have done it anyway, but that's the problem with dietit's a slow-motion threat; it doesn't trigger our anxiety. COVID does, so I say, let's catch the wave."

So, what can you do...today?

"There's never been a better time to have the 'let's get healthy, America' conversation," says Katz. That's because, even small, conscious changes can affect your health and immunity.

As for a healthy diet, Katz believes there's a basic theme to eating optimally, but there isn't a narrow prescription every person needs to adopt. To get you started on your own path, he shares a few tips for taking positive, dietary steps forwardand they're backed by other experts in the field, too:

1. Start with one healthy meal...but know the benefits get better over time.

"You can alter your immune response with a single meal," Katz says, "the magnitude of benefit will accrue over time, you certainly won't get the full measure from one good meal, but you can start the party." He notes that there is evidence in studies that observe how harvest cells in the immune system react to different stimuli. "They react in a way that's more likely to protect you following a high-quality meal, as opposed to a low-quality meal."

2. Opt for wholesome, natural foods.

"Essentially the closer you get to foods that come directly from nature, the better," says Katz. "So you want to avoid ultra-processed stuff and eat as much real, minimally or unprocessed foods as possible." Simple steps in the right direction might mean sipping water instead of soda or choosing whole grains instead of refined ones. "And if the ingredient list runs off the box, it's probably a bad idea."

And when it comes to choosing those foods...amid a pandemic, experts and the CDC agree that foods rich in vitamin C, vitamin D, and zinc may be particularly beneficial. "There's no disagreement between scientists and doctors that vitamin D is important for the immune system," David Sinclair, Ph.D., said during a recent mbg podcast episode. While Amy Shah, M.D., notes that vitamin C is such an important nutrient for immune support.

Supplements are also an option, but Katz points out these should be used as "supplemental to, not substitutes for a high-quality diet."

3. Swap in plants when you can.

"Since our diets tend to be heavy on animal foods, and most people consume too few fruits and vegetables, the more you can shift to plant foods the better," says Katz. That includes an array of fruits, veggies, whole grains, beans, lentils, nuts, and seeds. "Basically, any time you can eat a plant instead of an animal, do."

Other experts agree with this sentiment, including Jeffrey Bland, Ph.D. "If we go back to the cultures that have respected longevity and ask what they ate, we find that they're eating very hearty plants," he shared in a recent mbg podcast episode.

Of course, there are other measures you can takebut a healthy diet doesn't need to be overly complex, by any means. As Katz puts it, "It's just that simple; it's just that powerful. It's actionable, it's immediate, and there's never been a better time."

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Why Aren't We Talking More About Nutrition Amid COVID-19? - Anti Aging News

Ethical Machine Learning as a Wicked Problem Machine Learning Times – The Predictive Analytics Times

By: Sherril Hayes, Executive Director, Analytics and Data Science Institute and Professor of Conflict Management, Analytics & Data Science Institute, College of Computing and Software Engineering, Kennesaw State UniversityIn the 1950 and 1960s, social and behavioral sciences were at the cutting edge of innovation. Scientific techniques and quantitative analyses were being applied to some of the most pressing social problems. The thinking was If NASA can put men in space, why cant we use these techniques to solve the problems of housing discrimination and school desegregation? Despite the investment, effort, and professionalization of these fields, the consensus was that they were failing. Why? In 1973 Horst Rittel, a mathematician and Professor in the Science of Design at UC Berkeley, and his colleague Melvin Weber introduced the

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Ethical Machine Learning as a Wicked Problem Machine Learning Times - The Predictive Analytics Times

LPA announce VisiRule FastChart to combine Machine Learning and rule-based expert systems – PR Web

LONDON (PRWEB) November 02, 2020

VisiRule FastChart is an exciting new addition to the VisiRule family of visual AI expert system tools.

VisiRule FastChart can automatically interpret decision trees and use them to auto-construct a VisiRule chart without any user involvement. This means that historical data can be used to create VisiRule charts.

For example, given a historical log of machine data and fault logs, a decision tree can be induced which when exported to VisiRule FastChart will lead to a visual model being built in VisiRule. This chart can be used to predict future occurrences based on current data.

VisiRule incorporates Artificial Intelligence in the form of expert system rule-based inferencing. Complex behaviour and computation can be represented as a set of interconnected decision rules which in turn can be represented graphically in VisiRule.

Clive Spenser, Marketing Director, LPA, says, "VisiRule FastChart is an exciting new addition to the VisiRule family. It allows companies to utilise historical data to build current models to help predict and prescribe remedies for future situations". Clive adds "The highly visual philosophy of VisiRule makes building and testing such models more practical and opens up the world of AI to a much wider audience."

VisiRule FastChart is available for immediate release as part of the VisiRule product range at a price of 2500 USD.

VisiRule is an easy-to-use Low-Code No-Code tool for subject matter experts, like lawyers, tax advisors, engineers, to rapidly define and deliver intelligent advice and troubleshooting guides using decision tree flowcharts.

VisiRule allows experts to capture, evaluate, refine and deploy specialist expertise as smart AI solutions. Use cases include problem triage with recommended prescriptive actions plus document generation. https://www.visirule.co.uk/

LPA is a small dedicated AI company in London, England which has been providing logic-based software solutions since it was formed in 1981. LPA products have been used in a wide-range of commercial and research applications including legal document assembly, environmental engineering, information modeling, disease diagnosis, fault diagnosis, products sales and recommendations. https://www.lpa.co.uk/

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Resetting Covid-19 Impact On Artificial Intelligence and Machine Learning in IoT Market Report Explores Complete Research With Top Companies- Google,…

Earlier machine learning techniques have been used extensively for a wide range of tasks including classification, regression and density estimation in a variety of application areas such as bioinformatics, speech recognition, spam detection, computer vision, fraud detection and advertising networks. Machine learning is the main method among those computational application to IoT and there are lots of application both in research and industry including energy, routing, and home automation and so on.

Top Companies Covered in this Report:

Google Inc., Cisco, IBM Corp., Microsoft Corp., Amazon Inc., PTC (ColdLight), Infobright, Mtell, Predikto, Predixion Software and Sight Machine

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The report aims to provide an overview of global artificial intelligence and machine learning in IoT market with detailed market segmentation by application, and geography. The global artificial intelligence and machine learning in IoT market is expected to witness exponential growth during the forecast period so as to manage increasingly large amount of unstructured machine data available in almost all industry.

The objectives of this report are as follows:

To provide overview of the global artificial intelligence and machine learning in IoT market

To analyze and forecast the global artificial intelligence and machine learning in IoT market on the basis of its application

To provide market size and forecast till 2025 for overall artificial intelligence and machine learning in IoT market with respect to five major regions, namely; North America, Europe, Asia-Pacific (APAC), Middle East and Africa (MEA) and South America (SAM), which is later sub-segmented by respective countries

To evaluate market dynamics effecting the market during the forecast period i.e., drivers, restraints, opportunities, and future trend

To provide exhaustive PEST analysis for all five regions

To profiles key artificial intelligence and machine learning in IoT players influencing the market along with their SWOT analysis and market strategies

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Table Of Content

1 Introduction

2 Key Takeaways

3 Artificial Intelligence and Machine Learning in IoT Market Landscape

4 Artificial Intelligence and Machine Learning in IoT Market Key Industry Dynamics

5 Artificial Intelligence and Machine Learning in IoT Market Analysis- Global

6 Artificial Intelligence and Machine Learning in IoT Market Revenue and Forecasts to 2025 Application

7 Artificial Intelligence and Machine Learning in IoT Market Revenue and Forecasts to 2025 Geographical Analysis

8 Industry Landscape

9 Competitive Landscape

10 Artificial Intelligence and Machine Learning in IoT Market, Key Company Profiles

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Xbox Series X is more suited to machine learning than PS5 says David Cage – MSPoweruser – MSPoweruser

Xbox Series X may have one additional advantage over Sonys PlayStation 5 console: machine learning.

In an interview with WCCFTech, Quantic Dreams CEO David Cage revealed that the design of Microsofts Xbox Series X gives it the advantage in machine learning compared to the PlayStation 5.

Cage revealed that while the slightly better CPU and beefier GPU of the Xbox Series X gives Microsoft a slight edge over PS5, its really the machine learning capabilities of the Xbox console that may help it succeed against PlayStations faster SSD.

The shader cores of the Xbox are also more suitable to machine learning, which could be an advantage if Microsoft succeeds in implementing an equivalent to Nvidias DLSS, Cage explained.

However, the PlayStation-focused developer also explained that Sony has consistently punched up to deliver great looking games on not-so-powerful hardware in the past.

I think that the pure analysis of the hardware shows an advantage for Microsoft, but experience tells us that hardware is only part of the equation: Sony showed in the past that their consoles could deliver the best-looking games because their architecture and software were usually very consistent and efficient.

In a previous interview, Cage explained that he believes the split nature of Xbox Series X and Xbox Series S is confusing for consumers and developers.

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Xbox Series X is more suited to machine learning than PS5 says David Cage - MSPoweruser - MSPoweruser

Machine learning and predictive analytics work better together – TechTarget

Like many AI technologies, the difference between machine learning and predictive analytics lies in applications and use cases. Machine learning's ability to learn from previous data sets and stay nimble lends itself to diverse applications like neural networks or image detection, while predictive analytics' narrow focus is on forecasting specific target variables.

Instead of implementing one type of AI or choosing between the two strategies, companies that want to get the most out of their data should combine the processing power of predictive analytics and machine learning.

Artificial intelligence is the replication of human intelligence by machines. This includes numerous technologies such as robotic process automation (RPA), natural language processing (NLP) and machine learning. These diverse technologies each replicate human abilities but often operate differently in order to accomplish their specific tasks.

Machine learning is a form of AI that allows software applications to become progressively more accurate at prediction without being expressly programmed to do so. The algorithms applied to machine learning programs and software are created to be versatile and allow for developers to make changes via hyperparameter tuning. The machine 'learns' by processing large amounts of data and detecting patterns within this set. Machine learning is the foundational basis for advanced technologies like deep learning, neural networks and autonomous vehicle operation.

Machine learning can increase the speed at which data is processed and analyzed and is a clear candidate through which AI and predictive analytics can coalesce. Using machine learning, algorithms can train on even larger data sets and perform deeper analysis on multiple variables with minor changes in deployment.

Machine learning and AI have become enterprise staples, and the debate over value is obsolete in the eyes of Gartner analyst Whit Andrews. In years prior, operationalizing machine learning required a difficult transition for organizations, but the technology has now successful implementation in numerous industries due to the popularity of open source and private software machine learning development.

"Machine learning is easier to use now by far than it was five years ago," Andrews said. "And it's also likely to be more familiar to the organization's business leaders."

As a form of advanced analytics, predictive analytics uses new and historical data in order to predict and forecast behaviors and trends.

Software applications of predictive analytics use variables that can be analyzed to predict the future likely behavior, whether for individual consumers, machinery or sales trends. This form of analytics typically requires expertise in statistical methods and is therefore commonly the domain of data scientists, data analysts and statisticians -- but also requires major oversight in order to function.

For Gartner analyst Andrew White, the crucial piece of deploying predictive analytics is strong business leadership. In order to see successful implementation, enterprises need to be using predictive analytics and data to constantly try and improve business processes. The decisions and outcomes need to be based on the data analytics, which requires a hands-on data science team.

Because of the smaller training samples used to create a specific model that does not have much capacity for learning, White stressed the importance of quality training data. Predictive models and the data they are using need to be equally fine-tuned; confusing the analytics or the data as the main player is a mistake in White's eyes.

"The reality is [data and analytical models] are equal," White said. "You need to have ownership or leadership around prioritizing and governing data as much as you have the same for analytics, because analytics is just the last mile."

Data-rich enterprises have established successful applications for both machine learning and predictive analytics.

Retailers are one of the most predominant enterprises using predictive analytics tools in order to spot website user trends and hyperpersonalize ads and target emails. Massive amounts of data collected from points of sale, retail apps, social media, in-store sensors and voluntary email lists provide insights on sales forecasting, customer experience management, inventory and supply chain.

Another popular application of predictive analytics is predictive maintenance. Manufacturers use predictive analytics to monitor their equipment and machinery and predict when they need to replace or repair valuable pieces.

Predictive analytics is also popularly deployed in risk management, fraud and security, and healthcare applications across enterprises.

Machine learning, on the other hand, has a wider variety of applications, from customer relationship management to self-driving cars. These algorithms are in human resource information systems to identify candidates, within software sold by business intelligence and analytics vendors, as well as in customer relationship management systems.

In businesses, the most popular machine learning applications include chatbots, recommendation engines, market research and image recognition.

Enterprise trend applications are where predictive analytics and AI can converge. Maintaining best data practices as well as focusing on combining the powers of machine learning and predictive analytics is the only way for organizations to keep themselves at the cutting edge of predictive forecasting.

Machine learning algorithms can produce more accurate predictions, create cleaner data and empower predictive analytics to work faster and provide more insight with less oversight. Having a strong predictive analysis model and clean data fuels the machine learning application. While a combination does not necessarily provide more applications, it does mean that the application can be trusted more. Splitting hairs between the two shows that these terms are actually hierarchical and that when combined, they complete one another to strengthen the enterprise.

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Machine learning and predictive analytics work better together - TechTarget

Impact of Covid-19 on Machine Learning as a Service (MLaaS) Market is Projected to Grow Massively in Near Future with Profiling Eminent Players-…

Up-To-Date research on Machine Learning as a Service (MLaaS) Market 2020-2026 :

The Reputed Garner Insights website offers vast reports on different market.They cover all industry and these reports are very precise and reliable. It also offers Machine Learning as a Service (MLaaS) Market Report 2020 in its research report store. It is the most comprehensive report available on this market. The report study provides information on market trends and development, drivers, capacities, technologies, and on the changing investment structure of the Global Machine Learning as a Service (MLaaS) Market.

The study gives a transparent view on the Global Machine Learning as a Service (MLaaS) Market and includes a thorough competitive scenario and portfolio of the key players functioning in it. To get a clear idea of the competitive landscape in the market, the report conducts an analysis of Porters Five Forces Model. The report also provides a market attractiveness analysis, in which the segments and sub-segments are benchmarked on the basis of their market size, growth rate, and general attractiveness.

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Some of the major geographies included in the market are given below:North America (U.S., Canada)Europe (U.K., Germany, France, Italy)Asia Pacific (China, India, Japan, Singapore, Malaysia)Latin America (Brazil, Mexico)Middle East & Africa

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Components of the Machine Learning as a Service (MLaaS)Market report:-A detailed assessment of all opportunities and risk in this Market.-Recent innovations and major events-A comprehensive study of business strategies for the growth of the Machine Learning as a Service (MLaaS)leading market players.-Conclusive study about the growth plot of Machine Learning as a Service (MLaaS) Market for the upcoming years.-Understanding of Machine Learning as a Service (MLaaS)Industry-particular drivers, constraints and major micro markets in detail.-An evident impression of vital technological and latest market trends striking theMarket.

The objectives of the study are as follows:

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Impact of Covid-19 on Machine Learning as a Service (MLaaS) Market is Projected to Grow Massively in Near Future with Profiling Eminent Players-...

Machine Learning as a Service Market Qualitative Insights the COVID-19 by 2023 – Aerospace Journal

Market Overview

Machine learning has become a disruptive trend in the technology industry with computers learning to accomplish tasks without being explicitly programmed. The manufacturing industry is relatively new to the concept of machine learning. Machine learning is well aligned to deal with the complexities of the manufacturing industry. Manufacturers can improve their product quality, ensure supply chain efficiency, reduce time to market, fulfil reliability standards, and thus, enhance their customer base through the application of machine learning. Machine learning algorithms offer predictive insights at every stage of the production, which can ensure efficiency and accuracy. Problems that earlier took months to be addressed are now being resolved quickly. The predictive failure of equipment is the biggest use case of machine learning in manufacturing. The predictions can be utilized to create predictive maintenance to be done by the service technicians. Certain algorithms can even predict the type of failure that may occur so that correct replacement parts and tools can be brought by the technician for the job.

Market Analysis

According to Infoholic Research, Machine Learning as a Service (MLaaS) Market will witness a CAGR of 49% during the forecast period 20172023. The market is propelled by certain growth drivers such as the increased application of advanced analytics in manufacturing, high volume of structured and unstructured data, the integration of machine learning with big data and other technologies, the rising importance of predictive and preventive maintenance, and so on. The market growth is curbed to a certain extent by restraining factors such as implementation challenges, the dearth of skilled data scientists, and data inaccessibility and security concerns to name a few.

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Segmentation by Components

The market has been analyzed and segmented by the following components Software Tools, Cloud and Web-based Application Programming Interface (APIs), and Others.

Segmentation by End-users

The market has been analyzed and segmented by the following end-users, namely process industries and discrete industries. The application of machine learning is much higher in discrete than in process industries.

Segmentation by Deployment Mode

The market has been analyzed and segmented by the following deployment mode, namely public and private.

Regional Analysis

The market has been analyzed by the following regions as Americas, Europe, APAC, and MEA. The Americas holds the largest market share followed by Europe and APAC. The Americas is experiencing a high adoption rate of machine learning in manufacturing processes. The demand for enterprise mobility and cloud-based solutions is high in the Americas. The manufacturing sector is a major contributor to the GDP of the European countries and is witnessing AI driven transformation. Chinas dominant manufacturing industry is extensively applying machine learning techniques. China, India, Japan, and South Korea are investing significantly on AI and machine learning. MEA is also following a high growth trajectory.

Vendor Analysis

Some of the key players in the market are Microsoft, Amazon Web Services, Google, Inc., and IBM Corporation. The report also includes watchlist companies such as BigML Inc., Sight Machine, Eigen Innovations Inc., Seldon Technologies Ltd., and Citrine Informatics Inc.

Benefits

The study covers and analyzes the Global MLaaS Market in the manufacturing context. Bringing out the complete key insights of the industry, the report aims to provide an opportunity for players to understand the latest trends, current market scenario, government initiatives, and technologies related to the market. In addition, it helps the venture capitalists in understanding the companies better and take informed decisions.

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Machine Learning as a Service Market Qualitative Insights the COVID-19 by 2023 - Aerospace Journal

93% of security operations centers employing AI and machine learning tools to detect advanced threats – Security Magazine

93% of security operations center employing AI and machine learning tools to detect advanced threats | 2020-10-30 | Security Magazine This website requires certain cookies to work and uses other cookies to help you have the best experience. By visiting this website, certain cookies have already been set, which you may delete and block. By closing this message or continuing to use our site, you agree to the use of cookies. Visit our updated privacy and cookie policy to learn more. This Website Uses CookiesBy closing this message or continuing to use our site, you agree to our cookie policy. Learn MoreThis website requires certain cookies to work and uses other cookies to help you have the best experience. By visiting this website, certain cookies have already been set, which you may delete and block. By closing this message or continuing to use our site, you agree to the use of cookies. Visit our updated privacy and cookie policy to learn more.

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93% of security operations centers employing AI and machine learning tools to detect advanced threats - Security Magazine

Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study – DocWire News

This article was originally published here

Sci Rep. 2020 Oct 30;10(1):18716. doi: 10.1038/s41598-020-75767-2.

ABSTRACT

The rapid spread of COVID-19 has resulted in the shortage of medical resources, which necessitates accurate prognosis prediction to triage patients effectively. This study used the nationwide cohort of South Korea to develop a machine learning model to predict prognosis based on sociodemographic and medical information. Of 10,237 COVID-19 patients, 228 (2.2%) died, 7772 (75.9%) recovered, and 2237 (21.9%) were still in isolation or being treated at the last follow-up (April 16, 2020). The Cox proportional hazards regression analysis revealed that age > 70, male sex, moderate or severe disability, the presence of symptoms, nursing home residence, and comorbidities of diabetes mellitus (DM), chronic lung disease, or asthma were significantly associated with increased risk of mortality (p 0.047). For machine learning, the least absolute shrinkage and selection operator (LASSO), linear support vector machine (SVM), SVM with radial basis function kernel, random forest (RF), and k-nearest neighbors were tested. In prediction of mortality, LASSO and linear SVM demonstrated high sensitivities (90.7% [95% confidence interval: 83.3, 97.3] and 92.0% [85.9, 98.1], respectively) and specificities (91.4% [90.3, 92.5] and 91.8%, [90.7, 92.9], respectively) while maintaining high specificities > 90%, as well as high area under the receiver operating characteristics curves (0.963 [0.946, 0.979] and 0.962 [0.945, 0.979], respectively). The most significant predictors for LASSO included old age and preexisting DM or cancer; for RF they were old age, infection route (cluster infection or infection from personal contact), and underlying hypertension. The proposed prediction model may be helpful for the quick triage of patients without having to wait for the results of additional tests such as laboratory or radiologic studies, during a pandemic when limited medical resources must be wisely allocated without hesitation.

PMID:33127965 | DOI:10.1038/s41598-020-75767-2

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Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study - DocWire News

Facebook’s machine learning translation software raises the stakes – Verdict

Facebook has launched a multilingual machine learning translation model. Previous models tended to rely on English data as an intermediary. However, Facebooks many-to-many software, called M2M-100, can translate directly between any pair of 100 languages. The software is open-source with the model, raw data, training, and evaluation setup available on GitHub.

M2M-100, if it works correctly, provides a functional product with real-world applications, which can be built on by other developers. In a globalized world, accurate translation of a wide variety of languages is vital. It enables accurate communication between different communities, which is essential for multinational businesses. It also allows news articles and social media posts to be accurately portrayed, reducing instances of misinformation.

GlobalDatas recent thematic report on AI suggests that years of bold proclamations by tech companies eager for publicity have resulted in AI becoming overhyped. The reality has often fallen short of the rhetoric. Principal Microsoft researcher Katja Hofmann argues that AI is transitioning to a new phase, in which breakthroughs occur but at a slower rate than previously suggested. The next few years will require practical uses of AI with tangible benefits, addressing AI to specific use cases.

M2M-100 provides 2,200 translation combinations of 100 languages without relying on English data as a mediator. Among its main competitors, Amazon Translate and Microsoft Translator both support significantly fewer languages than Facebook. However, Google Translate supports 108 languages, both dead and alive, having added five new languages in February 2020.

Google and Facebooks products have offer differences. Google uses BookCorpus and English Wikipedia as training data, whereas Facebook analyzes the language of its users. Facebook is, therefore, more suitable for conversational translation, while Google excels at academic style web page translation. Google performs best when English is the target language, which correlates to the training data used. Facebooks multi-directional model claims there is no English bias, with translations functioning between 2,200 language pairs. Accurate conversational translations based on real-time data and multiple language pairs can fulfil global business needs, making Facebook a market leader.

Facebooks strength in this aspect of AI is unsurprising. GlobalData has given the company a thematic score of 5 out of 5 for machine learning, suggesting that this theme will significantly improve Facebooks future performance.

However, natural language processing (NLP) can be problematic, with language semantics making it hard for algorithms to provide accurate translations. In 2017, Facebook translated the phrase good morning in Arabic, posted on its platform by a Palestinian man, as attack them in Hebrew, resulting in the senders arrest by Israeli police. The open-source nature of the software will help developers recognize pain points. It also allows innovation, enabling multilingual models to be advanced in the future by developers.

Language translation is a high-profile use case for AI due to its applications in conversational plaforms like Amazons Alexa, Googles Assistant, and Apples Siri. The tech giants are racking to improve the performance of their virtual assistants. Facebooks M2M-100 announcement will raise the stakes in AI translation software, pushing the companys main competitors to respond.

In an interconnected, globalized world, accurate translation is essential. Facebook has used its global community and access to large datasets to progress machine learning and AI, creating a practical, real-world use case. Allowing access to the training data and models propels future developments, moving linguistic machine learning away from a traditionally Anglo-centric model.Related Report Download the full report from GlobalData's Report StoreGet the Report

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Microsoft/MITRE group declares war on machine learning vulnerabilities with Adversarial ML Threat Matrix – Diginomica

(Pixabay)

The extraordinary advances in machine learning that drive the increasing accuracy and reliability of artificial intelligence systems have been matched by a corresponding growth in malicious attacks by bad actors seeking to exploit a new breed of vulnerabilities designed to distort the results.

Microsoft reports it has seen a notable increase in attacks on commercial ML systems over the past four years. Other reports have also brought attention to this problem.Gartner's Top 10 Strategic Technology Trends for 2020, published in October 2019, predicts that:

Through 2022, 30% of all AI cyberattacks will leverage training-data poisoning, AI model theft, or adversarial samples to attack AI-powered systems.

Training data poisoning happens when an adversary is able to introduce bad data into your model's training pool, and hence get it to learn things that are wrong. One approach is to target your ML's availability; the other targets its integrity (commonly known as "backdoor" attacks). Availability attacks aim to inject so much bad data into your system that whatever boundaries your model learns are basically worthless. Integrity attacks are more insidious because the developer isn't aware of them so attackers can sneak in and get the system to do what they want.

Model theft techniques are used to recover models or information about data used during training which is a major concern because AI models represent valuable intellectual property trained on potentially sensitive data including financial trades, medical records, or user transactions.The aim of adversaries is to recreate AI models by utilizing the public API and refining their own model using it as a guide.

Adversarial examples are inputs to machine learning models that attackers haveintentionally designed to cause the model to make a mistake.Basically, they are like optical illusions for machines.

All of these methods are dangerous and growing in both volume and sophistication. As Ann Johnson Corporate Vice President, SCI Business Development at Microsoft wrote in ablog post:

Despite the compelling reasons to secure ML systems, Microsoft's survey spanning 28 businesses found that most industry practitioners have yet to come to terms with adversarial machine learning. Twenty-five out of the 28 businesses indicated that they don't have the right tools in place to secure their ML systems. What's more, they are explicitly looking for guidance. We found that preparation is not just limited to smaller organizations. We spoke to Fortune 500 companies, governments, non-profits, and small and mid-sized organizations.

Responding to the growing threat, last week, Microsoft, the nonprofit MITRE Corporation, and 11 organizations including IBM, Nvidia, Airbus, and Bosch released theAdversarial ML Threat Matrix, an industry-focused open framework designed to help security analysts to detect, respond to, and remediate threats against machine learning systems. Microsoft says it worked with MITRE to build a schema that organizes the approaches employed by malicious actors in subverting machine learning models, bolstering monitoring strategies around organizations' mission-critical systems.Said Johnson:

Microsoft worked with MITRE to create the Adversarial ML Threat Matrix, because we believe the first step in empowering security teams to defend against attacks on ML systems, is to have a framework that systematically organizes the techniques employed by malicious adversaries in subverting ML systems. We hope that the security community can use the tabulated tactics and techniques to bolster their monitoring strategies around their organization's mission critical ML systems.

The Adversarial ML Threat, modeled after the MITRE ATT&CK Framework, aims to address the problem with a curated set of vulnerabilities and adversary behaviors that Microsoft and MITRE vetted to be effective against production systems. With input from researchers at the University of Toronto, Cardiff University, and the Software Engineering Institute at Carnegie Mellon University, Microsoft and MITRE created a list of tactics that correspond to broad categories of adversary action.

Techniques in the schema fall within one tactic and are illustrated by a series of case studies covering how well-known attacks such as the Microsoft Tay poisoning, the Proofpoint evasion attack, and other attacks could be analyzed using the Threat Matrix. Noted Charles Clancy, MITRE's chief futurist, senior vice president, and general manager of MITRE Labs:

Unlike traditional cybersecurity vulnerabilities that are tied to specific software and hardware systems, adversarial ML vulnerabilities are enabled by inherent limitations underlying ML algorithms. Data can be weaponized in new ways which requires an extension of how we model cyber adversary behavior, to reflect emerging threat vectors and the rapidly evolving adversarial machine learning attack lifecycle.

Mikel Rodriguez, a machine learning researcher at MITRE who also oversees MITRE's Decision Science research programs, said that AI is now at the same stage now where the internet was in the late 1980s when people were focused on getting the technology to work and not thinking that much about longer term implications for security and privacy. That, he says, was a mistake that we can learn from.

The Adversarial ML Threat Matrix will allow security analysts to work with threat models that are grounded in real-world incidents that emulate adversary behavior with machine learning and to develop a common language that allows for better communications and collaboration.

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Microsoft/MITRE group declares war on machine learning vulnerabilities with Adversarial ML Threat Matrix - Diginomica

5 machine learning skills you need in the cloud – TechTarget

Machine learning and AI continue to reach further into IT services and complement applications developed by software engineers. IT teams need to sharpen their machine learning skills if they want to keep up.

Cloud computing services support an array of functionality needed to build and deploy AI and machine learning applications. In many ways, AI systems are managed much like other software that IT pros are familiar with in the cloud. But just because someone can deploy an application, that does not necessarily mean they can successfully deploy a machine learning model.

While the commonalities may partially smooth the transition, there are significant differences. Members of your IT teams need specific machine learning and AI knowledge, in addition to software engineering skills. Beyond the technological expertise, they also need to understand the cloud tools currently available to support their team's initiatives.

Explore the five machine learning skills IT pros need to successfully use AI in the cloud and get to know the products Amazon, Microsoft and Google offer to support them. There is some overlap in the skill sets, but don't expect one individual to do it all. Put your organization in the best position to utilize cloud-based machine learning by developing a team of people with these skills.

IT pros need to understand data engineering if they want to pursue any type of AI strategy in the cloud. Data engineering is comprised of a broad set of skills that requires data wrangling and workflow development, as well as some knowledge of software architecture.

These different areas of IT expertise can be broken down into different tasks IT pros should be able to accomplish. For example, data wrangling typically involves data source identification, data extraction, data quality assessments, data integration and pipeline development to carry out these operations in a production environment.

Data engineers should be comfortable working with relational databases, NoSQL databases and object storage systems. Python is a popular programming language that can be used with batch and stream processing platforms, like Apache Beam, and distributed computing platforms, such as Apache Spark. Even if you are not an expert Python programmer, having some knowledge of the language will enable you to draw from a broad array of open source tools for data engineering and machine learning.

Data engineering is well supported in all the major clouds. AWS has a full range of services to support data engineering, such as AWS Glue, Amazon Managed Streaming for Apache Kafka (MSK) and various Amazon Kinesis services. AWS Glue is a data catalog and extract, transform and load (ETL) service that includes support for scheduled jobs. MSK is a useful building block for data engineering pipelines, while Kinesis services are especially useful for deploying scalable stream processing pipelines.

Google Cloud Platform offers Cloud Dataflow, a managed Apache Beam service that supports batch and steam processing. For ETL processes, Google Cloud Data Fusion provides a Hadoop-based data integration service. Microsoft Azure also provides several managed data tools, such as Azure Cosmos DB, Data Catalog and Data Lake Analytics, among others.

Machine learning is a well-developed discipline, and you can make a career out of studying and developing machine learning algorithms.

IT teams use the data delivered by engineers to build models and create software that can make recommendations, predict values and classify items. It is important to understand the basics of machine learning technologies, even though much of the model building process is automated in the cloud.

As a model builder, you need to understand the data and business objectives. It's your job to formulate the solution to the problem and understand how it will integrate with existing systems.

Some products on the market include Google's Cloud AutoML, which is a suite of services that help build custom models using structured data as well as images, video and natural language without requiring much understanding of machine learning. Azure offers ML.NET Model Builder in Visual Studio, which provides an interface to build, train and deploy models. Amazon SageMaker is another managed service for building and deploying machine learning models in the cloud.

These tools can choose algorithms, determine which features or attributes in your data are most informative and optimize models using a process known as hyperparameter tuning. These kinds of services have expanded the potential use of machine learning and AI strategies. Just as you do not have to be a mechanical engineer to drive a car, you do not need a graduate degree in machine learning to build effective models.

Algorithms make decisions that directly and significantly impact individuals. For example, financial services use AI to make decisions about credit, which could be unintentionally biased against particular groups of people. This not only has the potential to harm individuals by denying credit but it also puts the financial institution at risk of violating regulations, like the Equal Credit Opportunity Act.

These seemingly menial tasks are imperative to AI and machine learning models. Detecting bias in a model can require savvy statistical and machine learning skills but, as with model building, some of the heavy lifting can be done by machines.

FairML is an open source tool for auditing predictive models that helps developers identify biases in their work. Experience with detecting bias in models can also help inform the data engineering and model building process. Google Cloud leads the market with fairness tools that include the What-If Tool, Fairness Indicators and Explainable AI services.

Part of the model building process is to evaluate how well a machine learning model performs. Classifiers, for example, are evaluated in terms of accuracy, precision and recall. Regression models, such as those that predict the price at which a house will sell, are evaluated by measuring their average error rate.

A model that performs well today may not perform as well in the future. The problem is not that the model is somehow broken, but that the model was trained on data that no longer reflects the world in which it is used. Even without sudden, major events, data drift can occur. It is important to evaluate models and continue to monitor them as long as they are in production.

Services such as Amazon SageMaker, Azure Machine Learning Studio and Google Cloud AutoML include an array of model performance evaluation tools.

Domain knowledge is not specifically a machine learning skill, but it is one of the most important parts of a successful machine learning strategy.

Every industry has a body of knowledge that must be studied in some capacity, especially when building algorithmic decision-makers. Machine learning models are constrained to reflect the data used to train them. Humans with domain knowledge are essential to knowing where to apply AI and to assess its effectiveness.

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5 machine learning skills you need in the cloud - TechTarget

Machine learning approach could detect drivers of atrial fibrillation – Cardiac Rhythm News

Mapping of the explanted human heart

Researchers have designed a new machine learning-based approach for detecting atrial fibrillation (AF) drivers, small patches of the heart muscle that are hypothesised to cause this most common type of cardiac arrhythmia. This approach may lead to more efficient targeted medical interventions to treat the condition, according to the authors of the paper published in the journal Circulation: Arrhythmia and Electrophysiology.

The mechanism behind AF is yet unclear, although research suggests it may be caused and maintained by re-entrant AF drivers, localised sources of repetitive rotational activity that lead to irregular heart rhythm. These drivers can be burnt via a surgical procedure, which can mitigate the condition or even restore the normal functioning of the heart.

To locate these re-entrant AF drivers for subsequent destruction, doctors use multi-electrode mapping, a technique that allows them to record multiple electrograms inside the using a catheter and build a map of electrical activity within the atria. However, clinical applications of this technique often produce a lot of false negatives, when an existing AF driver is not found, and false positives, when a driver is detected where there really is none.

Recently, researchers have tapped machine learning algorithms for the task of interpreting ECGs to look for AF; however, these algorithms require labelled data with the true location of the driver, and the accuracy of multi-electrode mapping is insufficient. The authors of the new study, co-led by Dmitry Dylov from the Skoltech Center of Computational and Data-Intensive Science and Engineering (CDISE, Moscow, Russia) and Vadim Fedorov from the Ohio State University (Columbus, USA) used high-resolution near-infrared optical mapping (NIOM) to locate AF drivers and stuck with it as a reference for training.

NIOM is based on well-penetrating infrared optical signals and therefore can record the electrical activity from within the heart muscle, whereas conventional clinical electrodes can only measure the signals on the surface. Add to this trait the excellent optical resolution, and the optical mapping becomes a no-brainer modality if you want to visualize and understand the electrical signal propagation through the heart tissue, said Dylov.

The team tested their approach on 11 explanted human hearts, all donated posthumously for research purposes. Researchers performed the simultaneous optical and multi-electrode mapping of AF episodes induced in the hearts. ML model can indeed efficiently interpret electrograms from multielectrode mapping to locate AF drivers, with an accuracy of up to 81%. They believe that larger training datasets, validated by NIOM, can improve machine learning-based algorithms enough for them to become complementary tools in clinical practice.

The dataset of recording from 11 human hearts is both extremely priceless and too small. We realiaed that clinical translation would require a much larger sample size for representative sampling, yet we had to make sure we extracted every piece of available information from the still-beating explanted human hearts. Dedication and scrutiny of two of our PhD students must be acknowledged here: Sasha Zolotarev spent several months on the academic mobility trip to Fedorovs lab understanding the specifics of the imaging workflow and present the pilot study at the HRS conference the biggest arrhythmology meeting in the world, and Katya Ivanova partook in the frequency and visualization analysis from within the walls of Skoltech. These two young researchers have squeezed out everything one possibly could, to train the machine learning model using optical measurements, Dylov notes.

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Machine learning approach could detect drivers of atrial fibrillation - Cardiac Rhythm News

Amwell CMO: Google partnership will focus on AI, machine learning to expand into new markets – FierceHealthcare

Amwell is looking to evolve virtual care beyond just imitating in-person care.

To do that, the telehealth companyexpects to use its latestpartnership with Google Cloud toenable it to tap into artificial intelligence and machine learning technologies to create a better healthcare experience, according to Peter Antall, M.D., Amwell's chief medical officer.

"We have a shared vision to advance universal access to care thats cost-effective. We have a shared vision to expand beyond our borders to look at other markets. Ultimately, its a strategic technology collaboration that were most interested in," Antall said of the company's partnership with the tech giant during a STATvirtual event Tuesday.

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"What we bring to the table is that we can help provide applications for those technologiesthat will have meaningful effects on consumers and providers," he said.

The use of AI and machine learning can improve bot-based interactions or decision support for providers, he said. The two companies also want to explore the use of natural language processing and automated translation to provide more "value to clients and consumers," he said.

Joining a rush of healthcare technology IPOs in 2020, Amwell went public in August, raising$742 million. Google Cloud and Amwell also announced amultiyear strategic partnership aimed at expanding access to virtual care, accompanied by a$100 million investmentfrom Google.

During an HLTH virtual event earlier this month, Google Cloud director of healthcare solutions Aashima Gupta said cloud and artificial intelligence will "revolutionize telemedicine as we know it."

RELATED:Amwell files to go public with $100M boost from Google

"There's a collective realization in the industry that the future will not look like the past," said Gupta during the HTLH panel.

During the STAT event, Antall said Amwellis putting a big focus onvirtual primary care, which has become an area of interest for health plans and employers.

"It seems to be the next big frontier. Weve been working on it for three years, and were very excited. So much of healthcare is ongoing chronic conditions and so much of the healthcare spend is taking care ofchronic conditionsandtaking care of those conditions in the right care setting and not in the emergency department," he said.

The companyworks with 55 health plans, which support over 36,000 employers and collectively represent more than 80million covered lives, as well as 150 of the nations largest health systems. To date, Amwell says it has powered over 5.6million telehealth visits for its clients, including more than 2.9million in the six months ended June 30, 2020.

Amwell is interested in interacting with patients beyond telehealth visits through what Antall called "nudges" and synchronous communication to encouragecompliance with healthy behaviors, he said.

RELATED:Amwell CEOs on the telehealth boom and why it will 'democratize' healthcare

It's an area where Livongo, recently acquired by Amwell competitor Teladoc,has become the category leader by using digital health tools to help with chronic condition management.

"Were moving into similar areas, but doing it in a slightly different matter interms of how we address ongoing continuity of care and how we address certain disease states and overall wellness," Antallsaid, in reference to Livongo's capabilities.

The telehealth company also wants to expand into home healthcare through the integration of telehealth and remote care devices.

Virtual care companies have been actively pursuing deals to build out their service and product lines as the use of telehealth soars. To this end, Amwell recently deepened its relationship with remote device company Tyto Care. Through the partnership, the TytoHome handheld examination device that allows patients to exam their heart, lungs, skin, ears, abdomen, and throat at home, is nowpaired withAmwells telehealth platform.

Looking forward, there is the potential for patients to getlab testing, diagnostic testing, and virtual visits with physicians all at home, Antall said.

"I think were going to see a real revolution in terms ofhow much more we can do in the home going forward," he said.

RELATED:Amwell's stock jumps on speculation of potential UnitedHealth deal: media report

Amwell also is exploring the use of televisions in the home to interact with patients, he said.

"We've done work with some partners and we're working toward a future where, if it's easier for you to click your remote and initiate a telehealth visit that way, thats one option. In some populations, particularly the elderly, a TV could serve as a remote patient device where a doctor or nurse could proactively 'ring the doorbell' on the TV and askto check on the patient," Antall said.

"Its video technology that'salready there in most homes, you just need a camera to go with it and a little bit of software.Its one part of our strategy to be available for the whole spectrum of care and be able to interact in a variety of ways," he said.

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Amwell CMO: Google partnership will focus on AI, machine learning to expand into new markets - FierceHealthcare