EARN IT ACT: Bipartisan Bill Gives Government Backdoor to Encrypted Data – The New American

A bill with broad bipartisan support is working its way through Congress and if passed would substantially impair safety and privacy online. How did such a bill become so popular? The way so much similar measures make it through the legislative process: the promise of protecting the children.

The measure the Eliminating Abusive and Rampant Neglect of Interactive Technologies (EARN IT) Act removes legal protection from many of the Internets most popular uses, including blogs, social media, instant message services, apps, and sites whose content is created by the public (think Wikipedia, for example). Should the EARN IT Act become the law, the Internet will be changed forever.

Of course, the bills sponsors insist that such drastic changes to such a broad spectrum of the U.S.s digital information infrastructure are necessary to prevent child sex trafficking.Were not going to live in a world where a bunch of child abusers have a safe haven to practice their craft. Period. End of discussion, said Senator Lindsey Graham (R-S.C.), EARN ITs GOP co-author.

Unless companies demonstrate that they are using best practices to protect children from being exploited online, they will forfeit any legal immunity. To that end, the EARN IT Act mandates that tech companies build backdoor access into all their encrypted offerings. Basically, this back door would give the government ultimate control over almost every public place on the Internet.

Remarkably, the bill would provide no additional power to victims to punish people preying on children online, nor would it give law enforcement any improved tools for investigating such despicable behavior. Nope. Not surprisingly, the only protection afforded by this act is that it gives the government the power to suspend the rights of free speech and privacy for any organization it the government deems irresponsible.

As the Naked Security blog explains:

If passed, the legislation will create a National Commission on Online Child Sexual Exploitation Prevention tasked with developing best practices for owners of Internet platforms to prevent, reduce, and respond to child exploitation online. But, as the EFF [Electronic Frontier Foundation] maintains, Best practices would essentially translate into legal requirements:

If a platform failed to adhere to them, it would lose essential legal protections for free speech.

The blog additionally pointed out that the best practices would be subject to approval or veto by the Attorney General (currently William Barr, whos issued a public call for backdoors), the Secretary of Homeland Security (ditto), and the Chair of the Federal Trade Commission (FTC).

Heres the Verges take on the measure and its likely effects:

For starters, its not clear that companies have to earn what are already protections provided under the First Amendment: to publish, and to allow their users to publish, with very few legal restrictions. But if the EARN IT Act were passed, tech companies could be held liable if their users posted illegal content. This would represent a significant and potentially devastating amendment to Section 230, a much-misunderstood law that many consider a pillar of the internet and the businesses that operate on top of it.

The Section 230 referred to in the Verge story is Section 230 of the Communications Decency Act (CDA). The Electronic Frontier Foundation (EFF) summarized Section 230s protections:Section 230 enforces the common-sense principle that if you say something illegal online, you should be the one held responsible, not the website or platform where you said it (with some important exceptions).

In other words, if a person publishes something on the Internet, the author alone wouldnt be responsible, but the platform on which the targeted content was posted would be held legally liable, as well.

Its called the chilling effect. Such statutes would keep people from posting anything on the Internet that could be subjected to second-guessing by bureaucrats in D.C.

The blog Protocol lays out the bureaucratic hurdles that would be placed between content providers, publishers, and the right to free speech and privacy:

The EARN IT Act would establish the National Commission on Online Child Sexual Exploitation Prevention, a 19-member commission, tasked with creating a set of best practices for online companies to abide by with regard to stopping child sexual abuse material. Those best practices would have to be approved by 14 members of the committee and submitted to the attorney general, the secretary of homeland security, and the chairman of the Federal Trade Commission for final approval. That list would then need to be enacted by Congress. Companies would have to certify that theyre following those best practices in order to retain their Section 230 immunity. Like FOSTA/SESTA before it, losing that immunity would be a significant blow to companies with millions, or billions, of users posting content every day.

The question now is whether the industry can convince lawmakers that the costs of the law outweigh the benefits. Its a debate that will test what tech companies have learned from the FOSTA/SESTA battle and how much clout they even have left on Capitol Hill.

The federal government in the form of the 19-man commission would be granted unfettered, unfiltered, unobstructed, decrypted access to any all online communication. All messages would be forced to pass federal muster. Of course, the supporters of the EARN IT Act reiterate that the committee would only exercise control over content that affects the ability of children to go online without being subjected to sexual exploitation.

As with most such schemes, the government asks for an inch of authority over a small segment of online activity, but will end up exercising a mile of tyranny over any content the ruling regime considers objectionable.

A report on the bill by EFF accurately predicts the likely latitude that will be given to the bills bureaucratic overlords:

The Commission wont be a body that seriously considers policy; it will be a vehicle for creating a law enforcement wish list. Barr has made clear, over and over again, that breaking encryption is at the top of that wish list. Once its broken, authoritarian regimes around the world will rejoice, as they have the ability to add their own types of mandatory scanning, not just for child sexual abuse material but for self-expression that those governments want to suppress.

Once it develops in the body politic, the muscle of tyranny never atrophies.

Photo: anyaberkut/iStock/Getty Images Plus

Joe Wolverton II, J.D., is the author of the books The Real James Madison and What Degree of Madness?: Madisons Method to Make America STATES Again. He also hosts the popular YouTube channel Teacher of Liberty.

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EARN IT ACT: Bipartisan Bill Gives Government Backdoor to Encrypted Data - The New American

Why AI might be the most effective weapon we have to fight COVID-19 – The Next Web

If not the most deadly, the novel coronavirus (COVID-19) is one of the most contagious diseases to have hit our green planet in the past decades. In little over three months since the virus was first spotted in mainland China, it has spread to more than 90 countries, infected more than 185,000 people, and taken more than 3,500 lives.

As governments and health organizations scramble to contain the spread of coronavirus, they need all the help they can get, including from artificial intelligence. Though current AI technologies arefar from replicating human intelligence, they are proving to be very helpful in tracking the outbreak, diagnosing patients, disinfecting areas, and speeding up the process of finding a cure for COVID-19.

Data science and machine learning might be two of the most effective weapons we have in the fight against the coronavirus outbreak.

Just before the turn of the year, BlueDot, an artificial intelligence platform that tracks infectious diseases around the world, flagged a cluster of unusual pneumonia cases happening around a market in Wuhan, China. Nine days later, the World Health Organization (WHO)released a statementdeclaring the discovery of a novel coronavirus in a hospitalized person with pneumonia in Wuhan.

BlueDot usesnatural language processingandmachine learning algorithmsto peruse information from hundreds of sources for early signs of infectious epidemics. The AI looks at statements from health organizations, commercial flights, livestock health reports, climate data from satellites, and news reports. With so much data being generated on coronavirus every day, the AI algorithms can help home in on the bits that can provide pertinent information on the spread of the virus. It can also find important correlations between data points, such as the movement patterns of the people who are living in the areas most affected by the virus.

The company also employs dozens of experts who specialize in a range of disciplines including geographic information systems, spatial analytics, data visualization, computer sciences, as well as medical experts in clinical infectious diseases, travel and tropical medicine, and public health. The experts review the information that has been flagged by the AI and send out reports on their findings.

Combined with the assistance of human experts, BlueDots AI can not only predict the start of an epidemic, but also forecast how it will spread. In the case of COVID-19, the AI successfully identified the cities where the virus would be transferred to after it surfaced in Wuhan. Machine learning algorithms studying travel patterns were able to predict where the people who had contracted coronavirus were likely to travel.

Coronavirus (COVID-19) (Image source:NIAID)

You have probably seen the COVID-19 screenings at border crossings and airports. Health officers use thermometer guns and visually check travelers for signs of fever, coughing, and breathing difficulties.

Now,computer vision algorithmscan perform the same at large scale. An AI system developed by Chinese tech giant Baidu uses cameras equipped with computer vision and infrared sensors to predict peoples temperatures in public areas. The system can screen up to 200 people per minute and detect their temperature within a range of 0.5 degrees Celsius. The AI flags anyone who has a temperature above 37.3 degrees. The technology is now in use in Beijings Qinghe Railway Station.

Alibaba, another Chinese tech giant, has developed an AI system that candetect coronavirus in chest CT scans. According to the researchers who developed the system, the AI has a 96-percent accuracy. The AI was trained on data from 5,000 coronavirus cases and can perform the test in 20 seconds as opposed to the 15 minutes it takes a human expert to diagnose patients. It can also tell the difference between coronavirus and ordinary viral pneumonia. The algorithm can give a boost to the medical centers that are already under a lot of pressure to screen patients for COVID-19 infection. The system is reportedly being adopted in 100 hospitals in China.

A separate AI developed by researchers from Renmin Hospital of Wuhan University, Wuhan EndoAngel Medical Technology Company, and the China University of Geosciences purportedly shows 95-percent accuracy on detecting COVID-19 in chest CT scans. The system is adeep learning algorithmtrained on 45,000 anonymized CT scans. According to a preprint paperpublished on medRxiv, the AIs performance is comparable to expert radiologists.

One of the main ways to prevent the spread of the novel coronavirus is to reduce contact between infected patients and people who have not contracted the virus. To this end, several companies and organizations have engaged in efforts to automate some of the procedures that previously required health workers and medical staff to interact with patients.

Chinese firms are using drones and robots to perform contactless delivery and to spray disinfectants in public areas to minimize the risk of cross-infection. Other robots are checking people for fever and other COVID-19 symptoms and dispensing free hand sanitizer foam and gel.

Inside hospitals, robots are delivering food and medicine to patients and disinfecting their rooms to obviate the need for the presence of nurses. Other robots are busy cooking rice without human supervision, reducing the number of staff required to run the facility.

In Seattle, doctors used a robot to communicate with and treat patients remotely to minimize exposure of medical staff to infected people.

At the end of the day, the war on the novel coronavirus is not over until we develop a vaccine that can immunize everyone against the virus. But developing new drugs and medicine is a very lengthy and costly process. It can cost more than a billion dollars and take up to 12 years. Thats the kind of timeframe we dont have as the virus continues to spread at an accelerating pace.

Fortunately, AI can help speed up the process. DeepMind, the AI research lab acquired by Google in 2014, recently declared that it has used deep learning to find new information about the structure of proteins associated with COVID-19. This is a process that could have taken many more months.

Understanding protein structures can provide important clues to the coronavirus vaccine formula. DeepMind is one of several organizations who are engaged in the race to unlock the coronavirus vaccine. It has leveraged the result of decades of machine learning progress as well as research on protein folding.

Its important to note that our structure prediction system is still in development and we cant be certain of the accuracy of the structures we are providing, although we are confident that the system is more accurate than our earlier CASP13 system, DeepMinds researchers wroteon the AI labs website. We confirmed that our system provided an accurate prediction for the experimentally determined SARS-CoV-2 spike protein structure shared in the Protein Data Bank, and this gave us confidence that our model predictions on other proteins may be useful.

Although its too early to tell whether were headed in the right direction, the efforts are commendable. Every day saved in finding the coronavirus vaccine can save hundredsor thousandsof lives.

This story is republished fromTechTalks, the blog that explores how technology is solving problems and creating new ones. Like them onFacebookhere and follow them down here:

Published March 21, 2020 17:00 UTC

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Why AI might be the most effective weapon we have to fight COVID-19 - The Next Web

Emerging Trend of Machine Learning in Retail Market 2019 by Company, Regions, Type and Application, Forecast to 2024 – Bandera County Courier

The latest report titled, Global Machine Learning in Retail Market 2019 by Company, Regions, Type and Application, Forecast to 2024 unveils the value at which the Machine Learning in Retail industry is anticipated to grow during the forecast period, 2019 to 2024. The report estimates CAGR analysis, competitive strategies, growth factors and regional outlook 2024. The report is a rich source of an exhaustive study of the driving elements, limiting components, and different market changes. It states market structure and then further forecasts several segments and sub-segments of the global market. The market study is provided on the basis of type, application, manufacturer as well as geography. Different elements such as opportunities, drivers, restraints, and challenges, market situation, market share, growth rate, future trends, risks, entry limits, sales channels, distributors are analyzed and examined within this report.

Exploring The Growth Rate Over A Period:

Business owners want to expand their business can refer to this report as it includes data regarding the rise in sales within a given consumer base for the forecast period, 2019 to 2024. The research analysts have mentioned a comparison between the Machine Learning in Retail market growth rate and product sales to allow business owners to discover the success or failure of a specific product or service. They have also added the driving factors such as demographics and revenue generated from other products to offer a better analysis of products and services by owners.

DOWNLOAD FREE SAMPLE REPORT: https://www.magnifierresearch.com/report-detail/7570/request-sample

Top industry players assessment: IBM, Microsoft, Amazon Web Services, Oracle, SAP, Intel, NVIDIA, Google, Sentient Technologies, Salesforce, ViSenze,

Product type assessment based on the following types: Cloud Based, On-Premises

Application assessment based on application mentioned below: Online, Offline

Leading market regions covered in the report are: North America (United States, Canada and Mexico), Europe (Germany, France, UK, Russia and Italy), Asia-Pacific (China, Japan, Korea, India and Southeast Asia), South America (Brazil, Argentina, Colombia), Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)

Main Features Covered In Global Machine Learning in Retail Market 2019 Report:

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Moreover in the report, supply chain analysis, regional marketing type analysis, international trade type analysis by the market as well as consumer analysis of Machine Learning in Retail market has been covered. Further, it determines the manufacturing plants and technical data analysis, capacity, and commercial production date, R&D Status, manufacturing area distribution, technology source, and raw materials sources analysis. It also depicts to depict sales, merchants, brokers, wholesalers, research findings and conclusion, and information sources.

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Emerging Trend of Machine Learning in Retail Market 2019 by Company, Regions, Type and Application, Forecast to 2024 - Bandera County Courier

Keeping Machine Learning Algorithms Humble and Honest in the Ethics-First Era – Datamation

By Davide Zilli, Client Services Director at Mind Foundry

Today in so many industries, from manufacturing and life sciences to financial services and retail, we rely on algorithms to conduct large-scale machine learning analysis. They are hugely effective for problem-solving and beneficial for augmenting human expertise within an organization. But they are now under the spotlight for many reasons and regulation is on the horizon, with Gartner projecting four of the G7 countries will establish dedicated associations to oversee AI and ML design by 2023. It remains vital that we understand their reasoning and decision-making process at every step.

Algorithms need to be fully transparent in their decisions, easily validated and monitored by a human expert. Machine learning tools must introduce this full accountability to evolve beyond unexplainable black box solutions and eliminate the easy excuse of the algorithm made me do it!"

Bias can be introduced into the machine learning process as early as the initial data upload and review stages. There are hundreds of parameters to take into consideration during data preparation, so it can often be difficult to strike a balance between removing bias and retaining useful data.

Gender for example might be a useful parameter when looking to identify specific disease risks or health threats, but using gender in many other scenarios is completely unacceptable if it risks introducing bias and, in turn, discrimination. Machine learning models will inevitably exploit any parameters such as gender in data sets they have access to, so it is vital for users to understand the steps taken for a model to reach a specific conclusion.

Removing the complexity of the data science procedure will help users discover and address bias faster and better understand the expected accuracy and outcomes of deploying a particular model.

Machine learning tools with built-in explainability allow users to demonstrate the reasoning behind applying ML to a tackle a specific problem, and ultimately justify the outcome. First steps towards this explainability would be features in the ML tool to enable the visual inspection of data with the platform alerting users to potential bias during preparation and metrics on model accuracy and health, including the ability to visualize what the model is doing.

Beyond this, ML platforms can take transparency further by introducing full user visibility, tracking each step through a consistent audit trail. This records how and when data sets have been imported, prepared and manipulated during the data science process. It also helps ensure compliance with national and industry regulations such as the European Unions GDPR right to explanation clause and helps effectively demonstrate transparency to consumers.

There is a further advantage here of allowing users to quickly replicate the same preparation and deployment steps, guaranteeing the same results from the same data particularly vital for achieving time efficiencies on repetitive tasks. We find for example in the Life Sciences sector, users are particularly keen on replicability and visibility for ML where it becomes an important facility in areas such as clinical trials and drug discovery.

There are so many different model types that it can be a challenge to select and deploy the best model for a task. Deep neural network models, for example, are inherently less transparent than probabilistic methods, which typically operate in a more honest and transparent manner.

Heres where many machine learning tools fall short. Theyre fully automated with no opportunity to review and select the most appropriate model. This may help users rapidly prepare data and deploy a machine learning model, but it provides little to no prospect of visual inspection to identify data and model issues.

An effective ML platform must be able to help identify and advise on resolving possible bias in a model during the preparation stage, and provide support through to creation where it will visualize what the chosen model is doing and provide accuracy metrics and then on to deployment, where it will evaluate model certainty and provide alerts when a model requires retraining.

Building greater visibility into data preparation and model deployment, we should look towards ML platforms that incorporate testing features, where users can test a new data set and receive best scores of the model performance. This helps identify bias and make changes to the model accordingly.

During model deployment, the most effective platforms will also extract extra features from data that are otherwise difficult to identify and help the user understand what is going on with the data at a granular level, beyond the most obvious insights.

The end goal is to put power directly into the hands of the users, enabling them to actively explore, visualize and manipulate data at each step, rather than simply delegating to an ML tool and risking the introduction of bias.

The introduction of explainability and enhanced governance into ML platforms is an important step towards ethical machine learning deployments, but we can and should go further.

Researchers and solution vendors hold a responsibility as ML educators to inform users of the use and abuses of bias in machine learning. We need to encourage businesses in this field to set up dedicated education programs on machine learning including specific modules that cover ethics and bias, explaining how users can identify and in turn tackle or outright avoid the dangers.

Raising awareness in this manner will be a key step towards establishing trust for AI and ML in sensitive deployments such as medical diagnoses, financial decision-making and criminal sentencing.

AI and machine learning offer truly limitless potential to transform the way we work, learn and tackle problems across a range of industriesbut ensuring these operations are conducted in an open and unbiased manner is paramount to winning and retaining both consumer and corporate trust in these applications.

The end goal is truly humble, honest algorithms that work for us and enable us to make unbiased, categorical predictions and consistently provide context, explainability and accuracy insights.

Recent research shows that 84% of CEOs agree that AI-based decisions must be explainable in order to be trusted. The time is ripe to embrace AI and ML solutions with baked in transparency.

About the author:

Davide Zilli, Client Services Director at Mind Foundry

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Keeping Machine Learning Algorithms Humble and Honest in the Ethics-First Era - Datamation

The Power of AI in ‘Next Best Actions’ – CMSWire

PHOTO:Charles

Lets say you have a customer who has taken a certain action: downloaded an ebook, filled out an application, added a product to their cart, called into your call center or walked into your branch office, to name a few. What content, offer or message should you deliver to them next? What next step should you recommend? How can you best add value for that individual, while nurturing the person, wherever they are in their relationship with your business?

Based on your history (or even lack of history) with a given individual, you and your company might also have questions such as: Whats the best product to upsell to this particular client? (and should I even try to upsell that person?); Whats the right promotion to show an engaged shopper on my ecommerce site? and Whats the right item to promote to someone logged into my application? The list goes on.

These types of questions are all important to businesses today, who often talk about next best actions. This customer-centric (often 1-to-1) approach and sequencing strategy can take a number of forms. But at a basic level, the concept means what it sounds like: determining the most relevant or appropriate next action (or offer, promotion, content, etc.) to show a person in the moment, based on their current and previous actions or other information youve gathered about them across your online and offline channels. Next best actions can also include triggering messages to call center agents or sales reps to alert them of important activity, or to suggest the next best action they should take with a customer.

Companies put awide variety of thought, time and effort into establishing sequencing paths from none at all (with a one-size-fits-all message, promotion, offer, etc.) to a lot. At a majority of organizations, though, determining the next best action for their customers is very important, involving multiple teams of people across functions and divisions.

There are teams of marketers and designers, for instance, who create elaborate promotions and offers with different media for different channels. And there are customer experience teams who devote many cycles to thinking about call-center scripts and next best actions.

So when it comes to deploying those next best actions, it can devolve into an inter-departmental war about who gets the prime real estate. For example, when new visitors hit the homepage or when customers log into the app, what gets displayed in the hero area?

Why all the effort and involvement? Its because next best actions are strategically important to engagement and the bottom line. Present the right, relevant offer or action to a customer or prospect, and youre helping elicit interest and drive conversions. Present the wrong (e.g., outdated, irrelevant, mismatched to sales cycle stage, etc.) one, and youre losing customer interest or even turning them off your brand.

Related Article: Good Personalization Hinges on Good Data

For many years, organizations have taken a rule-based approach to determining the right next best action for a particular customer in a particular channel or at a particular stage in their journey. Rules are manually created and structured with if-then logic (e.g., IF a person takes this action or belongs to this group, THEN display this next). They govern the experiences and actions for audience segments which can be broad or get very narrow.

Three types of rules are the most frequently applied to next-best-action decisioning. These can be used on their own or, typically, in concert:

Related Article: Why Personalization Efforts Fail

But one problem with rules is the more targeted and relevant you want to get, the greater the number of rules you need to make. With rules, personalization of the next best action is inversely correlated to simplicity. In other words, to deliver truly relevant and highly specific actions and experiences using rules only, you quickly enter a world of nearly unmanageable complexity.

Theres also the time factor to consider. As you have likely experienced, it takes a lot of hours to create and prescribe sequencing via rules for the multitude of scenarios customers can encounter and the paths they can take. And unraveling a heavily nested set of rules in order to make minor adjustments (and make them correctly) can take many more hours.

Another problem with rules is that they are just a human guessing. Suppose youre wrong in the next best action youve set up for a customer to receive in fact, it may actually be hurting revenues or customer loyalty.

So while rules do play a vital role in determining and displaying next best actions, a rules-only-based approach generally isnt optimal or scalable in the long-term.

Related Article: Refine Your Personalization Efforts by Ditching Tech-First Tendencies

Machine learning, a type of artificial intelligence (AI), can supplement rules and play a powerful role in prioritization and other next-best-action decisions: pulling in everything known about an individual in the channel of engagement and across channels, factoring in data from similar people, and then computing and displaying the optimal, relevant next best action or offer at the 1-to-1 level. Typically, this all occurs in milliseconds faster than you can blink an eye.

Across industries, theres an enormous amount of behavioral data to parse through to uncover trends and indicators of what to do next with any given individual. This can be combined with attribute and transaction data to build a rich profile and predictive intelligence. Machine-learning algorithms automate this process, make surprising discoveries and keep learning based on ever-growing data: from studying both the individual customer and customers with similar attributes and behaviors, and from learning from how customers are reacting to the actions being suggested to them.

In addition, when multiple promotions or next actions are valid, you can apply machine learning to decide on and display the truly optimal one, balancing whats best for the customer with whats best for your business.

Optimized machine-learning-driven next best actions outperform manual ones, even when what they suggest might seem counter-intuitive. For example, a banking institution might promote its most popular cash-back credit card offer to all new site visitors. But for return visitors located in colder climate regions, a continuous learning algorithm might determine that the banks travel rewards card offer performs much better. Only machine learning can pick up on behavioral signals and information at scale (including seemingly unimportant information) in a way that humans simply cannot.

Related Article: 5 Drivers of Personalized Experiences: A Walk Through the AI Food Chain

Determining and displaying next best actions involve integrations and interplay across channels. One system is informing another of an action a customer has taken and what to do next. For example: a customer who joined the loyalty program could be eligible to receive a certain promotion in their email. Or a shopper who browsed purses online can be push-notified a coupon code to use in-store, thanks to beacon technology. An alert might get triggered to a call center agent based on a customers unfinished loan application letting the agent know to provide information on interest rates or help set up an appointment at the customers local branch as that person is calling in.

Given the wide range of activity and vast quantities of data, its important to have a single system that can arbitrate all these actions, apply prioritization and act as the central brain. This helps keep customer information unified and up-to-date, and aids in real-time interaction management and experience delivery.

In the end, everything organizations do when communicating and relating to their customers could be viewed as next best actions. In fact, personalization and next best actions are closely intertwined, as two sides of the same coin. Its hard to separate a next best action from the personalization decisioning driving it, which is why the two areas should be (and sometimes are) tied together from a strategy and systems perspective.

By effectively determining and triggering personalized next steps, you can tell a cohesive and consistent cross-channel story that bolsters brand perception, improves the buyer journey and turns next best actions into must-take ones.

Karl Wirth is the CEO and co-founder of Evergage, a Salesforce Company and a leading real-time personalization and interaction management platform provider. Karl is also the author of the award-winning book One-to-One Personalization in the Age of Machine Learning.

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The Power of AI in 'Next Best Actions' - CMSWire

Thousands of These Computers Were Mining Cryptocurrency. Now They’re Working on Coronavirus Research – CoinDesk – CoinDesk

CoreWeave, the largest U.S. miner on the Ethereum blockchain, is redirecting the processing power of 6,000 specialized computer chips toward research to find a therapy for the coronavirus.

These graphics processing units (GPUs) will be pointed toward Stanford University's Folding@home, a long-standing research effort that unveiled a project on Feb. 27 specifically to boost coronavirus research by way of a unique approach to developing pharmaceutical drugs: connecting thousands of computers from around the world to form a distributed supercomputer for disease research.

CoreWeave co-founder and Chief Technology Officer (CTO) Brian Venturo said the project has at least a shot at finding a drug for the virus. As such, CoreWeave has responded by doubling the power of the entire network with its GPUs, which are designed to handle repetitive calculations.

According to Venturo, those 6,000 GPUs made up about 0.2 percent of Ethereum's total hashrate, earning roughly 28 ETH per day, worth about $3,600 at press time.

There is no cure for the coronavirus just yet (though various groups are working on vaccines and research to combat the disease, including IBM's supercomputer). Venturo noted that Folding@home has been used to contribute to breakthroughs in the creation of other important drugs.

"Their research had profound impacts on the development of front-line HIV defense drugs, and we are hoping our [computing power] will aid in the fight against coronavirus," Venturo said.

The coronavirus is taking a toll across the world. Italy and Spain are on lockdown. Conferences, stores and restaurants are closing to stem the spread of the disease; by stoking fears, it's slamming the financial markets in the process.

World computer

When the idea of using GPUs for coronavirus research was mentioned to CoreWeave, the team didn't think twice.

They had a test system up and running "within minutes," Venturo said. Since then, the project quickly snowballed. CoreWeave has been contributing over half of the overall computing power going into the coronavirus wing of Folding@home.

"The idea of 'should we do this?' was never really brought up, it kind of just happened. We were all enthusiastic that we might be able to help," Venturo added.

Folding@home is a decentralized project in the same vein as Bitcoin. Instead of one research firm alone using a massive computer to do research, Folding@home uses the computing power of anyone who wants to participate from around the world even if it's just a single laptop with a little unused computing power to spare.

In this case, the computing power is used to find helpful information relating to the coronavirus. Much like in bitcoin mining, one user might detect a "solution" to the problem at hand, distributing this information to the rest of the group.

"Their protein simulations attempt to find potential 'pockets' where existing [U.S. federal agency Food and Drug Administration (FDA)] approved drugs or other known compounds could help inhibit or treat the virus," Venturo said.

Viruses have proteins "that they use to suppress our immune systems and reproduce themselves. To help tackle coronavirus, we want to understand how these viral proteins work and how we can design therapeutics to stop them," a Folding@home blog post explains.

Simulating these proteins and then looking at them from different angles helps scientists to understand them better, with the potential of finding an antidote. Computers accelerate this process by shuffling through the variations very quickly.

"Our specialty is in using computer simulations to understand proteins moving parts. Watching how the atoms in a protein move relative to one another is important because it captures valuable information that is inaccessible by any other means," the post reads.

Long shot

Folding@home could use even more power. Venturo urges other GPU miners to join the cause.

Even without these calls for participation, though, miners of other cryptocurrencies are already independently taking action. Tulip.tools founder Johann Tanzer put out a call to action to Tezos bakers (that blockchains equivalent of miners) last week, promising to send the leading contributor to Folding@home a modest 15 XTZ, worth roughly $20 at press time.

The initiative blew up, to Tanzer's surprise. Though they might not be contributing as much power as CoreWeave, 20 groups of Tezos miners are now contributing a slice of their hashing power to the cause. Tanzer's pot has swelled to roughly $600 as Tezos users caught wind of the effort and donated.

But that's not to say all miners can participate. While GPUs are flexible, application-specific integrated circuits (ASICs), a type of chip designed specifically for mining, aren't, according to Venturo. Though ASICs are more powerful than GPUs, they're really only made for one thing: To mine cryptocurrency. This is one advantage Venturo thinks Ethereum has over Bitcoin, since GPU mining still works on the former, whereas the latter is now dominated by ASICs.

"This is one of the great things about the Ethereum mining ecosystem, it's basically the largest GPU compute resource on the planet. We were able to redeploy our hardware to help fight a global pandemic in minutes," Venturo said. (However, it's worth noting that Ethereum has seen ASICs enter the fray. Not to mention, ether miners might soon go extinct when a pivotal upgrade makes its way into the network.)

ASICs are useless for the Folding@Home effort, but if bitcoin miners have old GPUs lying around from the early days that they could contribute, too.

Even if other miners join up, though, it's still a long shot that the effort will lead to a helpful drug.

"After discussing with some industry experts [...] we believe the chance of success in utilizing the work done on Folding@Home to deliver a drug to market to be in the 2-5% range," Venturo said.

The leader in blockchain news, CoinDesk is a media outlet that strives for the highest journalistic standards and abides by a strict set of editorial policies. CoinDesk is an independent operating subsidiary of Digital Currency Group, which invests in cryptocurrencies and blockchain startups.

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Thousands of These Computers Were Mining Cryptocurrency. Now They're Working on Coronavirus Research - CoinDesk - CoinDesk

The Coder and the Dictator – The New York Times

Mr. Jimnez was fairly insulated. He had founded a start-up, The Social Us, that connected Venezuelan programmers and designers with American companies looking for cheap labor. Like many wealthier Venezuelans, Mr. Jimnez kept almost all his money in dollars, but this made transactions a headache. He had to illegally swap currency every few days, and a taxi ride would require a stack of bolvars so thick that most drivers accepted only wire transfers.

The situation rekindled Mr. Jimnezs long-running interest in cryptocurrencies. He began paying his employees in a digital coin; even with the crazy volatility of the crypto markets, it was more stable than a Venezuelan bank account, and it wasnt subject to the Maduro regimes diktats. The staff at The Social Us began touting cryptocurrency as a way for ordinary Venezuelans growing numbers of whom were buying Bitcoin on the street to deal with practical problems. One project they designed was a payment terminal that bypassed government limits on spending.

Initially, the Maduro regime saw Bitcoin as a threat. The technology, after all, used a decentralized network to create and move money, and no authority was in charge. But then some members of the government noticed that this cut both ways. Cryptocurrency could also be a way for Venezuela to escape sanctions levied by the United States and international organizations.

In September 2017, an official loyal to Mr. Maduro floated the idea of a digital currency backed by Venezuelas oil reserves. This was unorthodox: One of the tenets of Bitcoin is that its value does not derive from a natural resource or government fiat,only the laws of mathematics. But the distinction faded in the face of Venezuelas desperation. The official, Carlos Vargas, read about Mr. Jimnezs crypto work in a local publication and asked for a meeting.

Soon the hulking form of Mr. Vargas arrived at the office of The Social Us. As he consumed an entire bag of potato chips, Mr. Vargas flattered the young digital workers, saying they were among the only people in Venezuela capable of creating what he had proposed. The idea was exactly what Mr. Jimnez had hoped to hear. The goal was to create a new Venezuelan currency that would move freely over an open network, like Bitcoin. The government would be unable to control or bungle it. Mr. Vargas wanted to call it the Petro Global Coin, but Mr. Jimnez suggested something simpler: the Petro.

The Social Us put together a short pitch deck for the Petro project. But Venezuela is filled with people proposing crazy schemes, and Mr. Jimnez didnt put too much stock in it. Then, in early December, when Mr. Jimnez was at a conference in Colombia, he got an urgent text. Mr. Maduro had just announced a national cryptocurrency called the Petro. Mr. Jimnez threw open his laptop and found a video of the president, in his usual workmans shirt, telling a whooping crowd, This is something momentous.

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The Coder and the Dictator - The New York Times

Cryptocurrency Market Gains $25.7B In 24 Hours As It Recovers From Massive Sell-Off – Benzinga

The cryptocurrency market added nearly $21 billion in the 24 hours up to Friday 1:30 a.m., in a show of recovery following a market-wide sell-off over the past week.

The cryptocurrency market recovered even as stocks continue to battle the novel coronavirus (COVID-19) outbreak.

The world's apex work currency Bitcoin (BTC) traded 17.39% higher at $6,228, according to CoinMarketCap data. The cryptocurrency had dropped as low as $4,106.98 on March 13 and is still trading significantly lower than the price it opened this year, at$7,194.

Other cryptocurrencies followed suit with Ethereum trading 18.36% higher at $139.12. XRP (XRP), the asset backing the Ripple payment network, added 11.60% at 16 cents.

BTC hard fork Bitcoin SV made the largest gain among the top 20 cryptocurrencies. It added 38.12% at $167.32.

As the wider cryptocurrency market made a recovery, stablecoins held back.

Stablecoins are aimed at trading at a fixed price, with their value tied to a fiat currency or other stable assets.

The currencies typically surge when investors scramble to move their cryptocurrency assets to a safer position without needing to convert them to fiat currenciesand drop when the investments are changed back to other assets.

2020 Benzinga.com. Benzinga does not provide investment advice. All rights reserved.

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Cryptocurrency Market Gains $25.7B In 24 Hours As It Recovers From Massive Sell-Off - Benzinga

Cryptocurrency Donations Bring an Advanced Medical Post for Coronavirus Victims to Italy – The Merkle Hash

Bitcoin and other cryptocurrencies can often be used to support charitable events. During the novel coronavirus outbreak, Italys Red Cross is accepting Bitcoin donations for an advanced medical post.

It is evident that a lot of countries will need help to keep the novel coronavirus in check.

In Italy, the situation has spiraled out of control completely in the past week.

With so many patients awaiting treatment, new solutions need to be found.

One campaign on HelperBit was designed to achieve funding for an advanced medical post for pre-triage.

A goal of 10,000 Euro was set, which was reached on March 15th.

Following the success, the campaign was extended to complete core infrastructure with necessary accessories.

At the time of this campaigns creation, the number of confirmed cases and deaths was much lower compared to today.

These developments only highlight the need for medical supplies in Italy as of right now.

What makes the campaign so interesting is how all of the funding can be done through cryptocurrencies.

Both Bitcoin and various altcoins are accepted under the current circumstances.

It is a great way for cryptocurrency enthusiasts to contribute to the greater cause.

More efforts like these may need to be launched in the near future, as the coronavirus crisis is far from over.

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Cryptocurrency Donations Bring an Advanced Medical Post for Coronavirus Victims to Italy - The Merkle Hash

Top Analyst Explains Why Bitcoin Price is Up 78% amid Coronavirus Outbreak – newsBTC

Bitcoin resumed its roller-coaster rally even as the worsening Coronovirus pandemic discouraged investors from holding risky assets.

The benchmark cryptocurrency jumped 29.11 percent in the last 24 hours, hitting a new weekly high of $6,900 on Coinbase. The move uphill came after last weeks erratic sell-off that crashed bitcoin from $7,969 to as low as $3,858. Nevertheless, a renewed buying interest near the session lows helped the price rebound, eventually taking it up by 78 percent by this Friday.

BTC/USD jumps buy up to 78 percent | Source: TradingView.com

But the scale of bitcoins jump remained incalculable to many. The cryptocurrency last week threatened to move further down below the local bottom as investors appetite for cash boomed. Its rise, therefore, came as a surprise given the poor health of the global economy.

Dan Tapiero, the co-founder of US-based investment management firm, DCAP Holdings, attempted to explain the price rally. The macro investor credited negative interest rates for pumping bitcoin, explaining that people now need to pay the US government for keeping their money with them.

Central banks have intervened lately to control the economic slowdown caused by the spread of Coronavirus. The US Federal Reserve, European Central Bank, and Bank of England introduced stimulus packages, varying from swap lines to purchasing hundreds of billions of dollars in treasuries and lending rate cuts.

Negative interest rates have arrived in the US 6-month T-bill at -2bps, Mr. Tapiero noted. [It] means you need to PAY US govt for 6mo cash deposit. Rates to go much more negative to weaken the dollar. This is confiscation and it is bad but it needed for now to stabilize the system.

[It is] mega-bullish for Bitcoin, he added.

Bitcoins jump closely followed similar upside moves in the financial market. The latest central banks action helped global stocks, oil, bonds, and gold recover, but thinktanks feared that these markets have not bottomed-out yet.

Chris Turner, global head of markets at ING, told FT that market outlook remains uncertain with a clear bias to the downside, taking cues from the unknown extent to which Coronavirus pandemic can spread. The virus has infected more than 200,000 people across the world and has killed about 10,000 others.

The uncertainty has left bitcoin in a similar situation. Teddy Cleps, a prominent crypto trader and analyst, said Friday that buying cryptocurrencies is not peoples priority during a pandemic, adding that bitcoin could fall despite logging attractive gains.

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Top Analyst Explains Why Bitcoin Price is Up 78% amid Coronavirus Outbreak - newsBTC