Artificial Intelligence and Machine Learning Market by Application, Global Industry Share, Growth Opportunities, Regions & Forecast by 2025 – News…

Global Artificial Intelligence and Machine Learning Market 2020, presents a professional and in-depth study on the current state of the industry globally, providing basic overview of Artificial Intelligence and Machine Learning market including definitions, classifications, applications and industry chain structure. Historical data available in the report elaborates on the development of the Artificial Intelligence and Machine Learning market on a global and regional level. The report compares this data with the current state of the Artificial Intelligence and Machine Learning market and thus discuss upon the upcoming trends that have brought the Artificial Intelligence and Machine Learning market transformation.

Industry predictions along with the statistical implication presented in the report delivers an accurate scenario of the Artificial Intelligence and Machine Learning market. The market forces determining the shaping of the worldwide Artificial Intelligence and Machine Learning market have been evaluated in detail. In addition to this, the supervisory outlook of the Artificial Intelligence and Machine Learning market has been covered in the report from both the Global and local perspective. The demand and supply side of the Artificial Intelligence and Machine Learning market has been broadly covered in the report. Also the challenges faced by the players in the Artificial Intelligence and Machine Learning market in terms of demand and supply have been listed in the report.

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In Global Artificial Intelligence and Machine Learning Industry report, development policies and plans as well as market size, share, end users are analyzed. Growth prospects of the overall Artificial Intelligence and Machine Learning industry have been presented in the report. This industry study segments Artificial Intelligence and Machine Learning global market by types, applications and companies. However, to give an in-depth view to the readers, detailed geographical segmentation of Artificial Intelligence and Machine Learning market within the globe has been covered in this study. The key geographical regions along with Artificial Intelligence and Machine Learning revenue forecasts are included in the report.

The Artificial Intelligence and Machine Learning market is segmented on the basis of key players, types and applications.

The leading players of worldwide Artificial Intelligence and Machine Learning industry includes

AIBrainAmazonAnkiCloudMindsDeepmindGoogleFacebookIBMIris AIAppleLuminosoQualcomm

Type analysis classifies the Artificial Intelligence and Machine Learning market into

Deep LearningNatural Language ProcessingMachine VisionOthers

Various applications of Artificial Intelligence and Machine Learning market are

HealthcareBFSILawRetailAdvertising & MediaAutomotive & TransportationAgricultureManufacturing

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Global Artificial Intelligence and Machine Learning Market regional analysis covers:

The industry research presents Artificial Intelligence and Machine Learning market in North America mainly covers USA, Canada and Mexico. Artificial Intelligence and Machine Learning market in Asia-Pacific region cover-up China, Japan, Korea, India and Southeast Asia. Artificial Intelligence and Machine Learning market in Europe combines Germany, France, UK, Russia and Italy. Artificial Intelligence and Machine Learning market in South America includes Brazil, Argentina, Columbia etc. Artificial Intelligence and Machine Learning market in Middle East and Africa incorporates Saudi Arabia, UAE, Egypt, Nigeria and South Africa.

The competitive framework of the market in terms of the Global Artificial Intelligence and Machine Learning industry has been evaluated in the report. The Artificial Intelligence and Machine Learning market top companies with their overall share and share with respect to the global market have been included in the Artificial Intelligence and Machine Learning report. Furthermore, the factors on which the companies compete in the worldwide Artificial Intelligence and Machine Learning industry have been evaluated in the report. So the overall report helps the new aspirants to inspect the forthcoming opportunities in the Artificial Intelligence and Machine Learning market.

Chapter 1, to describe Artificial Intelligence and Machine Learning product scope, market overview, market opportunities, market driving force and market risks.

Chapter 2, to profile the top manufacturers of Artificial Intelligence and Machine Learning, with price, sales, revenue and global market share of Artificial Intelligence and Machine Learning in 2018 and 2019.

Chapter 3, the Artificial Intelligence and Machine Learning competitive situation, sales, revenue and global market share of top manufacturers are analyzed emphatically by landscape contrast.

Chapter 4, the Artificial Intelligence and Machine Learning breakdown data are shown at the regional level, to show the sales, revenue and growth by regions, from 2015 to 2020.

Chapter 5, 6, 7, 8 and 9, to break the sales data at the country level, with sales, revenue and market share for key countries in the world, from 2015 to 2020.

Chapter 10 and 11, to segment the sales by type and application, with sales market share and growth rate by type, application, from 2015 to 2020.

Chapter 12, Artificial Intelligence and Machine Learning market forecast, by regions, type and application, with sales and revenue, from 2020 to 2025.

Chapter 13, 14 and 15, to describe Artificial Intelligence and Machine Learning sales channel, distributors, customers, research findings and conclusion, appendix and data source.

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Orbis Reports is constantly motivated to offer superlative run-down on ongoing market developments. To fulfill this, our voluminous data archive is laden with genuine and legitimately sourced data, subject to intense validation by our in-house subject experts. A grueling validation process is implemented to double-check details of extensive publisher data pools, prior to including their diverse research reports catering to multiple industries on our coherent platform. With an astute inclination for impeccable data sourcing, rigorous quality control measures are a part and parcel in Orbis Reports.

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Artificial Intelligence and Machine Learning Market by Application, Global Industry Share, Growth Opportunities, Regions & Forecast by 2025 - News...

Machine Learning Market Anticipated to Expand at a CAGR of 43.8% For The Forecast Period From 2019 To 2025 | Grand View Reserach Inc. -…

Grand View Research, Inc. Market Research And Consulting.

According to report published by Grand View Research, The global machine learning market size was valued at USD 6.9 billion in 2018 and is anticipated to register a CAGR of 43.8% from 2019 to 2025.

The globalmachine learning marketsize is expected to reach USD 96.7 billion by 2025, according to a new report by Grand View Research, Inc. The market is anticipated to expand at a CAGR of 43.8% from 2019 to 2025.

Production of massive amounts of data has increased the adoption of technologies that can provide a smart analysis of that data. Technologies such as Machine Learning (ML) are being rapidly adopted across various applications in order to automatically detect meaningful patterns within a data set. Software based on ML algorithms, such as search engines, anti-spam software, and fraud detection software, are being increasingly used, thereby contributing to market growth.

The rapid emergence of ML technology has increased its adoption across various application areas. It provides cloud computing optimization along with intelligent voice assistance. In healthcare, it is used for the diagnosis of individuals. In case of businesses, the use of ML models that are open source and have a standards-based structure has increased in recent years. These models can be easily deployed in various business programs and can help companies bridge the skills gap between IT programmers and information scientists.

Developments such as fine-tuned personalization, hyper-targeting, searching engine optimization, no-code environment, self-learning bots, and others are projected to change the machine learning landscape. The development of capsule network has replaced neural networks in order to provide more accuracy in pattern detection, with fewer errors. These advanced developments are anticipated to proliferate market growth in the foreseeable future.

Request a sample Copy of theMachine Learning Market Research Report @https://www.grandviewresearch.com/industry-analysis/machine-learning-market/request/rs1

Key Takeaways Of The Report :

The emergence of connected AI is expected to enable ML algorithms to learn continuously based on newly available information. Such developments are anticipated to drive the market in the coming years

The advertising and media sector accounted for the largest share in 2018 owing to capabilities such as buyers optimization, data processing, and analysis provided by the technology

H2O.ai announced a partnership with IBM Corporation, a multinational IT company, in June 2018. Through this partnership, H2O.ai will offer its GPU-powered machine learning, next-generation AI platform, and best-of-breed deep learning on IBMs Power Systems platform

Key players in the machine learning market include Amazon Web Services, Inc.; Baidu Inc.; Google Inc.; H2O.ai; Hewlett Packard Enterprise Development LP; Intel Corporation; International Business Machines Corporation; Microsoft Corporation; SAS Institute Inc.; and SAP SE.

Have Any Query? Ask Our Experts @https://www.grandviewresearch.com/inquiry/7023/ibb

Grand View Research has segmented the global machine learning market based on component, enterprise size, end use, and region:

Machine Learning Component Outlook (Revenue, USD Million, 2014 2025)

Hardware

Software

Service

Machine Learning Enterprise Size Outlook (Revenue, USD Million, 2014 2025)

Machine Learning End-use Outlook (Revenue, USD Million, 2014 2025)

Machine Learning Regional Outlook (Revenue, USD Million, 2014 2025)

North America

Europe

Asia Pacific

South America

Middle East and Africa

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Education And Learning Analytics Market:The global education and learning analytics market size was valued at USD 17.01 billion in 2018. It is anticipated to witness a CAGR of 17.4% over the forecast period.

Smart Education and Learning Market:The global smart education and learning market size was valued at USD 135.5 billion in 2017 and is expected to register a CAGR of 15.2% from 2018 to 2025.

About Grand View Research

Grand View Research provides syndicated as well as customized research reports and consulting services on 46 industries across 25 major countries worldwide. This U.S.-based market research and consulting company is registered in California and headquartered in San Francisco. Comprising over 425 analysts and consultants, the company adds 1200+ market research reports to its extensive database each year. Supported by an interactive market intelligence platform, the team at Grand View Research guides Fortune 500 companies and prominent academic institutes in comprehending the global and regional business environment and carefully identifying future opportunities.

Media ContactCompany Name: Grand View Research, Inc.Contact Person: Sherry James, Corporate Sales Specialist U.S.A.Email: Send EmailPhone: 1-415-349-0058, Toll Free: 1-888-202-9519Address: 201, Spear Street, 1100 City: San FranciscoState: CaliforniaCountry: United StatesWebsite: https://www.grandviewresearch.com/industry-analysis/machine-learning-market

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Machine Learning Market Anticipated to Expand at a CAGR of 43.8% For The Forecast Period From 2019 To 2025 | Grand View Reserach Inc. -...

ABI Research: Installed base of machine vision systems in manufacturing to reach 100 million by 2025 – Modern Materials Handling

Machine vision is a mature technology with established incumbents. However, significant advancements in chipsets, software, and standards are bringing deep learning innovation into the machine vision sector.

According to a recent analysis by global tech market advisory firm ABI Research, total shipments for machine vision sensors and cameras will reach 16.9 million by 2025, creating an installed base of 94 million machine vision systems in industrial manufacturing. Of that installed base, 11% will be deep learning-based.

Machine vision systems are a staple in production lines for barcode reading, quality control, and inventory management. These solutions often have long replacement cycles and are less prone to disruption. Due to the increasing demands for automation, machine vision is finding its way into new applications, said Lian Jye Su, Principal Analyst at ABI Research. Robotics, for example, is a new growth area for machine vision: Collaborative robots rely on machine vision for guidance and object classification, while mobile robots rely on machine vision for SLAM and safety.

A different breed from conventional machine vision technology, deep learning-based machine vision is data-driven and utilizes a statistical approach, which allows the machine vision model to improve as more data is gathered for training and testing. Major machine vision vendors have realized the potential of deep learning-based machine learning. Cognex, for example, acquired SUALAB, a leading Korean-based developer of vision software using deep learning for industrial applications, and Zebra Technologies acquired Cortexica Vision Systems Ltd., a London-headquartered leader in business-to-business (B2B) AI-based computer vision solutions developer.

At the same time, chipset vendors are launching new chipsets and software stacks to facilitate the implementation of deep learning-based machine vision. Xilinx, a Field Programmable Gated Array (FPGA) vendor, partnered closely with camera sensor manufacturer Sony and camera vendors such as Framos and IDS Imaging to incorporate its Versal ACAP System on Chip (SoC). Intel, on the other hand, offers OpenVINO for developers to deploy pre-trained deep learning-based machine vision models through a common API to deliver inference solutions on various computing architectures. Another FPGA vendor, Lattice Semiconductor, focuses on low-powered Artificial Intelligence (AI) for embedded vision through its senseAI stack, which offers hardware accelerators, software tools, and reference designs. These technology stacks aim to ease development and deployment challenges and create platform stickiness.

On the standards front, vendors are bringing 10GigE (Gigabit Ethernet) and 25GigE cameras into industrial applications. Continual upgrades on video capturing and compression technologies also generate a better image and video quality for deep learning-based machine vision models. This ensures the futureproofing of machine vision systems. Therefore, when choosing machine vision systems, end implementers need to understand their machine vision requirements, consider integration with their backend system, and identify the right ecosystem partners. Deployment flexibility and future upgradability and scalability will be crucial as machine vision technology continues to evolve and improve, concludes Su.

These findings are from ABI Researchs Machine Vision in Industrial Applications application analysis report. This report is part of the companys Artificial Intelligence and Machine Learning research service, which includes research, data, and analyst insights. Based on extensive primary interviews, Application Analysis reports present in-depth analysis on key market trends and factors for a specific technology.

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ABI Research: Installed base of machine vision systems in manufacturing to reach 100 million by 2025 - Modern Materials Handling

How artificial intelligence outsmarted the superbugs – The Guardian

One of the seminal texts for anyone interested in technology and society is Melvin Kranzbergs Six Laws of Technology, the first of which says that technology is neither good nor bad; nor is it neutral. By this, Kranzberg meant that technologys interaction with society is such that technical developments frequently have environmental, social and human consequences that go far beyond the immediate purposes of the technical devices and practices themselves, and the same technology can have quite different results when introduced into different contexts or under different circumstances.

The saloon-bar version of this is that technology is both good and bad; it all depends on how its used a tactic that tech evangelists regularly deploy as a way of stopping the conversation. So a better way of using Kranzbergs law is to ask a simple Latin question: Cui bono? who benefits from any proposed or hyped technology? And, by implication, who loses?

With any general-purpose technology which is what the internet has become the answer is going to be complicated: various groups, societies, sectors, maybe even continents win and lose, so in the end the question comes down to: who benefits most? For the internet as a whole, its too early to say. But when we focus on a particular digital technology, then things become a bit clearer.

A case in point is the technology known as machine learning, a manifestation of artificial intelligence that is the tech obsession de nos jours. Its really a combination of algorithms that are trained on big data, ie huge datasets. In principle, anyone with the computational skills to use freely available software tools such as TensorFlow could do machine learning. But in practice they cant because they dont have access to the massive data needed to train their algorithms.

This means the outfits where most of the leading machine-learning research is being done are a small number of tech giants especially Google, Facebook and Amazon which have accumulated colossal silos of behavioural data over the last two decades. Since they have come to dominate the technology, the Kranzberg question who benefits? is easy to answer: they do. Machine learning now drives everything in those businesses personalisation of services, recommendations, precisely targeted advertising, behavioural prediction For them, AI (by which they mostly mean machine learning) is everywhere. And it is making them the most profitable enterprises in the history of capitalism.

As a consequence, a powerful technology with great potential for good is at the moment deployed mainly for privatised gain. In the process, it has been characterised by unregulated premature deployment, algorithmic bias, reinforcing inequality, undermining democratic processes and boosting covert surveillance to toxic levels. That it doesnt have to be like this was vividly demonstrated last week with a report in the leading biological journal Cell of an extraordinary project, which harnessed machine learning in the public (as compared to the private) interest. The researchers used the technology to tackle the problem of bacterial resistance to conventional antibiotics a problem that is rising dramatically worldwide, with predictions that, without a solution, resistant infections could kill 10 million people a year by 2050.

The team of MIT and Harvard researchers built a neural network (an algorithm inspired by the brains architecture) and trained it to spot molecules that inhibit the growth of the Escherichia coli bacterium using a dataset of 2,335 molecules for which the antibacterial activity was known including a library of 300 existing approved antibiotics and 800 natural products from plant, animal and microbial sources. They then asked the network to predict which would be effective against E coli but looked different from conventional antibiotics. This produced a hundred candidates for physical testing and led to one (which they named halicin after the HAL 9000 computer from 2001: A Space Odyssey) that was active against a wide spectrum of pathogens notably including two that are totally resistant to current antibiotics and are therefore a looming nightmare for hospitals worldwide.

There are a number of other examples of machine learning for public good rather than private gain. One thinks, for example, of the collaboration between Google DeepMind and Moorfields eye hospital. But this new example is the most spectacular to date because it goes beyond augmenting human screening capabilities to aiding the process of discovery. So while the main beneficiaries of machine learning for, say, a toxic technology like facial recognition are mostly authoritarian political regimes and a range of untrustworthy or unsavoury private companies, the beneficiaries of the technology as an aid to scientific discovery could be humanity as a species. The technology, in other words, is both good and bad. Kranzbergs first law rules OK.

Every cloud Zeynep Tufekci has written a perceptive essay for the Atlantic about how the coronavirus revealed authoritarianisms fatal flaw.

EU ideas explained Politico writers Laura Kayali, Melissa Heikkil and Janosch Delcker have delivered a shrewd analysis of the underlying strategy behind recent policy documents from the EU dealing with the digital future.

On the nature of loss Jill Lepore has written a knockout piece for the New Yorker under the heading The lingering of loss, on friendship, grief and remembrance. One of the best things Ive read in years.

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How artificial intelligence outsmarted the superbugs - The Guardian

Growing tomatoes with Amazon Web Services – hortidaily.com

30MHz is participating in the autonomous greenhouse challenge: growing tomatoes without entering the greenhouse. Theyre managing the greenhouse from behind their laptops, and have to guide their decisions based on the real-time data they receive from the indoor climate, outside conditions and weather forecasts. To be able to do this theyve been developing multiple machine learning applications. These applications guide the cultivation strategy and subsequently, the actions taken to reach the desired climate.

Machine learning challengesHowever, there are many challenges in developing and operationalising large scale machine learning applications. One reason is the inherent nature of machine learning. Data are ever-evolving and models are stochastic, which means you have no certainty about what will happen in advance.

In software engineering, code is version controlled to manage changes over time (i.e. the numbered software updates of your smartphone). In machine learning, there are no standardised solutions to manage changes in code, data and model characteristics at the same time. And this is largely due to the (im)maturity of the field. There are many initiatives trying to solve this problem, for example, MLflow and Data Version Control (DVC), but these have their own limitations which are out the scope of this blog.

AWS project & solutionsTo solve some of these problems 30MHz has been fortunate to receive the help of two machine learning engineers from Amazon Web Services (or AWS). AWS is a cloud provider, and the company is using their services to host among others servers, database and machine learning models. As a company, 30MHz has been closely working together with AWS for quite some years. For this reason, and because theyre excited about the work, 30MHz had the opportunity to learn from and work with AWS engineers at their own office in Amsterdam for more than two weeks.

The goals of the project were twofold:

Improve and automateWith AWS' knowledge and experience, 30MHz has been able to improve and automate a large part of their machine learning infrastructure. The result is a scalable and robust framework for machine learning applications on the 30MHz platform.

For more information:30MHzMoezelhavenweg 91043AM AmsterdamNetherlands+31 (0) 6 14551362contact@30mhz.comwww.30mhz.com

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Growing tomatoes with Amazon Web Services - hortidaily.com

Congress postponed a vote to extend Patriot Act surveillance programs – The Verge

Congress has postponed a planned vote to reauthorize controversial surveillance programs. As Politico reports, the House Judiciary Committee intended to renew parts of the Foreign Intelligence Surveillance Act (FISA) before they expire on March 15th. But the committee canceled a vote set for today after learning that Rep. Zoe Lofgren (D-CA) would offer new amendments ones that Democrats feared would hurt the bills chances in a House-wide vote.

The bill would extend a handful of domestic surveillance rules, particularly Section 215 of the Patriot Act, which lets government agencies demand sensitive business records with a secret court approval. Section 215 was set to expire in late 2019, but Congress extended it for three more months in a funding bill, pushing the debate to 2020.

Politico and other outlets describe a sensitive bargaining process over the reauthorization, complicated by political tensions. The resulting bill tweaks elements of the program to increase accountability and limit surveillance powers. Among other things, it would expand the power of a friend of the court who could challenge the governments arguments before the FISA court. And it would explicitly end a program that let the National Security Agency demand call records from phone companies although the NSA previously said it already abandoned that approach.

Lofgren, however, described the bill as a puny reform to Politico. The bill as introduced by the committee was not one I thought was worth supporting, she said.

Her amendments were expected to further limit government data collection and add more scrutiny to FISA court approvals. The changes could have pleased civil liberties advocates who have been frustrated by a lack of meaningful reform. They could also have garnered support from some Republicans who were once ambivalent of limiting the Patriot Act but have since echoed President Donald Trumps claims of an FBI conspiracy against him. An anonymous Democratic aide, however, called the amendments a poison pill that would sink the bill.

NSA contractor Edward Snowden revealed the extent of NSA phone surveillance which involved collecting data on millions of Americans to fight foreign terror threats in 2013. The revelations sparked public protest, a congressional reform effort, and several lawsuits, especially because the program was apparently unproductive. A recent report said the years-long effort revealed only two unique leads, only one of which led to an investigation. But reformers have seen only incremental results.

Now, the latest bills future is unclear. The committee could reschedule a vote and meet the March 15th deadline, either with the bill described above or with a straight reauthorization of the old rules. Lofgren also said that she would soon offer her own alternative bill. We have the opportunity to reform the system, she told The New York Times. We should take that opportunity.

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Congress postponed a vote to extend Patriot Act surveillance programs - The Verge

After blowing $100m to snoop on Americans’ phone call logs for four years, what did the NSA get? Just one lead – The Register

The controversial surveillance program that gave the NSA access to the phone call records of millions of Americans has cost US taxpayers $100m and resulted in just one useful lead over four years.

Thats the upshot of a report [PDF] from the US government's freshly revived Privacy and Civil Liberties Oversight Board (PCLOB). The panel dug into the super-snoops' so-called Section 215 program, which is due to be renewed next month.

Those findings reflect concerns expressed by lawmakers back in November when at a Congressional hearing, the NSA was unable to give a single example of how the spy program had been useful in the fight against terrorism. At the time, Senator Dianne Feinstein (D-CA) stated bluntly: If you cant give us any indication of specific value, there is no reason for us to reauthorize it.

That value appears to have been, in total, 15 intelligence reports at an overall cost of $100m between 2015 and 2019. Of the 15 reports that mentioned what the PCLOB now calls the call detail records (CDR) program, just two of them provided unique information. In other words, for the other 13 reports, use of the program reinforced what Uncle Sam's g-men already knew. In 2018 alone, the government collected more than 434 million records covering 19 million different phone numbers.

What of those two reports? According to the PCLOB overview: Based on one report, FBI vetted an individual, but, after vetting, determined that no further action was warranted. The second report provided unique information about a telephone number, previously known to US authorities, which led to the opening of a foreign intelligence investigation.

A short explanation of that sole useful investigation is redacted, so it is unknown what it covered or whether it proved useful or led to a prosecution.

So, overall, millions of Americans' phone logs were given to the NSA at a cost of $100m, and the result was the opening of one lone probe. It is perhaps no wonder that the NSA and the FBI has spent years stalling and refusing to hand over any information about the program.

Its also worth noting that the NSA has not once but twice shuttered the program because it ended up with millions of records it did not have a right to see ie: the program was twice found to have gone out of bounds and used illegally. And yet the intelligence services still want to keep the program even if the legislation supporting it, the USA Freedom Act of 2015, expires on March 15.

The Trump Administration has asked that Congress extend the law so the NSA can, if it wishes, turn the program back on at some future date.

The lengthy report is a welcome return for the PCLOB, which was turned into a zombie organization unable to do any work for several years after its previous report on NSA spying programs concluded that they were illegal and Congress was obliged to scale them back.

Those reports themselves stemmed from the fact that the full depth of the programs was exposed by NSA IT-admin-turned-whistleblower Edward Snowden in a vast leak of information.

And yet, despite it being made clear that neither Congress nor the PCLOB were able to adequately track or oversee what was really happening with Americas spying program, little or nothing has changed and repeat efforts by some in Congress to reform those programs have been repeatedly stymied.

Not to be beaten, several senators are again trying to scale back the various NSA surveillance programs, announcing a new bill last month aimed at ending NSA blanket snooping, protecting abuse of the FISA oversight process, closing various loopholes in the secret law that the spying agency uses, and expanding scrutiny of the programs.

The PCLOB notes in its report it was only able to gain access to the information it has now shared because the intelligence services agreed to declassify at least some of the details. And the only reason the snoops did that is because they concluded the program serves no real useful purpose anymore, thanks to the widespread use of encrypted messaging apps over telephone calls. Such apps are more secure.

As to what is happening with the NSAs other spy programs, neither the PCLOB, nor Congress, nor even the highly classified secret FISA oversight court knows. And the only way it seems likely we will find out is if there is another Edward Snowden.

Sponsored: Detecting cyber attacks as a small to medium business

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After blowing $100m to snoop on Americans' phone call logs for four years, what did the NSA get? Just one lead - The Register

Bernie Asked If Democrats Are ‘Staging Coup Against’ Him. He Refuses To Answer. – The Daily Wire

Socialist Bernie Sanders, the front-runner in the race for the Democratic nomination for president, refused to answer during an interview on CNN whether the Democratic Party was trying to take the nomination away from him.

President Trump questioned last night the timing of Buttigiegs withdrawal, tweeting in part, this is the real beginning of the Dems taking Bernie out of play, no nomination again, CNNs Anderson Cooper said to Sanders. He also also tweeted again this afternoon, they are staging a coup against you.

Is he right? Cooper asked.

Sanders refused to answer the question.

You know what, I really wish that the president of the United States, might kind of spend his time doing his job, Sanders responded. Maybe, just maybe, he might wanna worry about the coronavirus, he might wanna worry about the stock market, he might worry about the 500,000 people in this country who are homeless, or the massive level of income and wealth inequality that exists.

So, President Trump, stay out of the Democratic primary, Sanders continued. Why dont you do your job for a change as president? Stop lying, stop running a corruption administration, pay attention to the American people not just your own political aims.

WATCH:

Trump has repeatedly accused the Democrats of plotting to take the nomination away from Sanders again, writing in January, They are taking the nomination away from Bernie for a second time. Rigged!

Mini Mike is a 54 mass of dead energy who does not want to be on the debate stage with these professional politicians, Trump tweeted last month. No boxes please. He hates Crazy Bernie and will, with enough money, possibly stop him. Bernies people will go nuts!

The Dems are working hard to take the prized nomination away from Bernie. Back room politics, which Bernie is not very good at, Trump tweeted last week. His people will not let it happen again!

The Daily Wire highlighted Sanders extreme policy stances in a profile piece last year:

On The Issues: Sanders calls himself ademocratic socialistwho, whiledisavowingwhole-hearted socialist theory with respect to government ownership of the means of production, nonetheless has consistently advocated for economic class warfare that pits the lower and middle classes against the wealthy. He has routinely supported anti-capitalistic and anti-growth economic policies, heavy-handed government regulation over the private economy, robust labor unions, and the Nordic model of a sprawling welfare state. On foreign policy, he has frequently mollycoddled communist dictatorships and has often been hostile toward Americas closest geopolitical allies. Overall, he is a far-left progressive who has long defined the leftward flank of what it means to be a progressive in America.

Constitution: Sanders supports a living Constitution interpretive methodology that effectively empowers unelected federal judges to determine large swaths of the laws that govern Americans lives. He is hostile to the First Amendments protection of free speech and has supported a constitutional amendment to overturn the political speech-affirming 2010 U.S. Supreme Court decision ofCitizens United v. F.E.C.He has generally supported a more robust role for Congress and a more diminished role for the presidency in the context of foreign policy and the conduct of overseas military operations. He takes an expansive view of the Fourth Amendment and has even praised disgruntled NSA leaker Edward Snowden.

Continue reading The Daily Wires profile piece on Bernie Sanders here.

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Bernie Asked If Democrats Are 'Staging Coup Against' Him. He Refuses To Answer. - The Daily Wire

Who Needs Cryptocurrency FedCoin When We Already Have A National Digital Currency? – Forbes

UKRAINE - 2020/02/27: In this photo illustration one hundred US dollar banknotes are seen displayed. ... [+] (Photo Illustration by Sergei Chuzavkov/SOPA Images/LightRocket via Getty Images)

The cryptocurrency enthusiasts are at it again, with a new name and even more ambitious goals than before: now they want a national digital currency. Hurry! The Chinese will beat us to it, and well be left behind!

Somehow, no one in the debate acknowledges the obvious fact that we already HAVE a national digital currency. Its fast, cheap and secure! It has no issue with regulators, and its accepted everywhere. Who knew? Its called the US dollar. The wild-eyed national digital currency groupies prefer to ignore the fact yes, its a fact that the US dollar is a digital currency. Instead, theyre convinced it cant possibly be a good thing, because its not based on brand-new, cool, immutable distributed ledger blockchain-based cryptocurrency technology. Bzzzt! Wrong.

The people who talk about national digital currency are obsessively focused on cryptocurrencies. They make believe digital currencies are a recent invention, and that only things that have evolved from Bitcoin meet the description. Nonetheless, by any reasonable definition, here in the good old USA we already have a digital currency. Its called the US dollar. Its managed by the Federal Reserve Bank. But thats not digital, you might say what about that green stuff in my wallet, and those coins jangling in my pocket or purse?

I agree, we have cash. As of Feb 12, 2020 there was $1.75 trillion worth of paper cash in various denominations in circulation. Thats quite a bit. But its far from the whole story. For the rest of the story, we turn to the money supply, the total amount of which is one of the chief responsibilities of the Fed to maintain and grow and shrink, as needed. There are two main measures of the money supply, M1 and M2. See this for the Feds definition. Basically, M2 includes checking and savings bank deposits, money market funds, and similar cash-equivalents. As of December 2019, M2 was $15.434 trillion dollars.

What this means is simple: almost 90% of US dollars have no physical existence they are purely digital. But this isnt just for the USA; world-wide, only 8% of currency exists as physical cash!

The US dollar took many steps over more than a century to evolve from physical cash to todays largely digital currency. First, paper currency wasnt real money it was a promise by a bank to trade the paper for the equivalent in gold. For example, heres a $5,000 bill from 1882 thats a promise to exchange for $5,000 in gold coin on demand:

Bureau of Engraving and Printing color specimen of a $5,000 Gold certificate, Series of 1882

In practice, no one exchanged these large-dollar notes for gold; they were mostly used by banks and the government to move funds between themselves, a practice which stopped in 1934.

Long before the advent of computers, the gold exchange promise was dropped. Heres a bill as printed in 1928 that simply declares that its $5,000:

1928 Federal Reserve note

The last high-denomination bills were printed in 1945. Large inter-bank transfers were done without the exchange of cash; tightly controlled procedures were used to transfer money between bank ledgers before the advent of computers. In 1969 the large bills were officially discontinued, and the government started destroying them. In 1975, the government started depositing social security payments into recipients accounts electronically. By 1990, all money transfers between commercial and central banks were done electronically.

There is no single date when you can say that the dollar became digital. The process of transformation took place step by step, each leading to the next. The early steps took place long before computers; the principle was established and in universal use among banks and the federal reserve already in 1945! The invention and use of computers simply enabled further automation of the digitization of the US dollar, and enabled fully real-time transfers to take place.

What all this adds up to is that the US dollar is a national digital currency, by any reasonable definition, and has been for years. The vast majority of currency value is fully and completely digital, and all large-dollar transactions are completely digital. We also have cards, which are smaller, lighter and more convenient than smartphones, with the added convenience that they dont crash or run out of power. In addition, we have the added convenience of physical cash, 100% interchangeable with its digital currency equivalent, as we see with ATMs every day. Cash is convenient for small transactions and for people who dont have working, powered and connected small computers on their person. The US dollar is indeed a national digital currency, with the added convenience of cards and cash.

The vast majority of people know through everyday experience that the US dollar is a national digital currency. But almost no one talks in those terms.When people use that recently-coined term, they usually means something brand-new, a form of cryptocurrency. For example, a recent WSJ article describes a push towards a national digital currency. One of the quoted authors waxes eloquent about its virtues, but never really says what it is.

The only way to understand national digital currency is to back up and look at the history of where the concept came from. While no one likes to talk about it, the undisputed origin of the concept is a brilliant, well-implemented and widely used body of software called Bitcoin. The concept and every major feature of Bitcoin was designed to operate with no central authority of any kind in charge. Amazing. How can it be that anyone anywhere could declare themselves to be a Bitcoin bank (they call them miners) and the system works? See this for an explanation. Bitcoin was also designed to give total anonymity to the people who deposit, send and receive Bitcoin, making it a favorite of international criminals around the world.

Before long, Bitcoin competitors appeared, each claiming to add or correct something important in Bitcoin; for example, Ethereum introducing the so-called smart contract. Next, people started talking about blockchain and the distributed immutable ledger, taking out the concept of currency. Supposedly, these technologies would solve long-standing problems involving data that was in many locations. This led to loads of blockchain start-ups and service companies, with giant corporations infected with bad cases of FOMO funding pilots and proofs-of-concept. Major companies like Microsoft and IBM now offer blockchain-as-a-service in their cloud products.

More recently, we have seen highly publicized efforts to legitimize something like an enhanced Ethereum-like currency, most prominently Facebooks Libra, which has the backing of a large number of name-brand financial institutions.

All this leads up to the newly coined notion of a national digital currency lets have the US government implement it instead of Facebook and its consortium partners!

This is all-too-typical technology mania. Weve seen it many times. The true believers ignore evidence, ignore existing practice and fervently believe in the world-transforming new technology. Loads of highly-paid executives and government leaders pay obeisance, effectively paying insurance against the remote possibility that the cult delivers real value. Theres a strong lemming effect: dont be left behind!

People who advocate for a national digital currency like to ignore the one we already have, in favor of some variation of the currency beloved by human smugglers, drug lords and international illegal arms traffickers. Like the people at the Digital Currency Initiative at the much-revered Media Lab at MIT. In a recent WSJ article, the director of the lab immediately conceded that with direct deposit of salary and Venmo to split the cost of dinner with friends, it seems like we already have a digital currency. But this isnt good enough! After all, there are fees, and big banks are involved and sometimes transactions can take days. Ugh. With a real national digital currency, a federal cryptocurrency, payments would be faster, cheaper and more secure.

There are just a couple little problems. Here are some highlights:

Crypto-groupies love to talk about the slowest transactions in the multi-trillion dollar US digital dollar system. While large parts of the US digital dollar system execute huge numbers of transfers in seconds, Bitcoin takes on average ten minutes to execute a single transfer. And thats only if you pay an above-average fee if you dont pay much, you could wait for hours for your transaction to process.

Depending on the transaction size, Bitcoin can only process between 3 and 7 transactions per second. If there were always transactions waiting to be processed, 24 by 7, at 5 transactions per second Bitcoin could handle at most 158 million transactions per year. By contrast, over 10 billion transactions are performed at just ATM machines every year in the US alone. There were over 110 billion card transactions in the US in 2016. The growth in transactions from 2015 was over 7 billion; the growth in card transactions was about 50 times greater than the maximum capacity of Bitcoin.

Crypto-groupies love to talk about the high fees for doing certain dollar transactions, ignoring the immense transaction flow of cheap and easy transactions like direct deposit and ACH, which operate at huge volumes. They dont talk much about the costs of running cryptocurrency. Theyre smart to ignore the subject. Todays Bitcoin transactions are costly, and the second you try to correct the various problems (speed, scalability, security), the costs skyrocket.

Hardly anyone uses Bitcoin, and the volumes are tiny compared to the dollar. Nonetheless, Bitcoin is incredibly, mind-blowingly expensive to operate. Even at todays minuscule volumes, Bitcoin computer processing consumes about the same amount of electricity as the whole country of Switzerland!

If you lose your checkbook, your credit or bank card or anything else, youre OK; you contact the bank and they fix it. By contrast, if you lose your cryptocurrency key (a string of numbers), there is literally no way to recover your money. About 20% of all Bitcoin are believed to be lost, something like $20 billion!! If you lose your key, whoever gets it can take all your Bitcoin, unlike with for example a lost card, where you call the bank, report the lost card, and avoid losing any money.

The crypto folks love the fact that everyone imagines that crypto means cant be cracked. So they avoid the subject. The fact is, crypto banks are robbed and every Bitcoin stolen all too often. Nearly a million bitcoins have been lost in this way, a loss at todays prices of roughly $10 billion!! Even the core defense of Bitcoin has now been cracked.

To the outside, crypto people are all about ignoring the problems and promoting wonderfulness. Among themselves, the relatively sane advocates recognize the problems and try to solve them, with endless variations being rolled out. In doing so, they either make the problems worse or destroy what little value there is in cryptocurrency. One of the leading ideas is to make a private blockchain, which is a pathetic joke. For example, Microsoft and Intel spell out many problems by way of selling their ineffective solution, and the Facebook Libra coalition takes the solve it by making it worse approach to new lows.

The whiners will whine about whats wrong with todays US dollar. Is it really chock-full of problems, as the crypto-groupies like to say? Lets do something rare: focus on the positive. First and foremost, lets remember that the dollar has worked for a couple centuries now, and along the way transformed itself from physical to about 90% digital, all without breaking! In addition, it has benefited from tremendous private-sector innovation. Here are some highlights of the fastest, cheapest and most secure currency ever created:

Physical cash is great. When Im in the city and someone gets my car for me from the garage, I like to give a tip. Its easy: I pull out my wallet and hand over bills. Anything fully digital would require electronics and would be a pain.

Cards are great. When I pull into a gas station in New Jersey, where gas is pumped for you, I open the window, say fill with regular, please and hand over a card. When its done, I get the card and a receipt and drive off. Easier than cash because no change. This is fully digital. Today. And, at my great local gas station, they often clean my windows, so I get to hand the guy a couple bucks as a tip. Painless.

Cardless is great. I call for an Uber from the app. When the car arrives, we each check each others identities and away we go. On arrival, I get out. Thats it!

Wiring money for a house closing is great. I call USAA, my bank, who verifies my identity and gets it done in minutes. No going to a branch, certified checks, etc. The phone call is a good thing it reduces the chance of fraud to near-zero, unlike the fraud-riven crypto world.

P2P apps are great. There are zero-cost, instant transfer apps like Venmo, CashApp and Zelle. These are used by over a hundred million of people, and they work. Today. How could crypto in any form be better? Actually, it would be worse. See this.

What about those awful transactions that supposedly take days? Yup, there are some. Its called a step-by-step, no errors or crashes permitted transition to real-time. Transactions are already 100% digital, and with ACH (like electronic checks) very low cost. The version of ACH in the UK is already same-day, and ACH in the US is in the middle of a transition to same-day and real-time.

What about international payments? I guess the crypto-groupies are out of touch with whats going on here in the real world. For personal use, credit cards are already accepted nearly everywhere, with everyone involved getting or paying in their own currency. The big complaint of the crypto people is international business transactions, involving lots of time, transfers and fees. That was true. Which is why a handful of amazing new companies have emerged and are addressing the issue. A couple of them are operating at scale and in production today.

Currency Cloud, for example, has a brilliant solution. A company that has suppliers in many countries gets the suppliers to give Currency Cloud their preferred local bank accounts. Currency Cloud itself maintains local accounts for itself in all the countries it supports. The buyer sends a payment directive to Currency Cloud, who then does a local transfer of the requested amount from its account in the target country to the vendor in that country. As the network grows, each supported country has a larger number of companies both sending and receiving payments, so that a growing number of transfers can be done completely locally only the net payment imbalance between countries needs to be settled by Currency Cloud between its own accounts, which it optimizes for minimum cost. This is 100% digital, low cost, real-time, and operating at scale. Today.

For smaller business and individuals there are services exploding onto the scene for international payments. For example, Rapyd (disclosure: my VC fund is an investor) enables people without bank accounts to buy, sell and get paid for work in over 100 countries at over 2 million access points, where they either get or give local currency to complete international digital transactions. For example, you could be a driver for Uber and get paid, even though you have no card or bank account.

Get over it, crypto-fanatics and blockchain groupies. Yes, the Bitcoin technology is an impressive achievement, and highly useful to the criminal class. But it makes any real-world currency problem you can think of worse, and completely ignores the patent reality, which is that the wonderful future of a national digital currency is something we have today the US dollar!

Read the original:
Who Needs Cryptocurrency FedCoin When We Already Have A National Digital Currency? - Forbes

CoronaCoin: Cryptocurrency that bets on coronavirus deaths – Business Today

Some Europe-based developers have created a cryptocurrency -- named CoronaCoin -- that lets you cash in on the number of people who die or fall ill due to coronavirus. In a rather tasteless methodology, this Bitcoin-like cryptocurrency functions on coins -- called tokens -- that decrease as more people die or get infected. The scarcity of tokens lead to gain in the value of the digital currency. As per its developers, its total supply is based on the world population -- around 7,604,953,650 (over 7 billion).

The digital currency's tokens are burnt every 48 hours, depending upon the number of deaths during those hours. "As the number of infected/dead from the virus increases, the number of tokens is manually burned every 48 hours," says the website. With the rapid rise in the number of infected people, the tokens are also burning fast. "Some people speculate a large portion of the supply will be burned due to the spread of the virus, so they invest," Sunny Kemp, a user who identified himself as one of the developers, told Reuters.

As of Friday night, a total of 85,366 tokens have been burned. The digital currency developers, however, aim to donate 20 per cent of the money to Red Cross via its 2019-nCoV relief effort. Kemp said they'll use a well-known cryptocurrency payments processor to donate money every month.

On netizens raising questions over the "tasteless" idea, Kemp seems to disagree. He defended the idea, saying the World Health Organisation also issued pandemic bonds. "How's that different," he asked. Coronacoin is also backed by proof of death based on statistics obtained from the World Health Organisation (WHO), its developers claim.

How CoronaCoin functions

What's cryptocurrency

The underlying technology behind cryptocurrency is the blockchain technology. A blockchain is an anonymous online ledger that uses data structure to simplify the way we transact. Blockchain allows users to manipulate the ledger in a secure way without the help of a third party. The algorithm used in blockchain reduces the dependence on people to verify the transactions. This technology used for recording various transactions has the potential to disrupt the financial system.

Pandemic on the rise

The deadly virus that first emerged in China in December last year has spread to more than 70 countries and has infected more than 88,000 people, including over 80,000 in China. China's National Health Commission (NHC) said on Sunday it received reports of 202 new confirmed cases of coronavirus on Sunday, taking the total number of cases in the mainland to 80,026. On Sunday alone, China reported 42 new fatalities from the novel coronavirus outbreak, taking the death toll in the country to over 3,000.

Edited by Manoj Sharma

Originally posted here:
CoronaCoin: Cryptocurrency that bets on coronavirus deaths - Business Today