Bitcoin (BTC) Soft-Fork in 2020 Predicted By Analyst: Here’s Why – U.Today

The year of 2019 became the first that failed to bring a new well-recognized Bitcoin fork. The only live fork from 2019 is nowlisted in the third thousand of Coinmarketcap rankings. But, this year may bring some good news.

Yesterday, Lucas Nuzzi, the analyst from Digital Assets Research agency, tweeted about a possible Bitcoin (BTC) soft-fork in 2020. Moreover, according to him, this soft-fork will bring the most profound innovations to Bitcoin's Layer-One in its history.

Mr. Nuzzi predicted the implementation of three Bitcoin (BTC) Improvement Proposals (BIPs). It will contain the roll-out of Schnorr signatures, Taproot schemes and Tapscript language into the Bitcoin (BTC) network.

When asked about the exact time framework for the upcoming soft-work, Mr. Nuzzi answered:

I'm thinking Q4 if it goes through this year.

All three mechanisms will improveboth the scalability and privacy of the first blockchain. In a nutshell, with the Schnorr/Taproot upgrade, the mechanism of transaction signing in the Bitcoin (BTC) network will be reconsidered. In turn, it will allow the development of new multi-signature solutions.

One more use-case for the post-fork Bitcoin (BTC) network is proposed by Mr. Nuzzi. He supposes that numeroussimple peer-to-peer contracts that rely on safe oracles (e.g. arbitrators) will make use of it.

The Bitcoin (BTC) network has gonethrough one mass-adopted soft-fork so far, Segregated Witness (SegWit). It allows the processing of some data outside of the block and, therefore, unloads the main chain to upgrade its speed.

What do you think, will Bitcoin (BTC) fork in 2020? Share your predictions in the Comments!

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Bitcoin (BTC) Soft-Fork in 2020 Predicted By Analyst: Here's Why - U.Today

XRP Struggles At The 100MA As Bitcoin Attempts To Retake $9,000: Ripple Price Analysis – CryptoPotato

XRP/USD

Support: $0.22, $0.20, $0.185

Resistance: $0.2345, $0.25, $0.262.

XRP/BTC:

Support: 2600 SAT, 2350 SAT, 2455 SAT.

Resistance: 2710 SAT, 2800 SAT, 2900 SAT.

Since our last analysis, XRP managed to continue to surge higher. However, it met the expected resistance at $0.2345 and was unable to overcome here. This area of resistance is further bolstered by the 100-days EMA. XRP continues to remain supported by $0.228 as the bulls attempt to regroup to break the 100-days EMA.

XRP is on the cusp of turning bullish if it can pass above the resistance at $0.2345. For XRP to turn neutral, it must drop beneath $0.22, with a further drop beneath $0.185 turning it bearish.

If the bulls continue to pressure the market higher and break above the resistance at the 100-days EMA, immediate higher resistance lies at $0.24. Above this, resistance is to be expected at $0.25, $257 (1.414 FIb Extension), and $0.262 (bearish .618 Fib Retracement). The resistance at $0.262 is bolstered by the 200-days EMA. On the other hand, if the sellers push XRP beneath $0.228, initial support toward the downside sits at $0.22. Beneath this, support lies at $0.212, $0.20, and $0.185,

The RSI remains above the 50 level which shows that the bulls remain in control over the market momentum. However, the Stochastic RSI is preparing for a bearish crossover signal which might help to send the market lower.

Against BTC, XRP managed to bring itself back above the support at 2600 SAT. The cryptocurrency did also spike higher into 2780 SAT but quickly reversed and fell well beneath 2700 SAT again. It continues to trade sideways between 2600 SAT and 2700 SAT as we wait for the market to decide where to head toward next.

XRP still remains neutral at this moment in time and must pass above the resistance at 3000 SAT before it can turn bullish. Alternatively, if XRP drops beneath the support at 2350 SAT it would turn bearish.

If the bears push the market beneath 2600 SAT, immediate support is located at 2350 SAT which is provided by the .886 Fibonacci Retracement level. Beneath this, support lies at 2455 SAT, 2400 SAT and, 2360 SAT. On the other hand, if the bulls regroup and push higher, resistance is located at 2710 SAT, 2800 SAT, 2900 SAT, and 3000 SAT.

The RSI continues to trade along the 50 level as the indecision within the market continues. For a bullish break higher, we must see the RSI rising above 50 to confirm that the bulls have taken charge of the market momentum.

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Disclaimer: Information found on CryptoPotato is those of writers quoted. It does not represent the opinions of CryptoPotato on whether to buy, sell, or hold any investments. You are advised to conduct your own research before making any investment decisions. Use provided information at your own risk. See Disclaimer for more information.

Cryptocurrency chartsby TradingView.Technical analysis tools byCoinigy.

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More Bitcoin Scam Ads With Martin Lewis on Instagram Can We Get a Filter for That? – Cointelegraph

Suspected crypto con artists are once again using the likeness of British financial expert Martin Lewis to defraud unsuspecting victims. In 2019, Lewissettled a defamation suit against Facebook for similar Bitcoin (BTC) scam adverts.

Instagram says deceptive advertisements have no place on its platform and plans to continue improving its detection protocols for such content. Social platforms have been known to censor crypto-related content, instituting blanket bans on crypto ads on several occasions.

However,Facebook has recentlyrelaxed this policy amid the roll-out of its own digital currency project. The social media giant is one of the main backers of the Libra Association, which plans to release the Libra digital currency payment solution.

While many of these fraudulent crypto investments use fake endorsements, there are also cases where well-known crypto figures publicize such cons as legitimate investments. The presence of such backing seemingly provides legitimacy for otherwise obvious scams that end up siphoning millions of dollars from unsuspecting victims.

As previouslyreported by Cointelegraph, Bitcoin scam ads touting false endorsements from Martin Lewis are appearing once again on social media. Retweeting the scam ads now appearing on Instagram, Lewis warned the public to not fall victim to such obvious cons.

The misleading adverts show a fake article from British tabloid Mirror with the title, Martin Lewis lends a hand to British families with Revolutionary Bitcoin Home Based Opportunity. No such article exists on Mirror, with the media outlet issuing warnings about similar phony content as far back as August 2018.

The particular scam in question wasred-flagged in late 2019. In an email to Cointelegraph, a spokesperson for Facebook, the parent company of Instagram, explained that the platform has a zero-tolerance policy for scam ads. According to the company spokesperson:

Misleading or deceptive ads of any kind, have no place on Instagram. Our Advertising Policies do not allow scam ads, and when we detect an ad that violates our Advertising Policies, we disapprove it. All ads are subject to our ad review system, which relies primarily on automated, and in some cases manual review to check ads against these policies. This happens before ads begin running.

The Facebook representative further went on to state that while some misleading content may slip through the cracks, platform users should report such ads:

We incorporate signals of negative feedback from people, such as people reporting, hiding, or blocking an ad, into our ongoing review process. When we find ads that try to get around our enforcement, we go beyond simply rejecting the ad. We disable ad accounts and remove their ability to advertise in the future.

Back in 2018, Lewissued Facebook following the emergence of more than 1,000 scam ads featuring the financial expert. In 2019, the two parties settled the suit, with Facebook pledging to donate $3.9 million to Citizens Advice a Scams Action service for the United Kingdom.

The social media giant also agreed to create a unique tool for reporting scam ads in the U.K. Commenting at the time, Lewis remarked:

It shouldnt have taken the threat of legal action to get here. Yet once we started talking, Facebook quickly realised the scale of the problem, its impact on real people, and agreed to commit to making a difference both on its own platform and across the wider sector.

Lewis isnt the only person to sue Facebook because of Bitcoin scam ads. Back in mid-2019, Dutch billionaire John De Mol tooklegal action against the social media company over fraudulent cryptocurrency adverts using his image without permission.

Related: Dutch Billionaire Yet Another Victim of Deceptive Crypto Ads, Sues Facebook

At the time, De Mol argued that the scam ads were damaging to his reputation and had defrauded victims of close to $2 million. The court sided with the Big Brother reality show creator,ruling that Facebook must make efforts to remove such content or face significant monetary fines.

Scams featuring other public figures such as Tesla CEOElon Musk, Ethereum Co-FounderVitalik Buterin, British actress Kate Winslet and Australian business mogul Andrew Forrest have also emerged in the past. Each ad campaign typically attempts to use the images of these well-known people to trick uninformed investors into putting money (or crypto deposits) into an elaborate scam.

According to Alex Nguyen, founding partner at XNOVO legal a firm specializing in contracts and business structuring litigation holding social media platforms like Facebook liable for content published by users constitutes a slippery slope. In a private correspondence with Cointelegraph, Nguyen opined:

Subjecting the most ubiquitous social media platforms to secondary liability for their users illegal content or conduct is an arduous uphill battle, largely due to the broad application of the Communications Decency Act (CDA) created by the Telecommunications Act of 1996. The CDA allows a social media platform to avoid secondary liability for a users illegal content if a third party user originated the illegal content and the social media platform and its services merely served as a neutral tool for creating such content.

Nguyen argues that a court could include scam ads under the broad umbrella of third-party content. Thus, it is possible to liberally apply the protection afforded by the CDA to fraudulent cryptocurrency advertising.

Apart from crypto scam ads, social media platforms have also come undercriticism for allowing or failing to prevent the spread of misleading information, especially in the political scene. Facebook, in particular, continues to face backlash for its policies concerning political ads.

As is the case with crypto ads, it appears the burden of confirmation rests with users and not with the content creators or publishers. Thus, it is of paramount importance for consumers of information to do their own research and not take all information found online as gospel truth.

Reactions to the court ruling in the De Mol case raised questions about whether social media platforms like Facebook are fighting a losing battle against creators and publishers of misleading content. Facebooks attorney, Jens van den Brink speaking to Bloomberg following the trials close quipped: De Mol seeks a perfecting filter that doesnt exist.

Even with enduring blanket bans on crypto-related advertisements, scammers are still able to publish deceptive investment content on social media platforms. This reality points to the possibility that the filters employed by Facebook and others are ill-suited to completely eradicating all instances of scam ads.

As revealed by Facebook in its email to Cointelegraph, the company employs both automated and manual content review protocols. However, scammers are seemingly able to game these control systems, enabling their misleading content to find its way online. Facebook says it is taking steps to block fraudsters from publishing content on its platform.

For Vikram Singh, managing director of enterprise blockchain firm Antier Solutions, fraudsters will always find a way to bypass social media filters. In an email to Cointelegraph, Singh remarked:

It cannot be overlooked that there are always ways around whereby changing some different terminology you can still bypass computerized algorithms. So in my opinion it is more of a case of when people get lured by immediate gains and which can happen in any industry so curtailing cryptos for the same can eventually become a roadblock in adoption and awareness of crypto and blockchain looking at the outreach of Facebook and Insta.

XNOVOs Nguyen, however, believes that Facebook and other social media platforms could do more to stop the spread of misleading content. According to Nguyen, the current terms of use on social media platforms leads to termination of the account, which is not enough:

I think social media platforms are in the best position to implement better policies to identify and curb the continued proliferation of false or fraudulent cryptocurrency-related advertising ex ante, especially given their unfettered access to a tremendous amount of data, technologies (e.g. artificial intelligence and machine learning) to make sense of all that data, and limitless resources.

Concerning fact-checking, endorsements by seemingly trusted individuals in an industry can sometimes provide legitimacy for the published piece of information, especially when the end-user does not possess sufficient knowledge about the sector in question. Thus, it becomes an even greater problem when well-known personalities contribute to the spread of misleading content by providing backing.

While there are crypto scam ads with fake celebrity endorsements, there are also fraudulent advertisements promoted by crypto celebs. In late December 2019, a suspected Bitcoin scammer dubbed LONorchestrated an exit scam after defrauding victims of about 53 BTC (currently worth $424,000).

Before the exit scam, some popular crypto personalities endorsed LONs investment program via tweets and retweets. Following LONs alleged abscondment, some earlier backers deleted tweets promoting the scam.

Fraud has more to do with ignorance and lack of knowledge than any social media channel as a medium. Most of these cases occur to users who lack specialized expertise necessary to distinguish legitimate from an illegitimate offer, remarked Singh. Given the similarities in the scams adopted by these suspected crypto fraudsters, consumers need to employ more research, critical thinking and due diligence when making investment decisions.

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More Bitcoin Scam Ads With Martin Lewis on Instagram Can We Get a Filter for That? - Cointelegraph

Bitcoin Price Cheerfully Touches $9,000: Was This A False Breakout? – Coingape

Bitcoin is still the cheerleader in the cryptocurrency market. Besides, it is the largest digital asset and by far the most traded. Its impact on the altcoin market continues to be felt more than a decade later.

The surge at the beginning of this week aimed at $9,000. However, the bulls lost momentum slightly above $8,900. The retracement I discussed on Thursday seems to have been necessary for Bitcoins recovery. In other words, following the retreat below $8,600, Bitcoin bounced off the support at $8,570 and extended the movement marginally above $9,000.

Unfortunately, the gains were unsustainable, and Bitcoin succumbed to the pressure. At the time of writing, Bitcoin is trading at $8,884 following a 1.95% growth in value on the day. An extended reversal appears to be imminent, especially with the Relative Strength Index (RSI) retreating after touching the level at 70.

Both the short term and long term analyses have a bearish bias. As seen, Bitcoin is currently erasing accrued gains since the opening of the session. In addition to that, the formed rising wedge pattern (red dotted trendlines) signals that a reversal is underway, although it might not come immediately.

It is clear that Bitcoin is not technically nor fundamentally ready to take on the resistance at $9,000. This means a reversal is necessary to ensure that the bulls, regroup, gain strength and create fresh demand to push Bitcoin not only above $9,000 but also towards $10,000. In the meantime, the Andrews Pitchfork suggests that Bitcoin is still in the bullish phase of the ongoing surge.

Spot rate: $8,893

Relative change:

Percentage change:

RSI: Retreat from the overbought region signals rising selling activity.

Summary

Article Name

Bitcoin Price Cheerfully Touches $9,000: Was This A False Breakout?

Description

Bitcoin breaks the $9,000 resistance but fails to sustain the gains.A reversal could be necessary for Bitcoin to create fresh interest from the bulls.

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John Isige

Publisher Name

Coingape

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Bitcoin (BTC) Could Gain Even More This Year Than in 2019, Says Fundstrat’s Tom Lee – U.Today

Fundstrat's Tom Lee reiterated his earlier prediction about Bitcoin (BTC) posting bigger gains in 2020 than in 2019 during his most recent appearance on Yahoo! Finance.

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Lee claimed that the combination of "elevated" geopolitical tensions, the upcoming reward halving, and institutional money would form a perfect setup for Bitcoin's next rally in 2020.

As reported by U.Today, Fundstrat concluded that there was a strong probability that theprice of Bitcoin could surge by more than 100 percent in 2020, which means that BTC is expected to close this year at least at $14,000 by the famed Wall Street analyst.

Back in July 2019, Lee claimed that BTC could touch up to $40,000 by Q4 2019, which was an extremely inaccurate prediction. However, due to the aforementioned headwinds, the permabull might finally be spot-on this year.

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For those who do not want to directly buy Bitcoin but still want exposure to the volatile asset class, Lee recommends taking a look at companies like Square. The Jack Dorsey-helmed payments giant introduced Bitcoin deposits for its Cash App in June 2018.

Lee also mentioned Barry Silbert'sGrayscale Bitcoin Trust (GBTC)that can be accessed by many US investors. Grayscale's coffers increased by $600 mln in 2019 with 71 percent of this sum being attributed to institutional investors.

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Bitcoin (BTC) Could Gain Even More This Year Than in 2019, Says Fundstrat's Tom Lee - U.Today

4 Bitcoin Mixers for the Privacy Conscious – Bitcoin News

In an era of unprecedented global surveillance, it is unreasonable to expect the blockchain world to be any different. It is perfectly reasonable, though, to resist this surveillance through countermeasures that thwart the would-be surveillers. Digital privacy is a right that everyone is entitled to. Thanks to the provision of bitcoin mixers, you can claim that entitlement by shuffling your coins and emerging with untainted crypto whose origins have been obfuscated.

Also read: How Dropgangs and Dead Drops Are Transforming Darknet Practices

Just as using Tor doesnt give you internet anonymity, bitcoin mixing alone doesnt grant you automatic privacy. It helps, but only when used in conjunction with other privacy enhancing techniques, like not using exchanges that enforce KYC, and not recombining your freshly mixed UTXOs, thereby undoing all your hard work. News.Bitcoin.com will examine ways to enhance your anonymity when using mixing services in the near future. For now, just know that the following mixing services are not a silver bullet for privacy. When used as their developers recommend, however, they can significantly enhance the fungibility of your coins.

Bitcoin Mixer does exactly what its name sounds like, but it also does a lot more. In addition to mixing up your BTC, the service can do the same with LTC and ETH, providing privacy for three of the most popular cryptos. Its a custodial service, which generally means you can mix larger amounts of coins than with a noncustodial service, where youre reliant on your peers to provide privacy in numbers. Using the service is simple: enter the address youd like your shuffled coins to be sent to, and drag the slider to select your desired mixing time, ranging from 30 minutes to 20 hours. The longer youre prepared to wait, the greater the degree of anonymity you can expect. The platform charges a fee of 2-5%.

One for the BCH brigade, Cashshuffle provides noncustodial mixing of bitcoin cash. Its fully decentralized, and operates by mixing the UTXOs in your BCH wallet with those of other Cashshuffle users. Over $40 million of BCH has been mixed through Cashshuffle, which is compatible with wallets such as Electron Cash. If youre new to the world of bitcoin cash mixing, news.Bitcoin.com has published a detailed guide to using the service. Theres also plans for a Tor-integrated version of the service, known as Cashfusion, which will further diminish the ability for blockchain forensics firms to profile BCH users.

Most noncustodial BTC and BCH mixers are based on implementations of Coinjoin, a trustless method for combining bitcoin payments from multiple users into a single transaction, masking their origin. Cashshuffle is based on Coinjoin, and so are the two most popular wallet-integrated BTC mixers Whirlpool and Wasabi. The former is developed by Samourai Wallet, and enables users of the noncustodial wallet to mix their UTXOs with others through selecting from one of three pools of varying sizes: 0.01, 0.05, and 0.5 BTC. If you have 1 BTC to mix, for example, select the 0.5 BTC pool and your UXTOs will be sent through in two cycles, until all of your coins have been cleaned. The Whirlpool fee remains the same whether youre mixing one coin or 10, making Samourais Whirlpool Coinjoin implementation cost-effective. Its also fast.

Samourai and Wasabi are engaged in a dispute over whose mixing service provides greater anonymity. Samourai appears to have the upper hand at present, but that doesnt mean you should write off Wasabi its an excellent noncustodial BTC wallet for the privacy-conscious, and its integrated Chaumian Coinjoin mixing service is continually improving. The Plustoken scammers famously tried to wash thousands of BTC through Wasabi and failed due to the size of their transactions, which dwarfed those of all other users combined. For regular users seeking to mix modest amounts of BTC, greater anonymity and less scrutiny should be assured, making Wasabi perfect for everyday use.

Whether youre planning to use a custodial or noncustodial bitcoin mixer, do your homework, read some reviews, and familiarize yourself with its workings. Then, after successfully mixing your first set of UTXOs, make it a point of habitually repeating the exercise with new coins that come into your possession. Think of it as cleaning your digital house. In the process, youll also be enhancing the anonymity of your fellow coinjoiners.

What bitcoin mixers do you recommend? Let us know in the comments section below.

Disclaimer: This article is for informational purposes only. It is not an offer or solicitation of an offer to buy or sell, or a recommendation, endorsement, or sponsorship of any products, services, or companies. Bitcoin.com does not provide investment, tax, legal, or accounting advice. Neither the company nor the author is responsible, directly or indirectly, for any damage or loss caused or alleged to be caused by or in connection with the use of or reliance on any content, goods or services mentioned in this article.

Images courtesy of Shutterstock.

Did you know you can verify any unconfirmed Bitcoin transaction with our Bitcoin Block Explorer tool? Simply complete a Bitcoin address search to view it on the blockchain. Plus, visit our Bitcoin Charts to see whats happening in the industry.

Kai's been manipulating words for a living since 2009 and bought his first bitcoin at $12. It's long gone. He's previously written whitepapers for blockchain startups and is especially interested in P2P exchanges and DNMs.

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Essential AI & Machine Learning Certification Training Bundle Is Available For A Limited Time 93% Discount Offer Avail Now – Wccftech

Machine learning and AI are the future of technology. If you wish to become part of the world of technology, this is the place to begin. The world is becoming more dependent on technology every day and it wouldnt hurt to embrace it like it is. If you resist it, you will just be obsolete and will have trouble surviving. Wccftech is offering an amazing discount offer on the Essential AI & Machine Learning Certification Training Bundle. The offer will expire in less than a week, so avail it right away.

The bundle includes 4 extensive courses on NLP, Computer Vision, Data visualization and Machine Learning. Each course will help you understand the technology world a bit more and you will not regret investing your time and money on this. The courses have been created by experts so, you are in safe hands. Here are highlights of what the Essential AI & Machine Learning Certification Training Bundle has in store for you:

The bundle has been brought to you by GreyCampus. They are known for providing learning solutions to professionals in various fields including project management, data science, big data, quality management and more. They offer different kinds of teaching platforms including e-learning and live-online. All these courses have been specifically designed to meet the markets changing needs.

Original Price Essential AI & Machine Learning Certification Training Bundle: $656Wccftech Discount Price Essential AI & Machine Learning Certification Training Bundle: $39.99

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How machine learning and automation can modernize the network edge – SiliconANGLE

If you want to know the future of networking, follow the money right to the edge.

Applications are expected to move from data centers to edge facilities in record numbers, opening up a huge new market opportunity. The edge computing market is expected to grow at a compound annual growth rate of 36.3 percent between now and 2022, fueled by rapid adoption of the internet of things, autonomous vehicles, high-speed trading, content streaming and multiplayer games.

What these applications have in common is a need for near zero-latency data transfer, usually defined as less than five milliseconds, although even that figure is far too high for many emerging technologies.

The specific factors driving the need for low latency vary. In IoT applications, sensors and other devices capture enormous quantities of data, the value of which degrades by the millisecond. Autonomous vehicles require information in real-time to navigate effectively and avoid collisions. The best way to support such latency-sensitive applications is to move applications and data as close as possible to the data ingestion point, therefore reducing the overall round-trip time. Financial transactions now occur at sub-millisecond cycle times, leading one brokerage firm to invest more than $100 million to overhaul its stock trading platform in a quest for faster and faster trades.

As edge computing grows, so do the operational challenges for telecommunications service provider such as Verizon Communications Inc., AT&T Corp. and T-Mobile USA Inc. For one thing, moving to the edge essentially disaggregates the traditional data center. Instead of massive numbers of servers located in a few centralized data centers, the provider edge infrastructure consists of thousands of small sites, most with just a handful of servers. All of those sites require support to ensure peak performance, which strains the resources of the typical information technology group to the breaking point and sometimes beyond.

Another complicating factor is network functions moving toward cloud-native applications deployed on virtualized, shared and elastic infrastructure, a trend that has been accelerating in recent years. In a virtualized environment, each physical server hosts dozens of virtual machines and/or containers that are constantly being created and destroyed at rates far faster than humans can effectively manage. Orchestration tools automatically manage the dynamic virtual environment in normal operation, but when it comes to troubleshooting, humans are still in the drivers seat.

And its a hot seat to be in. Poor performance and service disruptions hurt the service providers business, so the organization puts enormous pressure on the IT staff to resolve problems quickly and effectively. The information needed to identify root causes is usually there. In fact, navigating the sheer volume of telemetry data from hardware and software components is one of the challenges facing network operators today.

A data-rich, highly dynamic, dispersed infrastructure is the perfect environment for artificial intelligence, specifically machine learning. The great strength of machine learning is the ability to find meaningful patterns in massive amounts of data that far outstrip the capabilities of network operators. Machine learning-based tools can self-learn from experience, adapt to new information and perform humanlike analyses with superhuman speed and accuracy.

To realize the full power of machine learning, insights must be translated into action a significant challenge in the dynamic, disaggregated world of edge computing. Thats where automation comes in.

Using the information gained by machine learning and real-time monitoring, automated tools can provision, instantiate and configure physical and virtual network functions far faster and more accurately than a human operator. The combination of machine learning and automation saves considerable staff time, which can be redirected to more strategic initiatives that create additional operational efficiencies and speed release cycles, ultimately driving additional revenue.

Until recently, the software development process for a typical telco consisted of a lengthy sequence of discrete stages that moved from department to department and took months or even years to complete. Cloud-native development has largely made obsolete this so-called waterfall methodology in favor of a high-velocity, integrated approach based on leading-edge technologies such as microservices, containers, agile development, continuous integration/continuous deployment and DevOps. As a result, telecom providers roll out services at unheard-of velocities, often multiple releases per week.

The move to the edge poses challenges for scaling cloud-native applications. When the environment consists of a few centralized data centers, human operators can manually determine the optimum configuration needed to ensure the proper performance for the virtual network functions or VNFs that make up the application.

However, as the environment disaggregates into thousands of small sites, each with slightly different operational characteristics, machine learning is required. Unsupervised learning algorithms can run all the individual components through a pre-production cycle to evaluate how they will behave in a production site. Operations staff can use this approach to develop a high level of confidence that the VNF being tested is going to come up in the desired operational state at the edge.

AI and automation can also add significant value in troubleshooting within cloud-native environments. Take the case of a service provider running 10 instances of a voice call processing application as a cloud-native application at an edge location. A remote operator notices that one VNF is performing significantly below the other nine.

The first question is, Do we really have a problem? Some variation in performance between application instances is not unusual, so answering the question requires a determination of the normal range of VNF performance values in actual operation. A human operator could take readings of a large number of instances of the VNF over a specified time period and then calculate the acceptable key performance indicator values a time-consuming and error-prone process that must repeated frequently to account for software upgrades, component replacements, traffic pattern variations and other parameters that affect performance.

In contrast, AI can determine KPIs in a fraction of the time and adjust the KPI values as needed when parameters change, all with no outside intervention. Once AI determines the KPI values, automation takes over. An automated tool can continuously monitor performance, compare the actual value to the AI-determined KPI and identify underperforming VNFs.

That information can then be forwarded to the orchestrator for remedial action such as spinning up a new VNF or moving the VNF to a new physical server. The combination of AI and automation helps ensure compliance with service-level agreements and removes the need for human intervention a welcome change for operators weary of late-night troubleshooting sessions.

As service providers accelerate their adoption of edge-oriented architectures, IT groups must find new ways to optimize network operations, troubleshoot underperforming VNFs and ensure SLA compliance at scale. Artificial intelligence technologies such as machine learning, combined with automation, can help them do that.

In particular, there have been a number of advancements over the last few years to enable this AI-driven future. They include systems and devices to provide high-fidelity, high-frequency telemetry that can be analyzed, highly scalable message buses such as Kafka and Redis that can capture and process that telemetry, and compute capacity and AI frameworks such as TensorFlow and PyTorch to create models from the raw telemetry streams. Taken together, they can determine in real time if operations of production systems are in conformance with standards and find problems when there are disruptions in operations.

All that has the potential to streamline operations and give service providers a competitive edge at the edge.

Sumeet Singh is vice president of engineering at Juniper Networks Inc., which provides telcos AI and automation capabilities to streamline network operations and helps them use automation capabilities to take advantage of business potential at the edge. He wrote this piece for SiliconANGLE.

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Predicting Healthcare Utilization With Applied Machine Learning – AJMC.com Managed Markets Network

On this episode of Managed Care Cast, we speak with John Showalter, MD, chief product officer at Jvion and an internal medicine physician, and Soy Chen, MS, director of data science at Jvion and part of their data science team. We discuss their research about using applied machine learning to predict healthcare utilization based on social determinants of health, appearing in the January 2019 Health IT issue of The American Journal of Managed Care.

They found that the social determinant of health most associated with risk was air quality. In addition, neighborhood in-migration, transportation, and purchasing channel preferences were more telling than ethnicity or gender in determining patients use of resources.

On this episode of Managed Care Cast, we speak to study authors Soy Chen, MS, and John Showalter, MD, about how they sourced data for the algorithm, the technology's impact on the future of healthcare, and privacy concerns raised by artificial intelligence.

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The open source licence debate: what we need to know – Open Source Insider – ComputerWeekly.com

As we have already noted on Computer Weekly Open Source Insider, open source grew, it proliferated and it became something that many previously proprietary-only software vendors embraced as a key means of development.

But the issue of how open source software is licenced is still the stuff of some debate.

Open Source Insider has already looked at the issues relating to dead projects (that are still walking and running) and the need for workable incentivisation models.

Chief operating officer (COO) for GitHub Erica Brescia noted that, from her perspective, she is seeing an increasing tension between open source projects and those that are building services on top of open source, such as cloud vendors with their database services.

Brescia notes that licenses applied to open source projects a decade ago did not consider the possibility of a cloud vendor delivering an as-a-Service SaaS layer using the project without contributing back to it, which is leaving some open companies in a difficult position.

Computer Weeklys Cliff Saran wrote, With friends like AWS, who needs an open source business? and noted thataNew York Timesarticle suggested that Amazon Web Services (AWS) was strip-mining open source projects by providingmanaged services based on open source code,without contributing back to the community.

We have also looked at the security aspects of open source licencing.

Exec VP at software intelligence company Cast is Rado Nikolov for his money, the open source licencing debate also has a security element in it.

Large organisations using open source code from GitHub, xs:code and other sources range from Walmart to NASA, collectively holding billions of pieces of sensitive data. Although open source code packages can be obtained at low or no cost, their various intellectual property and usage stipulations may lead to expensive legal implications if misunderstood or ignored, said Niklov.

Ilkka Turunen, global director of solutions architecture at DevSecOps automation company Sonatype further reminded us that there are 1001 ways of commercialising open source software but when releasing open source, the developer has a choice of publishing it under a license that is essentially a contract between them and the end user.

So theres security, theres fair and just contributions back to the community, theres layering over open for commercial use, theres the complexity of just so many open source licences existing out there to choose from and theres even concerns over whether trade sanctions can affect open source projects and see them becoming bifurcated along national borders.

Open source is supposed to be built around systems of meritocracy and be for the benefit of all, we must work hard to ensure that we can do this and shoulder the nuances of licensing to keep open source software as good as it should be let the debate continue.

More here:
The open source licence debate: what we need to know - Open Source Insider - ComputerWeekly.com