These Are the Bitcoin Stories You Loved in 2019 – Bitcoin News

2019 was a crazy year for cryptocurrency enthusiasts and a number of interesting events happened throughout the last 12 months. Its hard to keep track of the day-to-day activities taking place within the cryptosphere and there may be a few incidents some of our readers missed. At news.Bitcoin.com we took the opportunity to scan our most popular articles from 2019 in order to create a year-end list to share with our readers.

Also Read: A List of Self-Proclaimed Bitcoin Inventors and Satoshi Clues Debunked in 2019

Bitcoin.coms writers are entrenched in the cryptosphere and every day our writing team is on the hunt for cryptocurrency-related news. During the last 365 days, a number of our writers have published news stories seven days a week to keep our readers informed. Theres been a number of developments in 2019, as the community is dealing with digital asset regulations, crypto exchange hacks, central banks practicing monetary easing, and people claiming to be Satoshi Nakamoto. The following is a look at news.Bitcoin.coms most popular crypto articles in 2019 by order of the highest-trafficked content.

In September, news.Bitcoin.coms Kevin Helms reported on the Reserve Bank of Indias regulatory guidelines imposed, which limited bank customers withdrawals from 137 financial institutions. Customers from these bank branches were only allowed to withdraw 1,000 rupees (approximately $14) per account for six months.

After the news spread, a number of Indias citizens revolted and the government had to send police assistance to a few different banks located in Mumbai. The report highlighted how the current banking system, no matter what country you live in, continues to grow untrustworthy.

Across 2019, a great number of central banks and countries participated in practicing monetary easing. However, one specific economy, that has been considered the foundation of Europe, has been showing signs of financial failure. In August, Lubomir Tassev explained that the German economy is facing an economic crisis that could cause a domino effect throughout the EU.

2019 statistics had shown that Germanys strong industrial economy saw significant declines in production. [Germany] is now seeing a significant decline in production by 2.7% year-on-year in January and 1.9% in April compared to the previous month, Tassev detailed. Then in May, factory orders declined 2.2% from a month ago and registered an 8.6% annual drop, the biggest in a decade. The following month the country invoked a five-year rent freeze in Berlin.

In May, the cryptocurrency community found another suspect who might be the infamous creator of Bitcoin. Kai Sedgwick reported on how the name Paul Le Roux found its way into the cryptosphere last spring. During the Kleiman v. Wright lawsuit, Document 187 had shown an unredacted name and Wiki link which belongs to the criminal mastermind Paul Le Roux.

The document led people to believe that Le Roux was smart enough to create Bitcoin and the coincidental timing of his arrest was around the same time Nakamoto left the community. Speculators really started wondering if Le Roux was Nakamoto when an anon from 4chans /biz/ messageboard insisted that Bitcoin was a project of a evil genius Paul Solotshi Calder Le Roux.

Satoshi Nakamoto was a popular topic in 2019, and a few of news.Bitcoin.coms highest-trafficked articles are written about this legendary character. The day before Halloween, news.Bitcoin.com published a story about the fascinating clues left behind by Bitcoins notorious creator.

The editorial discusses Satoshis planning, theories as to why Nakamoto left the community, and conspiracy theories like the idea that Bitcoin was created by the CIA. Satoshi left behind a bunch of clues and said some interesting statements back when the monicker spoke on bitcointalk.org and the cryptography mailing list.

All year long Indian cryptocurrency enthusiasts have been waiting on the final word in regard to digital currency regulations in India. News about the regulatory situation started coming to life in the spring and in March, Indias government told the supreme court that the crypto regulations being drafted were near completion.

Attorney Jaideep Reddy of Nishith Desai, a lawyer behind a writ petition opposing the crypto banking ban by the Reserve Bank of India, told news.Bitcoin.com at the time: The matter was heard for a very short period of time The matter started with the counsel for the Union of India stating that its committee is in the final stages of deliberations and that the matter should be heard after that.

During the first week of August, it was discovered that the giant retail corporation Walmart patented plans for a stablecoin backed by USD. The news followed Facebooks announcement to launch a coin called Libra. Walmarts attempt also followed the time when the company attempted to start its own banking services back in 2006.

At the time, politicians and financial incumbents opposed Walmart joining the banking industry and the firm got so much pushback it decided to quit the banking attempt. However, with a Walmart Coin, the company could skip all the banking charter laws and offer customers a different kind of savings incentive through cryptocurrency dividends.

News about cryptocurrency laws in India was of great interest to news.Bitcoin.com readers in 2019. On July 26, headlines detailed that an Indian official who led the committee which had created the crypto ban bill resigned.

Former Department of Economic Affairs (DEA) Secretary Subhash Chandra Garg decided to apply for voluntary retirement after receiving flak from the Indian cryptocurrency community. Supporters of friendlier digital asset laws in India, called the drafted bill flawed and after a few controversial tweets about crypto, he left his post. Moreover, a few days prior, members of the Indian government told the public that digital currencies were not banned.

In the first month of 2019, news.Bitcoin.coms Lubomir Tassev wrote a review about eight different crypto debit cards people can use around the world. The editorial discussed cards issued by firms like Wirex, Bitpay, Revolut, Cryptopay, and Fuzex.

The report explained the negatives and the positive benefits to a loadable cryptocurrency card. In addition to detailed information about existing crypto cards on the market, Tassev also wrote about the upcoming card companies that planned to launch in 2019. The editorial highlights how the use of crypto debit cards significantly expands the usability of digital coins in the world.

There were a hell of a lot more popular stories last year and the eight mentioned above just scratch the surface when it comes to news.Bitcoin.coms 2019 archive. Other reader favorites in our library this year included subjects like a possible Deutsche Bank collapse, how Citi, Deutsche, and HSBC laid off thousands of employees, the Indian supreme courts struggles with drafting regulations, and the Philippines seeing 10 government approved exchanges.

There were plenty of Satoshi Nakamoto stories and unique editorials involving the mysterious creator of Bitcoin. The news about the U.S. tax agency telling the public they planned on sending 10,000 letters to American cryptocurrency owners shocked our readers. 2019 also saw the downfall of the biggest multi-level-marketing (MLM) crypto scam of all time when the so-called Bitcoin Killer Onecoin crumbled.

What do you think about the eight most popular news.Bitcoin.com articles from 2019? Let us know what you think about the subjects and articles 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 ideas, concepts, content, goods or services mentioned in this article.

Image credits: Shutterstock, Pixabay, Wiki Commons, Fair Use.

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.

Jamie Redman is a financial tech journalist living in Florida. Redman has been an active member of the cryptocurrency community since 2011. He has a passion for Bitcoin, open source code, and decentralized applications. Redman has written thousands of articles for news.Bitcoin.com about the disruptive protocols emerging today.

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These Are the Bitcoin Stories You Loved in 2019 - Bitcoin News

BTC’s Hashrate Touches 120 Exahash, But the Price Has Not Followed – Bitcoin News

On January 1, the BTC network hashrate touched an all-time high at close to 120 exahash per second (EH/s). Despite the crypto market lull and lower BTC prices, the 2020 milestone happened just before the blockchains 11th anniversary. BTCs curious jump in hashrate has the cryptosphere wondering whether or not the price truly follows hashpower.

Also Read: Market Update: Crypto Traders Search for Bullish and Bearish Trends

One of the crypto communitys favorite topics is hash power, which is a cryptocurrency mining rigs processing speed. The overall hashrate is the combined hash power used to mine cryptocurrencies like BCH, BTC, and a slew of other coins. The hashrate that analytical websites track is typically measured in calculated hashes per second. Data sites use terminology like terahash (1,000,000,000,000 hashes per second), petahash (one quadrillion hashes per second), and exahash, which equals one quintillion hashes per second.

For some perspective, most single-unit machines produce a number of terahash per second. Bigger facilities filled with mining rigs and collaborative pools produce petahash, and the entire network of single miners, giant facilities, and pools combined yield a number of exahash. Miners on the BTC network did not process one exahash until the last week of January 2016. At the time, the milestone was considered a noteworthy spike in overall hashrate. On January 1, 2020, the BTC network hashrate touch 119 EH/s surpassing the chains previous all-time high of 100 EH/s.

Similar to now, in 2016 BTC prices were low, at $380 per coin when the network crossed one exahash. Following the jump in hash power, the price did follow, and very slowly crept from $380 per BTC to $700 per coin in June of that year. Following the month of June, BTCs hashrate climbed above two EH/s but BTC prices remained stagnant fluctuating between $600-$775 per coin. At the end of November 2016, the price per BTC started climbing higher and the value continued to spike in the spring of 2017. From the spring months of 2017 all the way until December 2017, both BTCs fiat value and hashrate skyrocketed. In April 2017, the overall BTC network hashrate was around 4 EH/s and by the years end, it was hovering around 15 EH/s. Now, despite the price dropping from close to $20k per BTC all the way to the $3,500 range, the hashrate jumped to 56 EH/s in September 2018 without dropping much in between.

In September 2018, with a hashrate around 56 EH/s, the price per BTC was similar to todays market prices at $6,500 per coin. From September to December 2018, the BTC network lost a significant amount of hashrate as it plummeted to 31 EH/s. Of course, the price in December 2018 was between $3,200 to $4,000 per BTC. Bitcoin prices didnt start to recover until the end of March 2019, but from December 2018 until the spring months of 2019, BTCs hashrate regained the hash power it held in September 2018 at around 56 EH/s. The overall hashrate has doubled since then, touching a milestone of 100 EH/s in November 2019. The price per BTC has also followed suit up until it touched the 100 EH/s all-time high. Since then, BTCs fiat value has hovered around the $6,500 to $7,500 region.

There is always a lot of talk that the price follows hashrate and historically this has been true. However, statistics show that the hash power typically gets a decent lead before the prices start kicking into gear. This means that it could take some time for the price to follow the climbing hashrate.

If historical patterns remain consistent with future patterns, it could mean a few more months before the price comes around. People should also keep in mind that past price and hashrate patterns do not necessarily reflect what will happen in the future.

What do you think about the BTC network hashrate coming close to 120 EH/s on January 1, 2020? Do you agree that the price will follow BTCs hashrate? Or do you think that patterns like price and hash do not matter? Let us know what you think about this subject in the comments section below.

Disclaimer: Price articles and market updates are intended for informational purposes only and should not be considered as trading advice. Neither Bitcoin.com nor the author is responsible for any losses or gains, as the ultimate decision to conduct a trade is made by the reader. Always remember that only those in possession of the private keys are in control of the money. Cryptocurrency prices referenced in this article were recorded at different times using historical fiat prices and todays global exchange rate for BTC at 3:00 p.m. EST on January 2, 2020.

Image credits: Shutterstock, Pixabay, Twitter, Fair Use, Blockchain.com, markets.bitcoin.com, and Wiki Commons.

Verify and track bitcoin cash transactions on our BCH Block Explorer, the best of its kind anywhere in the world. Also, keep up with your holdings, BCH, and other coins, on our market charts at Bitcoin.com Markets, another original and free service from Bitcoin.com.

Jamie Redman is a financial tech journalist living in Florida. Redman has been an active member of the cryptocurrency community since 2011. He has a passion for Bitcoin, open source code, and decentralized applications. Redman has written thousands of articles for news.Bitcoin.com about the disruptive protocols emerging today.

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BTC's Hashrate Touches 120 Exahash, But the Price Has Not Followed - Bitcoin News

How Bitcoin Adoption Will Help India Achieve Its $5 Trillion… – Bitcoin Magazine

India has been one of the most notable emerging economies in the world in the last few decades. It currently stands at seventh place with a nominal GDP of $2.72 trillion and it is expected to overtake the United Kingdom in years to come. Indias PM Narendra Modi envisioned a dream of making India a $5 trillion economy by 2024. But achieving that dream for a country with a population of 1.3 billion might be a challenge if the current economic performance is to be considered. Indeed, bitcoin adoption in India could be the key to its economic future.

If Indias GDP is to reach $5 trillion by 2025, its minimum annual growth rate will need to be greater than 10.8 percent every year. Indias current GDP growth has fallen sharply from 8 percent last year to 5 percent in the second quarter of 2019. Manufacturing growth in India slumped to a 15-month low in August due to lower sales growth, resulting in factories being forced to shut down production.

Another reason for slowing economic growth is Indias rising unemployment rate, which was 8.5 percent in October, its highest in the last three years. Foreign portfolio investors were net sellers of Indian stocks during the July-September quarter, withdrawing over $3.2 billion from Indian capital markets. The Indian central governments fiscal deficit is projected to widen to about 3.7 percent in FY20, contrary to the plan of keeping it under 3.3 percent.

With all these lower-than-expected results, the Indian government has been taking a series of measures to boost the economy from the supply-side by pushing public capital expenditures, lowering the corporate tax from 35 percent to 25 percent. Indias central bank, the Reserve Bank of India (RBI) has cut its interest rate five times since the start of 2019 to boost spending.

Back in 1991, India adopted economic liberalization which helped to expand its economy and its role in private investment. Due to this radical reform, India achieved the status of a developing country but failed to adopt similar reforms which would have propelled it toward becoming fully developed. What the Indian government could do is adopt measures to stir the economy in an upward direction and also focus on opening the doors to supplement growth. Right now, India has a chance to live up to this potential by adopting another radical monetary innovation: Bitcoin.

Bitcoin is the worlds most powerful monetary innovation, with an idea to democratize exchange/store of value without any control from a single authority. Eleven years after its birth, it has been the best-performing asset class and, importantly, is on its way from being merely collectible to achieving the status of digital gold in years to come. Recently, Bitcoins hash rate just hit an all-time high of 111 EH/s, restoring confidence in its network despite the price dump. Every day (or, more precisely, every 10 minutes) as Bitcoins network becomes stronger than before, it will absorb more monetary value in proportion.

Whats important is that the rise in bitcoins monetary value will have a significant impact on Indias fiat currencies. Countries with the weakest monetary policies and currencies are most at risk of economic failure at the outset. Once they begin to fall, a domino effect of all fiat currencies which adopted the wrong monetary policy and engaged in excessive money printing will follow. This threat to fiat currencies is a large part of the reason why governments all around the world are hesitant toward directly adopting bitcoin. But governments should view bitcoin not as a threat but as an opportunity.

Long before bitcoin, gold was considered a robust store of value. In 1944, 44 countries signed onto the Bretton Woods system, agreeing to peg their currencies to the U.S. dollar (which was, itself, declared to be backed by gold). Due to this structure and overall confidence in its economy, the USA achieved the status of superpower in decades to come. The same sort of robust growth could be achieved by India through bitcoin adoption.

First, India can open the roads for everyone to directly invest in bitcoin legally through banking channels. As it has done with gold, the Indian central bank can continue accumulating bitcoin as the countrys reserves. Due to bitcoins fixed supply of 21 million, accumulating earlier than other countries will have a significant advantage in years to come. These bitcoin reserves will be beneficial for carrying out public capital expenditures and aiding the private sector through serial reforms.

Whenever a new investment opportunity knocks on a countrys doors, it has the potential to have an entire ecosystem built up around it. Same is true for bitcoin. It will give rise to new entrepreneurs, new start-ups, new businesses, new innovations, new products and services, new consumers, and altogether new markets. It happened with the internet and smartphones.

It is happening with other technologies like AI and IoT. It is happening with blockchain technology and Bitcoin in some countries like Singapore, Germany, and Switzerland. Hence, it makes eminent sense to let bitcoin flourish in a regulated environment around the world. This philosophy may run contrary to the rebellious roots of some Bitcoin enthusiasts who strive to challenge the current financial system. But lets be practical here. If bitcoin has to reach a population of 1.3 billion people like India, it will only happen through appreciation, acceptance and minor adjustments.

Coming to a common Indian individual, giving them access to invest in bitcoin through regulated channels will help increase their purchasing power. Currently, per capita income is just over $2,000 which is significantly lower than in other developed countries. Also, average retail investors are not allowed to have access to open global high performing markets, secluding them to limited options in Indias equities and commodities markets.

If the Indian government classifies bitcoin as a good/commodity/currency, a population of 1.3 billion will get access to store their wealth in the hardest money resulting in an increase in overall per capita income over time. More importantly, providing access to the hardest money will help maintain a base level of demand during critical times like recession or economic slowdown. Even today, 190 million people in India are unbanked, with the help of bitcoin these people can have access to money management like savings and transacting.

Bitcoins on-chain growth will give rise to multiple Bitcoin companies on second/third-layer solutions. With open Indian Bitcoin regulations, it will create many significant Bitcoin innovations and also create a plethora of Bitcoin-related jobs in technological engineering, marketing, etc.

India can take a proactive approach toward bitcoin by first accepting the innovation and later recognizing it. This will help India achieve the status of a $5 trillion economy, or maybe beyond.

In conclusion, it is fair to say, very soon, that bitcoin will help India be more visible and we can become the powerhouse that we have the potential to be. Coupled with our aim to integrate financial inclusivity, we will be unstoppable, as far as booming economies go.

This is an op ed by Sumit Gupta. Views expressed are his own and do not necessarily reflect those of Bitcoin Magazine or BTC Inc.

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How Bitcoin Adoption Will Help India Achieve Its $5 Trillion... - Bitcoin Magazine

Bitcoin records trading dominance of over 40% on Binance – AMBCrypto

Bitcoin began its year on a negative note. Its price fell below $7,200 on the second day of the year. The much-awaited Santa Claus rally didnt materialize for the worlds largest cryptocurrency as the optimism fueled by the holiday spirit did not really catch up with the coins price.

However, soon, the price did rebound. Bitcoin jumped by over 2% on the third day of 2020 as it entered its eleventh year. Following a strong upward movement against the U.S Dollar, the coin was valued at $7,363 with a market cap of $133 billion. Meanwhile, Bitcoin continued to hold a market dominance of over 68.5%

Despite such setbacks, Bitcoin continues to be the king as the coins trading dominance was recorded to be above 40%. In the latest December Markets Overview, Binance Research revealed that Bitcoins trading dominance for the month was 40.48%. The figures, however, had declined by 1.28% since November, a month when it registered a trading dominance of 41.76%. Binance Research defines Bitcoin trading dominance as the respective volume contribution from Bitcoin trading, with BTC as a base currency, relative to the total spot volume on a platform [e.g., Binance] over a period of time.

Binances report highlighted,

Bitcoin trading dominance remained above the 40% mark in December [40.48%], a slight decrease [-1.28%] from 41.76% in November. Meanwhile, the Bitcoin market dominance increased slightly from ~ 67% to ~ 68%.

The yearly low for this metric was recorded at 11% in March 2019. Overall, the figures largely remained positive as it gradually rose shortly after. Bitcoins monthly trading dominance rose significantly in August to 45.06%, a figure which happens to be its yearly high. This occasioned with Bitcoins price surge to over $12k. The figures, however, fell to 39.88% in October and coincided with Bitcoins price falling below $8.5k in the same month.

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Bitcoin records trading dominance of over 40% on Binance - AMBCrypto

Byrider to Partner With PointPredictive as Machine Learning AI Partner to Prevent Fraud – CloudWedge

Home > News > Byrider to Partner With PointPredictive as Machine Learning AI Partner to Prevent Fraud

Byrider selected PointPredictives machine learning AI scoring after extensive testing of the solution and evaluating retrospective results. In our retrospective test with PointPredictive, we saw a significant lift in identifying defaults tied to misrepresentation and fraud, said Gary Harmon, Chief Risk Officer of Byrider.

Aspart of the integration, Byrider will use the companys scoring solutionAuto Fraud Manager with Auto Fraud Alert Reporting to identifymisrepresentation and prevent default on high-risk applications whilestreamlining the approval process of low-risk applications to improve andexpedite both the consumer and dealer loan funding experience, ultimatelyexpanding their loan portfolio profitably.

PointPredictivelaunched Auto Fraud Manager with Auto Fraud Alert Reporting to help address the$6 billion-dollar annual problem of misrepresentation and fraud that plaguesthe auto lending industry. The solution uses machine learning to minehistorical data from applications across the industry to pinpoint where fraudis happening. Over 60 million applications have been evaluated and scored bythe unique machine learning AI system which is continuously learning newpatterns as they emerge.

PointPredictive is excited to partner with Byrider to help them achieve better relationships with their borrowers and their dealer network, advises Tim Grace, CEO of PointPredictive. Our solutions have proven to help lenders reduce their risk of early defaulted loans and, in the process, help them streamline loans for reduced stipulations and friction in the lending process. By better targeting risk, the end beneficiaries are their dealers and borrowers who can see a reduction in the time it takes to fund loans.

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Byrider to Partner With PointPredictive as Machine Learning AI Partner to Prevent Fraud - CloudWedge

What is deep learning and why is it in demand? – Express Computer

The human brain is complicated and for all the right reasons. A pathway of neurons, millions of cells, creating a response to stimuli and observation, all of this working simultaneously to help our brain give an appropriate response. Furthering its greatness, the brain is constantly observing and learning.

A well-known learning method- learning by example helps us develop an attitude or coping mechanism towards something we havent fully experienced but have a lot of information about. When we use the same logic for machines, deep learning comes closest to this.

Stemming out of the machine learning algorithm category, deep learning is a computer or a machines ability to learn things by example or data. It eliminates the need for manual feature extraction as it learns features directly from the data.

The most common example would be automated vehicles. An automated car is able to distinguish between people walking on the road, poles, traffic signals, and signboards by understanding the data it receives. How does it manage to detect things?

Unlike traditional learning, deep learning is an artificial neural network that uses many layers to form features from the data it acquires. This data includes image, text, and sound which helps the machine form a clearer picture of the object to easily detect it. Does this remind you of something? Yes, thats exactly how our brain works! However, we naturally possess a neural network.

As we move to an era that demands a higher level of data processing, deep learning justifies its existence for the world.

One major defining moment for it would be the use of artificial neural networks which brings out the best outcome. Unlike machine learning, there is no need to build new features and algorithms because deep learning directly identifies features from the data. It uses 150 layers of information to process features directly from the data received and also monitor its own performance.

Companies that are investing in deep learning are primarily looking to solve complex problems and this form of learning accomplishes that with its collection of large data sets.

Another reason deep learning thrives in the world today is that it powers the functions that need voice and image detection. Companies that require data on face recognition, object identification, voice-to-speech application, and translation can make optimal use of deep learning techniques.

Concluding

While we are not exempted from the fact that deep learning works best only with a huge amount of data and takes time to be set up, there is hope for better performance.

Moving from a structured and fixed architecture to an ever-evolving one, the next few years will see a rise in businesses moving to this new form of machine learning. Based on the existing data and examples of success, there seems to be an indication that companies using deep learning techniques perform better than ones that dont.

If you have an interesting article / experience / case study to share, please get in touch with us at [emailprotected]

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What is deep learning and why is it in demand? - Express Computer

Sports Organizations Using Machine Learning Technology to Drive Sponsorship Revenues – Sports Illustrated

The sports industry has begun to place a greater emphasis on data capture and the use of analytics over the past decade - particularly as it relates to on-field performance, but while sports has become big business, Adam Grossman (founder Block Six Analytics aka B6A) suggests from an economic and financial perspective - in terms of understanding concepts like asset valuation, cash-flow and regression - it remains behind the times. To help bring the industry up to speed, Grossman developed a sponsorship evaluation platform that values sports assets in the same manner that venture capitalists, private equity firms and investment banks look at investment opportunities. Using machine learning technology (think: natural language processing, computer vision), B6A's proprietary sponsorship model translates traditional fit and engagement benchmarks into probabilistic revenue growth metrics. Over the last 10 months, more than a dozen pro sports organizations have begun using Block Six technology - as opposed to relying on antiquated metrics like CPM - to drive sponsorship revenues.

Howie Long-Short: Sellers of sports sponsorships naturally seek brand partners that are demographically aligned. While most teams and media entities have historically managed to gather insights on their own organization, the challenge has always been capturing that of potential partners; the demographic data needed to ensure audience alignment, so that both parties can achieve their goals. Grossman explained that those on the sales side use the insights B6A provides to find new sponsors and to demonstrate their audience is a good fit for [a particular] brand. It should be noted that while were focused on rights holders, B6A also works with corporate partners investing in sports; typically, Fortune 500 companies who use the software to ensure theyre spending their marketing dollars efficiently.

Detailed knowledge about ones own audience can also be beneficial from an engagement perspective. Grossman explained that sports organizations have historically struggled to translate brand metrics into revenue metrics, but if [a seller] can prove that they have the right audience [for a buyer], that the audience is interested in the [prospective partner's] company and in their product(s) and that the seller will publish content that drives engagement and awareness [for the buyer] within the target demo, [they can say with a level of confidence that they are] maximizing the probability of increasing revenues. Statistically speaking (at least according to the way B6A measures lift in brand perception), there is significant correlation between engagement, sentiment, awareness of a brand and revenue growth.

Block Six was kind enough to run a complimentary analysis on thousands of posts attributed to followers of JohnWallStreets Twitter account to demonstrate how the platform's findings could be used. The report they turned over indicated that even in comparison to the golf companies and brands like Amazon and Apple, [JWS] disproportionately reaches a more educated and higher income audience; in fact, from an education perspective, JWS has the most educated following [analyzed to date]. While we know that a significant number of league commissioners, team owners and c-level team/league, media and agency executives read the newsletter daily, from an aggregate perspective, the data shows that JWS content is reaching a much wide range of senior leaders across the business world. Thats particularly valuable information to have as we continue in our search for the right title sponsor. To date, JWS sales efforts have been focused on service companies that seek to reach sports most influential decision makers, but the data born out of the B6A study shows that any business targeting highly educated, high-income earners should be pursued.

Taking it a step further, the psychographic observations gained reflect that technology and gambling are two topics that the JWS audience is particularly interested in. To date, JWS has not targeted brands in either field (technology due to a lack of time/resources, gambling because we incorrectly assumed they would be solely focused on consumer acquisition), but Grossman suggests that we should be as the data indicates businesses within those two sectors are natural advertisers for the brand.

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Sports Organizations Using Machine Learning Technology to Drive Sponsorship Revenues - Sports Illustrated

Machine Learning Answers: Sprint Stock Is Down 15% Over The Last Quarter, What Are The Chances It’ll Rebound? – Trefis

Sprint (NYSE:S) stock has seen significant volatility over recent months declining by about 15% over the last quarter and by close to 25% over the last six months on account of the companys underperforming postpaid wireless business and concerns on whether its proposed merger with larger rival T-Mobile will come to fruition.

We started with a simple question that investors could be asking about the Sprintstock: given a certain drop or rise, say a 5% drop in a week, what should we expect for the next week? Is it very likely that Sprint will recover the next week? What about the next month or a quarter?

In fact, we found that if the Sprint drops 15% in a quarter (63 trading days), there is a ~23% chance that it will rise by 10% over the subsequent month (21 trading days).Want to try other combinations? You can test a variety of scenarios on the Trefis Machine Learning Engine to calculate if Sprint stock dropped, whats the chance itll rise.

For example, after a 5% drop over a week (5 trading days), the Trefis machine learning engine says chances of an additional 5% drop over say the next month are about 34%. Quite significant, and helpful to know for someone trying to recover from a loss.

Knowing what to expect for almost any scenario is powerful. It can help you avoid rash moves. Given the recent volatility in the market owing to a mix of macroeconomic events like the trade war with China and the US Federal Reserves moves, we think investors can prepare better.

Below, we discuss a few scenarios and answer common investor questions:

Question 1: Does a rise in Sprint stock become more likely after a drop?

Answer:

Consider two situations,

Case 1: Sprint stock drops by 5% or more in a week

Case 2: Sprint stock rises by 5% or more in a week

Is the chance of say a 5% rise in Sprint stock over the subsequent month after Case 1 or Case 2 occurs much higher for one versus the other?

The answer is not really. The chance of a 5% rise over a month (21 trading days) is roughly that same at 34% for both cases.

Question 2: What about the other way around, does a drop in Sprint stock become more likely after a rise?

Answer:

Consider, once again, two cases:

Case 1: Sprint stock drops by 5% in a week

Case 2: Sprint stock rises by 5% in a week

The probability of a 5% drop after Case 1 or Case 2 is actually quite similar at 34% and 33%, respectively. The probability is also similar for theS&P 500, and for many other stocks.

Question 3: Does patience pay?

Answer:

If you buy and hold Sprint stock, the expectation is over time the near term fluctuations will cancel out, and the long-term positive trend will favor you at least if the company is otherwise strong. Overall, according to data and Trefis machine learning engines calculations, patience absolutely pays for most stocks!

After a drop of 5% in Sprint stock over a week (5 trading days), while there is only about 23% chance the stock will gain 5% over the subsequent week, there is more than 39% chance this will happen in 6 months, and 45% chance itll gain 5% over a year (about 252 trading days).

The table below shows the trend for Sprint Stock:

Question 4: What about the possibility of a drop after a rise if you wait for a while?

Answer:

After seeing a rise of 5% over 5 days, the chances of a 5% drop in Sprint stock are about 42% over the subsequent quarter of waiting (63 trading days). This chance increases slightly to about 45% when the waiting period is a year (252 trading days).

The table below shows the trend for Sprint Stock:

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Machine Learning Answers: Sprint Stock Is Down 15% Over The Last Quarter, What Are The Chances It'll Rebound? - Trefis

2010 2019: The rise of deep learning – The Next Web

No other technology was more important over the past decade than artificial intelligence. Stanfords Andrew Ng called it the new electricity, and both Microsoft and Google changed their business strategies to become AI-first companies. In the next decade, all technology will be considered AI technology. And we can thank deep learning for that.

Deep learning is a friendly facet of machine learning that lets AI sort through data and information in a manner that emulates the human brains neural network. Rather than simply running algorithms to completion, deep learning lets us tweak the parameters of a learning system until it outputs the results we desire.

The 2019 Turing Award, given for excellence in artificial intelligence research, was awarded to three of deep learnings most influential architects, Facebooks Yann LeCun, Googles Geoffrey Hinton, and University of Montreals Yoshua Bengio. This trio, along with many others over the past decade, developed the algorithms, systems, and techniques responsible for the onslaught of AI-powered products and services that are probably dominating your holiday shopping lists.

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Deep learning powers your phones face unlock feature and its the reason Alexa and Siri understand your voice. Its what makes Microsoft Translator and Google Maps work. If it werent for deep learning, Spotify and Netflix would have no clue what you want to hear or watch next.

How does it work? Its actually simpler than you might think. The machine uses algorithms to shake out answers like a series of sifters. You put a bunch of data in one side, it falls through sifters (abstraction layers) that pull specific information from it, and the machine outputs whats basically a curated insight. A lot of this happens in whats called the black box, a place where the algorithm crunches numbers in a way that we cant explain with simple math. But since the results can be tuned to our liking, it usually doesnt matter whether we can show our work or not when it comes to deep learning.

Deep learning, like all artificial intelligence technology, isnt new. The term was brought to prominence in the 1980s by computer scientists. And by 1986 a team of researchers including Geoffrey Hinton managed to come up with a back propagation-based training method that tickled at the beginnings of an unsupervised artificial neural network. Scant a few years later a young Yann LeCun would train an AI to recognize handwritten letters using similar techniques.

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But, as those of us over 30 can attest, Siri and Alexa werent around in the late 1980s and we didnt have Google Photos there to touch up our 35mm Kodak prints. Deep learning, in the useful sense we know it now, was still a long ways off. Eventually though, the next generation of AI superstars came along and put their mark on the field.

In 2009, the beginning of the modern deep learning era, Stanfords Fei-Fei Li created ImageNet. This massive training dataset made it easier than ever for researchers to develop computer vision algorithms and directly lead to similar paradigms for natural language processing and other bedrock AI technologies that we take for granted now. This lead to an age of friendly competition that saw teams around the globe competing to see which could train the most accurate AI.

The fire was lit. By 2010 there were thousands of AI startups focused on deep learning and every big tech company from Amazon to Intel was completely dug in on the future. AI had finally arrived.Young academics with notable ideas were propelled from campus libraries to seven and eight figure jobs at Google and Apple. Deep learning was well on its way to becoming a backbone technology for all sorts of big data problems.

And then 2014 came and Apples Ian Goodfellow (then at Google) invented the generative adverserial network (GAN). This is a type of deep learning artificial neural network that plays cat-and-mouse with itself in order create an output that appears to be a continuation of its input.

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When you hear about an AI painting a picture, the machine in question is probably running a GAN that takes thousands or millions of images of real paintings and then tries to imitate them all at once. A developer tunes the GAN to be more like one style or another so that it doesnt spit out blurry gibberish and then the AI tries to fool itself. Itll make a painting and then compare the painting to all the real paintings in its dataset, if it cant tell the difference then the painting passes. But if the AI discriminator can tell its own fake, it scraps that one and starts over. Its a bit more complex than that, but the technology is useful in myriad circumstances.

Rather than just spitting out paintings, Goodfellows GANs are also directly behind DeepFakes and just about any other AI tech that seeks to blur the line between human-generated and AI-made.

In the five years since the GAN was invented, weve seen the field of AI rise from parlor tricks to producing machines capable of full-fledged superhuman feats. Thanks to deep learning,Boston Dynamics has developed robots capable of traversing rugged terrain autonomously, to include an impressive amount of gymnastics. And Skydio developed the worlds first consumer drone capable of truly autonomous navigation. Were in the safety testing phase of truly useful robots, and driverless cars feel like theyre just around the corner.

Furthermore, deep learning is at the heart of current efforts to produce general artificial intelligence (GAI) otherwise known as human-level AI. As most of us dream of living in a world where robot butlers, maids, and chefs attend to our every need, AI researchers and developers across the globe are adapting deep learning techniques to develop machines that can think. While its clear well need more than just deep learning to achieve GAI, we wouldnt be on the cusp of the golden age of AI if it werent for deep learning and the dedicated superheroes of machine learning responsible for its explosion over the past decade.

AI defined the 2010s and deep learning was at the coreof its influence. Sure, big data companies have used algorithms and AI for decades to rule the world, but the hearts and minds of the consumer class the rest of us was captivated more by the disembodied voices that are our Google Assistant, Siri, and Alexa virtual assistants than any other AI technology. Deep learning may be a bit of a dinosaur, on its own, at this point. But wed be lost without it.

The next ten years will likely see the rise of a new class of algorithm, one thats better suited for use at the edge and, perhaps, one that harnesses the power of quantum computing. But you can be sure well still be using deep learning in 2029 and for the foreseeable future.

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Here’s what AI experts think will happen in 2020 – The Next Web

Its been another great year for robots. We didnt quite figure out how to imbue them with human-level intelligence, but we gave it the old college try and came up with GPT-2 (the text generator so scary it gives Freddy Krueger nightmares) and the AI magic responsible for these adorable robo-cheetahs:

But its time to let the past go and point our bows toward the future. Its no longer possible to estimate how much the machine learning and AI markets are worth, because the line between whats an AI-based technology and what isnt has become so blurred that Apple, Microsoft, and Google are all AI companies that also do other stuff.

Your local electricity provider uses AI and so does the person who takes those goofy real-estate agent pictures you see on park benches. Everything is AI an axiom thatll become even truer in 2020.

We solicited predictions for the AI industry over the next year from a panel of experts, heres what they had to say:

AI and human will collaborate. AI will not replace humans, it will collaborate with humans and enhance how we do things. People will be able to provide higher level work and service, powered by AI. At Intuit, our platform allows experts to connect with customers to provide tax advice and help small businesses with their books in a more accurate and efficient way, using AI. It helps work get done faster and helps customers make smarter financial decisions. As experts use the product, the product gets smarter, in turn making the experts more productive. This is the decade where, through this collaboration, AI will enhance human abilities and allow us to take our skills and work to a new level.

AI will eat the world in ways we cant imagine today: AI is often talked about as though it is a Sci-Fi concept, but it is and will continue to be all around us. We can already see how software and devices have become smarter in the past few years and AI has already been incorporated into many apps. AI enriched technology will continue to change our lives, every day, in what and how we operate. Personally, I am busy thinking about how AI will transform finances I think it will be ubiquitous. Just the same way that we cant imagine the world before the internet or mobile devices, our day-to-day will soon become different and unimaginable without AI all around us, making our lives today seem so obsolete and full of unneeded tasks.

We will see a surge of AI-first apps: As AI becomes part of every app, how we design and write apps will fundamentally change. Instead of writing apps the way we have during this decade and add AI, apps will be designed from the ground up, around AI and will be written differently. Just think of CUI and how it creates a new navigation paradigm in your app. Soon, a user will be able to ask any question from any place in the app, moving it outside of a regular flow. New tools, languages, practices and methods will also continue to emerge over the next decade.

We believe 2020 to be the year that industries that arent traditionally known to be adopters of sophisticated technologies like AI, reverse course. We expect industries like waste management, oil and gas, insurance, telecommunications and other SMBs to take on projects similar to the ones usually developed by the tech giants like Amazon, Microsoft and IBM. As the enterprise benefits of AI become more well-known, the industries outside of Silicon Valley will look to integrate these technologies.

If companies dont adapt to the current trends in AI, they could see tough times in the future. Increased productivity, operational efficiency gains, market share and revenue are some of the top line benefits that companies could either capitalize or miss out on in 2020, dependent on their implementation. We expect to see a large uptick in technology adoption and implementation from companies big and small as real-world AI applications, particularly within computer vision, become more widely available.

We dont see 2020 as another year of shiny new technology developments. We believe it will be more about the general availability of established technologies, and thats ok. Wed argue that, at times, true progress can be gauged by how widespread the availability of innovative technologies is, rather than the technologies themselves. With this in mind, we see technologies like neural networks, computer vision and 5G becoming more accessible as hardware continues to get smaller and more powerful, allowing edge deployment and unlocking new use cases for companies within these areas.

2020 is the year AI/ML capabilities will be truly operationalized, rather than companies pontificating about its abilities and potential ROI. Well see companies in the media and entertainment space deploy AI/ML to more effectively drive investment and priorities within the content supply chain and harness cloud technologies to expedite and streamline traditional services required for going to market with new offerings, whether that be original content or Direct to Consumer streaming experiences.

Leveraging AI toolsets to automate garnering insights into deep catalogs of content will increase efficiency for clients and partners, and help uphold the high-quality content that viewers demand. A greater number of studios and content creators will invest and leverage AI/ML to conform and localize premium and niche content, therefore reaching more diverse audiences in their native languages.

Im not an industry insider or a machine learning developer, but I covered more artificial intelligence stories this year than I can count. And I think 2019 showed us some disturbing trends that will continue in 2020. Amazon and Palantir are poised to sink their claws into the government surveillance business during what could potentially turn out to be President Donald Trumps final year in office. This will have significant ramifications for the AI industry.

The prospect of an Elizabeth Warren or Bernie Sanders taking office shakes the Facebooks and Microsofts of the world to their core, but companies who are already deeply invested in providing law enforcement agencies with AI systems that circumvent citizen privacy stand to lose even more. These AI companies could be inflated bubbles that pop in 2021, in the meantime theyll look to entrench with law enforcement over the next 12 months in hopes of surviving a Democrat-lead government.

Look for marketing teams to get slicker as AI-washing stops being such a big deal and AI rinsing disguising AI as something else becomes more common (ie: Ring is just a doorbell that keeps your packages safe, not an AI-powered portal for police surveillance, wink-wink).

Heres hoping your 2020 is fantastic. And, if we can venture a final prediction: stay tuned to TNW because were going to dive deeper into the world of artificial intelligence in 2020 than ever before. Its going to be a great year for humans and machines.

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Here's what AI experts think will happen in 2020 - The Next Web