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Preparing new machine learning models used to take weeks Activeloop teams up with NVIDIA to reduce that time to hours – MENAFN.COM

(MENAFN - EIN Presswire)

Activeloop user interface and toolset work with NVIDIA processing to help InteinAir achieve great ML results

Activeloop.ai logo

Y Combinator alum achieves better aerial data pipelines for IntelinAir in an industry-leading Agriculture Tech solution

MOUNTAIN VIEW, CA, USA, August 4, 2020 / EINPresswire.com / -- In a case study now available online, Activeloop ( [To enable links contact MENAFN] ), a Y Combinator-backed startup, is announcing a major success in helping an early customer, IntelinAir , improve the efficiency of their AI analysis of aerial footage. Activeloop's software builds plug-and-play data pipelines for unstructured data. The software helps data scientists streamline their data aggregation and preparation, and automates and optimizes their training of machine learning models. Together with NVIDIA , Activeloop has achieved a massive reduction in the time-to-value and cost of machine learning / deep learning efforts. The case study documents a breakthrough in the field of aerial imagery with their joint customer IntelinAir, a leading crop intelligence firm.

Activeloop's solution is becoming available just in time for the exploding artificial intelligence and advanced machine learning market, projected to grow up to $281.24 billion by 2026 with CAGR of 37.95%. This coincides with the massive growth of data available to be analyzed by AI. All data generated by the end of 2020 will be about 40 trillion gigabytes (40 zettabytes), with IBM estimating that 90% of it has been created over the past 2 years. As data gets bigger faster than ever, translating it into actionable insights is becoming increasingly difficult and expensive. As a result, the effort needed to set up a new model and get it running efficiently can be beyond the reach of many teams who could otherwise benefit from machine learning. Existing solutions often have large cloud storage and processing costs. These solutions can't be made more efficient without radical changes.

'Unstructured data - including text, images, or videos, comprises about 80-90% of the data people generate today', says Davit Buniatyan, Activeloop Founder and CEO. 'As it comes in different forms, sizes, and even shapes, analyzing and managing it is an extremely difficult and costly task. In fact, data scientists spend about 50 to 80% of their time setting up their unstructured dataset rather than analyzing it via machine or deep learning. We're changing that by creating a fast, simple platform for building and scaling data pipelines for machine learning.'

'We operate in an agile fashion: we want to focus on building high-quality models instead of fighting with data pipelines, infrastructure, and deployment challenges' says Jennifer Hobbs, Director of Machine Learning at IntelinAir. 'Thanks to Activeloop, we've been able to deploy new models in a matter of days instead of weeks. With the help of Activeloop's platform and NVIDIA's powerful GPUs, we were able to increase the inference speed threefold and improve the accuracy of the trained models at half the cost."

You can read more about the success story here: [To enable links contact MENAFN] .

###

About Activeloop

Activeloop ( [To enable links contact MENAFN] ), is a startup backed by Y Combinator and prominent Silicon Valley investors. The company has already been featured by major outlets including TechCrunch and is now coming out of stealth mode to make its product available to the machine learning community. Formerly named Snark AI, Activeloop aims to optimize the way machine and deep learning models are trained and streamline the huge amounts of data required for this work. Activeloop is a member of NVIDIA's Inception program for AI/ML development.

About IntelinAir

IntelinAir ( [To enable links contact MENAFN] ) is a full-season and full-spectrum crop intelligence company focused on agriculture that delivers actionable intelligence to help farmers make data-driven decisions to improve operational efficiency, yields, and ultimately their profitability.

Mikayel HarutyunyanActiveloop.ai+1 415-876-5667email us here Visit us on social media:Facebook Twitter LinkedIn

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Preparing new machine learning models used to take weeks Activeloop teams up with NVIDIA to reduce that time to hours - MENAFN.COM

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IoT automation trend rides the next wave of machine learning, Big Data – Urgent Communications

An array of new methods along with unexpected new pressures cast todays IoT automation efforts in an utterly new light.

Progress today in IoT automation is based on fresh methods employing big data, machine learning, asset intelligence and edge computing architecture. It is also enabled by emerging approaches to service orchestration and workflow, and by ITOps efforts that stress better links between IT and operations.

On one end, advances in IoT automation includerobotic process automation(RPA) tools that use sensor data to inform backroom and clerical tasks. On the other end are true robots that maintain the flow of goods onfactory floors.

Meanwhile, nothing has focused business leaders on automation like COVID-19. Automation technologies have gained priority in light of 2020s pandemic, which is spurring use of IoT sensors, robots and software to enable additional remote monitoring. Still, this work was well underway before COVID-19 emerged.

Cybersecurity Drives Advances in IoT Automation

In particular, automated discovery of IoT environments for cybersecurity purposes has been an ongoing driver of IoT automation. That is simply because there istoo much machine information to manually track,according to Lerry Wilson, senior director for innovation and digital ecosystems at Splunk. The target is anomalies found in data stream patterns.

Anomalous behavior starts to trickle into the environment, and theres too much for humans to do, Wilson said. And, while much of this still requires a human somewhere in the loop, the role of automation continues to grow.

Wilson said Splunk, which focuses on integrating a breadth of machine data, has worked with partners to ensure incoming data can now kick off useful functions in real time. These kinds of efforts are central to emerging information technology/operations technology (IT/OT) integration. This, along with machine learning (ML), promises increased automation of business workflows.

Today, we and our partners are creating machine learning that will automatically set up a work order people dont have to [manually] enter that anymore, he said, adding that what once took the form of analytical reports now is correlated with historic data for immediate execution.

We moved past reporting to action, Wilson said.

Notable use cases Splunk has encountered include systems that collect signals to monitor and optimize factory floor and campus activity as well as to correlate asset information, Wilson indicated.

To read the complete article, visit IoT World Today.

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Decoding Practical Problems and Business Implications of Machine Learning – Analytics Insight

Machine learning typically is used to solve a host of diverse problems within an organization, extracting predictive knowledge from both structured and unstructured data and using them to deliver value. The technology has already made its way into different aspects of a business ranging from finding data patterns to detect anomalies and making recommendations. Machine learning helps organizations gain a competitive edge by processing a voluminous amount of data and applying complex computations.

With machine learning, companies can develop better applications according to their business requirements. This technology is mainly designed to make everything programmatic. Applications of ML have the potential to drive business outcomes that can extensively affect a companys bottom line. The rapid evolution of new techniques in recent years has further expanded the machine learning application to nearly boundless possibilities.

Industries relying on massive volumes of data are significantly leveraging ML techniques to process their data and to build models, strategize, and plan.

While implementing the effective application of machine learning enables businesses to grow, gain competitive advantage and prepare for the future, there are some key practical issues in ML and their business implications organizations must consider.

As machine learning significantly relies on data, the occurrence of noisy data can considerably impact any information prediction. Generally, data from a dataset carries extraneous and meaningless information which can significantly affect data analysis, clustering and association analysis. Having a lack of quality data can also restrain the capabilities of building ML models. In order to cope with quality data and noise, businesses need to apply better and effective machine learning strategies through data cleansing and overall processing of data.

There is no doubt that the development of machine learning has made it possible to learn directly from data rather than human knowledge with a strong emphasis on accuracy. However, the lack of the ability to explain or present data in understandable terms to a human, often called interpretability, is one of the biggest issues in machine learning. The introduction of possible biases in data has also led to ethical and legal issues with ML models. Theinterpretabilitylevels in the field of machine learning and algorithms may significantly vary. Some methods are human-compatible as they are highly interpretable, while some are too complex to apprehend, thus require ad-hoc methods to gain an interpretation.

In the context of supervised machine learning, an imbalanced dataset often involves two or more classes. There is an imbalance among labels in the training data in several real-world datasets. This imbalance in a dataset has the potential to affect the choice of learning, the process of selecting algorithms, model evaluation and verification. The models can even suffer large biases, and the learning will not be effective if the right techniques are not employed properly. ML algorithms can generate insufficient classifiers when faced with imbalanced datasets. When trying to resolve certain business challenges with imbalanced data sets, the classifiers produced by standard ML algorithms might not deliver precise outcomes.

Thus, to address imbalanced datasets requires strategies like enhancing classification algorithms or balancing classes in the training data before providing the data as input to machine learning algorithms.

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Artificial Intelligence and Machine Learning Industry 2020 Market Manufacturers Analysis, Share, Size, Growth, Trends and Research Report 2026 -…

Artificial Intelligence and Machine Learning Market 2020-2026 Industry research report covers the market landscape and its growth prospects over the coming years, the report also brief deals with the product life cycle, comparing it to the relevant products from across industries that had already been commercialized details the potential for various applications, discussing about recent product innovations and gives an overview on potential regional market.

Get Sample Copy of this Report https://www.orianresearch.com/request-sample/1432622

The report includes executive summary, global economic outlook and overview section that provide a coherent analysis on the Artificial Intelligence and Machine Learning market. Besides, the report in the market overview section delineates PLC analysis and PESTLE analysis to provide thorough analysis on the market. The overview section further delves into Porters Five Force analysis that helps in revealing the competitive scenario with regards to Artificial Intelligence and Machine Learning market revealing the probable scenario of the market.

Analysis of Artificial Intelligence and Machine Learning Market Key Manufacturers: IBM Corporation, BigML, Inc., SAP SE, SAS Institute Inc., Fair Isaac Corporation, Microsoft Corporation, Google, Inc., Baidu, Inc., Amazon Web Services Inc., Intel Corporation and Hewlett Packard Enterprise Development LP (HPE)

The report provides a detailed overview of the industry including both qualitative and quantitative information. It provides overview and forecast of the global Artificial Intelligence and Machine Learning market based on various segments. It also provides market size and forecast estimates from year 2020 to 2026 with respect to five major regions, namely; North America, Europe, Asia-Pacific (APAC), Middle East and Africa (MEA) and South & Central America. The Artificial Intelligence and Machine Learning market by each region is later sub-segmented by respective countries and segments. The report covers analysis and forecast of countries globally along with current trend and opportunities prevailing in the region.

Global Artificial Intelligence and Machine Learning Industry 2020 Market Research Report is spread across 101 pages and provides exclusive vital statistics, data, information, trends and competitive landscape details in this niche sector.

With tables and figures helping analyze worldwide Global Artificial Intelligence and Machine Learning Market, this research provides key statistics on the state of the industry and is a valuable source of guidance and direction for companies and individuals interested in the market.

At the same time, we classify different Artificial Intelligence and Machine Learning based on their definitions. Upstream raw materials, equipment and downstream consumers analysis is also carried out. What is more, the Artificial Intelligence and Machine Learning industry development trends and marketing channels are analyzed.

Market Segment by Type:

Hardware

Software

Services

Market Segment by Application:

BFSI

Healthcare and Life Sciences

Retail

Telecommunication

Government and Defense

Manufacturing

Energy and Utilities

Others

The report strongly emphasizes prominent participants of the Artificial Intelligence and Machine Learning Industry to provide a valuable source of guidance and direction to companies, executive officials, and potential investors interested in this market. The study focuses on significant factors relevant to industry participants such as manufacturing technology, latest advancements, product description, manufacturing capacities, sources of raw material, and profound business strategies.

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Finally, the feasibility of new investment projects is assessed, and overall research conclusions are offered. In a word, the report provides major statistics on the state of the industry and is a valuable source of guidance and direction for companies and individuals interested in the market.

Global Artificial Intelligence and Machine Learning 2020 to 2026 includes:

Trends in Artificial Intelligence and Machine Learning deal making in the industry

Analysis of Artificial Intelligence and Machine Learning deal structure

Access to headline, upfront, milestone and royalty data

Access to hundreds of Artificial Intelligence and Machine Learning contract documents

Comprehensive access to Artificial Intelligence and Machine Learning records

TOC of Artificial Intelligence and Machine Learning Market Report Includes:

1 Artificial Intelligence and Machine Learning Market Overview

2 Company Profiles

3 Global Artificial Intelligence and Machine Learning Market Competition, by Players

4 Global Artificial Intelligence and Machine Learning Market Size by Regions

5 North America Artificial Intelligence and Machine Learning Revenue by Countries

6 Europe Artificial Intelligence and Machine Learning Revenue by Countries

7 Asia-Pacific Artificial Intelligence and Machine Learning Revenue by Countries

8 South America Artificial Intelligence and Machine Learning Revenue by Countries

9 Middle East and Africa Revenue Artificial Intelligence and Machine Learning by Countries

10 Global Artificial Intelligence and Machine Learning Market Segment by Type

11 Global Artificial Intelligence and Machine Learning Market Segment by Application

12 Global Artificial Intelligence and Machine Learning Market Size Forecast (2020-2026)

13 Research Findings and Conclusion

14 Appendix

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Orian Research is one of the most comprehensive collections of market intelligence reports on the World Wide Web. Our reports repository boasts of over 500000+ industry and country research reports from over 100 top publishers. We continuously update our repository so as to provide our clients easy access to the worlds most complete and current database of expert insights on global industries, companies, and products. We also specialize in custom research in situations where our syndicate research offerings do not meet the specific requirements of our esteemed clients.

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Is This The Real Reason Behind Bitcoins Huge Weekend Flash Crash? – Forbes

Bitcoin volatility is back. After months of relative stability the bitcoin price ricocheted this weekend, rapidly losing and gaining over $1,000 in mere minutes.

Bitcoin's Sunday morning flash crash was initially attributed by some to so-called "whales" who control large amounts of bitcoin moving the market, however others have now suggested it could be due to "algo misbehavior."

Bitcoin and cryptocurrency markets have come alive again after months of stability, with the bitcoin ... [+] price climbing and crashing over the weekend.

The bitcoin price broke $12,000 per bitcoin early Sunday morning only to plummet 12% to $10,500 within the hour before bouncing back to over $11,300 almost immediately.

"Such spikes are still inherent to the crypto market structure, with prolific unregulated leveraged trading going on," Anatoliy Knyazev, the chief executive of brokerage Exante, said via email, adding the flash crash "could be a case of an algo misbehavior."

Algorithmic trading is used to automate trades based on time, price, and volume with traders programming buy or sell orders to happen when certain market conditions are met, such as an asset price reaching a particular level or if it sharply falls.

The effects of algorithmic trading can be exacerbated by leveraged trading, allowing traders to take larger positions with smaller amounts of capitalsomething that is now being offered by many of the biggest bitcoin and cryptocurrency exchanges.

"There's a lot more leverage now than ever before, especially in crypto," Mati Greenspan, the founder of Quantum Economics told subscribers of his markets newsletter.

"This could lead to some extreme volatility," Greenspan wrote, but added he thinks "bitcoin, along with the rest of the digital asset market, is in a bull market right now."

The bitcoin price has shot up by more than 20% over the last month, climbing to levels not seen since August last year.

The bitcoin price has soared by over 20% through July with the weekend's flash crash barely denting ... [+] its upward trajectory.

Meanwhile, it's also been suggested the sharp Sunday morning downturn was due to market participants "profit-taking."

"Bitcoin has been increasing, and on Sunday morning the first digital currency touched $12,000," Alex Kuptsikevich, senior financial analyst at FxPro, said via email.

"However, due to the wave of profit-taking, it quickly corrected to $11,000. Taking into account the relatively low liquidity of the crypto market, a small number of large orders is capable of launching waves in both directions."

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First Mover: As Fed Nears Inflation Rubicon, Analysts See $50K Bitcoin in Play – CoinDesk – CoinDesk

The Federal Reserve appears ready to pursue yet another untestedstrategy that could ultimately boost inflation and possibly prices for bitcoin.

The Fedis preparing to effectively abandon its strategy of pre-emptively lifting interest rates to head off higher inflation,according to a new report in theWall Street Journal.

The shift signals an explicit willingness by the central bankto tolerate higher inflation, at a time when the spreading coronavirus continues to ravage the economy. TheU.S.unemployment rate stands at11%, a levelnot witnessed since the early 1940s until this year.

TheFeds extra loosening ofmonetary policycould help support prices for bitcoin, which many cryptocurrency investors speculate could serve as an effective hedge against inflation, similar to gold. Bitcoin prices have already soared 58% this year, beating silvers 36% andgolds 30%, not to mention the 2% gain in the Standard & Poors 500 Index of large stocks.

Bitcoin rose 1.5% on Monday to $11,338.

As more investors look to digital goldas an inflation hedge in an increasingly digitized world amidst unprecedented government money printing, the cryptocurrency research firm Messari wrote Monday, we know that it wont take much of an institutional allocation until $50,000 bitcoin is back on the table.

The Fedalready has taken monetary policy to a new level of extraordinary this year,pumpingnearly $3 trillion of freshly created money into financial markets earlier and pushing its total assets to about $7 trillion.A growing number of investorsin both digital-asset and traditional markets say theflood of dollars could whittle downthe U.S. currencys purchasing power.

The dollar index, a gauge of the the currencys strength in foreign exchange markets, fell 4% in July, thebiggest monthly dropsince 2010. And the Wall Street brokerage firm Jefferies now predicts that the dollar could fall as much as 15%, according to CNBC.

Bank of America analysts wrote Monday in a report that its becoming a popular trade to bet against the dollar, since investors are worried about the long-term impact of the rapid accumulation of U.S. debt for the U.S. dollars reserve-currency status.

As gold, silver, equities, and long bonds reach record high levels, and the U.S. dollar slumps, the king of cryptocurrenciesmay be back in the spotlight for the foreseeable future,Jeff Dorman, chief investment officer of the cryptocurrency-focused firm Arca, wrote Monday in a weekly blog.

Under the Feds policy shift, according to the Wall Street Journal, the central bankwould allow inflation to drift above a 2% target before raising rates. The idea is that above-target inflation would offsetperiods where consumer price increases were previously below the mark, as has been the case for most of the past two decades.

The goal is not to increase inflation per se, but to provide assurances to investors that interest rates would remain lowfor a long time, according to the paper. Such accommodation could help to assure a faster economic recovery.

Yet, higher inflation could further distortalready uncanny signals emanating from bond markets, further undermining the dollars attractiveness. Nominal yields on 10-year U.S. Treasury bonds are currently around 0.6%, close to historic lows. Once inflation is factored in, thereal yields equate tonegative 1%.

Assuming nominal yields dont rise much anytime soon, an inflation rate above 2% would cause bond investors to fall even further behind.

Negative real rates imply a loss in purchasing power from holding U.S. Treasuries,the ideal conditions for non-income producing assets such as gold and silver but also crypto assets like bitcoin, the analysis firm Delphi Digital wrote on July 31.

Theres some risk that a fresh panic in markets might prompt investors to rush back into dollars, which couldmeana redux of the March crash inbitcoin prices.

But according to an Aug. 2 Bloomberg News story, the next risk-off scenario might not see investors rushing into dollars, due to theflood of liquidity unleashed by the Fed.

Any haven rally is likely to be shallower than in previous years, according to the report, while the possible extent of depreciation remains the same.

Everything hinges on the dollar right now, Mati Greenspan, founder of the cryptocurrency-focused research firm Quantum Economics, wrote Monday in an emailto subscribers.

Tweet of the day

Bitcoin watch

BTC: Price: $11,186 (BPI) | 24-Hr High: $11,480 | 24-Hr Low: $11,164

Trend:Bitcoin is again struggling to find a foothold above $11,400 amid signs of buyer exhaustion on the three-day chart.

The number one cryptocurrency by market value is currently trading near $11,290, having hit a high of $11,424 during the Asian trading hours. Tuesday is the second straight day of bull failure above $11,400. Prices hit a high of $11,480 on Monday, but printed a UTC close below $11,240.

Essentially, bitcoins recovery rally from Sundays flash crash low of $10,659 has stalled with the area above $11,400 acting as stiff resistance.

The bulls need quick progress now, or the focus would shift to the uptrend exhaustion signaled by a major doji candle seen on the three-day chart.

A doji occurs when prices see two-way business during a specific period. While it is usually considered a sign of indecision, in this case, it has appeared following a notable rally to 11-month highs above $12,100. As such, it represents buyer fatigue.

The three-day charts relative strength index (RSI) is also reporting overbought conditions with an above-70 reading. Thus, a pullback to $11,000 cant be ruled out. A move below that psychological support would expose the former hurdle-turned-support at $10,500 (February high).

Alternatively, a sustained move above $11,400 on the hourly chart would strengthen the case for a re-test of recent highs above $12,000.

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

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TUM team finds Bitcoin accounts for 2/3 of total energy consumption of cryptocurrencies – Green Car Congress

Researchers from the Technical University of Munich (TUM) have analyzed 20 cryptocurrenciesaccounting for more than 98% of the total market capitalization of cryptocurrenciesand found that Bitcoin accounts for 2/3 of the total energy consumption of cryptocurrencies. Understudied cryptocurrencies represent the remaining 1/3. Their paper is published in the journal Joule.

Bitcoin is a digital currency based on a cryptographically secured distributed ledger; it is the first and best-known blockchain application. Bitcoin relies on a computationally intensive validation process called mining that requires specific hardware and considerable amounts of electricity to reach consensus about ownership and transactions.

Estimates of Bitcoin energy consumption based on different methodologies and assumptions, 20172020. Energy consumption is presented in gigawatt (GW). Gallersdrfer et al.

However, the authors note, most studies have been focusing exclusively on Bitcoin and have ignored the more than 500 further mineable coins and tokens. In the Joule paper, the researchers analyze 20 cryptocurrencies, which account for more than 98% of the total market capitalization of cryptocurrencies.

To estimate the energy consumption of cryptocurrencies beyond Bitcoin, we resort to a methodology proposed by Krause and Tolaymatthat employs hash rates of cryptocurrency networks and suitable mining devices. Hash rates measure the processing power; they describe the number of attempts per second to solve a block in the so-called proof-of-work mining process.

Based on the underlying algorithms, current hash rates, and suitable mining devices, we conclude that Bitcoin accounts for 2/3 of the total energy consumption, and understudied cryptocurrencies represent the remaining 1/3. Therefore, understudied currencies add nearly 50% on top of Bitcoins energy hunger, which already alone may cause considerable environmental damage. Including the remaining hundreds of mineable coins and tokens, which account for the 1.77% market capitalization not captured by the top 20, would further increase the share of energy consumption caused by cryptocurrencies besides Bitcoin.

Gallersdrfer et al.

Resources

Gallersdrfer et al. (2020) Energy Consumption of Cryptocurrencies Beyond Bitcoin, Joule (2020) doi: 10.1016/j.joule.2020.07.013

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Bitcoin’s flash crash and recent rally explained – BNN

The notoriously volatile price of Bitcoin is rallying, surging past the US$11,000 mark for the first time this year in late July.

The cryptocurrency set a new record high for 2020 on Sunday when it briefly surpassed US$12,000, marking an important technical and psychological milestone for cryptocurrency investors, before plunging more than US$1,000 within minutes.

There are several key factors fueling this price boost including the so-called Bitcoin Halving, which occurred on May 11. The algorithm that controls market supply of Bitcoin was adjusted to reduce new market supply by half. In the 11 years since its inception, this inflation-fighting feature of Bitcoins programming has historically been a driver of price movement.

Brian Mosoff, CEO of Toronto-based fintech firm Ether Capital, says a big part of the Bitcoin narrative has been a hedge against the inflation of fiat currencies, such as the U.S. dollar.

The timing of COVID and the expansion of government and central bank balance sheets have investors nervous about inflation. Investors are seeking ways to hedge and Bitcoin stands to be an important beneficiary of this, he said in an email.

Bitcoins algorithm dictates that only 21 million Bitcoin will ever be minted, leading many investors to draw the comparison to golds scarcity. Industry watchers say many investors view Bitcoin as a kind of digital gold and for that reason, it has piggy-backed on golds current rally.

According to Bilal Hammoud, CEO and Founder of a Calgary-based cryptocurrency exchange NDAX.io, Bitcoin is the perfect long-term hedge against both monetary policy instability and political instability. In an email he said ongoing trade tensions between the U.S. and China as well as unlimited money-printing stimulus due to the pandemic make the greenback a less attractive safe haven, driving the smart money to anti-inflationary assets such as gold and bitcoin as a hedge.

Another significant development driving the price of Bitcoin higher is a July 22 letter from the U.S. federal agency responsible for monitoring national banks and federal branches of foreign banks. It states that the Office of the Comptroller of the Currency concluded providing custody of cryptocurrency is a modern form of traditional bank activities related to custody services.

Banks can continue satisfying their customers needs for safeguarding their most valuable assets, which today for tens of millions of Americans includes cryptocurrency, the OCC letter says.

The letter opened the door for all national banks to provide cryptocurrency custody services for customers, according to Taras Kulyk, a senior vice-president at Bellevue, Washington-based blockchain and Artificial Intelligence firm, Core Scientific.

Following the release ofthis letter,the price of Bitcoin jumped 10 per cent in the span of 48 hours. Kulyk said in a phoneinterview that the market realized that this truly was a fundamental shift in regulatory positioning in the United States.

American law firm Sullivan Worcester LLP, which is based in New York City, described the OCCs letter in a July 24 note to clients as a potential turning point in the notorious frenemy relationship between banks and cryptocurrency. It goes on to say its possible that this could lead to a lower barrier for consumer and merchant crypto transactions.

Kulyk said in addition to the OCCs statement, many U.S. consumer-facing fintech apps including Venmo and Paypal have recently announced plans to integrate cryptocurrency in their offerings.

In July, popular Canadian investment app Wealthsimple announced plans to offer cryptocurrency trading on its platform asBitcoin trade volumes reached record highs on major cryptocurrency exchanges around the world.

Mosoff said that historically, the majority of Bitcoin trading is done by retail investors, although institutional capital and interest is increasing.

"It's difficult and often impossible to know what leads to big, short-term price moves in crypto," he said. "Bitcoin is traded globally and traders often employ leverage; with leverage a lot of volatility is possible."

On Sunday, Bitcoin experienced a flash crash, with US$1,000 wiped from its trading price on major platforms within minutes. It has since somewhatrecovered, and the move was attributed to profit-taking by so-called whales who own large amounts of the cryptocurrency.

Bitcoins overall price increase has provided fuel for other major cryptocurrencies, including ether which runs on the Ethereum network, and was pioneered by Canadian Vitalik Buterin. Historically the largest cryptocurrencies tend to move in lockstep, though Mosoff says Ethereum, which marked its fifth anniversary at the end of July, is one to watch.

While Bitcoin is treated as a stock, commodity or store of value, Mosoff says Etheris poised to take a different path.

Later this year, the network will begin to undergo a significant upgrade that will allow investors to generate yield in what weve termed a digital bond, Mosoff said.I believe this upgrade will put substantial upwards pressure on the price.

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Over 90% of ETH’s Supply Now in Profit | Markets and Prices Bitcoin News – Bitcoin News

More than 90% of ETHs circulating supply is now in profit. The last time this level was observed was in early 2018 when the price of the cryptocurrency was $925.

Research and analytics firm Glassnode reported Monday that the percentage of ETHs supply in profit has reached a level not seen since early 2018. The firm tweeted:

Over 90% of the circulating ETH supply is now in a state of profit, i.e. the current price is higher compared to the price at the time the coins last moved.

Last time this we saw this level was in Feb 2018 when the ETH price was at $925, the firm continued. The price of ETH has been surging significantly over the past weeks, rising about 47% since July 23. At the time of this writing, the price of ETH stands at $390.63, having breached the $400 mark.

Anthony Sassano, co-founder of Ethhub.io, believes that At this stage of the cycle Id say that its very bullish, he tweeted, emphasizing that Over 90% of the current supply of ETH is now considered in the money aka in profit.

The Spartan Groups co-founder, Kelvin Koh, commented: The strong move in ethereum has to do with the upcoming ETH 2.0 launch which is a major catalyst. Every phase of ETH 2.0 over the next 2-3 years brings Ethereum closer to its final state and will be catalysts for ETH.

Furthermore, news.Bitcoin.com recently reported that the total value locked within the decentralized finance (defi) ecosystem had surpassed $4.22 billion. The value is currently at $4.32 billion. A very large portion of defi applications, tokens, and platforms are hosted on the Ethereum network; defis massive growth has contributed to the significant rise in the price of ETH.

What do you think about 90% of ETHs supply being in profit? Let us know in the comments section below.

Image Credits: Shutterstock, Pixabay, Wiki Commons, Glassnode, Intotheblock

Disclaimer: This article is for informational purposes only. It is not a direct offer or solicitation of an offer to buy or sell, or a recommendation or endorsement 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.

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Chinese Bitcoin Miners Face Tougher Than Ever Rainy Season in 2020 – CoinDesk – CoinDesk

The rain has come. The machines are humming. This should be the best time of the year for Chinas bitcoin miners. The monsoon season, generally from June to October, brings excessive rain and thus cheap hydro electricity.

But this year is different, proving to be harder than ever for Chinas bitcoin miners and mining farm operators who are estimated to dominate 65% of the global multi-billion dollar bitcoin mining industry.

Since last summer, many mining farm operators rushed to build new facilities in Chinas southwestern region in anticipation of a dramatic price rise with bitcoins halving.

But mining difficulty has now almost doubled compared to the monsoon season last year, while block rewards have halved, meaning it is more difficult to mine, with less rewards. Bitcoin miners that have entered the market since last year have to wait much longer to see a return on their investment in mining hardware and facilities.

Thomas Heller, global business director of mining pool F2Pool, summarized the situation in a recent blog post: Were halfway through 2020 and the mining industry has already faced several enormous challenges.

Miners had to battle off the macroeconomic black swan of March, pass through the smoke of the halving and a pandemic, and now theyre gearing up for the rest of the years competitive battlefield, he wrote.

A year with a bitcoin halving and global epidemic rolled into one, its truly one of a kind.

Harder than ever

Many miners expected bitcoins price to rise sharply after the halving, said Kevin Pan, CEO and co-founder of the China-based PoolIn, one of the two biggest bitcoin mining pools in the world (along with F2Pool).

In reality, not only there was not much price momentum driven by halving, there came the mega sell-off on March 12, which caused a large scale of forced liquidation and loss, he said.

For two months after halving, bitcoins price largely remained static around $9,000. Although it jumped above $10,000 last week and is now changing hands over $11,000, it is still at a similar price level seen at this time last year.

In contrast, the networks mining difficulty rose to an all-time-level within two months after halving. Its now almost twice as difficult to mine bitcoin compared to last July, while block rewards have halved.

Without a significant price breakout, bitcoin miners daily revenue has dropped by 70% compared to last year, said Pan, although the recent bitcoin price jump has helped improving the situation.

Indeed, Bitinfocharts data shows bitcoins daily mining revenue was around $0.33 per one terahashes second (TH/s) of computing power in July 2019. It has since then declined to now around $0.1 per TH/s.

Overcapacity

Meanwhile, a surge in interest and investment in bitcoin mining since last year have led to a surplus of newly constructed mining facilities in China.

In April, the oversupply issue had already shifted the hosting business from a sellers market to a buyers market, with mining farms generally offering a 20% electricity discount compared to last year.

Pan estimates that during this rainy season, 20% to 30% of mining facility capacity in Sichuan and Yunnan provinces still remains unused.

To be clear, bitcoin miners and mining farms can still make a profit. But they have to endure a much longer period than expected to break even on their investments.

A payback period of six months to a year used to be common for bitcoin miners in China, but if bitcoin maintains its current prices around $11,000, that could be extended to as long as two years.

In the eyes of many old Chinese miners, the electricity price right now is not only lower than the similar situation of the halving and hydro season in 2016, but also even lower than the electricity prices during the 2015 bear market, said Heller of F2Pool.

Lower electricity may be appealing to miners, but it also means mining farm operators are facing an unprecedented investment challenge as the business shifted to a buyers market, Heller said.

Long-term bullish

Despite this years tough market environment, some are still bullish over the long term and are rolling out products to attract investors. Jiang Zhuoer, CEO and founder of mining pool BTC.Top who also runs his own mining farms, recently launched joint-mining contracts dubbed B.top.

It essentially sells mining equipment by TH/s and farm electricity at cost to retailers who want to participate in mining. The company will not charge customers hosting and management fees until the mining profits they receive break even on their investment.

HashAge and Heng Jia, two long-running bitcoin mining farm operators with over a dozen facilities in Sichuan, also announced a partnership with Chinese crypto lending startup Babel last Friday.

Flex Yang, CEO and co-founder of Babel, said the firm is allocating up to $50 million in USDT as a loan for those who choose to host their miners at HashAge and Heng Jias facilities.

In contrast to previous crypto loans that require borrowers to pledge bitcoin as collateral, this new partnership accepts debtors miners hosted at HashAge and Heng Jia as collateral.

This effort is also one of the industrys first in terms of treating specialized mining equipment, known as ASIC miners, as a tradable asset in crypto-based debt financing.

Luxor, a U.S.-based mining pool, rolled out a bitcoin hashrate price index earlier last month in an effort to provide better transparency into the traditionally opaque market of how much bitcoin mining equipment is changing hands.

Floods

But rain cuts both ways for the mining industry. Flooding in China is among the worst in decades, and has affected over 50 million residents, with nearly four million people displaced and over 150 dead or missing.

The good news is it could have been much worse. Pan said the flood has so far mainly affected the middle and lower reaches of the Yangtze river.

Since most mining farms in Sichuan and Yunnan are located along the upper reaches in the mountain area, which are some 1,200 km, or 800 miles, away from the middle reaches, there are fewer instances where facilities are directly flooded due to the rainfall.

But Pan said there have been more regular instances of mining farms hydropower plants temporarily cutting off electricity generation because the increasing water reserve levels would otherwise cause pressure on the dam.

The places that are suffering the most severe damage so far are provinces in Central China including Jiangxi, Hubei, Hunan and Anhui provinces, as illustrated in this multimedia article from the South China Morning Post.

Johnson Xu, chief analyst at Beijing-based research startup TokenInsight, said mining farm operators nowadays are more experienced in choosing the right location for construction, after witnessing events in previous years where facilities were destroyed by floods and mudslides.

Chinese mining farms have already conducted thorough due diligence to pick the locations where potential flooding risk is minimal, so the floods havent caused a major impact on the mining community, said Xu.

Tug of war

Another reason why there are too many bitcoin mining farms is the push by local governments in Sichuan for establishing the so-called Demonstration Zone for Utilizing Excessive Hydropower Electricity since late last year.

Mining farms and hydropower plants that choose to be based in these industrial parks can typically enjoy a stable operational environment with a steady and cheap power supply. In return, they give a portion of their profits to local governments as well as Chinas State Grid, the state-owned utility monopoly.

In previous years, many mining farms in Sichuan and Yunnan have been using whats called direct-supply electricity. That means power plants sell electricity directly to mining farm operators without having to share the profits with other parties.

As local governments have stepped up efforts to rectify the direct-supply model adopted by many power plants, this has created a sort of tug of war among local governments, hydropower plants as well as the State Grid, Pan said.

Some bitcoin mining farm operators using direct-supply electricity wish to sell their facilities at a low valuation given tough market conditions. This tug-of-war will continue to be a risk factor for potential investors in those mining farms.

Overall, the latest regulatory policies in China tend to have a negative impact on those unregulated smaller mining farms, but positive towards firms who meet the local regulatory requirements, Xu added.

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Chinese Bitcoin Miners Face Tougher Than Ever Rainy Season in 2020 - CoinDesk - CoinDesk

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