How To Read Cryptocurrency Charts – NuWire Investor

Cryptocurrency is the future. When we talk about crypto, we talk about a whole new system of money that is outside what we currently use in our banking system. Cryptocurrency is based off of a technology called blockchain. When you look at crypto charts, it can often be confusing and a bit jargon heavy. Here, we will cut through the technicalities and give a clear concise means to read the charts:

The first thing you have to know is what time frame you are looking at. Based off of your range, you can be looking at the Price variability of one hour, one day, one month, or one year. Be sure to look at the X axis and the demarcations of each candle stick. There, you will know what kind of timeframe youre looking at. The purpose for these time frames are they inform traders of a pattern happening in real time. If you are an experienced trader, the minutes and hours mean a lot. If youre looking for a long-term investment, stick to the month-to-month.

More often than not, your Y axis is going to compose of the price. Youre going to see the highs and lows according to the range you said it. Once again, if youre looking at the minutes an hour, youre probably going to have a smaller price variability on your Y axis. It is not very likely that the price of any cryptocurrency will fall or rise significantly relative to the amount that you own.

When we talk about candlesticks, were talking about those red and green lines that we see across the chart. Those represent the highs and lows of price within a certain timeframe. You can see the shift within your specific range change by watching where on the chart your candlesticks are. If the closing price of your crypto asset is higher than the opening price within the timeframe you select, the candlestick will appear green. If the closing price of your crypto asset is lower than the opening price within the timeframe, your candlestick will appear red. Trends appear, when you watch the green and red candlesticks move up and down the price over the time you select.

Reading a crypto chart is very similar to reading any that of any publicly traded stock.All the variables are the same within the chart. What changes are the market caps, P/E ratio, international opinions, and other factors that affect how well cryptocurrency does. If you know how to read the chart, and with the handy guide above, you can make decisions based off of informed and calculated moves. Good luck, and happy trading.

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How To Read Cryptocurrency Charts - NuWire Investor

Cryptocurrency and Blockchain to be Applied in the Italian Energy Industry – Coin Idol

Mar 08, 2020 at 11:34 // News

There has long been talk of the great potential of cryptocurrency, blockchain and distributed ledger technology (DLT) to be applied to the energy industry. But until now it was more than just educational disquisitions.

This is because no company had yet implemented this technology in the energy market, which between now and two years will see the end of the market protected with its full liberalization. But from March 2020 thanks to the enlightened mind of a handful of Italian entrepreneurs, it will be possible to pay the electricity and gas bills with tokens or cryptocurrencies, all in blockchain technology.

Some innovative startups have come up with the idea to solve power problems in this growing liberalized market, which covers about 37 million users in Italy for an average household spend of about 1200 euros per year. The idea was presented on Monday 27 January during a press conference in Milan, with the presence of the top companies and investors who immediately believed in the innovative idea of applying the sharing economy in blockchain to the energy sector.

An innovative solution for all users who want to change power operator has to be invented. In fact, customers should not only enjoy prices that are certainly competitive compared to the competition, but also have all the advantages in terms of efficiency, privacy and disintermediation that DLT offers. Also, the company and users will be in a position to use of a cryptocurrency and blockchain sharing economy platform.

The digital currency to be used will have to be listed in different cryptocurrency markets, with which it will be possible to pay the power bills. And it is precisely on the basis of experience that DLT experts believe before others in the potential of new technology in the market.

Later on, the platform will use all the benefits of revolutionary blockchain technology to ensure greater transparency with so-called smart contracts, greater efficiency and lower costs thanks to disintermediation.

A real revolution that could solve some of the problems created by the complete liberalization of the energy market planned in two years' time and which would give customers the opportunity to make a conscious, free and safe choice. The objectives are certainly ambitious but the premises that this new idea can play an important role in the market in the coming years are all there.

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Cryptocurrency and Blockchain to be Applied in the Italian Energy Industry - Coin Idol

Over $21 billion wiped off cryptocurrency market in 24 hours after massive oil price plunge – CNBC

A visual representation of the cryptocurrency Bitcoin on November 20, 2018 in London, England.

Jordan Mansfield | Getty Images News | Getty Images

Cryptocurrency markets plunged following a plummet in oil prices and further sell-off in stocks.

The market capitalization or entire value of cryptocurrencies was down $21.58 billion from a day earlier at around 10 a.m. Singapore time, according to data from Coinmarketcap.com. It was down even further earlier in the day, but pared some of those losses.

Bitcoin, the biggest cryptocurrency by value, fell 8% in 24 hours at around the same time.

The violent sell-off in the cryptocurrency market comes after international oil benchmarkBrent crudefutures plummeted 30% to $31.02 per barrel, its lowest level since Feb. 2016. That was sparked by Saudi Arabia slashing its official selling prices for oil after OPEC failed to agree a deal on production cuts. This has led to fears of an oil price war. Brent has since pared some of its losses.

Meanwhile, stock markets in Japan and Hong Kong fell sharplywhile U.S. stocks are set for a steep drop at start of trading on Monday.

The other big digital coins ethereum, XRP and bitcoin cash, posted double-digit percentage point losses.

Despite the losses posted Monday, bitcoin is up over 12% year-to-date.

Huge moves in cryptocurrency prices are not unusual and these digital coins are known for their volatility. Market players however said this could be an opportunity to buy some bitcoin.

"For those who have long term investment horizons, bitcoin is absolutely a buy during these dips," Jehan Chu, co-founder of Kenetic Capital, an investor in blockchain start-ups toldCNBC. "We can expect more of this volatility sparked by macro health and financial shocks, but ultimately long term investments in the digital future and it's key asset Bitcoin will be a winning strategy"

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Over $21 billion wiped off cryptocurrency market in 24 hours after massive oil price plunge - CNBC

Retail Demand May Force the SEC to Approve a Bitcoin ETF – Cointelegraph

Several investment firm executives have debated the likelihood of the U.S. Securities and Exchange Commission (SEC) licensing a U.S.-based Bitcoin (BTC) exchange-traded fund (ETF) during a CNBC broadcast on March 7.

The discussion follows the SECs recent rejection of its last pending Bitcoin ETF application.

Wilshire Phoenix had first filed the application for its proposed United States Bitcoin and Treasury Investment Trust with the SEC during January 2019.

Despite amending their application six times in 13 months, the SEC rejected Wilshire Phoenixs ETF, citing concerns about manipulation of Bitcoins market, and limited investor protections.

Chris Hempstead, the director of institutional business development at ETF and hedge fund provider IndexIQ, predicts that a Bitcoin ETF will come as retail demand for the product grows.

I doubt very heavily that its going to be the last straw, Hempstead stated. I think everyone will continue to listen to the feedback and the notes from the SEC, what their comments are, and they will continue to address it.

Despite predicting that the commission will reconsider its stance if faced with widespread demand in coming years, Hempstead does not predict any significant changes to the SECs decision in the near future.

At some point, when market demand and investor demand pushes the pendulum to a certain area, they will probably take another look at it and have different kinds of considerations.

Nick Colas, the co-founder of investment analysis firm DataTrek Research, expressed skepticism at the prospect of the SEC licensing a Bitcoin ETF any time soon.

You will see a central bank cryptocurrency before you will see a Bitcoin ETF, he stated.

When asked whether stablecoins make imminent sense to consumers, Hempstead responded: I think youre onto something.

Hempstead predicts stablecoins and other cryptocurrency products will become regulated as the sector matures and the public gain a greater understanding of the inner workings of distributed ledger technology (DLT).

I think that maybe part of what theyre waiting for is a little bit more structure and oversight into the operational complexity of cryptocurrency transactions [...] I think when we start to see more risk diversification, and more understanding about how these various products, not just Bitcoin, how they operate - I think thats probably whats needed at the Commission level.

According to Dan Wiener, the chairman of Adviser Investments and the senior editor of The Independent Adviser for Vanguard Investors, business adoption of blockchain technology is more important than cryptocurrency.

Wiener dismissed the notion that there is a need for Bitcoin altogether, arguing that payment platforms like Venmo have attracted far greater adoption than cryptocurrencies.

Do we really need bitcoin? Im not a drug dealer. Im not worried about moving money [...] We have many, many ways to move money around, I dont know that we need to be able to hide ourselves, or our identities.

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Retail Demand May Force the SEC to Approve a Bitcoin ETF - Cointelegraph

Proving That Tether Manipulated Bitcoin 2017 Bull Run Wont Be Easy – Cointelegraph

The iFinexTether market manipulation lawsuit continues. Last week, Judge Katherine Failla of the Southern District of New York selected Roche Cyrulnik Freedman as interim lead plaintiff counsel, and four civil actions were consolidated into a single class action: Leibowitz v. iFinex Inc.

In the complaints, iFinexs subsidiary, Bitfinex, and related stablecoin Tether (USDT) are charged with manipulating the Bitcoin market in 2017 something the firm strenuously denied.

This isnt shaping up as an ordinary civil action. As Failla observed in announcing her lead counsel decision on Feb. 27 via a telephone conference call, she claimed that the case combines old and new:

The cryptocurrency law is quite novel [with] lots of issues and not a lot of resolution, but there is a lot of established law out there as well with respect to pleading requirements, with respect to traditional antitrust issues and RICO and the Commodities Exchange Act.

The case has reached an inflection point where the plaintiff groups that had been competing among themselves for primacy must now coalesce and confront iFinex Inc. directly. Its a good time to ask: What sort of challenges await the litigants?

Felix Shipkevich, an attorney specializing in cryptocurrency-related legal and regulatory matters at Shipkevich PLLC, told Cointelegraph: I am pessimistic that they [i.e., plaintiffs] will be able to overcome the hurdle of proving market manipulation of a decentralized currency like Bitcoin.

The scope of market manipulation can differ from industry to industry, said Shipkevich. Its one thing to prove market manipulation with commodities futures but another to prove it with equity securities. Cryptocurrencies are still so new that it isnt clear which way the courts will lean with regard to market manipulation.

Price manipulation claims under the Commodity Exchange Act (CEA) are difficult to prove, according to a statement to Cointelegraph by Anne Termine, an attorney with Covington & Burling LLP and former chief trial attorney for the United States Commodity Futures Trading Commissions (CFTC) enforcement division. She added: Proving a price manipulation charge where Bitcoin is the underlying commodity just adds another layer of complexity.

In proving market manipulation, there are typically four prongs, or factors, that have to be taken into account, said Shipkevich. Two of these may be problematic for the plaintiffs: Was there deceptive intent to manipulate the market? In other words, did people collude to move the price of a commodity up or down? Because of the decentralized nature of crypto exchanges and ledgers, this could be difficult to prove in the case of Bitcoin.

Another factor is market dominance. A firm typically has to be able to dominate a market to manipulate it. If one buys up all the crypto in an initial coin offering, thats a closed loop, and the path to dominating or monopolizing that market becomes a real possibility, said Shipkevich. But how do you prove price dominance with regard to BTC, which had a market capitalization of $166 billion on March 6? It might be difficult. Termine added to the notion:

Price manipulation requires proof of the ability to create/cause artificial prices and proof that the defendants, in fact, caused the price of the futures contract the Bitcoin futures contract, in this case to be artificial. While facts can be used to establish the requisite specific intent and the ability to cause artificial prices, proving an artificial price did, in fact, occur can often be a difficult and technical but-for analysis.

What seems clear, however, is that Tether continues to play an outsized role in Bitcoin trading. In December 2019, BTC trading into USDT represented 76.2% of total BTC volume traded into fiat currencies or stablecoins, according to CryptoCompares Exchange Review December 2019. Its been even higher in the past and suggests at least the possibility of leverage if not dominance. As Ohio State Professor John Griffin told Newsweek in November: Crypto can be pushed around easily by big whales. In a statement sent to Cointelegraph, Tether General Counsel Stuart Hoegner vehemently denied any wrongdoing:

Tether and its affiliates have never used Tether tokens or issuances to manipulate the cryptocurrency market or token pricing. All Tether tokens are fully backed by reserves and are issued pursuant to market demand and not for the purpose of controlling the pricing of crypto assets.

Sidharth Sogani, founder and CEO of Crebaco Global Inc., a crypto and blockchain credit rating and audit firm, told Cointelegraph that stablecoins, in general, are detrimental to both cryptocurrencies and fiat currencies because they create manipulation and creation of artificial wealth, resulting in economic inflation.

As for Tether, specifically, the company is incorporated in the British Virgin Islands, which doesnt inspire confidence from a regulatory compliance standpoint, Sogani said. The British Virgin Islands and the Cayman Islands country risk assessment is Category C, per Crebacos standards, adding:

There are more chances of frauds, MLMs [multi-level marketing schemes] and scams arising out of these countries due to the lack of regulations for digital assets.

Since 2014, iFinexTether has been essentially self-regulated. In its intelligence reports, Crebaco uncovers serious flaws in USDTs compliance, reserves and circulation throughout many exchanges and wallets, Sogani informed Cointelegraph.

In October, Shipkevich told Cointelegraph that he was not surprised that a class-action lawsuit had been brought forth against both Tether and Bitfinex, considering the legal pursuit these entities have been facing by the New York attorney general over the past year.

The New York State Attorney Generals office has been investigating the company for potential securities and commodities fraud after the company allegedly moved Tether reserves over to affiliate exchange Bifinex after it lost $850 million earmarked for user redemptions. In a Dec. 13 filing, lawyers for Bifinex and Tether said that the NYAG didnt have the authority to investigate the companies because Tethers are not securities or commodities.

The issues in the current case arent entirely clear, and this may have figured in Faillas selection of Roche Cyrulnik Freedman as lead plaintiff counsel. According to the transcript of the telephone conference, the judge had four criteria in mind for picking a lead counsel: The work that counsel has done, the experience of counsel, the knowledge of the applicable law, and the resources that have or will be committed. Here, any of the three competing firms would have sufficed, she said.

Related: Top Cryptocurrencies Are Exponentially More Liquid Than Ever Before

The definition of the injured class differed among some of the firms, however. As reported by Cointelegraph, two of the vying legal groups Roche Cyrulnik Freedman LLP and Kirby Mcinerney LLP defined the class action of their respective injured parties in a broad sense, while a third, Robbins Geller Rudman & Dowd LLP, restricted its class definition to investors in Bitcoin and Bitcoin futures. According to Brian Cochrane of Robbins Geller:

Roche defined it as anyone who owned crypto over the last six years. Thats overwrought much too broad. Bitcoin and Bitcoin futures are closer to my definition of the class. Not all cryptos should be included. That would simply be taking money from real victims and giving it to others.

Failla, however, decided against this more restricted definition of the injured class: I can't agree with a class as narrow as that initially defined by the Robbins Geller firm, and my concern here is they're cutting off the line too soon into the matter. Robbins Geller was thus eliminated. Next, Failla had to choose between Roche and Kirby.

This was close a call, she recounted, but after looking at the firms work products in other cases, the judge felt that the Roche firm would best illuminate the issues, new and old, that I believed are going to be implicated by this litigation So, I am granting their motion for appointment as interim lead plaintiff counsel.

Is this likely to be a significant case for the crypto world? Generally, yes, answered Shipkevich, but not as significant as some other cases, like Telegram or others involving the Securities and Exchange Commission, CFTC or the states. This case is in such an early stage that it is difficult to say if it will be a precedent case for market manipulation in the crypto world.

According to Sogani, USDT remains the largest stablecoin by far and is listed on all the major exchanges and wallets. Any [court] decision will impact the industry directly. Furthermore, Termine told Cointelegraph:

There are some courts that have found that Bitcoin is a commodity in interstate commerce, but it is by no means a settled issue. It does help that the agency responsible for enforcing the CEA, the CFTC, has publicly taken the position that Bitcoin is a commodity. How a jury will see the issue is not a certainty. As such, any decision by the court on each of these issues will be closely watched by the industry.

The case is complex, Termine added, and the charges here go beyond price manipulation they also include fraudulent manipulation. Then, what has to be alleged and proven in private lawsuits, like this one, is often different from what is required when a government agency like the CFTC brings an action.

Related: Tether Stablecoin: Can the Crypto Market Live Without It?

Shipkevich wouldnt venture to say whether a settlement as opposed to a court decision in this case, is likely. But if I were Tether, Id be litigating until I ran out of money. To settle would be to declare open hunting season, he told Cointelegraph. The firm could expect to be besieged by lawsuits.

One can expect that the defense, led by Walden Macht & Haran LLP, will now file a motion to dismiss the case. This process, which would culminate in an oral argument before Failla, might take six months.

If the defense prevails, iFinexTether wins the case. If the plaintiff group survives the motion, however, things could really heat up. Plaintiffs take depositions, they gain access to trading data, and all sorts of scenarios could emerge. When Cointelegraph asked iFinex Inc. to comment for this story, a company spokesperson replied:

We have no further comment at this time beyond our most recent statement and look forward to putting the facts before the court, and addressing the baseless allegations in the judicial forum.

The SEC and CFTC both agree that Bitcoin is a commodity and should be regulated as such and there is established law to determine if and when the price of a commodity has been manipulated.

However, Bitcoin isnt a material commodity like oil or silver, and as recently as October, CFTC Chairman Heath Tarbert speculated that a cryptocurrency could move from being a security to a commodity and change back and forth. Cryptocurrency law, too, is still novel, as Failla observed i.e., it is a work in progress. It comes as no surprise, then, that proving price manipulation in regard to something as elusive as BTC might be a challenging task for the aggrieved parties in this case.

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Proving That Tether Manipulated Bitcoin 2017 Bull Run Wont Be Easy - Cointelegraph

Bitcoin (BTC) Plunges to $8,300; Heres What Analysts Are Thinking – Ethereum World News

Bitcoin really hasnt done well over the past day. After printing a false breakout candle on Saturday that brought the price of the asset to $9,200, there was a rapid and violent reversal. What followed was a dramatic and steep downtrend that has taken the price of BTC as low as $8,300 just last hour (as of the time of this articles writing), marking a 10% drop from the highs.

Here are what analysts expecting is next for Bitcoin.

Although the price action that has transpired over the past 24 hours has been decisively bearish for Bitcoin, there are some expecting the asset to bounce.

NebraskanGooner, a founder of exchange Level and a noted crypto trader, remarked that as scary as that drop was, Bitcoin has fallen to his daily trendline support, depicted below. The trendline has acted as both resistance and support for at least two months now, suggesting it is a crucial level to keep an eye on.

With BTC currently holding the trendline NebraskanGooner indicated, he suggested that there is a chance that it can rally 15% or so back to $9,500, the top of the range he defined in the chart above.

NebraskanGooner isnt the only bull in this environment.

Prominent trader Big Cheds recently wrote that Bitcoin has his permission to bounce now. Backing this lofty sentiment, hepointed to a chart that showed that Bitcoin has found support at $8,400 over three times in the past few weeks.

Despite this bullish sentiment, the ball is seemingly in the court of bears, so to say. (Case in point, the price of Bitcoin has fallen to $8,200 in the minutes that Ive been writing this article.)

Cryptocurrency consultancy founder Burger remarked that Bitcoin could be printing a bearish head and shoulders pattern, which could mark a medium-term reversal for the price of BTC:

H&S pattern on the daily chart for $BTC which often marks the start of a reversal.

He added that with the existence of the coronavirus FUD, there may be some adverse effects on the cryptocurrency market as can be seen with traditional markets already.

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Bitcoin (BTC) Plunges to $8,300; Heres What Analysts Are Thinking - Ethereum World News

Bitcoin Halving is Less Than 10000 Blocks Away, Will Prices Soar? – Bitcoinist

With just over two months to go and BTC still struggling under $9K, will Bitcoins halving really affect its price?

The Bitcoin halving is currently less than 10000 blocks away, as tweeted out by Bitcoin core developer and educator Jimmy Song. The majority of people in the space anticipate it will have a major impact on bitcoins price. This is for several reasons.

Just as the supply of bitcoins is limited to 21 million, the mining reward for generating new blocks is reduced every four years or every 210,000 blocks. It is cut in half, hence the term halving (or halving). This will carry on until all the 21 million bitcoins are released into circulation.

With the capped supply, Nakamoto ensured that Bitcoin, unlike fiat currencies will never lose its purchasing power over time. In fact, a capped supply dramatically increases BTCs odds of steadily increasing in price in the future.

This rise in price is what allows mining bitcoins to still be profitable to miners even with a reduced reward over time.

The mining reward is made up of the block subsidy and the transaction fees. The subsidy consists of newly generated bitcoins and is currently the largest part of the reward. The other part is made up of transaction fees paid by all the transactions included in the block.

The current reward is 12.5 bitcoins plus TX fees for the discovery of a new block. After the next Bitcoin halving the mining reward will be cut in half to 6.25 BTC. This will carry on until all bitcoins are released, at which point the network should be sustainable on transaction fees alone.

The first Bitcoin halving happened on Nov 28, 2012, when the mining reward was reduced to 25 bitcoins. At the time of the halving, the price of BTC was approximately $11. Over the next year, Bitcoin would see its price increase to as much as $1,135 on Nov 29, 2013. A dramatic hike of 10,218%.

The second Bitcoin halving occurred on July 16, 2016, when the reward was reduced to its current rate of 12.5 bitcoins per block. This time around, the price did not react immediately.

In fact, after the last halving, BTC was locked in a rather dull trading range of between $500 and $800. This lasted all the way through to the end of the year. Then, on Dec 21, 2016, the price penetrated $800 and the halving rally was underway at last.

Over the next 12 months, an explosive bull market ensued with Bitcoin reaching its all-time high os $19,862 on Dec 18, 2017. A 2,827% percentage hike. So, based on these past results, its not surprising the community is getting excited.

Many prominent analysts in the space expect the halving to have a dramatic impact on bitcoins price. These include Fundstrat Managing Partner Thomas Lee, who sees bitcoins price more than tripling in 2020.

Other major influencers including Morgan Creek Digitals Anthony Pompliano have frequently tweeted out their excitement over the upcoming event.

Their enthusiasm is echoed by traders and HODLers alike who believe that the price of bitcoin will explode to the upside very soon.

However, its not a hard and fast rule that history will repeat itself. As one Redditor commented:

Its a game of supply and demand. The halving reduces the supply.. so if demand stays the same price will have to go up.

Februarys price decline was a decisive blow to the Bitcoin bulls. If demand decreases and prices dwindle, the mining reward could leave miners struggling and even force them out of business.

Even though bitcoin maximalists like Max Keiser are calling for a $400K bitcoin soon, its quite unlikely that bitcoin will see a dramatic price increase the likes of the previous two halvings.

In fact, there was a large reduction in terms of percentage gains from 2016 halving compared to 2012some 72% less.

So lets make an educated guess. If we take in the assumption that the rally will be 72% less than the 2016 halving, then we can expect BTC to make a substantial gain of 797% this time around.

Based on a BTC price of $9k on the next halving, we could expect to see its price reach as much as $71,730 in about 12 to 18 months from May 2020.This means that BTC price may not see any dramatic action for at least a year after the next halving.

Of course, these are just predictions and its impossible to predict the future direction of any speculative asset. But, with the information at hand, it looks likely that 2021 will be a good year for BTC price.

Will Bitcoins price react positively to the upcoming BTC halving? Let us know your thoughts below!

Images via Shutterstock, Twitter: @jimmysong, @CryptoManagers, @PBlockstar, @APompliano

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Bitcoin Halving is Less Than 10000 Blocks Away, Will Prices Soar? - Bitcoinist

Crypto Bulls Roadshow Coming to Over 15 Indian Cities With Government Participation – Bitcoin News

Indias Crypto Bulls Roadshow, a nonprofit initiative to prepare India for the next bull run, is coming up, and government organizations are joining the drive. Currently, 15 cities in India are planned for but more may be added based on demand. There is no fee to participate in the roadshow and there will be online voting for top influencers of the Indian crypto ecosystem.

Also read: Bitcoin Legal in India Exchanges Resume INR Banking Service After Supreme Court Verdict Allows Cryptocurrency

Indias Crypto Bulls Roadshow is a nonprofit initiative by Kumar Gaurav, CEO of crypto banking platform Cashaa, and Gaurav Dubey, CEO of blockchain investment advisory firm O1ex. Cashaa launched its Indian operations in October last year. O1ex, a Dubai-headquartered company with IT operations based out of Kanpur, India, will be the sponsor of the roadshow.

The event aims to educate Indian crypto users about real blockchain technology to prepare India for the next bull run, the roadshow website details, adding that it will showcase crypto projects, create public awareness, and build a strong Indian crypto community. The website continues:

Now, its time to prepare India for the next bull run and show the world that India is not less than the USA or China.

Cashaas Gaurav shared with news.Bitcoin.com that the roadshow is an initiative to bring back the Indian crypto industry together after the huge damage, due to the banking restriction imposed by the central bank.

The roadshow document describes: In the recent supreme court hearing, it became clear that crypto is not illegal in India. It was nearly two years ago that the Reserve Bank of India clamped down on a fast-growing market for cryptocurrencies in the country. That impacted the cash on-ramp for the crypto market in India even though there is no legal ban on their use in the country. The Supreme Court of India quashed the RBI ban on the crypto industry on Wednesday.

Cashaas CEO added:

Many government organizations such as law enforcement (police) and municipal corporations are also joining this drive, to educate citizens about the bitcoin and bring awareness to protect people from scams crypto. After the holiday (12/03/2020) we are expecting huge participation from the other organization and regulators such as Income Tax, SEBI to be part of this program.

The current plan is for the roadshow to start on April 3 and run through April 26. But due to current excitement in India, we might add a few more cities during the roadshow, due to which it may last up to 30th April, Gaurav revealed to news.Bitcoin.com, elaborating:

Due to the supreme court verdict, the revolution has grown up and bitcoin community managers and evangelists from many different cities have joined it, so far we have added Chennai, Visakhapatnam, Bhubaneswar, Kolkata, Patna, Kanpur, covering total 7,000 Kms. The start date will be 3rd April.

The 15 cities planned for so far are Delhi, Jaipur, Udaipur, Ahmedabad, Surat, Mumbai, Pune, Hyderabad, Bengaluru, Chennai, Visakhapatnam, Bhubaneswar, Kolkata, Patna, and Kanpur.

Prior to the actual roadshow, there will be meetings with all the exchanges and projects participating in the event. The chain of meetups, meetings with the local governments, large enterprises, and sessions at the top accelerators with 500 plus startups will create an everlasting ripple effect across the nation, the roadshow website notes.

The current roadshow timetable is as follows:New Delhi April 3 and 4 (2 events in North and South Delhi),Jaipur April 5Udaipur April 7Ahmedabad April 8Surat April 9Mumbai April 10 and 11Pune April 12 and 13Hyderabad April 15, andBangalore April 17

Crypto projects, influencers, event organizers, traders, and investors from around the world are invited to participate in the roadshow. Attendees will soon be able to select the city and register for the event on the Crypto Bulls Roadshow website (Cryptobulls.in), Gaurav confirmed, noting:

There is no fee unlike any other events in India for participants. The India Crypto Bulls will be a pure crypto event with a focus on the adoption of the public chain.

As part of the event, there will be Online voting to pick the best exchange, best blockchain project and Indian influencer, Gaurav further said. Winner of each category will receive the award at the Gala dinner at the end of the roadshow (venue will be announced by 30th March) and represent India in New York, the USA on 12th May as a speaker at Consensus 2020 in the India Crypto Bulls segment on stage. He added that online voting will start on March 16 and will continue to the end of the roadshow but nominations are open now.

What do you think of this India Crypto Bulls Roadshow? Do you want to participate? Let us know in the comments section below.

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

Images courtesy of Shutterstock, Cashaa, and India Crypto Bulls Roadshow.

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A student of Austrian Economics, Kevin found Bitcoin in 2011 and has been an evangelist ever since. His interests lie in Bitcoin security, open-source systems, network effects and the intersection between economics and cryptography.

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Doing machine learning the right way – MIT News

The work of MIT computer scientist Aleksander Madry is fueled by one core mission: doing machine learning the right way.

Madrys research centers largely on making machine learning a type of artificial intelligence more accurate, efficient, and robust against errors. In his classroom and beyond, he also worries about questions of ethical computing, as we approach an age where artificial intelligence will have great impact on many sectors of society.

I want society to truly embrace machine learning, says Madry, a recently tenured professor in the Department of Electrical Engineering and Computer Science. To do that, we need to figure out how to train models that people can use safely, reliably, and in a way that they understand.

Interestingly, his work with machine learning dates back only a couple of years, to shortly after he joined MIT in 2015. In that time, his research group has published several critical papers demonstrating that certain models can be easily tricked to produce inaccurate results and showing how to make them more robust.

In the end, he aims to make each models decisions more interpretable by humans, so researchers can peer inside to see where things went awry. At the same time, he wants to enable nonexperts to deploy the improved models in the real world for, say, helping diagnose disease or control driverless cars.

Its not just about trying to crack open the machine-learning black box. I want to open it up, see how it works, and pack it back up, so people can use it without needing to understand whats going on inside, he says.

For the love of algorithms

Madry was born in Wroclaw, Poland, where he attended the University of Wroclaw as an undergraduate in the mid-2000s. While he harbored interest in computer science and physics, I actually never thought Id become a scientist, he says.

An avid video gamer, Madry initially enrolled in the computer science program with intentions of programming his own games. But in joining friends in a few classes in theoretical computer science and, in particular, theory of algorithms, he fell in love with the material. Algorithm theory aims to find efficient optimization procedures for solving computational problems, which requires tackling difficult mathematical questions. I realized I enjoy thinking deeply about something and trying to figure it out, says Madry, who wound up double-majoring in physics and computer science.

When it came to delving deeper into algorithms in graduate school, he went to his first choice: MIT. Here, he worked under both Michel X. Goemans, who was a major figure in applied math and algorithm optimization, and Jonathan A. Kelner, who had just arrived to MIT as a junior faculty working in that field. For his PhD dissertation, Madry developed algorithms that solved a number of longstanding problems in graph algorithms, earning the 2011 George M. Sprowls Doctoral Dissertation Award for the best MIT doctoral thesis in computer science.

After his PhD, Madry spent a year as a postdoc at Microsoft Research New England, before teaching for three years at the Swiss Federal Institute of Technology Lausanne which Madry calls the Swiss version of MIT. But his alma mater kept calling him back: MIT has the thrilling energy I was missing. Its in my DNA.

Getting adversarial

Shortly after joining MIT, Madry found himself swept up in a novel science: machine learning. In particular, he focused on understanding the re-emerging paradigm of deep learning. Thats an artificial-intelligence application that uses multiple computing layers to extract high-level features from raw input such as using pixel-level data to classify images. MITs campus was, at the time, buzzing with new innovations in the domain.

But that begged the question: Was machine learning all hype or solid science? It seemed to work, but no one actually understood how and why, Madry says.

Answering that question set his group on a long journey, running experiment after experiment on deep-learning models to understand the underlying principles. A major milestone in this journey was an influential paper they published in 2018, developing a methodology for making machine-learning models more resistant to adversarial examples. Adversarial examples are slight perturbations to input data that are imperceptible to humans such as changing the color of one pixel in an image but cause a model to make inaccurate predictions. They illuminate a major shortcoming of existing machine-learning tools.

Continuing this line of work, Madrys group showed that the existence of these mysterious adversarial examples may contribute to how machine-learning models make decisions. In particular, models designed to differentiate images of, say, cats and dogs, make decisions based on features that do not align with how humans make classifications. Simply changing these features can make the model consistently misclassify cats as dogs, without changing anything in the image thats really meaningful to humans.

Results indicated some models which may be used to, say, identify abnormalities in medical images or help autonomous cars identify objects in the road arent exactly up to snuff. People often think these models are superhuman, but they didnt actually solve the classification problem we intend them to solve, Madry says. And their complete vulnerability to adversarial examples was a manifestation of that fact. That was an eye-opening finding.

Thats why Madry seeks to make machine-learning models more interpretable to humans. New models hes developed show how much certain pixels in images the system is trained on can influence the systems predictions. Researchers can then tweak the models to focus on pixels clusters more closely correlated with identifiable features such as detecting an animals snout, ears, and tail. In the end, that will help make the models more humanlike or superhumanlike in their decisions. To further this work, Madry and his colleagues recently founded the MIT Center for Deployable Machine Learning, a collaborative research effort working toward building machine-learning tools ready for real-world deployment.

We want machine learning not just as a toy, but as something you can use in, say, an autonomous car, or health care. Right now, we dont understand enough to have sufficient confidence in it for those critical applications, Madry says.

Shaping education and policy

Madry views artificial intelligence and decision making (AI+D is one of the three new academic units in the Department of Electrical Engineering and Computer Science) as the interface of computing thats going to have the biggest impact on society.

In that regard, he makes sure to expose his students to the human aspect of computing. In part, that means considering consequences of what theyre building. Often, he says, students will be overly ambitious in creating new technologies, but they havent thought through potential ramifications on individuals and society. Building something cool isnt a good enough reason to build something, Madry says. Its about thinking about not if we can build something, but if we should build something.

Madry has also been engaging in conversations about laws and policies to help regulate machine learning. A point of these discussions, he says, is to better understand the costs and benefits of unleashing machine-learning technologies on society.

Sometimes we overestimate the power of machine learning, thinking it will be our salvation. Sometimes we underestimate the cost it may have on society, Madry says. To do machine learning right, theres still a lot still left to figure out.

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Doing machine learning the right way - MIT News

What would machine learning look like if you mixed in DevOps? Wonder no more, we lift the lid on MLOps – The Register

Achieving production-level governance with machine-learning projects currently presents unique challenges. A new space of tools and practices is emerging under the name MLOps. The space is analogous to DevOps but tailored to the practices and workflows of machine learning.

Machine learning models make predictions for new data based on the data they have been trained on. Managing this data in a way that can be safely used in live environments is challenging, and one of the key reasons why 80 per cent of data science projects never make it to production an estimate from Gartner.

It is essential that the data is clean, correct, and safe to use without any privacy or bias issues. Real-world data can also continuously change, so inputs and predictions have to be monitored for any shifts that may be problematic for the model. These are complex challenges that are distinct from those found in traditional DevOps.

DevOps practices are centred on the build and release process and continuous integration. Traditional development builds are packages of executable artifacts compiled from source code. Non-code supporting data in these builds tends to be limited to relatively small static config files. In essence, traditional DevOps is geared to building programs consisting of sets of explicitly defined rules that give specific outputs in response to specific inputs.

In contrast, machine-learning models make predictions by indirectly capturing patterns from data, not by formulating all the rules. A characteristic machine-learning problem involves making new predictions based on known data, such as predicting the price of a house using known house prices and details such as the number of bedrooms, square footage, and location. Machine-learning builds run a pipeline that extracts patterns from data and creates a weighted machine-learning model artifact. This makes these builds far more complex and the whole data science workflow more experimental. As a result, a key part of the MLOps challenge is supporting multi-step machine learning model builds that involve large data volumes and varying parameters.

To run projects safely in live environments, we need to be able to monitor for problem situations and see how to fix things when they go wrong. There are pretty standard DevOps practices for how to record code builds in order to go back to old versions. But MLOps does not yet have standardisation on how to record and go back to the data that was used to train a version of a model.

There are also special MLOps challenges to face in the live environment. There are largely agreed DevOps approaches for monitoring for error codes or an increase in latency. But its a different challenge to monitor for bad predictions. You may not have any direct way of knowing whether a prediction is good, and may have to instead monitor indirect signals such as customer behaviour (conversions, rate of customers leaving the site, any feedback submitted). It can also be hard to know in advance how well your training data represents your live data. For example, it might match well at a general level but there could be specific kinds of exceptions. This risk can be mitigated with careful monitoring and cautious management of the rollout of new versions.

The effort involved in solving MLOps challenges can be reduced by leveraging a platform and applying it to the particular case. Many organisations face a choice of whether to use an off-the-shelf machine-learning platform or try to put an in-house platform together themselves by assembling open-source components.

Some machine-learning platforms are part of a cloud providers offering, such as AWS SageMaker or AzureML. This may or may not appeal, depending on the cloud strategy of the organisation. Other platforms are not cloud-specific and instead offer self-install or a custom hosted solution (eg, Databricks MLflow).

Instead of choosing a platform, organisations can instead choose to assemble their own. This may be a preferred route when requirements are too niche to fit a current platform, such as needing integrations to other in-house systems or if data has to be stored in a particular location or format. Choosing to assemble an in-house platform requires learning to navigate the ML tool landscape. This landscape is complex with different tools specialising in different niches and in some cases there are competing tools approaching similar problems in different ways (see the Linux Foundations LF AI project for a visualization or categorised lists from the Institute for Ethical AI).

The Linux Foundations diagram of MLOps tools ... Click for full detail

For organisations using Kubernetes, the kubeflow project presents an interesting option as it aims to curate a set of open-source tools and make them work well together on kubernetes. The project is led by Google, and top contributors (as listed by IBM) include IBM, Cisco, Caicloud, Amazon, and Microsoft, as well as ML tooling provider Seldon, Chinese tech giant NetEase, Japanese tech conglomerate NTT, and hardware giant Intel.

Challenges around reproducibility and monitoring of machine learning systems are governance problems. They need to be addressed in order to be confident that a production system can be maintained and that any challenges from auditors or customers can be answered. For many projects these are not the only challenges as customers might reasonably expect to be able to ask why a prediction concerning them was made. In some cases this may also be a legal requirement as the European Unions General Data Protection Regulation states that a "data subject" has a right to "meaningful information about the logic involved" in any automated decision that relates to them.

Explainability is a data science problem in itself. Modelling techniques can be divided into black-box and white-box, depending on whether the method can naturally be inspected to provide insight into the reasons for particular predictions. With black-box models, such as proprietary neural networks, the options for interpreting results are more restricted and more difficult to use than the options for interpreting a white-box linear model. In highly regulated industries, it can be impossible for AI projects to move forward without supporting explainability. For example, medical diagnosis systems may need to be highly interpretable so that they can be investigated when things go wrong or so that the model can aid a human doctor. This can mean that projects are restricted to working with models that admit of acceptable interpretability. Making black-box models more interpretable is a fast-growth area, with new techniques rapidly becoming available.

The MLOps scene is evolving as machine-learning becomes more widely adopted, and we learn more about what counts as best practice for different use cases. Different organisations have different machine learning use cases and therefore differing needs. As the field evolves well likely see greater standardisation, and even the more challenging use cases will become better supported.

Ryan Dawson is a core member of the Seldon open-source team, providing tooling for machine-learning deployments to Kubernetes. He has spent 10 years working in the Java development scene in London across a variety of industries.

Bringing DevOps principles to machine learning throws up some unique challenges, not least very different workflows and artifacts. Ryan will dive into this topic in May at Continuous Lifecycle London 2020 a conference organized by The Register's mothership, Situation Publishing.

You can find out more, and book tickets, right here.

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What would machine learning look like if you mixed in DevOps? Wonder no more, we lift the lid on MLOps - The Register