Artificial Intelligence In Transportation Market Outlook 2020-2025 Forecast Research, Applications, Key Developments, Competitive Landscape &…

Global Artificial Intelligence In Transportation Market report shows the Industry Chain Structure as well as Macroeconomic Environment Analysis and Development Trend. The Artificial Intelligence In Transportation Market report also provides the market impact and new opportunities created due to the COVID19/CORONA Virus catastrophe. The total market is further divided by company, by country, and by application/types for the competitive landscape analysis. The report then estimates 2020-2025 market development trends of Artificial Intelligence In Transportation Industry.

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Continental AG, NVIDIA Corporation, Intel Corporation, Microsoft Corporation, Alphabet Inc., ZF Friedrichshafen AG, Robert Bosch GmbH, Valeo SA, and more others.

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Initially, the report provides a basic overview of the industry including definitions, classifications, applications, and industry chain structure. The Artificial Intelligence In Transportation market analysis is provided for the international markets including development trends, competitive landscape analysis, and key regions development status.

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By Application, (Autonomous Trucks,HMI in Trucks,Semi-Autonomous Truck)

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Cloud Encryption Market: Current Status, In-depth Analysis and Forecast Outlook 2025 By IBM Corporation, Trend Micro Inc, Dell Technologies Inc,…

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Here we go again: CJEU issues its judgment in Schrems II – Lexology

The CJEU has released its judgment in the long running Max Schrems/Facebook Ireland story. We explain what the case is about and why it's important.

Background

To recap (and as set out in our briefing following the Attorney General's preliminary opinion at the end of last year), Max Schrems objected to the transfer by Facebook of his personal data from the EU to the US, following revelations made by one Mr Edward Snowden about access by US surveillance authorities to personal data. The initial case resulted in the downfall of the US "Safe Harbor" regime, which had been put in place as a mechanism for allowing personal data to be transferred from the EEA to the US in a way which complied with EU data protection laws requiring destination countries outside the EEA to keep the data safe and to the same standards as set out in the EEA. Today's decision of the CJEU, is important because it examined the ongoing validity of two important mechanisms, mandated by the General Data Protection Regulation 2016 (GDPR), for transferring personal data outside the EEA in such as a way as to maintain the safety of that data in the destination country:

KEY TAKEAWAYS FROM THE LATEST SCHREMS JUDGMENT

Impact on business

Whilst the judgment is going to be a big headache for those businesses, many of which are in the tech sector, which did rely on the EU-US Privacy Shield to govern their data transfers, standard contractual clauses can still, for now, provide a fall back for them, as they did in the immediate aftermath following the initial decision about the US "Safe Harbor" regime and, out of a range of transfer mechanisms which GDPR provides, such as BCRs (binding corporate rules), and specific derogations such as consent, they are the still often the most practical tool available for large scale, ongoing data transfers. In addition, many contracts will already deal with this issue (i.e. by stating that the parties will put in place an approved mechanism in the event that another falls away). For those businesses which haven't used them, and relied instead on the Privacy Shield, additional papering and due diligence (see below) will be required, but at least standard contractual clauses do provide another possible option.

Standard contractual clauses: some points to note

Although the court's ruling on standard contractual clauses is likely to be welcomed by many businesses, the following points are worth noting:

IT'S NOT JUST ABOUT PAPERWORK

The CJEU's observations about standard contractual clauses were similar to the Advocate General's: businesses shouldn't just assume that once you've signed the paperwork the job is done; the parties need to do their due diligence to ensure that any data being transferred will in fact be kept safe by the data importer, and the clauses themselves do impose obligations on the data importer to put in place technical and organisational measures to keep the data safe and to verify and inform the data exporter if there are any local laws which might compromise the safety of the data, so that the transfer can then be suspended. The judgment also highlighted the role which supervisory authorities have to play too in stepping in to suspend or prohibit data transfers where they take a view that standard contractual clauses cannot be complied with in a particular country, and that the protection of the data cannot be ensured by other means. In a sense, none of this is new much of the judgment and the conclusions on why standard contractual clauses are still a valid mechanism, was based on wording contained in the clauses which has always been there. However, the judgment does shine a spotlight on the fact that there needs to be a genuine assessment by the parties in any given situation of all the risks associated with the transfer of personal data to a third country, taking into consideration the nature of the data that is being transferred, the volume, and how it will be used by the data importer in the third country.

THE BREXIT DIMENSION

The CJEU's decision on standard contractual clauses will be very welcome to UK businesses which rely on data flows from the EEA, if the UK is unable to persuade the EU to grant it an adequacy decision, as part of a trade deal or otherwise, before the expiry of the transition period at the end of the year. In this situation the main mechanism which EEA businesses will rely on to transfer personal data to the UK, will be standard contractual clauses and we recommend that you make contingency plans for that eventuality now. Readour guidance on preparing for Brexit. Having said this, the ruling does potentially have wider political ramifications, in terms of the UK's position post transition vis--vis data flows to the United States, and in terms of any assessment of the adequacy of the UK's own data protection regime, and the way these ramifications play out remains to be seen.

What next for EU-US data transfers?

There is a now a job to be done on what precisely will replace the EU-US Privacy Shield, and EU Justice Chief, Didier Reynders has already said that the EU will look at ways to boost data transfers to the US. How quickly and effectively that can be achieved in the current climate, remains to be seen. The CJEU's comments on how standard contractual clauses work to protect personal data, coupled with its conclusion about the US data protection regime (and the resulting decision to invalidate the Privacy Shield), mean that whilst they are likely to be the best (and in many cases the only) alternative mechanism for businesses to work with if they want to transfer data, their use in respect of transfers to the US, is not without risk though for the reasons set out above, it could be said that this has always been the case. The European Commission's recent Evaluation Report into GDPR implementation, did highlight the fact that standard contractual clauses are being modernised to cover all relevant transfer scenarios and better reflect modern business practices, however, we do not as yet have an indication of when a new version will become available for use, and in any event, it is difficult to see, given the CJEU's comments, how the clauses can be reconciled to apply in some situations where the data protection regime of a third country simply does not come anywhere near the standards set by the EU for protection of personal data. Meanwhile, we await the reaction of supervisory authorities. We will continue to monitor any guidance which is issued by such authorities over the coming weeks,as to whether the use of standard contractual clauses in respect of data transfers to the US, or indeed, any other jurisdiction, is problematic.

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Here we go again: CJEU issues its judgment in Schrems II - Lexology

Chinese TV Regulators Appear to Increase Story Supervision – Variety

Gay stories, excessive romance, unscientific fantasy, and narratives that glorify the pre-Communist republican era are reportedly among 20 genres of fiction to be banned or subject to further censorship in China, according to what appears to be new measures emanating from Chinese authorities.

Photos of what appears to be a new directive issued by the National Radio and Television Administration have been widely circulated on Chinese social media platforms Weibo and WeChat over the past few days. However, social media posts containing similar photos from last year are also to be found on the internet. The date and authenticity of the new directives cannot be independently verified.

The directives appear to provide clear instructions on what can and cannot be shown in 20 genres of TV and web drama show. Youth drama, for example, should avoid puppy love, crime, and violence. Romantic dramas should avoid intimacy, but be bold about clashes and conflicts. Fantasy stories will be subject to particular attention and must be told from a scientific perspective.

Time travel stories must be explained with scientific theories and characters involved must be positive and cannot change the course of history. Gay stories are banned and should be replaced with friendship among characters of the same sex.

Dramas that glorify the republican government or warlords in the republican era are to be strictly censored. And crime thrillers will be subject to censorship by the police department. Plots must not reveal how the crimes are solved, though analysis of criminal psychology is allowed. Criminals must be punished. Police cannot be portrayed negatively.

Social media posts containing these pictures have sparked heated discussions online in China. Puppy love, violence and crime in youth dramas have already been banned, but does it mean they do not exist in reality?, one post on WeChat questioned. If the directives are real, then there are no stories left to tell in Chinas film and television.

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Chinese TV Regulators Appear to Increase Story Supervision - Variety

Cancel culture, George Orwell and reasoned debate – The Guardian

Thank you to Billy Bragg (Cancel culture doesnt stifle debate, but it does challenge the old order, 10 July) for a thought-provoking article and for drawing attention to the statue of George Orwell outside the BBC in London. Mr Bragg says that the quotation on the wall next to the statue If liberty means anything at all, it means the right to tell people what they do not want to hear is a demand for licence, not a defence of liberty, and argues that liberty must be tempered by equality and accountability.

There is no doubt that very many tweet-friendly quotations are taken from Orwells works and used out of context by people from all parts of the political spectrum. However, the statue quotation remains a powerful statement against censorship. The essay it is taken from was titled The Freedom of the Press. As Mr Bragg says, it was written as a preface to Animal Farm. In fact, it was not used at the time and was only published long after Orwells death, in 1972.

Orwell argued for equality and democracy (accountability was not a term much used at the time) to go hand in hand with the liberty he defended. We are delighted that Orwell is the English writer that Mr Bragg admires the most and that he continues to engage in the reasoned debate for which Orwell is renowned.Quentin KoppChair, The Orwell Society

Re Nesrine Maliks piece (The cancel culture war is really about old elites losing power in the social media age, 13 July), what is at issue here is not the right of people to attack the opinions of others on social media, but the tendency to overreaction when someone expresses an opinion that is at variance with that of self-defining justice warriors.

Opinions that are lawfully expressed may well deserve robust criticism; what they do not deserve is for the person expressing them to be no-platformed, hounded out of a position of influence or traduced as some sort of fascist. Years ago, that sort of behaviour was confined to the wilder fringes of the Socialist Workers party. Now it seems to be all too common among people who should know better. Roger Fisken Ashampstead, Berkshire

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Cancel culture, George Orwell and reasoned debate - The Guardian

The Falcon and the Winter Soldier is officially delayed on Disney+ – Winter is Coming

Image: Disney/The Falcon and the Winter Soldier

Disney has released its schedule of new movies and TV shows coming to Disney+ this August. Theres some good stuff on there, including the originalX-Men movie,Ant-Man and the Wasp and the 2017 remake ofBeauty and the Beast. But whats nowhere to be seen isThe Falcon and the Winter Soldier, the first of the studios high-profile Marvel Cinematic Universe shows.

And thats not too surprising. Production on the show, which follows Sam Wilson (Anthony Mackie) and Bucky Barnes (Sebastian Stan) in the wake of the events ofAvengers: Endgamehad to shut down due to the coronavirus. Although it started back up eventually, apparently it wasnt in time to get the series out in time for August.

Disney is working on other Marvel shows, likeWandaVisionandLoki. The studio was also teasing that the former could be out this year, but with this news, you wonder if it too wont be pushed back. We were always gonna get to a point where the coronavirus started delaying new shows, and it looks like that point has arrived.

As long asthe second season ofThe Mandaloriancomes out this year, Ill be happy; so far as I can tell, most of all of that was shot before the pandemic started shutting everything down, so it should hopefully be alright. Its scheduled for October, but Id take November. Theres no new release date forThe Falcon and the Winter Soldierset as of yet.

As long as were talking about Marvels misfortunes, did you know that US attorney general William Barr called them out in a speech yesterday for censoring their movies in order to appease China, which is the worlds second-largest film market? Hollywood now regularly censors its own movies to appease the Chinese Communist Party, the worlds most powerful violator of human rights, he said. This censorship infects not only versions of movies that are released in China, but also many that are shown in American theaters to American audiences.

As an example, Barr pointed to Disneys decision to rethink the character of the Ancient One, played by Tilda Swinton inDoctor StrangeandAvengers: Endgame. In the comics, this character is Tibetan, but because Tibet is a hot-button issue for China, she was made Celtic for the movie. TheDoctor Strangewriter admittedthis was done to avoid angering China, since if they do, its possible the country wont allow the film into Chinese theaters.

Its no secret that China is a huge deal for movie studios, and its true that lots of them clip and edit their films for that market, or even alter them at the outset likeDoctor Strange. Chinese government censors dont need to say a word, because Hollywood is doing their work for them, Barr claimed. Whether the US government wants to crack down on that sort of thing remains to be seen.

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If your data science rollout is failing, this may be why – VentureBeat

Ive seen some advanced analytics transformations succeed and many others fail. Its well known that most of these transformations end in failure, wasting huge amounts of time and money. And what is worse, these failures typically sour the organization on data science and machine learning for the future. So failures in advanced analytics transformations cause longer-term damage than is typically appreciated.

I was recently challenged by a colleague of mine to write down what I thought our business would look like in five years if our own analytics transformation was successful. A few items I wrote were typical: wed have the ability to deploy machine learning models into production quickly; wed have data science embedded across the org chart, and so on. But what I quickly realized was that although all of those goals are good ones, the real difference Id like to see in five years is not technical, but cultural. We should be aiming first and foremost at cultural change. I now believe that this is the right way to think about how to add data science and machine learning to your business.

Think about how typical business decisions are made. In a well-run business, experts are consulted and possible solutions to business problems are debated in an open and collaborative way. In those discussions, there is a lot of conventional wisdom and many assumptions about potential risks and rewards. Decision makers adopt a measure twice, cut once approach that is risk-averse and intended to commit to a reasonable course of action over the long run.

In contrast, think about how science is done. I happen to have worked alongside some excellent researchers in my academic career at universities and in the national laboratory system. An effective scientist, in my experience, prioritizes generating and testing hypotheses over commitment to a solution. The most brilliant researchers dont adopt a measure twice, cut once mentality. Instead, they generate many hypotheses and they focus on testing them rapidly. If this sounds like a startup mentality, thats because it is. The best methodologies in the tech startup world, in my opinion, are successful because theyre scientific.

Lets consider an example. Suppose your business wants to do a better job reaching high-value customers. A common, but unscientific, approach is to bring in a team of consultants who will develop personas of your customers over the course of weeks or months. Some of those personas will be of more desirable customers. The assumptions built into the desirable personas are used to develop marketing campaigns. It is understood that the company is committing over the long term to using those personas to segment its customers and measure performance.

If this story seems natural, and you can easily imagine it happening in your business, then you dont have a scientific culture. No scientist worth their salt would be willing to commit to a theory in such a manner. A scientific approach would be to come up with lots of hypotheses and a wide variety of potential personas. To a good scientist, those hypotheses could come from anywhere. They could be conventional wisdom, or they could have occurred to someone in a fever dream. They might be the result of some exploratory analysis, or not. But they are all provisional and tentative. All the hypotheses would be parked somewhere until theyve been tested. Discussion quickly turns from generating hypotheses to the more important question of how to test them.

Of course, this is a simplistic view of what a scientific culture looks like. But the main elements are there, the most important of which is a focus on generating and testing hypotheses.

When my colleague asked me to describe what our organization would look like after our analytics transformation, this sort of scientific culture is what I described. I will judge our transformation as successful if the default way to make decisions is scientific. And most importantly, this is not limited to the technical workers. Well only be successful if this mentality is adopted across the entire org chart.

All the technical and organizational goals of an analytics transformation fall out of this overarching goal. For example, its unfair to ask your marketing team to generate and test hypotheses if they dont have access to data and the ability to rapidly roll out marketing campaigns. You cant ask your designers to be scientific if you dont provide the ability to A-B test their designs.

Adopting a scientific culture entails all the technical and organizational changes that were used to hearing about, and it places those changes into context, making them understandable. For example, its commonly suggested that an organization should integrate its data science team with the other teams across the business. This is perfectly true, but why should we do this?

A common but inadequate answer is that the data scientists need to understand the business context of their work, and the other people need to be able to take advantage of data science expertise. This is true, so far as it goes. But it misses the real point. You wont get any benefits if the team doesnt adopt a scientific mindset. If they dont change their methods to emphasize the quick generation and testing of hypotheses, the business will not enjoy the benefits that data science has to offer. With this in mind, theres a new reason for integrating data science into other teams: to infect teams with the data-driven, scientific mindset that you (hopefully) have within the data science team.

When we think about the goal of creating a scientific culture, we can see some of the more subtle changes that have to occur. The most important of these changes, in my opinion, is that incentives have to be realigned. Returning to our previous example, if were developing customer personas, we have to reward people for testing their ideas and gathering data. Theyll be doing a good job if they incrementally improve these personas over time based on measurements. Contrast that with the usual way of doing things: People would be rewarded for rolling out marketing campaigns based on polished and detailed descriptions of customer personas a distinctly unscientific approach.

Most analytics transformations fail because a new data science team is simply bolted onto the organization without being given the support it needs. In other cases, the data science team gets the right level of technical and non-technical support, but the effort still fails because of a lack of understanding of the business. But the transformation can also fail even when it seems like the business has made all the changes necessary for success.

In my experience, this latter kind of failure is especially mysterious and frustrating for everyone. The failure seems inexplicable because it feels like the business did everything right. But thinking about your analytics transformation in terms of effecting a cultural change helps put into focus the rest of the changes that the business is undergoing. All the rest, including organizational, technological, and other changes should be understood as means toward the end of creating a more scientific culture.

Zac Ernst is Head of Data Science at insurance tech startup Clearcover.

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If your data science rollout is failing, this may be why - VentureBeat

Why the Twitter Hack Was Good for Bitcoin (and Its Not the Media Attention) – CoinDesk – CoinDesk

Every year has a handful of days that youll never forget. Sometimes for great reasons, sometimes for awful ones, and sometimes because a level of noise and action coalesces into an awareness that something big has shifted.

Wednesday was one of those days, with the staccato of compromised Twitter accounts (including ours) escalating to reach prominent public figures including current and former heads of state. The scale of the hack was spectacular.

Youre readingCrypto Long & Short, a newsletter that looks closely at the forces driving cryptocurrency markets. Authored by CoinDesks head of research, Noelle Acheson, it goes out every Sunday and offers a recap of the week with insights and analysis from a professional investors point of view.You can subscribe here.

The mainstream press called this a bitcoin scam, and to some extent it was the hacker set up the typical ploy of promising to send back double whatever amount of bitcoin anyone sent to a certain wallet. Its amazing that people fall for this.

But some people do a total of $123,000 worth of BTC was sent in approximately 400 transactions in total (some may have been the hacker recycling coins to inflate the activity). 17 transactions sent more than $1,000. Glossing over the fact that this is an astonishingly small amount for the scale of the hack, some skeptics took the opportunity to remind everyone how bitcoin was a scammers paradise.

Close the door

Some commentators went as far as to call for the banning of bitcoin. If bitcoin wereillegal, goes the reasoning, this wouldnt happen. Of course, this brought out the defenders by the droves, who pointed out among other compelling arguments that making something illegal doesnt stop it from happening; it often just makes it harder to monitor. And banning bitcoin would not stop its use nor eliminate its value.

But it did highlight a pervasive concern among many mainstream investors: a lack of regulatory clarity. Could the U.S. decide to outlaw bitcoin transactions within its jurisdiction? The very possibility is understandably enough to keep cautious investors away.

Technically, the U.S. could not ban bitcoin on a global scale bitcoin lives on a distributed network that would continue to exist even if U.S.-based nodes shut off and U.S.-based users dropped out. One of the strengths of bitcoin is that it is out of the range of state actors.

But, realistically, making the holding or transacting of bitcoin illegal for U.S.-based entities and individuals would be a big shock to the price as its store of value narrative would take a significant hit.

Whats more, the U.S. has considerable influence over the FATF, which sets anti-money laundering and anti-terrorist financing systems for the worlds banks and payments companies. The organization could be pressured to penalize governments that allow cryptocurrency services within their jurisdiction.Yet all of these concerns seem unfounded.Last week, the FATF announced its intention to step up crypto asset supervision with a view to building a global framework, which implies an interest in monitoring rather than stopping.

And in the wake of the Twitter hack, the talk coming out of Washington is not about bitcoin. The concern is the centralization of platforms. Twitter is under scrutiny much more than bitcoin.

If regulators were going to jump on the ban-bitcoin bandwagon, given the media frenzy, now would be the time. That they have notdone so is a strong sign of acceptance. True, there may yet be hiccups ahead in the road to systemic support but so far, the concern is more about the vulnerabilities in centralized services.

Furthermore, the amount of bitcoin involved in the scam is minusculecompared to what the take couldhave been, given the scale of the operation. Maybe the public is becoming more scam-savvy? And we should all be grateful that the hackers only wanted bitcoin, when you consider that they had control of the Twitter accounts of the likes of Elon Musk, Joe Biden, Benjamin Netanyahu, Barack Obama, Apple

The lack of focus on bitcoin in Washington this weekis a step forward,especially in the eyes of professional investors eager for greater regulatory clarity. If indeed bitcoin does weather this without louder calls for a clampdown, that is a strong sign that regulators acknowledge that bitcoin is here to stay.

I see you

Another way in which the Twitter hack was positive for bitcoin is the spotlight shone on the forensic transparency of the network.

Within hours of the hack, blockchain analysts were already constructing profiles of the hackers history and tracking the movements of the ill-gotten funds.

The wallets in question may not have a name and address associated with them, but they are indelibly there for anyone to monitor, and transactions into and out of these wallets cannot be hidden nor undone. Digital fiat money transfers may have associated names and addresses, but movements are easier to obscure. And names and addresses can be faked.

Whats more, the fact that anyone can access this data spreads the potential for useful information coming to light. While there may initially be different interpretations of the addresses and transfers, a consensus interpretation tends to emerge, which is likely to be a help to law enforcement. And forensic techniques are advancing, as is the diversity of approaches to blockchain data analysis. This should reassure regulators that bitcoin-related crime is not the threat to society some skeptics claim.

The bigger question

It is true that having bitcoin in mainstream headlines is good for its brand recognition but, in this case, the association with scams is not in its favor. Yet while politicians do pay attention to what the media is saying, by next week the bitcoin scam headlines will have faded into the pixels of time, giventhe frenetic news cycle we live in.

And the market itself does not seem worried the bitcoin price barely budged in the aftermath of the news.

The lasting effect will be a deeper understanding for those willing to ask the right questions, not just about bitcoins seizure resistance but also about how one weak access point left so much power in a bad actors hands.

That is a third beneficial outcome that should strengthen interest in blockchain applications beyond crypto assets. Growing concern over centralized vulnerabilities in communication platforms is just the beginning. From there to worrying about vulnerabilities in the centralized financial systems on which our society runs is not that big a step.

Anyone know what's going on yet?

This week exemplified the seesaw of good news swiftly followed by dampeners. Better-than-expectedearnings were offset by forecast downgrades, which seem to be spooking investors. Vaccine optimism, buoyed by positive results from a few laboratories, yet again got tempered by vaccine realism, as even really good candidates could take years before they become widely available. And when it comes to the evolution of the number of COVID-19 cases, positive news in some areas was offset by devastating news in others.

The S&P 500 is very close to having clawed back its losses for the year-to-date, and by the time you read this, might well have done so. It is 7% higher than this time last year. Ive given up wondering what economic outlook it is discounting.

Bitcoin has had a lackluster performance so far this month, yet it continues to outperform other major indices and assets on a year-to-date basis. Its lack of volatility has traders champing at the bit, however, and the emotional tension of waiting for a breakout, any breakout, could soon start to impact trading patterns.

CHAIN LINKS

Crypto asset fund manager Grayscale Investments* released its Q2 report, which revealed new investment of over $900 million over the quarter, its largest quarterly inflow to date, and 80% more than the previous quarterly high in Q1. TAKEAWAY: While the BTC price has been stagnant of late, it appreciated over 40% in Q2, largely as part of a broader market recovery from post-crash lows, but also perhaps partially because of growing institutional support. We dont know, however, how much of the inflow is new investment and how much is recycled as qualified investors sell their holding in the market at a premium and buy back in at par. (*Grayscale is owned by DCG, also the parent of CoinDesk.)

Lex Sokolin, the CMO and Global Fintech Co-Head at Ethereum laboratory ConsenSys, published an analysis of the rumored upcoming listing of crypto exchange Coinbase. TAKEAWAY: The lack of available data at the current time is one major obstacle for analysts trying to get a feel for what a listing valuation could be, but Lex does an admirable job of scraping information from public sources. Yet even if/when listing documents are filed and more numbers become available, analysts will still have a hard time figuring this one out: what exactly isCoinbase? Is it an exchange? A bank? A custodian?

Crypto data provider Coin Metrics has published areport on stablecoins that looks into their explosive growth: It took supply five years to reach $6 billion in March 2012, and only four months since then to double that. TAKEAWAY: The report takes a close look at pegs not everyone realizes that dollar stablecoins are not always worth $1, and that the difference can exert a material influence on supply as issuers arbitrage profit opportunities.

Crypto fund manager Arca reviewed its 2020 predictions from January, and updated them for the rest of the year. TAKEAWAY: The ones I found particularly interesting included the growth in non-crypto companies issuing crypto tokens, the rise of non-fungible tokens as an asset group and the growing influence of younger generations (I wrote more about this here).

Bitcoin miners sent less bitcoin to exchanges during the second quarter of 2020 than at any time over the past 12 months. TAKEAWAY: This can be taken as bullish (miners are choosing to hold onto their mined bitcoin because they believe the price will go up) or bearish (they would rather not sell into what they think will be a weak market). Either way, we should remember that newly mined bitcoins now account for a very small fraction of trading volume, so the influence of miners decisions is mitigated. Their actions are worth watching, however, as most have close relationships with OTC desks that move high volumes and have their ear to the ground.

The number of addresses holding a large number of bitcoins, known as whale addresses, has declined to a 14-month low. TAKEAWAY: As with the miner flows metric above, this can also be either bearish or bullish. Its not positive news for the asset price outlook to see large holders reduce their stakes; but a broader distribution of ownership is better for price resilience.

U.S.-based digital asset firm BitOoda published a report, together with the Fidelity Center for Applied Technology, that shows 50% of bitcoin mining is in China, and 14% in the U.S. TAKEAWAY: Earlier estimates had put Chinas market share at 65%, so if these figures are accurate, the bitcoin mining industry is becoming more decentralized and less dependent on China.

Crypto financial app Abra has settled charges from the SEC and the CFTC relating to its offering of synthetic swaps to retail investors without registering or selling them on a recognized national exchange. Abra and its Philippines-based partner company Plutus will pay $300,000 in fines and do not have to acknowledge the accusations. TAKEAWAY: This is the long arm of the law in action. Abra limited its offering to non-U.S. investors, and moved most of its operations overseas. But the regulators determined that having an office in San Francisco from which the contracts were marketed and hedged, serving a handful of U.S. retail investors that got through the geofencing, and having marketed to retail investors in the early days of the contract, put Abra in violation of securities laws. In other words, it doesnt matter where your main base is if your activity touches U.S. citizens and/or U.S. soil, you fall under U.S. jurisdiction.

On a recent panel, Linda Lacewell, superintendent of the New York Department of Financial Services (NYDFS),said that the recent changes to the BitLicense law were being well received. TAKEAWAY: The original BitLicense, which emerged just over five years ago as a requirement for any crypto business wanting to operate in the state of New York, received significant criticism for its onerous application obligations and the high cost of compliance. Lacewell introduced some reforms to the regulation that aimed to lower the barriers and encourage more experimentation. Its not surprising they are being well received, but it is good news to get the confirmation. Many crypto businesses chose to not do business in New York as a result of the original design, and the update does not mean they will come back. But Wall Street is one of the greatest financial centers in the world, and if crypto is going to run with the big boys, a presence at the heart of finance will be a step forward in the push to position crypto assets as a respectable investment for institutional portfolios.

Bitcoin Core contributor Jeremy Rubin has revealed his work on a new smart-contract language for Bitcoin called Sapio, which he hopes will increase the financial self-sovereignty of users. TAKEAWAY: Its worth keeping an eye on technological developments even in assets that, for many, are based on the store-of-value narrative. Enhanced smart contract ability will not only potentially lend application functionalities to Bitcoin, giving it a residual value and further likening it to gold (which, as well as an investment asset, is used in jewelry, technology, dentistry, etc.); it could also make it easier and/or safer to custody and exchange.

BitGo will offer API support for the latest Travel Rule guidelines from the FATF that stipulate originators and beneficiaries of financial transactions over $1,000 be identified. TAKEAWAY: This was always going to be a difficult proposition with crypto assets, since identification of both ends of a transaction isoften not possible, and goes against the integral idea that transfers can be decentralized and independent of a third party. The FATFs reach is long, however, and non-compliance is likely to be costly for jurisdictions that cannot control crypto activity within their boundaries. Technological aids like BitGos API, provided by a firm with a long history of custodial services, are likely to calm fears of both regulators and clients. Plus, BitGos origin is as a custody technology provider in 2013, it launched the first multi-sig wallet, a staple of custody technology today.

Also, Shyft Network this week announced that it is releasingits blockchain-based solution to help crypto companies comply with the FATFrequirements. TAKEAWAY:Tools like this are trying to get ahead of what is going to be a significant problem: the security vulnerabilities inherent in the FATFs requirement to send sensitive financial information back and forth.

The crypto options marketis growing fast, both in volumes and in number of platforms. Gate.io, a relatively small offshore exchange, has launched a new options trading feature, and Singapore-based exchange Huobi, which already offers futures and perpetual swaps, plans to do so later this year. TAKEAWAY: Growth in options is a sign of a maturing market, and a necessary step for greater institutional involvement. How long this growth will continue is an open question, especially given the declining volumes in crypto spot and futures markets.

Switzerland-based crypto custodian Metaco has closed a Series A round that was reportedly oversubscribed by a factor of two. Investors include Standard Chartered, smart-card and currency note printer Giesecke+Devrient, Zrcher Kantonalbank (the fourth largest bank in Switzerland), and the countrys postal service Swiss Post. TAKEAWAY: That this was reportedly oversubscribed is a sign of growing interest in Europe in digital asset market infrastructure. Also, the mix and profile of the investors is intriguing.

Podcast episodes worth listening to:

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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Why the Twitter Hack Was Good for Bitcoin (and Its Not the Media Attention) - CoinDesk - CoinDesk

AI Fueling The Oil And Gas Industry: Interview With Tim Custer At Apache – Forbes

In industries where data is key to gaining competitive advantage, artificial intelligence and machine learning have become necessities. This is most definitely the case in the oil and gas industries that ebb and flow over time as market demand waxes and wanes for critical resources weve come to depend on.

In a recent AI Today podcast episode, Tim Custer, Senior Vice president

Tim Custer, Apache

of North America land, business development and real estate with Apache, a major energy firm, shares how AI is impacting the way the energy business operates.. After taking the role of land manager for the past ten years, Custer has shared how tied to real estate and traditional non-energy businesses the oil and gas sector is, and the role that machine learning and AI is playing to greatly change the way that the energy industry deals with documents.

According to Custer, AI and machine learning are extracting valuable data from unstructured data. The oil and gas industry is particularly dependent on an intricate set of processes and document-centric needs for land leases. Gas

leases are vital to the energy industry as they determine legal rights and claims to an oil or gas deposit while regulating the trade and extraction of those resources. At Apache, Custer notes they have around 60,000 paper and document-centric leases which can vary in length from just two pages to over fifty. Moreover, there are provisions contained on each page that must be located and interpreted every time an inquiry is made on a lease. This task can prove quite laborious with the added task of finding the correct hardcopy lease to begin with.

The first step to wrangling control of these leases is to digitize the documents so that machines can understand them. Apache has succeeded in digitizing the majority of their gas leases using Optical Character Recognition (OCR) and natural language processing (NLP). They are capable of searching through these documents for not only the required lease but the provision within it in a matter of seconds. This not only speeds up searching processes at Apache but is also time-saving for teams in need of specific provisions for their projects. Custer continues by describing the optimization of the process as grouping provisions with like wording across vast amounts of data. These digitization and NLP systems ensure higher data integrity by increasing accuracy and removing human interpretation.

One curious particularity of these leases is that they are often old, with the documents done in handwriting, usually in dated calligraphic and handwritten styles. Some of the later documents were hand type-written. As such there is a lot of variability of legibility, fonts, spacing, and overall document quality. Apache has applied machine learning to complete data organizing and searching processes more effectively and quickly than otherwise would be possible with humans having to read and process each document. Custer notes that there are a variety of document insights that must be considered such as letters, correspondence, or internal memos that are attached to the gas lease itself. The AI-enabled systems allow for significantly improved organization of this additional information due to its ability to classify and categorize the documents. In addition to higher efficiency and effectiveness, using a digitization and ML-based approach here also eliminates the need to store documents on-hand in file cabinets. Instead, these documents, once scanned, can be moved to long-term archival storage for use only when necessary as backup.

The energy is heavily regulated and that this can pose a challenge when attempting technology implementation. Custer however sees AI realistically being applied to the energy industry to many unique use-cases that he envisions for the future. In particular, Custer notes the relative inefficiency of logistics and management of the energy industry. He notes that technological advancements have already been made within the industry and give examples such as seismic imaging that can scan for underground reservoirs and drills that can drill both vertically and horizontally into the ground. Custer recognizes these applications as huge technology advancements within the industry. However, Custer notes that there has been minimal advancement in his domain of land management and business development over the years, referring to how people were apprehensive to begin with but have slowly warmed up to the idea of AI within the energy industry. He adds that this acceptance is particularly prominent when provisions and leases are involved due to its time-saving and file organizing abilities. Apache as well as other energy firms are increasingly welcoming the continual technological advancements enjoying the cost and time-saving benefits.

Custer also elaborates on the time-saving aspect of these AI systems, in particular when applied to a provision known as the consent to assign. This provision is involved within a contractual obligation that decides whether ownership can be transferred or not. Custer notes that a consent to assign provision can take hours to review manually while AI-enabled systems can shorten the process to a matter of minutes.

In general, Custer believes that this is just the tip of the iceberg with regards to the ways that AI can dramatically impact the energy industry. He states that there are many possible advancements to be made in the industry that can be learned from other industries such as finance, healthcare, and manufacturing, looking at similar use cases where documents, processes, and data can be effectively leveraged and optimized. Custer notes just how much more efficient data analysis will become and that our ability to extract valuable information from data will only be improved as time goes on.

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AI Fueling The Oil And Gas Industry: Interview With Tim Custer At Apache - Forbes

Bitcoin Price Predictions by Top Analysts Are Usually Wrong Heres Why – Cointelegraph

Since Bitcoin (BTC) price rallied above $19,000 in 2017, crypto analysts have issued an amazingly wide range of price predictions on the date and value of the next all-time high or low.

Sometimes these predictions are rooted in deep fundamental and technical analysis, whereas other times they are simply nothing more than off-the-cuff estimates issued at whim.

Options markets provide useful insights into traders' expectations, including mathematical probabilities for an asset's future prices. Using Black & Scholes model allows one to better assess the likelihood of analysts' estimates.

The Black & Scholes valuation algorithm has been the basis for the pricing of options on traditional assets since the early 1970s, and remains widely used.

Although the Black & Scholes option pricing model tends to underestimate the odds of substantial movements, it does provide precise and conservative estimates.

Similar to weather forecasting, adding more than a couple of days to an estimate reduces its precision by a logarithmic proportion. One must also consider that the model has to predict a binary outcome because a $9,500 option will be deemed worthless if the expiry price is $9,499.

Many analysts tend to exaggerate their estimates to make a bold statement and attract media attention, or their predictions are based on various types of bias.

No one expects gold bugs like Peter Schiff to draw a bullish Bitcoin estimate, and the same can be said for expecting a bearish prediction from Stock-to-Flow model author PlanB.

The question investors should be asking is exactly how far off were those estimates compared to Bitcoin options pricing? Furthermore, should one even consider these analysts and pundits opinions?

Although the Black & Scholes options pricing model can be complicated, it's usage is pretty straight forward. By informing the current BTC price, strike level, days until expiry, and annual volatility, the model will instantly provide the odds above and below a specific price.

Skipping the complex calculations, one can refer to Skew Analytics to find current probabilities for each expiry based on options pricing.

Bitcoin probability at options maturity. Source: Deribit

Most active option strikes expire on the last Friday of every month. As previously explained, those figures will seem conservative. Both August and September strikes signify a mere 50% probability that Bitcoin price will remain above $9,000.

A 50% odd should is effectively neutral, as the mathematical model states that odds above and below such target are pretty much the same.

By contrast, the probability for a $8,500 on the July expiry (just two weeks from now) sits at 76%. The model becomes more confident as we approach maturity, so one should not expect options to price 90% plus odds for contracts with more than two weeks left.

To assert whether analysts and pundits' predictions fare better than options markets pricing, one needs to stack those odds against the Black & Scholes options model, which requires four basic inputs: current price, strike (prediction), days until expiry, and implied volatility.

Bitcoin price and pundits predictions. Source: TradingView

The above chart depicts six predictions over a 100 day period, which will be individually tested against the options markets model.

Despite having said numerous times that he isn't an active trader, Binance founder Changpeng Zhao often likes to publicize his predictions. In early November, CZ declared that BTC would hit $16K 'soonish,' so one should assume four months.

CZ missed the mark by 35% as Bitcoin failed to break $10,500 level within four months. This was not a lousy call, but rather way too optimistic as indicated by the Black & Scholes model.

Analyst Willy Woo reflected on the previous year cycle low of $3,100 and estimated that Bitcoin could drop 71% from its $12,800 high, reaching $4,500 before the next halving. It seemed rather unlikely at the time, but a six-month timeframe in cryptocurrency is a very long time.

Hats off to Willy Woo on this call as the March 13 infamous crash caused a brief test of the $4,000 level. Despite being correct, buying protection for such a long time frame costs substantial money. A $6K put option would have cost Woo $540 back then.

Notorious Bitcoin basher Peter Schiff spotted a head and shoulders pattern and issued a $1,000 prediction. Although no timeframe was set, based on such a pattern, a three-month time frame seems reasonable.

One doesn't need to be a statistician to deem such predictions as unreasonable. According to the options model, a $5,000 target back would still have shown a limited 10.7% probability.

Peter could have remained bearish using a more reasonable goal, according to options markets at that time.

The 40-year market stalwart said that BTC had already hit its floor; hence investors waiting for a price dip to $6,000 have "missed" their opportunity. No timeline was mentioned, although a 3-month prediction would have pleased most investors.

Less than two months after that tweet, the sudden Bitcoin collapse on March 13 proved Peter Brandt's prediction wrong.

In an interview with Yahoo! Finance, Fundstrat senior analyst Tom Lee suggested that Bitcoin's technical achievements paved the way for 200% gains within six months, with halving acting as a catalyst.

With less than 20 days to fulfill such a prediction, it seems very unlikely to occur. At least buying a $23K call option would have cost $65, a bargain considering a $4,000 upside to Lee's target.

The creator of the stock-to-flow model revealed his belief that Bitcoin would not return below $8,200. PlanB also mentioned that he was expecting levels above $10,000 near Bitcoin halving in May.

Less than a month later, PlanB's $8,200 support level was broken, although his $10,000 prediction for halving was pretty close as it was off by only 3% to 5%.

One might say PlanB got it 50% right, although the bold $10,000 prediction could have earned him good money using a butterfly spread strategy.

Black & Scholes can be a useful tool to understand how far a prediction might be from options pricing. Whats clear is that pundits seem to exaggerate their takes, which leads to huge misses and misinformation in the form of bad analysis being spread through major media outlets.

In some cases, the wild guesses do hit the mark. For example, Willy Woo and PlanB could certainly have profited by defying options model pricing, but generally it's better to do your own research instead of following calls from leading analysts.

The views and opinions expressed here are solely those of the author and do not necessarily reflect the views of Cointelegraph. Every investment and trading move involves risk. You should conduct your own research when making a decision.

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Bitcoin Price Predictions by Top Analysts Are Usually Wrong Heres Why - Cointelegraph