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News Article | April 17, 2017
Site: www.scientificamerican.com

The brain processes sights, sounds and other sensory information—and even makes decisions—based on a calculation of probabilities. At least, that’s what a number of leading theories of mental processing tell us: The body’s master controller builds an internal model from past experiences, and then predicts how best to behave. Although studies have shown humans and other animals make varied behavioral choices even when performing the same task in an identical environment, these hypotheses often attribute such fluctuations to “noise”—to an error in the system. But not everyone agrees this provides the complete picture. After all, sometimes it really does pay off for randomness to enter the equation. A prey animal has a higher chance of escaping predators if its behavior cannot be anticipated easily, something made possible by introducing greater variability into its decision-making. Or in less stable conditions, when prior experience can no longer provide an accurate gauge for how to act, this kind of complex behavior allows the animal to explore more diverse options, improving its odds of finding the optimal solution. One 2014 study found rats resorted to random behavior when they realized nonrandom behavior was insufficient for outsmarting a computer algorithm. Perhaps, then, this variance cannot simply be chalked up to mere noise. Instead, it plays an essential role in how the brain functions. Now, in a study published April 12 in PLoS Computational Biology, a group of researchers in the Algorithmic Nature Group at LABORES Scientific Research Lab for the Natural and Digital Sciences in Paris hope to illuminate how this complexity unfolds in humans. “When the rats tried to behave randomly [in 2014],” says Hector Zenil, a computer scientist who is one of the study’s authors, “researchers saw that they were computing how to behave randomly. This computation is what we wanted to capture in our study.” Zenil’s team found that, on average, people’s ability to behave randomly peaks at age 25, then slowly declines until age 60, when it starts to decrease much more rapidly. To test this, the researchers had more than 3,400 participants, aged four to 91, complete a series of tasks—“a sort of reversed Turing test,” Zenil says, determining how well a human can outcompete a computer when it comes to producing and recognizing random patterns. The subjects had to create sequences of coin tosses and die rolls they believed would look random to another person, guess which card would be drawn from a randomly shuffled deck, point to circles on a screen and color in a grid to form a seemingly random design. The team then analyzed these responses to quantify their level of randomness by determining the probability that a computer algorithm could generate the same decisions, measuring algorithmic complexity as the length of the shortest possible computer program that could model the participants’ choices. In other words, the more random a person’s behavior, the more difficult it would be to describe his or her responses mathematically, and the longer the algorithm would be. If a sequence were truly random, it would not be possible for such a program to compress the data at all—it would be the same length as the original sequence. After controlling for factors such as language, sex and education, the researchers concluded age was the only characteristic that affected how randomly someone behaved. “At age 25, people can outsmart computers at generating this kind of randomness,” Zenil says. This developmental  trajectory, he adds, reflects what scientists would expect measures of higher cognitive abilities to look like. In fact, a sense of complexity and randomness is based on cognitive functions including attention, inhibition and working memory (which were involved in the study’s five tasks)—although the exact mechanisms behind this relationship remain unknown. “It is around 25, then, that minds are the sharpest.” This makes biological sense, according to Zenil: Natural selection would favor a greater capacity for generating randomness during key reproductive years. The study’s results may even have implications for understanding human creativity. After all, a large part of being creative is the ability to develop new approaches and test different outcomes. “That means accessing a larger repository of diversity,” Zenil says, “which is essentially randomness. So at 25, people have more resources to behave creatively.” Zenil’s findings support previous research, which also showed a decline in random behavior with age. But this is the first study to employ an algorithmic approach to measuring complexity as well as the first to do so over a continuous age range. “Earlier studies considered groups of young and older adults, capturing specific statistical aspects such as repetition rate in very long response sequences,” says Gordana Dodig-Crnkovic, a computer scientist at Mälardalen University in Sweden, who was not involved in the research. “The present article goes a step further.” Using algorithmic measures of randomness, rather than statistical ones, allowed Zenil’s team to examine true random behavior instead of statistical, or pseudorandom, behavior—which, although satisfying statistical tests for randomness, would not necessarily be “incompressible” the way truly random data is. The fact that algorithmic capability differed with age implies the brain is algorithmic in nature—that it does not assume the world is statistically random but takes a more generalized approach without the biases described in more traditional statistical models of the brain. These results may open up a wider perspective on how the brain works: as an algorithmic probability estimator. The theory would update and eliminate some of the biases in statistical models of decision-making that lie at the heart of prevalent theories—prominent among them is the Bayesian brain hypothesis, which holds that the mind assigns a probability to a conjecture and revises it when new information is received from the senses.  “The brain is highly algorithmic,” Zenil says. “It doesn’t behave stochastically, or as a sort of coin-tossing mechanism.” Neglecting an algorithmic approach in favor of only statistical ones gives us an incomplete understanding of the brain, he adds. For instance, a statistical approach does not explain why we can remember sequences of digits such as a phone number—take “246-810-1214,” whose digits are simply even counting numbers: This is not a statistical property, but an algorithmic one. We can recognize the pattern and use it to memorize the number. Algorithmic probability, moreover, allows us to more easily find (and compress) patterns in information that appears random. “This is a paradigm shift,” Zenil says, “because even though most researchers agree that there is this algorithmic component in the way the mind works, we had been unable to measure it because we did not have the right tools, which we have now developed and introduced in our study.” Zenil and his team plan to continue exploring human algorithmic complexity, and hope to shed light on the cognitive mechanisms underlying the relationship between behavioral randomness and age. First, however, they plan to conduct their experiments with people who have been diagnosed with neurodegenerative diseases and mental disorders, including Alzheimer’s and schizophrenia. Zenil predicts, for example, that participants diagnosed with the latter will not generate or perceive randomness as well as their counterparts in the control group, because they often make more associations and observe more patterns than the average person does. The researchers’ colleagues are standing by. Their work on complexity, says Dodig-Crnkovic, “presents a very promising approach.”


News Article | April 21, 2017
Site: www.eurekalert.org

New Apress book explains technical foundations of the Ethereum project -- view to new products and services Cryptocurrencies are on the rise, and blockchain protocols are taking the world by storm. Ethereum is an open-source public blockchain featuring smart contracts and which uses the Turing-complete scripting language Solidity. The open source Ethereum protocol was first proposed in 2013, along with its native cryptocurrency ether. Since then ether has grown to become the second largest cryptocurrency by market capitalization after bitcoin. Introducing Ethereum and Solidity, written by Chris Dannen and published by Apress, compiles the basic technical principles underlying Ethereum and situates the project within the existing world of hardware and software. The book familiarizes readers with blockchain programming paradigms, and introduces the programming language Solidity. With this book as their guide, readers will be able to learn the foundations of smart contract programming and distributed application development. The author starts out by reviewing the fundamentals of programming and networking, and describing how blockchains can solve long-standing technology challenges. The book also outlines the new discipline of crypto-economics, the study of game theoretical systems written in pure software. Readers are then guided into deploying smart contracts of their own, and learning how those can serve as a back-end for JavaScript and HTML applications on the web. "Unlike other tutorials, Introducing Ethereum and Solidity is written for both technology professionals, financial services professionals, and enthusiasts of all levels. It provides creative technologists with a gateway from concept to deployment," says the author. Chris Dannen graduated from the University of Virginia. He is founder and partner at Iterative Instinct, a hybrid investment fund focused on cryptocurrency trading and seed-stage venture investments. He worked previously as a business journalist and corporate strategist. A self-taught programmer, he holds one computer hardware patent. Chris Dannen was formerly a Senior Editor at Fast Company and today consults on technical content for major publishers. Chris Dannen Introducing Ethereum and Solidity Foundations of Cryptocurrency and Blockchain Programming for Beginners 2017, 206 p. 34 illus. 31 illus. in colour Softcover € 36.99 (D) | £ 27.99 | $ 39.99 ISBN 978-1-4842-2534-9 Also available as an eBook


News Article | April 17, 2017
Site: www.nature.com

This year marks the centenary of what seems now to be an extraordinary event in publishing: the time when a UK local newspaper reviewed a dense, nearly 800-page treatise on mathematical biology that sought to place physical constraints on the processes of Darwinism. And what’s more, the Dundee Advertiser loved the book and recommended it to readers. When the author, it noted, wrote of maths, “he never fails to translate his mathematics into English; and he is one of the relatively few men of science who can write in flawless English and who never grudge the effort to make every sentence balanced and good.” The Dundee Advertiser is still going, although it has changed identity: a decade after the review was published, it merged with The Courier, and that is how most people refer to it today. The book is still going, too. If anything, its title — alongside its balanced and good sentences — has become more iconic and recognized as the years have ticked by. The book is On Growth and Form by D’Arcy Thompson. This week, Nature offers its own appreciation, with a series of articles in print and online that celebrate the book’s impact, ideas and lasting legacy. Still in print, On Growth and Form was more than a decade in the planning. Thompson would regularly tell colleagues and students — he taught at what is now the University of Dundee, hence the local media interest — about his big idea before he wrote it all down. In part, he was reacting against one of the biggest ideas in scientific history. Thompson used his book to argue that Charles Darwin’s natural selection was not the only major influence on the origin and development of species and their unique forms: “In general no organic forms exist save such as are in conformity with physical and mathematical laws.” Biological response to physical forces remains a live topic for research. In a research paper, for example, researchers report how physical stresses generated at defects in the structures of epithelial cell layers cause excess cells to be extruded. In a separate online publication (K. Kawaguchi et al. Nature http://dx.doi.org/10.1038/nature22321; 2017), other scientists show that topological defects have a role in cell dynamics, as a result of the balance of forces. In high-density cultures of neural progenitor cells, the direction in which cells travel around defects affects whether cells become more densely packed (leading to pile-ups) or spread out (leading to a cellular fast-lane where travel speeds up). A Technology Feature investigates in depth the innovative methods developed to detect and measure forces generated by cells and proteins. Such techniques help researchers to understand how force is translated into biological function. Thompson’s influence also flourishes in other active areas of interdisciplinary research. A research paper offers a mathematical explanation for the colour changes that appear in the scales of ocellated lizards (Timon lepidus) during development (also featured on this week’s cover). It suggests that the patterns are generated by a system called a hexagonal cellular automaton, and that such a discrete system can emerge from the continuous reaction-diffusion framework developed by mathematician Alan Turing to explain the distinctive patterning on animals, such as spots and stripes. (Some of the research findings are explored in detail in the News and Views section.) To complete the link to Thompson, Turing cited On Growth and Form in his original work on reaction-diffusion theory in living systems. Finally, we have also prepared an online collection of research and comment from Nature and the Nature research journals in support of the centenary, some of which we have made freely available to view for one month. Nature is far from the only organization to recognize the centenary of Thompson’s book. A full programme of events will run this year around the world, and at the D’Arcy Thompson Zoology Museum in Dundee, skulls and other specimens are being scanned to create digital 3D models. Late last month, this work was featured in The Courier. One hundred years on, Thompson’s story has some way to run yet.


News Article | April 25, 2017
Site: www.techradar.com

It's far from clear what the UK's immigration policy will look like post-Brexit, but it's almost certain that stricter controls will come into force in the not-too-distant future. There's been much talk in the news recently about how changes to immigration policy can affect the technology sector, with leading tech figures in the US penning a letter to president Trump about the impact of his ‘travel ban’ on the industry there. Now The Royal Society, the UK's national academy of science, has released a report looking at what these uncertain times might mean for the machine learning industry in the UK, and what steps need to be taken to ensure we remain a world leader in this field. Put simply, machine learning is a process by which a computer can learn to complete a task by looking at previously entered data, rather than by being given a specific set of instructions. Facebook’s facial recognition software is a good example. The computer doesn’t need to be given individual instructions to identify people in the picture, then facial features, then compare those facial features against a database, then ask if that person is you. It knows it has to identify a person, and runs a system of checks based on previous results to reach the desired result. The UK has a strong track record in the field of machine learning, dating back to the 1950s when the computer scientist and former Bletchley Park codebreaker Alan Turing created the Turing test, a test that's still used today as a marker of machine intelligence. More recently there have been some hugely successful UK startups dedicated to machine learning, including the now-Google-owned DeepMind, speech recognition company VocalIQ, which is now owned by Apple, and the now-Twitter-owned Magic Pony, which works to process visual data. With the shifts in the current political climate, The Royal Society is keen for the UK not to lose any ground in the world of machine learning, which is a rapidly developing industry. The report says: “As it considers its future approach to immigration policy, the UK must ensure that research and innovation systems continue to be able to access the skills they need. The UK’s approach to immigration should support the UK’s aim to be one of the best places in the world to research and innovate, and machine learning is an area of opportunity in support of this aim.” The future of the UK's tech sector fits into a larger conversation about how  changes in the digital landscape are going to affect the job market. One of the major fears the study identified during 60,000 digital interactions and 15,000 face-to-face encounters was that machines would replace humans in many jobs. The report seems to conclude that rather than being a job-taker, this industry is capable of creating jobs, as long as adequate funding is put into education in the field. “Because of the substantial skills shortage in this area, near-term funding should be made available so that the capacity to train UK PhD students in machine learning is able to increase with the level of demand for candidates of a sufficiently high quality,” the report adds. “This could be supported through allocation of the expected 1,000 extra PhD places.” One of the interesting discrepancies highlighted in the study was the vast difference between the amount of people who knew what machine learning was and those who use machine learning. Only 9% of all the people asked knew what machine learning was, whereas almost all of them used machine learning in one form or another.


VANCOUVER, BC / ACCESSWIRE / April 26, 2017 / CoinQx Exchange LIMITED, a wholly owned subsidiary of FIRST BITCOIN CAPITAL CORP (OTC PINK: BITCF or "Company", "We", "Us" or "Our") is the world's first underwriter of Initial Coin Offerings. The emerging proliferation of altcoins is rife with unverifiable ICOs which some say could result in the next South Seas Company Bubble not unlike that of the 16 th Century that resulted in Great Britain enacting the Bubble Act. What makes cryptocurrencies unlike that bubble is that cryptocurrencies have a new type of value and usage unknown to the 1700s. Comparisons were early made of Bitcoin to the Tulip frenzy of 15th Century Holland which turned out to be unfounded as well. Undoubtedly there will be many bubbles popping in most altcoins along the path to weeding out the fly-by-nights and perhaps a final bubble like that of the Internet when the stock market boom peaked on March 10 2000. This has presented First Bitcoin Capital with a unique opportunity to act as an underwriter of ICOs which will make us also a gatekeeper to weed out undesirable coin offerings. We envision that by acting as underwriter it will help differentiate the fly-by-night operators so that many potential speculators will avoid those that do not pass through our due diligence gateway. We believe that we are uniquely suited for this endeavor in as much as being a transparent public company; filing reports with OTC Markets; the trading of our shares regulated by FINRA; our CoinQx exchange registered with FINCEN; experienced in peer-to-peer cryptocurrency creation; having launched our own ICO; as well as the first pubco dedicated to this space. We are currently negotiating several underwritings including "Bonanza" now being offered as symbol "XZA" which has undergone our due diligence process sufficient to have agreed to act as best efforts underwriter at this point. As another way to ferret out the less credible coin offerings that are already trading on cryptocurrency exchanges our www.altcoinmarket.com web site (under development) will give the altcoin community access to up and down vote all altcoins covered therein. There also lacks conformity in this new space, therefore, First Bitcoin Capital Corp is in the process of developing systems like FINRA and CUSIP developed for identifiers including numbering and symbols generation yet to be placed on an open source blockchain. Another aspect of peer to peer coins that makes it unlike the South Seas Company Bubble is the emergence of Decentralized Autonomous States and Organizations (DAS or DAO) brining the world into a previously unknown legal structure opening new horizons. What makes this brave new world like the South Seas Bubble is the rampant speculation in new ICOs that may also similarly be seen as gambling. A decentralized autonomous organization (DAO), sometimes labeled a decentralized autonomous corporation (DAC), is an organization that is run through rules encoded as computer programs called smart contracts.A DAO's financial transaction record and program rules are maintained on a blockchain. There are several examples of this business model. The precise legal status of this type of business organization is unclear. The best-known example was The DAO, a DAO for venture capital funding, which was launched with $150 million in crowdfunding in June 2016 and was immediately hacked and drained of US$50 million in cryptocurrency. Decentralized autonomous organizations have been seen by some as difficult to describe. Nevertheless, the conceptual essence of a decentralized autonomous organization has been typified as the ability of blockchain technology to provide a secure digital ledger that tracks financial interactions across the internet, hardened against forgery by trusted timestamping and by dissemination of a distributed database. This approach eliminates the need to involve a bilaterally accepted trusted third party in a financial transaction, thus simplifying the sequence. [2]The costs of a blockchain enabled transaction and of making available the associated data may be substantially lessened by the elimination of both the trusted third party and of the need for repetitious recording of contract exchanges in different records: for example, the blockchain data could in principle, if regulatory structures permitted, replace public documents such as deeds and titles. In theory, a blockchain approach allows multiple cloud computing users to enter a loosely coupled peer-to-peer smart contract collaboration. Buterin proposed that after a DAO was launched, it might be organized to run without human managerial interactivity, provided the smart contracts were supported by a Turing complete platform. Ethereum, built on a blockchain and launched in 2015, has been described as meeting that Turing threshold, thus enabling DAOs. Decentralized autonomous organizations aim to be open platforms where individuals control their identities and their personal data. Examples of DAOs are Dash, The DAO and Digix.io.[12] A value token called DigixDAO finished its crowdfunding campaign and the token began trading on exchanges on 28 April 2016. Shareholder participation in DAOs can be problematic. For example, BitShares has seen a lack of voting participation, because it takes time and energy to consider proposals. The precise legal status of this type of business organization is unclear;some similar approaches have been regarded by the U.S. Securities and Exchange Commission as illegal offers of unregistered securities.Although unclear, a DAO may functionally be a corporation without legal status as a corporation: a general partnership.This means potentially unlimited legal liability for participants, even if the smart contract code or the DAO's promoters say otherwise.Known participants, or those at the interface between a DAO and regulated financial systems, may be targets for regulatory enforcement or civil actions. The code of a given DAO will be difficult to alter once the system is up and running, including bug fixes that would be trivial in centralised code. Corrections for a DAO would require writing new code and agreement to migrate all the funds. Although the code is visible to all, it is hard to repair, thus leaving known security holes open to exploitation unless a moratorium is called to enable bug fixing. In 2016, a specific DAO, The DAO, set a record for the largest crowdfunding campaign to date. However, researchers pointed out multiple issues in the code of The DAO. The operational procedure for The DAO allows investors to withdraw at will any money that has not yet been committed to a project; the funds could thus deplete quickly. Although safeguards aim to prevent gaming the voting of shareholders to win investments, there were a "number of security vulnerabilities". These enabled an attempted large withdrawal of funds from The DAO that was initiated in mid-June 2016. However, after much debate, on the 20th July 2016, the Ethereum community arrived at a consensus decision to hard fork the Ethereum blockchain to bailout the original contract and thus $ETC was a result. First Bitcoin Capital Corp is planning to launch a series of DAOs in 2017. The Company continues to actively offer AltCoin (ALT), its first ICO as it approaches completion via http://www.altcoinmarketcap.com We are proud to report that ALT is now listed on 4 exchanges including CCEX.com, Cryptopia, OMNIDEX, and our own CoinQX.com. Altcoin (symbol ALT) was added to our competitor's website today, coinmarketcap.com. We have also listed the recently launched 6 (see list below) indicative Bitcoin hard fork outcomes for speculators to predict the successful one(s) on www.CoinQX.com as well as adding "Bond." First Bitcoin Capital is engaged in developing digital currencies, proprietary Blockchain technologies, and the digital currency exchange- www.CoinQX.com. We see this step as a tremendous opportunity to create further shareholder value by leveraging management's experience in developing and managing complex Blockchain technologies, developing new types of digital assets. Being the first publicly-traded cryptocurrency and blockchain-centered company (with shares both traded in the US OTC Markets as [BITCF] and as [BIT] in crypto exchanges) we want to provide our shareholders with diversified exposure to digital cryptocurrencies and blockchain technologies. At this time the Company owns and operates more than the following digital assets. List of Omni protocol coins issued on the Bitcoin Blockchain owned by the Company: http://omnichest.info/lookupadd.aspx?address=1FwADyEvdvaLNxjN1v3q6tNJCgHEBuABrS Certain statements contained in this press release may constitute "forward-looking statements." Forward-looking statements provide current expectations of future events based on certain assumptions and include any statement that does not directly relate to any historical or current fact. Actual results may differ materially from those indicated by such forward-looking statements as a result of various important factors as may be disclosed in company's filings. In addition to these factors, actual future performance, outcomes, and results may differ materially because of more general factors including (without limitation) general industry and market conditions and growth rates, economic conditions, and governmental and public policy changes. The forward-looking statements included in this press release represent the Company's views as of the date of this press release and these views could change. However, while the Company may elect to update these forward-looking statements at some point in the future, the Company specifically disclaims any obligation to do so. These forward-looking statements should not be relied upon as representing the Company's views as of any date subsequent to the date of the press release .Such forward-looking statements are risks that are detailed in the Company's filings, which are on file at www.OTCMarkets.com. Contact us via: info@bitcoincapitalcorp.com or visit http://www.bitcoincapitalcorp.com VANCOUVER, BC / ACCESSWIRE / April 26, 2017 / CoinQx Exchange LIMITED, a wholly owned subsidiary of FIRST BITCOIN CAPITAL CORP (OTC PINK: BITCF or "Company", "We", "Us" or "Our") is the world's first underwriter of Initial Coin Offerings. The emerging proliferation of altcoins is rife with unverifiable ICOs which some say could result in the next South Seas Company Bubble not unlike that of the 16 th Century that resulted in Great Britain enacting the Bubble Act. What makes cryptocurrencies unlike that bubble is that cryptocurrencies have a new type of value and usage unknown to the 1700s. Comparisons were early made of Bitcoin to the Tulip frenzy of 15th Century Holland which turned out to be unfounded as well. Undoubtedly there will be many bubbles popping in most altcoins along the path to weeding out the fly-by-nights and perhaps a final bubble like that of the Internet when the stock market boom peaked on March 10 2000. This has presented First Bitcoin Capital with a unique opportunity to act as an underwriter of ICOs which will make us also a gatekeeper to weed out undesirable coin offerings. We envision that by acting as underwriter it will help differentiate the fly-by-night operators so that many potential speculators will avoid those that do not pass through our due diligence gateway. We believe that we are uniquely suited for this endeavor in as much as being a transparent public company; filing reports with OTC Markets; the trading of our shares regulated by FINRA; our CoinQx exchange registered with FINCEN; experienced in peer-to-peer cryptocurrency creation; having launched our own ICO; as well as the first pubco dedicated to this space. We are currently negotiating several underwritings including "Bonanza" now being offered as symbol "XZA" which has undergone our due diligence process sufficient to have agreed to act as best efforts underwriter at this point. As another way to ferret out the less credible coin offerings that are already trading on cryptocurrency exchanges our www.altcoinmarket.com web site (under development) will give the altcoin community access to up and down vote all altcoins covered therein. There also lacks conformity in this new space, therefore, First Bitcoin Capital Corp is in the process of developing systems like FINRA and CUSIP developed for identifiers including numbering and symbols generation yet to be placed on an open source blockchain. Another aspect of peer to peer coins that makes it unlike the South Seas Company Bubble is the emergence of Decentralized Autonomous States and Organizations (DAS or DAO) brining the world into a previously unknown legal structure opening new horizons. What makes this brave new world like the South Seas Bubble is the rampant speculation in new ICOs that may also similarly be seen as gambling. A decentralized autonomous organization (DAO), sometimes labeled a decentralized autonomous corporation (DAC), is an organization that is run through rules encoded as computer programs called smart contracts.A DAO's financial transaction record and program rules are maintained on a blockchain. There are several examples of this business model. The precise legal status of this type of business organization is unclear. The best-known example was The DAO, a DAO for venture capital funding, which was launched with $150 million in crowdfunding in June 2016 and was immediately hacked and drained of US$50 million in cryptocurrency. Decentralized autonomous organizations have been seen by some as difficult to describe. Nevertheless, the conceptual essence of a decentralized autonomous organization has been typified as the ability of blockchain technology to provide a secure digital ledger that tracks financial interactions across the internet, hardened against forgery by trusted timestamping and by dissemination of a distributed database. This approach eliminates the need to involve a bilaterally accepted trusted third party in a financial transaction, thus simplifying the sequence. [2]The costs of a blockchain enabled transaction and of making available the associated data may be substantially lessened by the elimination of both the trusted third party and of the need for repetitious recording of contract exchanges in different records: for example, the blockchain data could in principle, if regulatory structures permitted, replace public documents such as deeds and titles. In theory, a blockchain approach allows multiple cloud computing users to enter a loosely coupled peer-to-peer smart contract collaboration. Buterin proposed that after a DAO was launched, it might be organized to run without human managerial interactivity, provided the smart contracts were supported by a Turing complete platform. Ethereum, built on a blockchain and launched in 2015, has been described as meeting that Turing threshold, thus enabling DAOs. Decentralized autonomous organizations aim to be open platforms where individuals control their identities and their personal data. Examples of DAOs are Dash, The DAO and Digix.io.[12] A value token called DigixDAO finished its crowdfunding campaign and the token began trading on exchanges on 28 April 2016. Shareholder participation in DAOs can be problematic. For example, BitShares has seen a lack of voting participation, because it takes time and energy to consider proposals. The precise legal status of this type of business organization is unclear;some similar approaches have been regarded by the U.S. Securities and Exchange Commission as illegal offers of unregistered securities.Although unclear, a DAO may functionally be a corporation without legal status as a corporation: a general partnership.This means potentially unlimited legal liability for participants, even if the smart contract code or the DAO's promoters say otherwise.Known participants, or those at the interface between a DAO and regulated financial systems, may be targets for regulatory enforcement or civil actions. The code of a given DAO will be difficult to alter once the system is up and running, including bug fixes that would be trivial in centralised code. Corrections for a DAO would require writing new code and agreement to migrate all the funds. Although the code is visible to all, it is hard to repair, thus leaving known security holes open to exploitation unless a moratorium is called to enable bug fixing. In 2016, a specific DAO, The DAO, set a record for the largest crowdfunding campaign to date. However, researchers pointed out multiple issues in the code of The DAO. The operational procedure for The DAO allows investors to withdraw at will any money that has not yet been committed to a project; the funds could thus deplete quickly. Although safeguards aim to prevent gaming the voting of shareholders to win investments, there were a "number of security vulnerabilities". These enabled an attempted large withdrawal of funds from The DAO that was initiated in mid-June 2016. However, after much debate, on the 20th July 2016, the Ethereum community arrived at a consensus decision to hard fork the Ethereum blockchain to bailout the original contract and thus $ETC was a result. First Bitcoin Capital Corp is planning to launch a series of DAOs in 2017. The Company continues to actively offer AltCoin (ALT), its first ICO as it approaches completion via http://www.altcoinmarketcap.com We are proud to report that ALT is now listed on 4 exchanges including CCEX.com, Cryptopia, OMNIDEX, and our own CoinQX.com. Altcoin (symbol ALT) was added to our competitor's website today, coinmarketcap.com. We have also listed the recently launched 6 (see list below) indicative Bitcoin hard fork outcomes for speculators to predict the successful one(s) on www.CoinQX.com as well as adding "Bond." First Bitcoin Capital is engaged in developing digital currencies, proprietary Blockchain technologies, and the digital currency exchange- www.CoinQX.com. We see this step as a tremendous opportunity to create further shareholder value by leveraging management's experience in developing and managing complex Blockchain technologies, developing new types of digital assets. Being the first publicly-traded cryptocurrency and blockchain-centered company (with shares both traded in the US OTC Markets as [BITCF] and as [BIT] in crypto exchanges) we want to provide our shareholders with diversified exposure to digital cryptocurrencies and blockchain technologies. At this time the Company owns and operates more than the following digital assets. List of Omni protocol coins issued on the Bitcoin Blockchain owned by the Company: http://omnichest.info/lookupadd.aspx?address=1FwADyEvdvaLNxjN1v3q6tNJCgHEBuABrS Certain statements contained in this press release may constitute "forward-looking statements." Forward-looking statements provide current expectations of future events based on certain assumptions and include any statement that does not directly relate to any historical or current fact. Actual results may differ materially from those indicated by such forward-looking statements as a result of various important factors as may be disclosed in company's filings. In addition to these factors, actual future performance, outcomes, and results may differ materially because of more general factors including (without limitation) general industry and market conditions and growth rates, economic conditions, and governmental and public policy changes. The forward-looking statements included in this press release represent the Company's views as of the date of this press release and these views could change. However, while the Company may elect to update these forward-looking statements at some point in the future, the Company specifically disclaims any obligation to do so. These forward-looking statements should not be relied upon as representing the Company's views as of any date subsequent to the date of the press release .Such forward-looking statements are risks that are detailed in the Company's filings, which are on file at www.OTCMarkets.com. Contact us via: info@bitcoincapitalcorp.com or visit http://www.bitcoincapitalcorp.com


News Article | May 25, 2017
Site: globenewswire.com

BEIJING, May 25, 2017 (GLOBE NEWSWIRE) -- Gridsum Holding Inc. (“Gridsum” or the “Company”) (NASDAQ:GSUM), a leading provider of cloud-based big-data analytics, machine learning and AI solutions in China, today announced that, as a part of its strategic evolution, it has consolidated all of its artificial intelligence (“AI”) activities strategically, technically and organizationally into a new division called the Gridsum Prophet. Gridsum is a first mover in China in big data intelligence. Since 2005, the Company has utilized a distributed big-data computing architecture, developed and implemented sophisticated natural language processing (“NLP”), and leveraged machine learning directed toward large enterprise clients. During that time, from serving large enterprise customers, the Company has accumulated deep domain knowledge and expertise as well as a massive amount of data that fuels its machine learning algorithms. Since this early inception, the Company has continued to stay at the forefront through focus and investment, hiring and training extraordinary engineers and architects and, importantly, playing an active and leading role in the AI academic and developer communities. The Company believes it is currently the technology market leader in the China enterprise-focused AI space with a global best-of-breed Enterprise-AI engine. As of March 31, 2017, Gridsum has filed 1,653 patent applications in China, of which 583 are big-data focused and 148 are explicitly Enterprise-AI focused. The Company believes this is the largest number of Enterprise-AI focused patents for a company in China today. Mr. Guosheng Qi, Chief Executive Officer of Gridsum, stated, “We are excited to launch our new AI engine, Gridsum Prophet. AI is playing an increasingly key role in developing and optimizing our intelligent products and solutions across the board and providing quantifiable value-add to our enterprise and other customers. Through Gridsum Prophet, we are taking strategic moves to further accelerate our AI development in a more concentrated way. New product features driven by Gridsum Prophet have already been key in a double-digit number of both client wins and subscription renewals with substantial ARPU uplift.” In terms of scope, Gridsum Prophet encompasses all of the Company’s AI capabilities: machine learning, natural language processing, image recognition, predictive industry modeling, and knowledge graph. Gridsum Prophet powers the Company’s intelligent products and solutions across the matrix of clients and industry verticals. It also allows the Company to see cross-industry, product and demographic correlations and relationships to add further value for its clients. Gridsum Prophet is an explicit and important development area for the Company and is increasingly key in creating new differentiated product features for the Company’s existing products (e.g. marketing automation) and is central in defining and shaping new products, such as the Company’s social listening and brand management suite as well as its legal services and Intelligent CRM product suites. Gridsum Prophet already plays a key role in driving quantifiable value for the Company’s customers. It also defines a key competitive advantage for Gridsum versus other local and global players. This is the case in the marketing technology and broader Business Intelligence ecosystems where Gridsum Prophet is instrumental for the Company in driving new customer acquisition as well as ARPU growth with existing customers. Gridsum Prophet is also key to the Company’s legal services product suite which, in some areas, Gridsum believes is consistent with passing a “soft” Turing Test - delivering results in a few seconds consistent with multiple man-hours of work by a team of junior lawyers and paralegals. Gridsum Prophet is also central to the development and success, so far in current Beta trials, for the Company’s Intelligent CRM solution. Broadly speaking, consumer AI often leverages the ubiquitous consumer Internet ecosystems across e-commerce, social, mobile, entertainment and others and focuses to quickly solve or facilitate relatively simple (from a mathematical standpoint) but often time-consuming, “painful” or distracting consumer challenges. Consumer AI is incredibly broad in scope, having the potential to revolutionize and facilitate the way people live and, within the next 10 years, will likely touch the lives of most of the people on the planet. Enterprise AI is different in focus, structure, development, management, application and goals. It requires a very different and focused “organizational DNA” which is particularly rare in China (and elsewhere in Asia). It is focused on creating immediate and quantifiable value for companies with an immediate KPI impact and evolving and increasing that value-add over time. Enterprise AI hence requires deep domain expertise and knowledge of the target industry and its ecosystem. This allows Gridsum to understand the challenges and opportunities where AI technologies can be effectively applied to delivering immediate and quantifiable value to an enterprise customer whether it is improving efficiency, reducing cost, or allocating marketing budget for optimized ROI. These are often industry-specific drivers and dynamics, typically requiring more focus and depth in a narrower area than consumer AI. To accomplish this, enterprise AI tends to heavily leverage supervised learning techniques to infuse human expertise into the resulting intelligent solutions. To drive high-value machine learning, Gridsum utilizes its growing team of industry experts and data scientists who also help tailor the Company’s products, solutions, features and UI for specific industries and verticals. This is a key differentiator of enterprise AI (and Gridsum) when compared to the demands and development process for typical consumer AI. Gridsum uses an iterative learning process whereby high quality raw data is sourced. That data is organized across multiple dimensions and machine learning then produces intermediate results from analyzing the complex relationships within the data. The Company’s industry experts review the results suggested by the system and, based on deep industry expertise, tag these results as relevant, less relevant or irrelevant which is then fed back into the system’s machine learning cycle to further optimize the results, thereby forming a positive optimizing feedback loop. In addition to the Company’s strong emphasis on supervised learning, it is important to note that Gridsum utilizes deep learning, supervised or unsupervised where appropriate, in areas such as abnormal pattern discovery, image recognition and customer value prediction. To further explain how Gridsum Prophet works in practice, below are three specific examples of the engine in action. The Company estimates that over 35% of Internet traffic in China can be defined as “suspicious” (including non-human/bot and “malvertising”). Powered by the vast amount of online behavior data that the Company has been collecting and analyzing for over a decade across a broad range of industries and diverse sets of computing devices and software systems, Gridsum’s supervised and deep learning algorithms continuously improve the coverage and accuracy of click fraud detection and prevention within its Web Dissector and other products. By consolidating all of its abnormal pattern recognition technology into the Prophet engine, the system is now leveraging cross-industry and cross-product machine learning resulting in improved performance. This helps the Company’s clients to substantially and quantifiably improve their ROI from their digital market dollars. By mining historical media buying and performance data and leveraging Gridsum’s deep knowledge and expertise in the China marketing space, the Company’s data science team has developed an automated industry modeling and marketing budget allocation model. This means that a customer can input a desired range of KPI results and the system will then define and allocate a marketing budget to achieve this. This also allows a customer to have an accurate prediction of likely KPI outcomes from their marketing budget and strategy. This heavily leverages Gridsum’s supervised machine learning algorithms that the Company has already successfully used to help clients in the financial industry to allocate their annual digital marketing budget across media channels to achieve optimal return on investment. This moves Gridsum’s products up the value chain to a marketing strategy creation level, and has already resulted in significantly increased ARPU for a number of clients This is increasingly important in China today where the largest brands are finding it increasingly difficult to differentiate in an increasingly “noisy” and crowded landscape defined by a proliferation of lower-end brands. The Gridsum Prophet engine here also importantly helps clients to explore new media such as newsfeed, short video and social video in an ROI-focused, controlled, data-driven and results-oriented manner. These features have recently been a key differentiator in a number of the Company’s new customer additions. In another demonstration of the power of data and deep learning, Gridsum has built a customer value prediction system by analyzing the customers’ online (spanning mobile app, browser and PC) and offline behavior data (contained within a customer’s CRM system and other areas). Using predictive modeling and machine learning, the system has already been helping clients in the automotive industry to effectively improve their sales and call center performance by reducing the average number of calls needed for customer acquisition by as much as 90%. Going forward, over the medium and longer term, Gridsum Prophet opens the opportunity for significant future product development. For example: Gridsum Holding Inc. is a leading provider of cloud-based big-data analytics, machine learning and AI solutions for multinational and domestic enterprises and government agencies in China. Gridsum’s core technology, the Gridsum Big Data Platform, is built on a distributed computing framework and performs real-time multi-dimensional correlation analysis of both structured and unstructured data. This enables Gridsum’s customers to identify complex relationships within their data and gain new insights that help them make better business decisions. The Company is named “Gridsum” to symbolize the combination of distributed computing (Grid) and analytics (sum). As a digital intelligence pioneer, the Company’s mission is to help enterprises and government organizations in China use data in new and powerful ways to make better informed decisions and be more productive. For more information, please visit http://www.gridsum.com/. This announcement contains forward-looking statements. These forward-looking statements are made under the "safe harbor" provisions of the U.S. Private Securities Litigation Reform Act of 1995. These statements can be identified by terminology such as “may,” "will," "expects," "anticipates," “aims,” "future," "intends," "plans," "believes," "estimates," “likely to” and similar statements. Among other things, quotations from management in this announcement, Gridsum’s financial outlook as well as Gridsum's strategic and operational plans contain forward-looking statements. Gridsum may also make written or oral forward-looking statements in its reports filed with, or furnished to, the U.S. Securities and Exchange Commission, in its annual reports to shareholders, in press releases and other written materials and in oral statements made by its officers, directors or employees to third parties. Statements that are not historical facts, including statements about Gridsum's beliefs and expectations, are forward-looking statements. Forward-looking statements involve inherent risks and uncertainties. A number of factors could cause actual results to differ materially from those contained in any forward-looking statement, including but not limited to the following: unexpected difficulties in Gridsum's pursuit of its goals and strategies; the unexpected developments, including slow growth, in the digital intelligence market; reduced demand for, and market acceptance of, Gridsum's solutions; difficulties keeping and strengthening relationships with customers; potentially costly research and development activities; competitions in the digital intelligence market; PRC governmental policies relating to media, software, big data, the internet, internet content providers and online advertising; and general economic and business conditions in the regions where Gridsum provides solutions and services. Further information regarding these and other risks is included in Gridsum’s reports filed with, or furnished to, the Securities and Exchange Commission. All information provided in this press release and in the attachments is as of the date of this press release, and Gridsum undertakes no duty to update such information except as required under applicable law.


News Article | May 23, 2017
Site: www.techradar.com

The OnePlus 3 has undoubtedly become the company’s most successful smartphone, both critically and commercially. But OnePlus is not resting on its laurels and has already begun work on its successor, the OnePlus 4. With a tentative launch date of June-July, leaks and rumours regarding the upcoming flagship have already started making rounds. Fresh leaks indicate that the OnePlus 4 will be powered by Qualcomm’s upcoming flagship Snapdragon 830 processor and will come with 6GB of RAM. The Snapdragon 830 processor will reportedly be built on Samsung’s 10nm manufacturing process, have an octal-core setup, support upto 8GB of RAM and also support 4K display resolutions. While Qualcomm hasn’t officially announced the Snapdragon 830, it is expected to ship with quite a few upcoming smartphones including the Samsung Galaxy S8, Microsoft Surface Phone, And the upcoming Turing phones. Further details about the OnePlus 4 indicate that it will run on OnePlus’s own Oxygen OS running atop Android 7.0 Nougat (no confirmation for 7.1), the processor will be clocked at 3.0GHz and it will have a 4,000mAH battery. Before the OnePlus 4 is launched though, OnePlus may launch an upgraded version of the current OnePlus 3 called the OnePlus 3T. The 3T will run on the latest Android 7.0 Nougat and come with an upgraded 16MP camera with a Sony IMX398 sensor. Additional rumours indicate a shift from the current OLED panel used for the display to an LCD panel with a price increase from $399 to $479.


News Article | May 25, 2017
Site: globenewswire.com

BEIJING, May 25, 2017 (GLOBE NEWSWIRE) -- Gridsum Holding Inc. (“Gridsum” or the “Company”) (NASDAQ:GSUM), a leading provider of cloud-based big-data analytics, machine learning and AI solutions in China, today announced that, as a part of its strategic evolution, it has consolidated all of its artificial intelligence (“AI”) activities strategically, technically and organizationally into a new division called the Gridsum Prophet. Gridsum is a first mover in China in big data intelligence. Since 2005, the Company has utilized a distributed big-data computing architecture, developed and implemented sophisticated natural language processing (“NLP”), and leveraged machine learning directed toward large enterprise clients. During that time, from serving large enterprise customers, the Company has accumulated deep domain knowledge and expertise as well as a massive amount of data that fuels its machine learning algorithms. Since this early inception, the Company has continued to stay at the forefront through focus and investment, hiring and training extraordinary engineers and architects and, importantly, playing an active and leading role in the AI academic and developer communities. The Company believes it is currently the technology market leader in the China enterprise-focused AI space with a global best-of-breed Enterprise-AI engine. As of March 31, 2017, Gridsum has filed 1,653 patent applications in China, of which 583 are big-data focused and 148 are explicitly Enterprise-AI focused. The Company believes this is the largest number of Enterprise-AI focused patents for a company in China today. Mr. Guosheng Qi, Chief Executive Officer of Gridsum, stated, “We are excited to launch our new AI engine, Gridsum Prophet. AI is playing an increasingly key role in developing and optimizing our intelligent products and solutions across the board and providing quantifiable value-add to our enterprise and other customers. Through Gridsum Prophet, we are taking strategic moves to further accelerate our AI development in a more concentrated way. New product features driven by Gridsum Prophet have already been key in a double-digit number of both client wins and subscription renewals with substantial ARPU uplift.” In terms of scope, Gridsum Prophet encompasses all of the Company’s AI capabilities: machine learning, natural language processing, image recognition, predictive industry modeling, and knowledge graph. Gridsum Prophet powers the Company’s intelligent products and solutions across the matrix of clients and industry verticals. It also allows the Company to see cross-industry, product and demographic correlations and relationships to add further value for its clients. Gridsum Prophet is an explicit and important development area for the Company and is increasingly key in creating new differentiated product features for the Company’s existing products (e.g. marketing automation) and is central in defining and shaping new products, such as the Company’s social listening and brand management suite as well as its legal services and Intelligent CRM product suites. Gridsum Prophet already plays a key role in driving quantifiable value for the Company’s customers. It also defines a key competitive advantage for Gridsum versus other local and global players. This is the case in the marketing technology and broader Business Intelligence ecosystems where Gridsum Prophet is instrumental for the Company in driving new customer acquisition as well as ARPU growth with existing customers. Gridsum Prophet is also key to the Company’s legal services product suite which, in some areas, Gridsum believes is consistent with passing a “soft” Turing Test - delivering results in a few seconds consistent with multiple man-hours of work by a team of junior lawyers and paralegals. Gridsum Prophet is also central to the development and success, so far in current Beta trials, for the Company’s Intelligent CRM solution. Broadly speaking, consumer AI often leverages the ubiquitous consumer Internet ecosystems across e-commerce, social, mobile, entertainment and others and focuses to quickly solve or facilitate relatively simple (from a mathematical standpoint) but often time-consuming, “painful” or distracting consumer challenges. Consumer AI is incredibly broad in scope, having the potential to revolutionize and facilitate the way people live and, within the next 10 years, will likely touch the lives of most of the people on the planet. Enterprise AI is different in focus, structure, development, management, application and goals. It requires a very different and focused “organizational DNA” which is particularly rare in China (and elsewhere in Asia). It is focused on creating immediate and quantifiable value for companies with an immediate KPI impact and evolving and increasing that value-add over time. Enterprise AI hence requires deep domain expertise and knowledge of the target industry and its ecosystem. This allows Gridsum to understand the challenges and opportunities where AI technologies can be effectively applied to delivering immediate and quantifiable value to an enterprise customer whether it is improving efficiency, reducing cost, or allocating marketing budget for optimized ROI. These are often industry-specific drivers and dynamics, typically requiring more focus and depth in a narrower area than consumer AI. To accomplish this, enterprise AI tends to heavily leverage supervised learning techniques to infuse human expertise into the resulting intelligent solutions. To drive high-value machine learning, Gridsum utilizes its growing team of industry experts and data scientists who also help tailor the Company’s products, solutions, features and UI for specific industries and verticals. This is a key differentiator of enterprise AI (and Gridsum) when compared to the demands and development process for typical consumer AI. Gridsum uses an iterative learning process whereby high quality raw data is sourced. That data is organized across multiple dimensions and machine learning then produces intermediate results from analyzing the complex relationships within the data. The Company’s industry experts review the results suggested by the system and, based on deep industry expertise, tag these results as relevant, less relevant or irrelevant which is then fed back into the system’s machine learning cycle to further optimize the results, thereby forming a positive optimizing feedback loop. In addition to the Company’s strong emphasis on supervised learning, it is important to note that Gridsum utilizes deep learning, supervised or unsupervised where appropriate, in areas such as abnormal pattern discovery, image recognition and customer value prediction. To further explain how Gridsum Prophet works in practice, below are three specific examples of the engine in action. The Company estimates that over 35% of Internet traffic in China can be defined as “suspicious” (including non-human/bot and “malvertising”). Powered by the vast amount of online behavior data that the Company has been collecting and analyzing for over a decade across a broad range of industries and diverse sets of computing devices and software systems, Gridsum’s supervised and deep learning algorithms continuously improve the coverage and accuracy of click fraud detection and prevention within its Web Dissector and other products. By consolidating all of its abnormal pattern recognition technology into the Prophet engine, the system is now leveraging cross-industry and cross-product machine learning resulting in improved performance. This helps the Company’s clients to substantially and quantifiably improve their ROI from their digital market dollars. By mining historical media buying and performance data and leveraging Gridsum’s deep knowledge and expertise in the China marketing space, the Company’s data science team has developed an automated industry modeling and marketing budget allocation model. This means that a customer can input a desired range of KPI results and the system will then define and allocate a marketing budget to achieve this. This also allows a customer to have an accurate prediction of likely KPI outcomes from their marketing budget and strategy. This heavily leverages Gridsum’s supervised machine learning algorithms that the Company has already successfully used to help clients in the financial industry to allocate their annual digital marketing budget across media channels to achieve optimal return on investment. This moves Gridsum’s products up the value chain to a marketing strategy creation level, and has already resulted in significantly increased ARPU for a number of clients This is increasingly important in China today where the largest brands are finding it increasingly difficult to differentiate in an increasingly “noisy” and crowded landscape defined by a proliferation of lower-end brands. The Gridsum Prophet engine here also importantly helps clients to explore new media such as newsfeed, short video and social video in an ROI-focused, controlled, data-driven and results-oriented manner. These features have recently been a key differentiator in a number of the Company’s new customer additions. In another demonstration of the power of data and deep learning, Gridsum has built a customer value prediction system by analyzing the customers’ online (spanning mobile app, browser and PC) and offline behavior data (contained within a customer’s CRM system and other areas). Using predictive modeling and machine learning, the system has already been helping clients in the automotive industry to effectively improve their sales and call center performance by reducing the average number of calls needed for customer acquisition by as much as 90%. Going forward, over the medium and longer term, Gridsum Prophet opens the opportunity for significant future product development. For example: Gridsum Holding Inc. is a leading provider of cloud-based big-data analytics, machine learning and AI solutions for multinational and domestic enterprises and government agencies in China. Gridsum’s core technology, the Gridsum Big Data Platform, is built on a distributed computing framework and performs real-time multi-dimensional correlation analysis of both structured and unstructured data. This enables Gridsum’s customers to identify complex relationships within their data and gain new insights that help them make better business decisions. The Company is named “Gridsum” to symbolize the combination of distributed computing (Grid) and analytics (sum). As a digital intelligence pioneer, the Company’s mission is to help enterprises and government organizations in China use data in new and powerful ways to make better informed decisions and be more productive. For more information, please visit http://www.gridsum.com/. This announcement contains forward-looking statements. These forward-looking statements are made under the "safe harbor" provisions of the U.S. Private Securities Litigation Reform Act of 1995. These statements can be identified by terminology such as “may,” "will," "expects," "anticipates," “aims,” "future," "intends," "plans," "believes," "estimates," “likely to” and similar statements. Among other things, quotations from management in this announcement, Gridsum’s financial outlook as well as Gridsum's strategic and operational plans contain forward-looking statements. Gridsum may also make written or oral forward-looking statements in its reports filed with, or furnished to, the U.S. Securities and Exchange Commission, in its annual reports to shareholders, in press releases and other written materials and in oral statements made by its officers, directors or employees to third parties. Statements that are not historical facts, including statements about Gridsum's beliefs and expectations, are forward-looking statements. Forward-looking statements involve inherent risks and uncertainties. A number of factors could cause actual results to differ materially from those contained in any forward-looking statement, including but not limited to the following: unexpected difficulties in Gridsum's pursuit of its goals and strategies; the unexpected developments, including slow growth, in the digital intelligence market; reduced demand for, and market acceptance of, Gridsum's solutions; difficulties keeping and strengthening relationships with customers; potentially costly research and development activities; competitions in the digital intelligence market; PRC governmental policies relating to media, software, big data, the internet, internet content providers and online advertising; and general economic and business conditions in the regions where Gridsum provides solutions and services. Further information regarding these and other risks is included in Gridsum’s reports filed with, or furnished to, the Securities and Exchange Commission. All information provided in this press release and in the attachments is as of the date of this press release, and Gridsum undertakes no duty to update such information except as required under applicable law.


News Article | May 25, 2017
Site: globenewswire.com

BEIJING, May 25, 2017 (GLOBE NEWSWIRE) -- Gridsum Holding Inc. (“Gridsum” or the “Company”) (NASDAQ:GSUM), a leading provider of cloud-based big-data analytics, machine learning and AI solutions in China, today announced that, as a part of its strategic evolution, it has consolidated all of its artificial intelligence (“AI”) activities strategically, technically and organizationally into a new division called the Gridsum Prophet. Gridsum is a first mover in China in big data intelligence. Since 2005, the Company has utilized a distributed big-data computing architecture, developed and implemented sophisticated natural language processing (“NLP”), and leveraged machine learning directed toward large enterprise clients. During that time, from serving large enterprise customers, the Company has accumulated deep domain knowledge and expertise as well as a massive amount of data that fuels its machine learning algorithms. Since this early inception, the Company has continued to stay at the forefront through focus and investment, hiring and training extraordinary engineers and architects and, importantly, playing an active and leading role in the AI academic and developer communities. The Company believes it is currently the technology market leader in the China enterprise-focused AI space with a global best-of-breed Enterprise-AI engine. As of March 31, 2017, Gridsum has filed 1,653 patent applications in China, of which 583 are big-data focused and 148 are explicitly Enterprise-AI focused. The Company believes this is the largest number of Enterprise-AI focused patents for a company in China today. Mr. Guosheng Qi, Chief Executive Officer of Gridsum, stated, “We are excited to launch our new AI engine, Gridsum Prophet. AI is playing an increasingly key role in developing and optimizing our intelligent products and solutions across the board and providing quantifiable value-add to our enterprise and other customers. Through Gridsum Prophet, we are taking strategic moves to further accelerate our AI development in a more concentrated way. New product features driven by Gridsum Prophet have already been key in a double-digit number of both client wins and subscription renewals with substantial ARPU uplift.” In terms of scope, Gridsum Prophet encompasses all of the Company’s AI capabilities: machine learning, natural language processing, image recognition, predictive industry modeling, and knowledge graph. Gridsum Prophet powers the Company’s intelligent products and solutions across the matrix of clients and industry verticals. It also allows the Company to see cross-industry, product and demographic correlations and relationships to add further value for its clients. Gridsum Prophet is an explicit and important development area for the Company and is increasingly key in creating new differentiated product features for the Company’s existing products (e.g. marketing automation) and is central in defining and shaping new products, such as the Company’s social listening and brand management suite as well as its legal services and Intelligent CRM product suites. Gridsum Prophet already plays a key role in driving quantifiable value for the Company’s customers. It also defines a key competitive advantage for Gridsum versus other local and global players. This is the case in the marketing technology and broader Business Intelligence ecosystems where Gridsum Prophet is instrumental for the Company in driving new customer acquisition as well as ARPU growth with existing customers. Gridsum Prophet is also key to the Company’s legal services product suite which, in some areas, Gridsum believes is consistent with passing a “soft” Turing Test - delivering results in a few seconds consistent with multiple man-hours of work by a team of junior lawyers and paralegals. Gridsum Prophet is also central to the development and success, so far in current Beta trials, for the Company’s Intelligent CRM solution. Broadly speaking, consumer AI often leverages the ubiquitous consumer Internet ecosystems across e-commerce, social, mobile, entertainment and others and focuses to quickly solve or facilitate relatively simple (from a mathematical standpoint) but often time-consuming, “painful” or distracting consumer challenges. Consumer AI is incredibly broad in scope, having the potential to revolutionize and facilitate the way people live and, within the next 10 years, will likely touch the lives of most of the people on the planet. Enterprise AI is different in focus, structure, development, management, application and goals. It requires a very different and focused “organizational DNA” which is particularly rare in China (and elsewhere in Asia). It is focused on creating immediate and quantifiable value for companies with an immediate KPI impact and evolving and increasing that value-add over time. Enterprise AI hence requires deep domain expertise and knowledge of the target industry and its ecosystem. This allows Gridsum to understand the challenges and opportunities where AI technologies can be effectively applied to delivering immediate and quantifiable value to an enterprise customer whether it is improving efficiency, reducing cost, or allocating marketing budget for optimized ROI. These are often industry-specific drivers and dynamics, typically requiring more focus and depth in a narrower area than consumer AI. To accomplish this, enterprise AI tends to heavily leverage supervised learning techniques to infuse human expertise into the resulting intelligent solutions. To drive high-value machine learning, Gridsum utilizes its growing team of industry experts and data scientists who also help tailor the Company’s products, solutions, features and UI for specific industries and verticals. This is a key differentiator of enterprise AI (and Gridsum) when compared to the demands and development process for typical consumer AI. Gridsum uses an iterative learning process whereby high quality raw data is sourced. That data is organized across multiple dimensions and machine learning then produces intermediate results from analyzing the complex relationships within the data. The Company’s industry experts review the results suggested by the system and, based on deep industry expertise, tag these results as relevant, less relevant or irrelevant which is then fed back into the system’s machine learning cycle to further optimize the results, thereby forming a positive optimizing feedback loop. In addition to the Company’s strong emphasis on supervised learning, it is important to note that Gridsum utilizes deep learning, supervised or unsupervised where appropriate, in areas such as abnormal pattern discovery, image recognition and customer value prediction. To further explain how Gridsum Prophet works in practice, below are three specific examples of the engine in action. The Company estimates that over 35% of Internet traffic in China can be defined as “suspicious” (including non-human/bot and “malvertising”). Powered by the vast amount of online behavior data that the Company has been collecting and analyzing for over a decade across a broad range of industries and diverse sets of computing devices and software systems, Gridsum’s supervised and deep learning algorithms continuously improve the coverage and accuracy of click fraud detection and prevention within its Web Dissector and other products. By consolidating all of its abnormal pattern recognition technology into the Prophet engine, the system is now leveraging cross-industry and cross-product machine learning resulting in improved performance. This helps the Company’s clients to substantially and quantifiably improve their ROI from their digital market dollars. By mining historical media buying and performance data and leveraging Gridsum’s deep knowledge and expertise in the China marketing space, the Company’s data science team has developed an automated industry modeling and marketing budget allocation model. This means that a customer can input a desired range of KPI results and the system will then define and allocate a marketing budget to achieve this. This also allows a customer to have an accurate prediction of likely KPI outcomes from their marketing budget and strategy. This heavily leverages Gridsum’s supervised machine learning algorithms that the Company has already successfully used to help clients in the financial industry to allocate their annual digital marketing budget across media channels to achieve optimal return on investment. This moves Gridsum’s products up the value chain to a marketing strategy creation level, and has already resulted in significantly increased ARPU for a number of clients This is increasingly important in China today where the largest brands are finding it increasingly difficult to differentiate in an increasingly “noisy” and crowded landscape defined by a proliferation of lower-end brands. The Gridsum Prophet engine here also importantly helps clients to explore new media such as newsfeed, short video and social video in an ROI-focused, controlled, data-driven and results-oriented manner. These features have recently been a key differentiator in a number of the Company’s new customer additions. In another demonstration of the power of data and deep learning, Gridsum has built a customer value prediction system by analyzing the customers’ online (spanning mobile app, browser and PC) and offline behavior data (contained within a customer’s CRM system and other areas). Using predictive modeling and machine learning, the system has already been helping clients in the automotive industry to effectively improve their sales and call center performance by reducing the average number of calls needed for customer acquisition by as much as 90%. Going forward, over the medium and longer term, Gridsum Prophet opens the opportunity for significant future product development. For example: Gridsum Holding Inc. is a leading provider of cloud-based big-data analytics, machine learning and AI solutions for multinational and domestic enterprises and government agencies in China. Gridsum’s core technology, the Gridsum Big Data Platform, is built on a distributed computing framework and performs real-time multi-dimensional correlation analysis of both structured and unstructured data. This enables Gridsum’s customers to identify complex relationships within their data and gain new insights that help them make better business decisions. The Company is named “Gridsum” to symbolize the combination of distributed computing (Grid) and analytics (sum). As a digital intelligence pioneer, the Company’s mission is to help enterprises and government organizations in China use data in new and powerful ways to make better informed decisions and be more productive. For more information, please visit http://www.gridsum.com/. This announcement contains forward-looking statements. These forward-looking statements are made under the "safe harbor" provisions of the U.S. Private Securities Litigation Reform Act of 1995. These statements can be identified by terminology such as “may,” "will," "expects," "anticipates," “aims,” "future," "intends," "plans," "believes," "estimates," “likely to” and similar statements. Among other things, quotations from management in this announcement, Gridsum’s financial outlook as well as Gridsum's strategic and operational plans contain forward-looking statements. Gridsum may also make written or oral forward-looking statements in its reports filed with, or furnished to, the U.S. Securities and Exchange Commission, in its annual reports to shareholders, in press releases and other written materials and in oral statements made by its officers, directors or employees to third parties. Statements that are not historical facts, including statements about Gridsum's beliefs and expectations, are forward-looking statements. Forward-looking statements involve inherent risks and uncertainties. A number of factors could cause actual results to differ materially from those contained in any forward-looking statement, including but not limited to the following: unexpected difficulties in Gridsum's pursuit of its goals and strategies; the unexpected developments, including slow growth, in the digital intelligence market; reduced demand for, and market acceptance of, Gridsum's solutions; difficulties keeping and strengthening relationships with customers; potentially costly research and development activities; competitions in the digital intelligence market; PRC governmental policies relating to media, software, big data, the internet, internet content providers and online advertising; and general economic and business conditions in the regions where Gridsum provides solutions and services. Further information regarding these and other risks is included in Gridsum’s reports filed with, or furnished to, the Securities and Exchange Commission. All information provided in this press release and in the attachments is as of the date of this press release, and Gridsum undertakes no duty to update such information except as required under applicable law.


WEST PALM BEACH, Fla.--(BUSINESS WIRE)--SubscriberWise, the nation's largest issuing CRA for the communications industry and the nation’s leading protector of children victimized by identity fraud, announced today company founder and global master-champion all-time worldwide highest FICO and Vantage Score achiever David E. Howe has contacted Credit Karma public relations with concerns involving a TV advertisement that may inadvertently mislead current and future members of the credit and financial educational site. “Yesterday I watched an advertisement from Credit Karma involving what appears to be a prospective tenant and her landlord,” said David Howe, SubscriberWise founder and FICO worldwide highest achiever since Alan Turing invented the computer. “Near the conclusion of the commercial, after the prospective renter expressed interest for the unit, the landlord inquired about the renter’s credit. The renter responded to the request by showing her Credit Karma scores on her personal device. The landlord glanced at the scores and instantly offered an approval, based on what must have been favorable credit from her perspective. “Although I eagerly acknowledge that Credit Karma didn’t produce this content to mislead or misinform consumers in any way, this particular advertisement nevertheless has the possibility of giving consumers an inaccurate understanding of the strict federal regulations that are required for permissible-purpose access to federally regulated consumer credit products for underwriting purposes -- including for individuals seeking apartment rentals and other access to credit,” Howe commented. “The fundamental concern with the advertisement is that Credit Karma is not approved for underwriting purposes and some -- if not many -- consumers watching this advertisement may not understand how the federal government strictly regulates credit products and scores for this purpose. Some consumers watching the ad may actually believe that they can rely on their personal Credit Karma account to obtain approvals from landlords and creditors in the way depicted in this advertisement. Worse, they may expect landlords and credit grantors to imitate this and other scenarios. And while Credit Karma may predict outcomes with profound accuracy based on their modeling, ultimately the creditor sets the terms. “So there are many good reasons why consumers cannot and should not rely on Credit Karma in this way,” added Howe. “Let’s say in this same scenario, if the prospective renter revealed what may be considered adverse credit to the landlord, the landlord instantly denied based on the Credit Karma score -- then there could be a violation of federal law because the landlord may not have access to other critical information which would have to be part of a legally required response. Moreover, it’s also possible that the landlord would violate federal ‘Red Flag’ rules because Credit Karma doesn’t comply with current laws related to Red Flags since the company is not currently engaged in underwriting and approval from the standpoint of existing federal requirements, at least in the capacity presented in this advertisement. Specifically, in addition to Red Flags and any other regulatory requirements, the landlord would be required to provide score reason factors derived from the credit report and also listed in the order of the most significant factor to the least in terms of the impact on the scores. In other words, when there’s a denial based on a credit, consumers are entitled to an ‘Adverse Action Notice’ as indicated by the FCRA. In other instances, a ‘Risk-Based Pricing Notice’ or ‘Credit Score Disclosure’ may be indicated (https://www.ftc.gov/tips-advice/business-center/guidance/using-consumer-reports-credit-decisions-what-know-about-adverse),” noted Howe. “From my perspective, this particular Credit Karma spot fails to deliver this big-picture and critical message to viewers. “Credit Karma, in its current commercial application, does not currently meet the stringent federal requirements for FCRA underwriting requirements and it’s imperative that current and future members clearly understand this fact,” Howe emphasized. “It’s also imperative Credit Karma members understand what their federal rights are when adverse credit translates into denial or less favorable terms (https://www.consumer.ftc.gov/articles/pdf-0096-fair-credit-reporting-act.pdf). “And just to reiterate, I know that Credit Karma didn’t intend to mislead or misinform consumers in this ad. Rather, I’m convinced, their goal is to continue to expand membership while also empowering individuals with credit knowledge at the same time exposing the myriad ways that credit plays a critical role in our lives today. Credit education, for the record, is the primary reason I recommended Credit Karma last year. “Yes, the Credit Karma site does an excellent job achieving their goals,” concluded Howe. “I would even argue that this particular advertisement does deliver an overall positive message, despite this important weakness that I feel compelled to point out.” Related: Global Credit Czar David Howe, FICO All-Time Highest Achiever and the United States’ Most Prolific Protector of Children Victimized by Identity Fraud, Takes Advocacy and Education to Twitter and Facebook SubscriberWise® launched as the first issuing consumer reporting agency exclusively for the cable industry in 2006. In 2009, SubscriberWise and TransUnion announced a joint marketing agreement for the benefit of America's cable operators. Today SubscriberWise is a risk management preferred-solutions provider for the National Cable Television Cooperative. SubscriberWise is a federally registered trademark of the SubscriberWise Limited Liability Co.

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