Binance Changpeng Zhao, the CEO of the largest cryptobirge, said he did not consider ETFs "a key factor in the growth of the industry."
– Binance (@binance) 7 February 2019 p.
“I think that in order to grow our industry, we need more entrepreneurs to create real projects ,” Zhao stressed during the Periscope broadcast.
The head of Binance was asked to comment on the situation with the problem of QuadrigaCX cryptographic trading . Zhao replied that he usually reluctantly talks about other trading floors, but in this case "something suspicious is happening."
Changpen Zhao also revealed some details about the upcoming decentralized Binance DEX exchange. According to him, the testnet Binance Chain, which forms the basis of the trading platform, will start “in one two-week time period”.
The most popular tokens of the ERC20 standard – Binance Coin (BNB) – will soon be transferred to their own blockchain. The other day BNB asset entered the top ten of the CoinMarketCap rating.
Binance Chain is built on the Tendermint protocol from the Cosmos Network project. According to Zhao, the architecture of this protocol is best suited for the tasks of the new site. The blockchain at the heart of Binance DEX will operate on the basis of the delegated share (Delegated Proof of Stake, DPoS) and the Byzantine Consensus (Byzantine Fault Tolerant, BFT).
Also, according to Zhao, Binance Chain is a fork of the Tendermint, in which "many things are cut out", including smart contracts. This is done to increase productivity, which is more important for exchange tasks than an abundance of opportunities.
The platform interface will allow projects to release tokens based on Binance Chain and “effectively carry out ICO”, attracting funds in BNB tokens.
According to Zhao, Binance DEX commissions will be “small.” In addition, there is a fee for launching tokens on the Binance Chain, which will initially be “about $ 100,000”, but will vary in the future.
“I intentionally want to set a rather high listing commission. This will help reduce spam and scam projects. Also validators will be involved in the process of voting for candidates for listing DEX »
Binance DEX will be a non-custodial solution. This means that only users will control their private keys. However, Zhao noted that at first Binance will have ample opportunities for influencing the network and validators. The last in Binance Chain will be only 11, at least at the initial stage.
“I think at the early stages we need to maintain a significant impact on the network ,” the head of the largest stock exchange shares his thoughts. “ Over time, more and more validators will join the network, and our influence will diminish.”
He also added that such a small number of validators will be used for performance reasons:
“It will be like NEO or even Ripple: a small number of nodes, with each one quite powerful”
The disk space requirements for validator nodes will be quite large, since a new block will be generated every second in Binance Chain. This approach will allow the network to process "several thousand" transactions per second. Moreover, Zhao noted that a decentralized platform could already handle the current trading volume on the main Binance exchange.
After launching the test network, access to the Binance DEX user interface will appear via a web browser. The company has already provided early access to manufacturers of wallets, blockchain browsers and other solutions wishing to integrate with the new platform. The first hardware wallet supporting Binance Chain and DEX will be Ledger Nano S. Then integration with KeepKey and Trezor will follow.
Over time, Binance intends to develop crosschain interaction between different chains.
Recall Binance eyeing the technology xRapid from Ripple , which involves the use of XRP tokens.
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On October 29, Moscow will host the Tech Week 19 digital conference
The Tech Week platform brings together the best entrepreneurs from different parts of the Russian Federation and abroad twice a year. At the autumn meeting, the organizers plan to gather 2,000 people. The conference will bring together more than 200 speakers, among which 5 leading foreign entrepreneurs are scheduled to speak. Traditionally, an exhibition of smart projects to solve business problems will function during the event.
Promo video of the event :
Tech Week 19 is a four-day applied conference and exhibition on innovative technologies for business. Unlike other events, Tech Week is a huge educational hub where people communicate, get inspired and can find ready-made solutions for their business. The knowledge and content presented at the event is definitely not on the Internet. The connections that can be obtained during the conference have been developed over the years.
The current event will be held at the Skolkovo Technopark.
As part of the event:
- Pre-party evening;
- Multi-format conference;
- Technology exhibition;
- Day of applied master classes;
- Access to an online platform with video recordings of presentations and presentations.
- Digital business transformation;
- Virtual and Augmented Reality;
- Artificial Intelligence and Big Data;
- Blockchain technology;
- Digital marketing
- Digital Sales
- Management methodology;
- Product & Design Management;
- Business analytics;
- Investment pitches.
Among the speakers:
- Nadezhda Surova – member of the Council for the Development of the Digital Economy under the Council of the Federation of the Federal Assembly of the Russian Federation
- Mikhail Matveev – Director of Digital Transformation, VTB
- Sergey Lukashkin – Director of Digital Transformation Project Management, VTB
- Sergey Parshikov – Project Director, Digital Corporate Bank Division, Corporate Investment Banking Unit, Sberbank
- Valeria Kurmak – Inclusive Design Expert, Sberbank
- Sergey Kononenko – Chief Technical Officer, Raiffeisenbank Russia
- Alexey Kudachkin – Product Manager, IT Department, Gazprombank
- Maxim Avdeev – Founder and CEO, QIWI Platform
- Andrey Ivashentsev – Regional Director, Microsoft
- Yakov Peysakhzon – Head of Customer Relations, Mail.ru Group
- Vladislav Prishchepov – product manager AppMetrica, Yandex
- Mikhail Vysokovsky – Service Manager, Yandex.Navigator
- Yuri Smagarinsky – Executive Vice President, Sales and Customer Service, VimpelCom (Beeline)
- Stanislav Milykh – AI Implementation Project Manager, Megaphone
- Elena Kaplieva – Head of Digital Marketing, MTS
- Elena Levochkina – Head of the Department of Automation of Business Processes, Human Resources Management Unit, MTS
- Eduard Segal – Head of Design, Digital Identity Design Office, Rostelecom
- Vitaly Porubov – Head of Strategy and Innovation, X5 Retail Group (Pyaterochka, Perekrestok, Carousel)
- Mikhail Dmitriev – Head of Monitoring and Rapid Response, X5 Retail Group (Pyaterochka, Perekrestok, Carousel)
- Olesya Mashkova – Innovation Manager, VkusVill
- Anton Beskhodarny – IT Strategic Development Project Manager, Gazprom Neft
- Alexander Kalmykov – Head of Blockchain Technology Center, Gazprom Neft
- Alexey Aldoshkin – Investment Project Analyst, AtomInvest Company, State Atomic Energy Corporation Rosatom
- Yuri Bubnov – Leading Engineer, Research and Development, Siemens Russia
- Denis Dudorov – Head of Organizational Development, Avito
- Ekaterina Skizhevskaya – Investment Manager, S7 Ventures
and many others ( download the conference program ).
Why is it necessary to study the process of introducing digital technologies into business and what successful cases are there right now:
Experience of M. Video on the implementation of artificial intelligence systems
Today, artificial intelligence is used by many companies in various sectors: logistics, personnel management, increasing revenues, reducing costs, etc. In his activity, he is also used by M.Video.
A special group has been created in M.Video that is engaged in the development of data monetization solutions and the application of artificial intelligence and machine learning technologies to optimize various company business processes.
As a starting line of development, the company settled on solutions to improve customer service and provide a personalized offer for its customers. M.Video introduced a special system that allows you to evaluate the customer’s behavior on the site, his browsing history and the products left in the basket. Based on these data, the system offers the client personalized product recommendations. If a registered user of the site put some product in the basket, but left the online store without placing an order, the system reminds him of this and offers alternative options in terms of characteristics. The company has developed a model for choosing the optimal time for interaction with the client – when he is most inclined to purchase. In addition, “M. Video” introduced a mechanism for selecting accessories on the main product page – including, based on the preferences of other customers. All these implementations significantly increased the conversion to purchases.
Today, M.Video does not stop there and is actively developing artificial intelligence and machine learning technologies, expanding the scope of their application. Next in line are projects on the aggregation of product reviews, assortment management, logistics supply and demand forecasting.
You can learn detailed insiders about digitalization of sales from the report of marketing director of M.Video Anton Volodkin in the Digital Sales section of Tech Week 19.
The event is organized by the Technokrat company. Successful conferences are behind her:
- Russian Blockchain Week (REU named after Plekhanov, 2017-2018);
- Russian Tech Week (Skolkovo, 2018-2019);
- Digital Technology in Retail (Deworkacy, 2019).
- Mandarin Solutions (https://mandarin.solutions/ru)
- Asana (https://asana.com/)
- AboutBrand (https://aboutbrand.ru/)
Why visit Tech Week:
- Get an edge over your competitors. Change your mind, find new connections and turnkey solutions for your business.
- Top speakers. Listen to over 200 reports from the best experts in your field.
- Practical workshops. Get to know the practical side of technology adoption as a team.
- Exhibition of smart projects. Get to know more than 80 technological solutions in the expo zone.
- Presentation of your project. Rent a booth and tell us about your company. Get quick feedback about your product.
- Networking. Exchange ideas with colleagues and speakers in an informal setting for a pre-party evening.
Book your seats in advance with a 10% discount on the promo code:
- IFPCRTW-MININGCRYPTOCURRENCY by phone +7 (499) 348-20-04 or on the website .
Publication date 08/22/2019
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What is Big data in simple words? Application and perspectives of big data
After 10 years, the world will move into a new era – the era of big data. Instead of a weather widget on the smartphone screen, he himself will tell you what is best to wear. At breakfast, the phone will show the road along which you will quickly get to work and when you need to leave.
Under the influence of Big Data, everything that a person does not touch will change. We will figure out what it is, and also consider the real application and prospects of the technology.
What is Big data?
Big data is an information processing technology that surpasses hundreds of terabytes and grows exponentially over time.
Such data is so large and complex that none of the traditional data management tools can store or efficiently process it. A person is not able to analyze this volume. For this, special algorithms have been developed that, after analyzing big data, give a person understandable results.
Big Data includes petabytes (1024 terabytes) or exabytes (1024 petabytes) of information that make up billions or trillions of records of millions of people and all from different sources (Internet, sales, contact center, social networks, mobile devices). As a rule, information is poorly structured and often incomplete and inaccessible.
How does Big-Data technology work?
Users of the social network Facebook upload photos, videos and perform actions every day for hundreds of terabytes. No matter how many people participate in the development, they will not cope with the constant flow of information. In order to further develop the service and make sites more comfortable – introduce smart content recommendations, display ads relevant to the user, hundreds of thousands of terabytes are passed through the algorithm and receive structured and understandable information.
Comparing a huge amount of information, it finds the relationship. These relationships are likely to predict the future. To find and analyze a person helps artificial intelligence.
The neural network scans thousands of photos, videos, comments – the very hundreds of terabytes of big data and gives the result: how many satisfied customers leave the store, whether there will be a traffic jam in the coming hours, what discussions are popular on the social network and much more.
Methods of working with big data:
- Machine learning
- Mood analysis
- Social network analysis
- Learning Rules Association
- Classification Tree Analysis
- Genetic Algorithms
- Regression analysis
You look at the news feed, like Instagram posts, and the algorithm examines your content and recommends similar ones. Artificial intelligence learns without explicit programming and focuses on forecasting based on well-known properties extracted from sets of “training data”.
Machine learning helps :
- Distinguish between spam and non-spam email
- Explore user preferences and give recommendations
- Identify the best content to attract potential customers.
- Determine the probability of winning a case and set legal fees
Mood analysis helps :
- Improve hotel chain service by analyzing guest comments
- Customize incentives and services to meet customer needs
- Determine from opinions on the social network what customers are thinking.
Social Network Analysis
Social network analysis was first used in the telecommunications industry. The method is used by sociologists to analyze relationships between people in many fields and commercial activities.
This analysis is used to :
- See how people from different population groups form relationships with outsiders
- Find out the importance and influence of a specific person in a group
- Find the minimum number of direct connections to connect two people
- Understand the social structure of the customer base
Learning Association Rules
People who don’t buy alcohol take juices more often than lovers of strong drinks?
Studying association rules is a method for discovering interesting relationships between variables in large databases. For the first time, it was used by large supermarket chains to discover interesting relationships between products using information from supermarket outlet systems (POS).
Using association rules :
- Place products closer to each other to increase sales
- Extract website visitor information from web server logs
- Analyze biological data
- Track system logs to detect intruders
- Determine if tea shoppers take sodas more often
Classification Tree Analysis
Statistical classification defines the categories to which the new observation belongs.
Statistical classification is used for :
- Automatic assignment of documents to categories
- Classification of organisms into groups
- Developing profiles of students taking online courses
Genetic algorithms are inspired by how evolution works, that is, through mechanisms such as inheritance, mutation, and natural selection.
Genetic algorithms are used for :
- Scheduling doctors for emergency departments in hospitals
- Calculation of optimal materials for the development of fuel-efficient cars
- Creating “artificially creative” content such as puns and jokes
How does a person’s age affect the type of car he buys?
At a basic level, regression analysis involves manipulating some independent variable (like background music) to see how it affects the dependent variable (time spent in the store).
Regression analysis is used to determine:
- Customer satisfaction levels
- How the weather forecast for the previous day affects the number of calls to support
- How the area and size of houses affect the price of housing
Data Mining – how Big Date is collected and processed
Uploading big data to a traditional relational database for analysis takes a lot of time and money. For this reason, special approaches have appeared for the collection and analysis of information. To obtain and then retrieve the information, combine and place it in a “data lake”. From there, artificial intelligence programs using complex algorithms look for repeating patterns.
Storage and processing takes place with the following tools:
- Apache HADOOP is a packet-oriented data processing system. The system stores and tracks information on several machines and scales to several thousand servers.
- HPPC is an open source platform developed by LexisNexis Risk Solutions. HPPC is known as the Data Analytics Supercomputer (DAS), which supports data processing both in batch mode and in real time. The system uses supercomputers and clusters from conventional computers.
- Storm – processes information in real time. Uses the open source Eclipse Public License.
The real application of Big Data
The fastest growth in spending on big data technology occurs in the banking, healthcare, insurance, securities and investment services, as well as in telecommunications. Three of these industries are in the financial sector, which has many useful options for Big Data analysis: fraud detection, risk management, and customer service optimization.
Banks and credit card companies use big data to identify patterns that indicate criminal activity. Because of this, some analysts believe that big data can benefit cryptocurrency . Algorithms will be able to detect fraud and illegal activities in the crypto industry.
Thanks to cryptocurrencies such as Bitcoin and Ethereum, the blockchain can actually support any type of digitized information. It can be used in the Big Data field, especially to improve the security or quality of information.
For example, a hospital can use it to ensure the safety, relevance of patient data and to fully preserve their quality. By placing health databases on the blockchain, the hospital provides all its employees with access to a single, unchanging source of information.
Just as people associate cryptocurrency with volatility, they often associate big data with the ability to sift through large amounts of information. Big Data helps track trends. A lot of factors influence the price and big data algorithms will take this into account and then provide a solution.
Prospects for Using Big Date
Blockchain and Big Data are two evolving and complementary technologies. Since 2016, blockchain is often discussed in the media. This is a cryptographically secure distributed database technology for storing and transmitting information. Protecting private and confidential information is an urgent and future big data problem that the blockchain can solve.
Almost every industry has begun investing in Big Data analytics, but some are investing more than others. According to IDC, they spend more on banking, discrete manufacturing, process manufacturing and professional services. According to Wikibon research, revenue from sales of programs and services on the world market in 2018 amounted to $ 42 billion, and in 2027 it will overcome the mark of $ 100 billion.
Neimeth estimates that the blockchain will account for up to 20% of the total big data market by 2030, generating up to $ 100 billion in annual revenue. This exceeds the profit of PayPal, Visa and Mastercard combined.
Big Data analytics will be important for tracking transactions and will allow companies using the blockchain to identify hidden schemes and find out who they interact with on the blockchain.
Big data market in Russia
The whole world, including Russia, uses Big Data technology in banking, communications and retail. Experts believe that in the future technology will be used by the transport industry, the oil and gas and food industries, as well as energy.
IDC analysts have recognized Russia as the largest regional BDA market. According to estimates this year, revenue will approach $ 1.4 billion and will account for 40% of total investment in the big data sector and business intelligence applications.
Publication date 08/22/2019
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Study: in July 2019, the trading volume on “doubtful” cryptocurrency exchanges exceeded $ 300 billion
Despite the fact that reputable exchanges have recently increased trading volumes, dubious trading platforms continue to dominate the market, whose approaches to meeting regulatory requirements and ensuring user safety are far from the standards set. This conclusion is contained in the latest study of the analytical portal CryptoCompare. So, according to the recently launched Exchange Benchmark rating system, in July […]
Despite the fact that reputable exchanges have recently increased trading volumes, dubious trading platforms continue to dominate the market, whose approaches to meeting regulatory requirements and ensuring user safety are far from the standards set. This conclusion is contained in the latest study of the analytical portal CryptoCompare .
Thus, according to the recently launched Exchange Benchmark rating system, in July trading volumes of AA exchanges grew by 29% compared to the previous month. In total, they accounted for 5% of the total volume ($ 31 billion), for category A and B exchanges, these indicators amounted to 19% ($ 119 billion) and 8% ($ 47 billion), respectively.
At the same time, the aggregate trading volumes of D and E exchanges amounted to 64% ($ 316 billion).
In general, trading volumes on “trusted” exchanges (AA-B categories) grew by 4.4% in July, while for exchanges from CF categories the increase was only 0.7%. The total volume on trusted exchanges nevertheless amounted to only one third (32%) of the total market.
Among CF category exchanges, the largest average transaction sizes were on LBank, Coinsbit and CoinBene – 3.7, 1.6 and 1.1 BTC, respectively. It is noteworthy that on LBank (category D), the average transaction size in the BTC / USDT pair was 15 times higher than in the BTC / USD pair on the leading American exchange Coinbase (category AA).
In terms of the number of transactions concluded daily, on LBank this figure amounted to 25 thousand in the BTC / USDT pair against 100 thousand in the BTC / USD pair on Coinbase. The leaders in the number of daily transactions concluded in July were Liquid (more than 400 thousand) and Binance (more than 300 thousand). More than 200 thousand transactions were made on OKEx and BitFlyer.
The study also showed that the largest cryptocurrency exchange in terms of trading volume with an indicator of $ 20.4 billion turned out to be Bithumb. At the same time, the South Korean platform showed impressive growth rates – in July its volumes grew 46.4%.
Coinbase ($ 12.5 billion) and Bitfinex ($ 9.35 billion), which ran second and third, trade volumes, on the contrary, increased: minus 2% and 15.2%, respectively.
At the same time, exchanges where trading is exclusively in cryptocurrency pairs accounted for 84% of the total trading volume ($ 497 billion). For exchanges with fiat support, this figure was 16% ($ 93 billion). These data are consistent with the previous two months.
Recall, earlier DataLight analysts said that in relation to the highs of 2017, the volume of trading in bitcoin in the first half of 2019 increased by 1.5 times.
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