Discover a world where courts can return stolen cryptocurrency and manage smart contracts in case of a breach. All of this is possible with Jurat, a powerful tool that connects courts with blockchains to make digital assets safer and more versatile. Jurat is solving some of the toughest challenges confronting blockchain and Web3 today. We offer branded cryptocurrencies, smart contracts, and public blockchain forks that protect legal rights and keep digital commerce operating within the letter of the law. We also provide our court-connectivity infrastructure directly to blockchain developers, digital asset custodians, and Web3 organizations. To learn more, visit us at www.jurat.io. #JusticeOnTheBlockchain

Jurat

$JTC, the native token of a legal recourse Bitcoin branch, is soon to be . Chicago, Illinois, January 9th, 2024/Chainwire/-- listed on BitMart The highly anticipated $JTC listing on BitMart has been confirmed. The coin will be listed at 10 AM UTC on 09/01. The listing announcement earlier in December brings the crypto industry one step closer to accessing the same legal remedies available in mainstream finance. The listing is a significant milestone as it means the JTC blockchain’s native crypto will be available on an exchange that handles billions of dollars in volume daily and serves customers worldwide. Users will be able to trade $JTC on BitMart with a USDT pairing. BitMart Confirms JTC Listing on Its Official X Profile The BitMart listing comes after almost two years of development to integrate the $JTC coin with official state and federal court systems. For the past two years, the Jurat ecosystem has been undergoing a series of developments, including the launch of , the onboarding of legal firms and attorneys to mine the JTC blockchain, and the growth of the online community that now has thousands of members across , , and . JTC Ordinals X Telegram Discord The decision to list on BitMart has given $JTC massive market access as the exchange has over two million users and operates in 180 countries. According to CoinMarketCap, BitMart’s 24-hour trading volume fluctuates between $1-2 billion and has over 700 trading pairs. $JTC: The Multi-Utility Crypto Bringing Legal Recourse To The Blockchain $JTC is the native cryptocurrency of the JTC blockchain, which was created as a branch of Bitcoin on January 8, 2022, at Block Height 717808. When the JTC branch was implemented, all $BTC wallets were mirrored, meaning anyone who held $BTC before the branch date is eligible to claim $JTC at a 1:1 ratio. Similarly to $BTC, new $JTC tokens are created through mining on a Proof-of-Work blockchain, with the supply also being limited to 21 million. The miners on the JTC blockchain are attorneys and legal firms that have joined due to their interest in contributing to a legally advanced version of Bitcoin. Among the group of Jurat Attorney Miners is , a prominent figure within the Web3 regulatory space and practicing lawyer at . Jeremy Hogan Hogan & Hogan Jurat connects the blockchain with existing legal procedures in state and federal courts. As a result, it brings legal recourse to cryptocurrency transactions without requiring the existing legal systems to make any major changes. “The benefits of blockchain technology are vast. Just about every industry and person stands to benefit, but before $JTC, the risk of storing assets on-chain was too great for most people. How can the average person entrust their life savings to technology without legal protections?” Said Kanovitz. “The BitMart listing is a key milestone for crypto’s mainstream adoption because it makes $JTC more accessible for mainstream users.” $JTC on BitMart: Coming Soon The $JTC listing is expected to go live in the coming weeks, with both USDT and USDC pairings being made available. Users can follow Jurat across , , and and be among the first to hear further updates about the listing. X Telegram Discord About Jurat Blockchains is positioning itself as the leading provider of decentralized legal enforcement solutions for commercial and government users. Its technologies power $JTC, a court-connected Bitcoin ($BTC) fork with the unique ability to charge back unauthorized transactions. Jurat Blockchains Jurat technologies enable blockchain’s seamless integration with both the civil and criminal justice systems, making it attractive to businesses, regulators, and those keen on tokenizing off-chain assets. For more information, visit Jurat’s | | Official Website Twitter Medium Contact CEO Mike Kanovitz Jurat mike@jurat.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program . here

$JTC Network To List On BitMart Exchange

Chicago, Illinois, August 8th, 2023/Chainwire/--For years, cryptocurrencies such as Bitcoin (BTC) and Ether (ETH) have been the favored methods of payment for cybercriminals. Ransomware crime rings rely almost exclusively on Bitcoin payments to extract vast sums from major institutions, including hospitals and providers of critical infrastructure. But, in a groundbreaking move, a in Chicago has turned the tables on cybercriminals by using inventive technology to digitally lock the criminals out of their blockchain accounts. federal court case The case was brought against Russian and North Korean hackers who are under sanctions by the United States Treasury Department. Mike Kanovitz, CEO of , the firm behind the technology deployed in the case, remarked, “It is encouraging to witness this invention in action, fighting criminals who misuse blockchains and give cryptocurrency a bad name.” Jurat Blockchains Kanovitz explained that “Jurat technology provides the base layer for legal enforcement on blockchains by working in conjunction with official courts. It can help government officials prevent crime and lets cryptocurrency users access courts if they have had their cryptocurrency stolen, been defrauded, or lost their private keys.” The court’s order applies to 16 individuals and international criminal organizations sanctioned under Executive Orders of the President of the United States. Among those targeted was Hydra Market, the focus of “a collaborative initiative joined by the U.S. Department of Justice, Federal Bureau of Investigations, Drug Enforcement Administration, Internal Revenue Service Criminal Investigation, and Homeland Security Investigations,” according to a . U.S. Treasury Department press release Another major target named in the lawsuit is the , which engaged in “malicious cyber activities and money-laundering of stolen virtual currency” on behalf of the Democratic People’s Republic of Korea. The state-sponsored hackers committed “the largest virtual currency heist to date, worth almost $620 million, from a blockchain project linked to the online game Axie Infinity.” Lazarus Group Using the first-of-its-kind judicial injunction, the federal court has locked all accounts belonging to the 16 criminals that contain the cryptocurrency $JTC. The coin emerged following an update to Bitcoin’s software, resulting in a “fork” that left two blockchains, BTC and JTC, hosting the same accounts. Victims who previously lost $BTC can claim their $JTC using the Jurat Wallet app or by filing an action to enforce their legal rights in court. Industry estimates of cryptocurrency crimes range above $20 billion annually. The case was handled by Jon Loevy, of Loevy & Loevy Attorneys at Law in Chicago. About Jurat Blockchains provides Web3 legal tech and reg tech for blockchains, smart contracts, and DAOs. It launched its first public blockchain, JTC, in 2022 and has additional layer 1 projects and dispute resolution system dApps in the pipeline. For further information about Jurat contact info@jurat.io or find us at . To reach Loevy & Loevy call 312-243-5900. Jurat Blockchains www.jurat.io Contact Media Manager Andy Thayer Loevy & Loevy info@jurat.io 312-243-5900 This story was distributed as a release by Chainwire under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author

Federal Court Case Uses Blockchain Tech to Fight Russian and North Korean Hackers

“Jurassic Park” may not be as far-fetched as it seems. Scientists have been trying to find ways to bring extinct species back to life for some time now, and AI could finally make it happen. While some regulators call AI an extinction-level threat, the opposite may also be true. It could reverse extinction — perhaps not for dinosaurs, but feasibly for species like the woolly mammoth. Doing so could also play an important role in the fight against climate change. How De-Extinction Works De-extinction is the culmination of many cutting-edge technologies. It starts with gene-editing technologies like CRISPR, and AI helps throughout the entire process. Here’s a closer look at how it works. Genome Sequencing The first step in de-extinction is to collect the DNA of an extinct animal — like the woolly mammoth — and a close living relative — like the Asian elephant. While usable dinosaur DNA samples may be long gone, the mammoth’s relatively recent extinction and cold habitat mean it still has well-preserved genes. Once scientists have gathered this DNA, they must sequence each species’ genome. This process uncovers the order of each gene’s building blocks to learn how they work and what characteristics they produce. It’s difficult and time-consuming work, so it’s an ideal use case for AI. Studies show that AI shortens the time spent in genome sequencing and reduces errors in the process. Speed and accuracy are big concerns here, so AI is now essential in this stage of de-extinction. Filling in the Gaps After sequencing both species’ genomes, scientists compare them to see what elephant genes they must change to resemble the woolly mammoth’s. AI is crucial in this step, as there are gaps left to fill after sequencing. DNA has a 521-year half-life, so even well-preserved samples from over 1,000 years ago aren’t complete. AI can analyze genes to predict the order of the missing bits. It can then compare them to those of the Asian elephant to determine what elephant genes it can keep and what it must modify to look more like a mammoth. Machine learning’s predictive capabilities make it the perfect tool for this job. Accurately predicting what customers want is how AI saves Netflix $1 billion annually, and finding similarities in DNA sequences is a similar task. Gene Insertion and Verification It’s now time to modify Asian elephant genes to essentially create complete woolly mammoth DNA. This process is eerily similar to how it works in “Jurassic Park,” at least in the basics. Like in Michael Crichton’s work, real-life scientists cut out bits of living species’ DNA that don’t match and insert genes from the extinct species. This is possible through a technology called CRISPR gene editing — the same innovation that helps fight diseases like sickle cell anemia and malaria. After inserting mammoth genes into elephant DNA, scientists must verify that the resulting cells possess mammoth traits. Once again, AI is the ideal solution. Machine learning can test these cells faster and more accurately than manual methods. Embryo Transfer, Growth, and Birth At this point, scientists will have created hybrid elephant-mammoth cells. They remove the nucleus from an Asian elephant egg cell and replace it with the hybrid mammoth nucleus. They then stimulate the egg so it fertilizes and grows into an embryo. Once they have a healthy embryo, scientists transfer it to an African elephant to carry to term. African elephants are larger and have the longest pregnancy of any mammal, so they’re ideal mothers to grow a baby mammoth. After roughly 22 months, the elephant will give birth to the hybrid mammoth. While the mammoth’s genes won’t be identical to its extinct ancestors, it’ll essentially be a real woolly mammoth brought back from extinction. Ongoing De-Extinction Attempts Several research organizations are following this process to de-extinct species like the woolly mammoth. One biotechnology company — Colossal Laboratories — aims to resurrect the woolly mammoth by 2027 and is currently building the necessary genetic libraries to create the hybrid DNA. Colossal also hopes to de-extinct species like the dodo and Tasmanian tiger. While no project has successfully cloned an extinct species that lived more than a few minutes, advances in AI and gene editing technology are bringing the possibility closer. Other researchers are focusing on critically endangered species — not quite extinct but about to be. Scientists have successfully cloned a black-footed ferret using decades-old DNA, showing it’s possible to de-extinct animals with old genes. Researchers at the San Diego Zoo Alliance are trying to do the same for the northern white rhino, just two of which exist today. Why It Matters As cool as it is, why would scientists bother de-extincting species? It’s an expensive, complicated process, so what’s the point? Unlike in “Jurassic Park,” there is a noble goal here. Bringing extinct or critically endangered species back could help protect the planet. Woolly mammoths could help fight climate change, as these animals clear dead or invasive tree species to restore grasslands. Bringing more recently extinct animals back to life would restore a full, healthy ecosystem in some areas to protect other species. In cases like the white rhino, it would right the wrongs of humanity’s past by reversing forced extinction. If nothing else, successful de-extinction would be a big step forward for AI in genetic engineering. It would prove this technology’s potential to fight seemingly impossible odds. Scientists already believe AI-powered gene editing could improve global health and combat climate change, so any advances in the field are worth noticing. Ethical Complications Despite this vast potential, AI-powered de-extinction has some controversy. Some argue that bringing species like the woolly mammoth into a warmer, harsher climate, which they’re not well-adapted to, may be cruel to these animals. There’s also the argument that the money and time spent on this research could go to other, more immediate climate and health issues. Some also have fears about AI gene editing going too far. Successful de-extinction could snowball into issues of people genetically modifying humans to have unique abilities or creating dangerous genetic weapons. Many of these fears are theoretical but deserve attention as this field advances. AI Could Bring Species Back From the Dead While there are still some ethical and technological complications, de-extinction shows promise. It would be a major step forward for biotechnology if these projects work out, and AI will likely be the difference-maker. AI has plenty of ethical and environmental concerns. However, it’s hard to deny its potential for protecting the natural world. Using it for conservation efforts could ensure the world benefits more from this technology than it suffers. “Jurassic Park” may not be as far-fetched as it seems. Scientists have been trying to find ways to bring extinct species back to life for some time now, and AI could finally make it happen. While some regulators call AI an extinction-level threat , the opposite may also be true. It could reverse extinction — perhaps not for dinosaurs, but feasibly for species like the woolly mammoth. Doing so could also play an important role in the fight against climate change. call AI an extinction-level threat call AI an extinction-level threat How De-Extinction Works De-extinction is the culmination of many cutting-edge technologies. It starts with gene-editing technologies like CRISPR, and AI helps throughout the entire process. Here’s a closer look at how it works. Genome Sequencing The first step in de-extinction is to collect the DNA of an extinct animal — like the woolly mammoth — and a close living relative — like the Asian elephant. While usable dinosaur DNA samples may be long gone, the mammoth’s relatively recent extinction and cold habitat mean it still has well-preserved genes. Once scientists have gathered this DNA, they must sequence each species’ genome. This process uncovers the order of each gene’s building blocks to learn how they work and what characteristics they produce. It’s difficult and time-consuming work, so it’s an ideal use case for AI. Studies show that AI shortens the time spent in genome sequencing and reduces errors in the process. Speed and accuracy are big concerns here, so AI is now essential in this stage of de-extinction. shortens the time spent in genome sequencing shortens the time spent in genome sequencing Filling in the Gaps After sequencing both species’ genomes, scientists compare them to see what elephant genes they must change to resemble the woolly mammoth’s. AI is crucial in this step, as there are gaps left to fill after sequencing. DNA has a 521-year half-life , so even well-preserved samples from over 1,000 years ago aren’t complete. has a 521-year half-life has a 521-year half-life AI can analyze genes to predict the order of the missing bits. It can then compare them to those of the Asian elephant to determine what elephant genes it can keep and what it must modify to look more like a mammoth. Machine learning’s predictive capabilities make it the perfect tool for this job. Accurately predicting what customers want is how AI saves Netflix $1 billion annually , and finding similarities in DNA sequences is a similar task. saves Netflix $1 billion annually saves Netflix $1 billion annually Gene Insertion and Verification It’s now time to modify Asian elephant genes to essentially create complete woolly mammoth DNA. This process is eerily similar to how it works in “Jurassic Park,” at least in the basics. Like in Michael Crichton’s work, real-life scientists cut out bits of living species’ DNA that don’t match and insert genes from the extinct species. This is possible through a technology called CRISPR gene editing — the same innovation that helps fight diseases like sickle cell anemia and malaria. After inserting mammoth genes into elephant DNA, scientists must verify that the resulting cells possess mammoth traits. Once again, AI is the ideal solution. Machine learning can test these cells faster and more accurately than manual methods. Embryo Transfer, Growth, and Birth At this point, scientists will have created hybrid elephant-mammoth cells. They remove the nucleus from an Asian elephant egg cell and replace it with the hybrid mammoth nucleus. They then stimulate the egg so it fertilizes and grows into an embryo. Once they have a healthy embryo, scientists transfer it to an African elephant to carry to term. African elephants are larger and have the longest pregnancy of any mammal , so they’re ideal mothers to grow a baby mammoth. the longest pregnancy of any mammal the longest pregnancy of any mammal After roughly 22 months, the elephant will give birth to the hybrid mammoth. While the mammoth’s genes won’t be identical to its extinct ancestors, it’ll essentially be a real woolly mammoth brought back from extinction. Ongoing De-Extinction Attempts Several research organizations are following this process to de-extinct species like the woolly mammoth. One biotechnology company — Colossal Laboratories — aims to resurrect the woolly mammoth by 2027 and is currently building the necessary genetic libraries to create the hybrid DNA. resurrect the woolly mammoth by 2027 resurrect the woolly mammoth by 2027 Colossal also hopes to de-extinct species like the dodo and Tasmanian tiger. While no project has successfully cloned an extinct species that lived more than a few minutes, advances in AI and gene editing technology are bringing the possibility closer. Other researchers are focusing on critically endangered species — not quite extinct but about to be. Scientists have successfully cloned a black-footed ferret using decades-old DNA, showing it’s possible to de-extinct animals with old genes. Researchers at the San Diego Zoo Alliance are trying to do the same for the northern white rhino, just two of which exist today. successfully cloned a black-footed ferret successfully cloned a black-footed ferret Why It Matters As cool as it is, why would scientists bother de-extincting species? It’s an expensive, complicated process, so what’s the point? Unlike in “Jurassic Park,” there is a noble goal here. Bringing extinct or critically endangered species back could help protect the planet. Woolly mammoths could help fight climate change, as these animals clear dead or invasive tree species to restore grasslands. Bringing more recently extinct animals back to life would restore a full, healthy ecosystem in some areas to protect other species. In cases like the white rhino, it would right the wrongs of humanity’s past by reversing forced extinction. If nothing else, successful de-extinction would be a big step forward for AI in genetic engineering. It would prove this technology’s potential to fight seemingly impossible odds. Scientists already believe AI-powered gene editing could improve global health and combat climate change, so any advances in the field are worth noticing. could improve global health could improve global health Ethical Complications Despite this vast potential, AI-powered de-extinction has some controversy. Some argue that bringing species like the woolly mammoth into a warmer, harsher climate, which they’re not well-adapted to, may be cruel to these animals. There’s also the argument that the money and time spent on this research could go to other, more immediate climate and health issues. Some also have fears about AI gene editing going too far. Successful de-extinction could snowball into issues of people genetically modifying humans to have unique abilities or creating dangerous genetic weapons. Many of these fears are theoretical but deserve attention as this field advances. AI Could Bring Species Back From the Dead While there are still some ethical and technological complications, de-extinction shows promise. It would be a major step forward for biotechnology if these projects work out, and AI will likely be the difference-maker. AI has plenty of ethical and environmental concerns. However, it’s hard to deny its potential for protecting the natural world. Using it for conservation efforts could ensure the world benefits more from this technology than it suffers.

The Role of AI in Resurrecting Extinct Species

বিলুপ্ত প্রজাতির পুনরুত্থানে এআই-এর ভূমিকা

Die Rolle der KI bei der Wiederbelebung ausgestorbener Arten

El papel de la IA en la resurrección de especies extintas

Le rôle de l’IA dans la résurrection des espèces disparues

विलुप्त प्रजातियों को पुनर्जीवित करने में एआई की भूमिका

絶滅した種の復活におけるAIの役割

멸종된 종을 부활시키는 데 있어 AI의 역할

O papel da IA na ressurreição de espécies extintas

Роль ИИ в воскрешении вымерших видов

Soyu Tükenmiş Türlerin Diriltilmesinde Yapay Zekanın Rolü

Vai trò của AI trong việc hồi sinh các loài đã tuyệt chủng

人工智能在复活灭绝物种中的作用

Remember when code was just lines on a screen, without personality or dialogue? Well, buckle up, fellow developers and AI enthusiasts, because the landscape is changing at breakneck speed! Large Language Models (LLMs) are bursting onto the scene, harnessing the power of natural language processing to transform the way we code, create, and communicate. Yan Lekun, the director of Meta AI, says, "The potential of LLMs is huge; they can help us automate tasks, create content, and translate languages," but because there are so many open options, choosing the right LLMs is blindfolded, It’s like walking through a jungle gym. Don't worry, brave developers! This is where I come in, your friendly local AI sherpa, ready to guide you through the hottest open LLMs that will supercharge your career and unleash your inner AI wizard. But there are so many options. Choosing the right LLM can feel like walking into a jungle gym with your eyes closed. Fear not, brave developers! I'm here to act as your Sherpa, guiding you through the hottest open-source LLMs to increase your productivity and unleash your inner AI wizard. So, let's crack open the treasure chest and unveil the top 10 gems: No introduction needed. Bard, Google’s behemoth LLM, boasts an impressive 137B standard, making it a powerhouse for handling complex NLP tasks. While still in development, Bard showed early promise in areas such as rule generation and summaries. Bard (Google AI): Coming in hot at 178B parameters, Jurassic-1 Jumbo is a monster LLM with a reputation for amazing realistic speech understanding. If your project requires you to work with large datasets of text and code, Jurassic-1 Jumbo could be your new best friend. Jurassic-1 Jumbo (AI21 Labs): Together, NVIDIA and researchers at Megatron developed a monster 530B-parameter LLM specifically geared to generate a variety of creative texts, such as poetry, code, writing, pieces of music, emails, documents, and more etc. If you want to add a bit of AI-powered creativity to your project, this is the LLM for you. Megatron-Turing NLG (NVIDIA): This multilingual marvel, backed by a consortium of AI researchers, boasts impressive performance in 46 languages. Need to translate code comments, document international projects, or build multilingual chatbots? BLOOM is your one-stop shop. BLOOM: Clocking in at a whopping 1.75T parameters, WuDao 2.0 is a Chinese superpower LLM that focuses on multilingual capabilities. This model is particularly adept at understanding and responding to natural language in Chinese. WuDao 2.0 (BAAI): This community-driven project offers a smorgasbord of open-source LLMs of various sizes, all trained on a huge dataset of text and code. EleutherAI is a good choice for developers of different LLMs who want to experiment with construction design to find the one that best suits their specific needs. EleutherAI (EleutherAI): Another gem from EleutherAI GPT-J is a 6B parameter LLM based on the OpenAI GPT-3 framework. Although smaller than some of the other options on this list, GPT-J offers a good balance of power and functionality, making it a great choice for developers with limited resources. GPT-J (EleutherAI): Although not strictly open-source, Cohere provides a free layer to their powerful LLM APIs. This makes it a good choice for developers who want to try out LLM before diving headfirst into their models. Cohere (Cohere Corp): LaMDA, short for Language Modeling for Conversational Programming, is Google’s take on the conversational AI model. LaMDA is specifically designed to create realistic and engaging interactions, making it ideal for developers of chatbots or virtual assistants. LaMDA (Google AI): Rounding out our list is Cicero, a real language LLM from Meta AI. Trained in the extensive use of information and law, Cicero excels at such tasks as interviewing and taking information. Cicero (Meta AI): Conclusion: Your Personal AI Playground Awaits Note that this list is just a taste of the vibrant and ever-growing world of open LLMs. Each model has unique strengths and weaknesses, so experiment, explore, and find the one that fits your specific needs and projects. As Fei-Fei Li, executive director of the Stanford Human-Centered AI Institute, aptly puts it, "AI Think of these LLMs as powerful tools in your developer toolbox, in order to maximize your creativity.” has made your work easier, what." Expect to push the limits of possibility and open your eyes. So, dive in, explore, and prepare to be amazed at the potential of an open-source LLM to transform your growth journey. The future is bright and powered by AI, but the story of how it unfolds is yours to write, code by code, with the help of these wonderful language models.

10 Open-Source LLMs That Will Rock Your Dev World in 2024

This is a continuation of my article on CBDC (Central Bank Digital Currency) from the “Now You Understand” series. , I looked at what a CBDC is, and how this form of digital currency can be used, how it differs from stablecoins, and why private rather than public blockchains are used to implement CBDC solutions. In the first part You can read it here ⬇️ https://hackernoon.com/now-you-understand-central-bank-digital-currency-cbdc-explanation-and-usage-part-i?embedable=true In this part, I will look at what solutions are being used in different states to implement CBDCs, take a closer look at the Brazilian DREX case study, and follow the dynamics of CBDC implementation around the world. Enjoy reading! What solutions are countries using to implement CBDC? To implement CBDC, central banks will need a blockchain infrastructure. In this section, we’ll discuss the technical characteristics of the most common blockchain platforms for implementing digital currencies. During the research for this article, I conducted a thorough analysis of different solutions and platforms. My findings show that Based on the data and sources studied, it is evident that a few select blockchain platforms will be used prominently in various countries for CBDC implementation. Hyperledger Blockchain is emerging as a dominant tool in this domain. In addition, notable platforms such as Quorum and R3 Corda are actively used in significant projects (e.g., Dunbar, Jura). The selection of these platforms is driven by their popularity and track record, particularly in facilitating CBDC implementation. Hyperledger Besu Besu is an EVM-compatible blockchain platform with additional functionality to provide privacy capabilities. It supports private smart contracts and transactions. In the corporate sector, this is a must-have for technology adoption at all stages. Privacy became possible here due to a separate set of nodes that is responsible for encrypting private transactions. The Besu client does not include built-in private key management. External tools, such as Ethereum-compatible wallets, are used to manage private keys and sign transactions. This provides a greater level of security and flexibility. Besu is a permissioned network. It allows you to list approved accounts and deny all transactions from accounts that are not on the list. It allows you to determine which nodes can connect and participate in the network and which cannot. This is also achieved by adding nodes to the list of approved nodes, if a node is not on this list it is denied access to the network. This increases overall security and access control as well. Since only selected members can participate in the network, it significantly increases throughput. Hyperledger Fabric Fabric is a private blockchain platform designed for enterprise use, with a focus on privacy. It was initially developed by IBM and was transferred to the Linux Foundation in 2015. Unlike Besu, Fabric is not an EVM-compliant platform and has a different architecture, smart contracts are called chaincodes which are developed using , JS/TS, and . Go Java Fabric architecture includes a special type of node that is responsible for transaction processing, computation, and state management, they are called endorsers/committers. Another type of node called Orderers is responsible for reaching consensus, sequencing transactions, and placing them into blocks. For a higher level of privacy, another element called channels is implemented. Channels are represented as subnets in which only participating nodes have access to chain codes and transactions, while participants outside the channel are unaware of their existence, providing a high level of privacy and confidentiality. This functionality can be especially useful when there is a requirement to separate several directions and isolate them from each other. In Fabric, private transactions are also possible through direct P2P communications. In this scenario, only the hashes of the input data of the transaction are shared with the Orderers nodes and written to the ledger along with the transaction. Fabric supports pluggable consensus mechanisms, which means there is flexibility to choose the most suitable option for specific challenges and requirements. As Fabris is a private solution, this also means that a node in this network can only be started with the permission of authorized entities, which restricts the network participants. Hyperledger Iroha Launched in 2019, Iroha is a private blockchain platform that is also focused on enterprise use. The difference is that it has a simpler design and is easier to integrate thanks to its modular architecture. It also supports smart contracts. This platform is primarily performance-oriented with its own fault-tolerant consensus algorithm under the hood. It provides high throughput and low delay in transaction processing. A node can only be launched on this network with the permission of authorized entities. On this platform, Cambodia launched Bakong, a project controlled by the Central Bank of Cambodia, which is designed for mobile payments and banking. There was a problem with the coverage of banking services, and this project solved that problem by allowing all citizens to open an account and make cashless and secure payments. And for banks, it opened up the possibility of interbank transfers at a lower cost. This project is not entirely a CBDC, although it is administered by the Central Bank, but rather a tokenized deposit system. Quorum Quorum is an enterprise blockchain platform that is based on Ethereum but extended with functionality customized to the specific requirements of corporate clients. The platform was founded by J. P. Morgan Chase in 2016. In 2020, Consensys bought Quorum and now supports and develops the platform. The special feature of this platform is that it has a built-in privacy functionality, which opens up opportunities for corporate use. For example, there is the option for private transactions. This is very important compared to an open network because when we transfer money, we don't want anyone to know about it. The control of this platform is based on consensus mechanisms which assume that only selected participants are allowed to participate and maintain control of the network. For example, governance may be centralized at the Central Bank. Such a mechanism not only consolidates power in the system, but also provides a number of benefits, such as increased throughput and transaction speed. This is achieved by the fact that the number of nodes to reach a consensus between is minimal and limited. There is no need to wait for a large number of participants to agree. Unlike Ethereum, Quorum has zero gas costs, which means you don't need to pay for gas for transactions. As Quorum is based on Ethereum, it supports smart contracts in Solidity. Smart contracts open up the possibility of creating their applications, e.g. by commercial banks with permission from central banks. Smart contracts in Quorum can be as private as transactions. Similarly to a regular contract, for confidentiality reasons, it is necessary that they are not available to anyone. When we buy or sell a property, we don't want anyone to know the terms of the transaction and its participants. Smart contracts are deployed on the blockchain and are always available for interaction, the code is executed when called, so there is no need for a third party. For example, when we make a transaction, we have to wait for a third party, such as a bank, to execute it. In the case of smart contracts, we have no such need because everything is done by the executable code. Such interaction is always available 24/7, unlike standard organizations. They are also an excellent way to tokenize assets or access rights. Corda Corda platform is based on distributed ledger technology (DLT), but it is not a blockchain. That's why it's different from all the others. This platform is fully focused on privacy and by default all transactions are private. It does not have a global shared ledger and only supports transactions in which only counterparties are involved. Transactions are only related to bilateral or multilateral agreements between counterparties. Here each node maintains its own register of transactions, where they are linked to each other and complete in real time since there is no blockchain. But in this case the issue of double spending and others arise. Here this is solved by installing a notary which is a third party and controls the validity of transactions. Notary is a separate set of nodes and the level of decentralization is directly related to Notary. The model is based on strict confidentiality and each node must prove that it has been validly connected to the network using a certificate from authorized members. The signature capability here exists only on the nodes. Each application here exists only in the nodes of the network as well as the ability to sign transactions. This is significantly different from the standard Ethereum model where signature control is maintained by the client wallets. In fact, they simply run the process and control its execution. Here these applications are called CorDapp. : Drex (Digital Brazilian Real) Example of a CBDC implementation Brazil has launched its own CBDC called Drex (Digital Brazilian Real) in pilot mode on the Hyperledger Besu platform. As we know, it is an EVM-compliant blockchain, which means that it supports smart contracts in Solidity. The control architecture of the network is structured in such a way that it is centralized at the Central Bank and therefore managed completely privately. A node can only launch on this network with the permission of the Central Bank. End users can only have custodial wallets managed by an organization that has been authorized by the Central Bank, such as a commercial bank. There are retail and wholesale forms of CBDC. The wholesale form is intended only for high-volume transactions, such as between banks, cooperatives and possibly with the participation of large corporations. The retail form is designed for payments and transfers between individuals and businesses of any size and can be used for everyday financial transactions in any value range. In this implementation, CBDCs are smart contracts on Solidity ERC-20 standards, there are two main ones: RealDigital and RealTokenizado. The main token is RealDigital, which is controlled by the central bank. Other participants in the system, such as commercial banks and other financial institutions control their own token, RealTokenizado, which actually extends the RealDigital contract and its functionality. RealTokenizado is used to manage deposits and accounts. RealDigital itself stands as a reserve for all tokens of other participants. As we have different ERC-20 tokens for each participant they cannot be transferred directly, for this purpose swap contracts are used additionally. Therefore, when different tokens (RealTokenizado) are transferred between participants, it looks this way: RealTokenizado tokens (of one participant) are burned from the sender, RealDigital tokens (represent a reserve) are transferred from the sender to the receiver and RealTokenizado tokens (of another participant) are issued to the receiver. In this architecture there are also roles responsible for issuing, burning, moving and freezing funds. It is also important to say that participants, such as commercial banks, operate through a special account established by the Central Bank and cannot independently issue or burn RealTokenizado tokens. Stages of CBDC implementation worldwide In the first half of 2021, only 74 countries were interested in CBDC. Of these, only 3 decided not to develop this direction, 28 countries were in the research stage, 14 countries were in the development stage, 18 countries launched their own CBDCs in pilot mode and 1 country completely launched. By mid-2023, the picture has changed significantly, from the key parameters - now 130 countries have expressed interest, of which 46 are in the research stage and 32 are in the development stage. Pilot mode has been launched in 21 countries and 11 countries have fully launched. It is important to note that these 130 countries that are expressing interest in CBDC represent 98% of the world's GDP. From this we can conclude that this direction is of interest to the majority of countries in the world and has huge potential, and the active growth in the rate of research and development indicate that it is likely to be realised on a grand scale. The world situation at the moment is as follows: My opinion on CBDCs Even though many crypto enthusiasts believe that current CBDC implementations by central banks are not in line with the basic principles of the crypto community, I suppose that CBDC implementations should not be measured by public blockchains. CBDC implementations most often have fully centralized control, which crypto enthusiasts are against, but it should be understood that CBDCs cannot and should not be implemented otherwise. This is due to many considerations, including privacy and legality of transactions.  Yes, such a centralized implementation loses some of the advantages of public blockchains, but at the same time, it solves more important country-specific issues and reduces risks. Conclusion There is a significant global interest in CBDC implementation, which indicates a high promise of changing the traditional system towards CBDC and tokenization of assets. For CBDC implementation, private solutions are used that provide privacy control and centralized control over the Central Bank. They also support a high level of programmability, which makes it possible to create your own rules for CBDC. Unlike public networks, there are no transaction fees. Because transactions occur according to programmed rules - there is no need for a third party to verify them, and therefore these solutions are available 24/7. Control over the private network is limited to selected members and there is no need to wait for the majority to agree as in the case of public networks. CBDCs enable the tokenization of assets and property, optimizing the costs of the current registry infrastructure and increasing security, speed, and cost of transactions. Smart contracts in CBDCs open up opportunities to automate many processes and increase their security, including cross-border payments. Digitizing money makes it more transparent, efficient, controllable and accessible, removing vulnerabilities and inefficiencies in the current infrastructure.

Now You Understand: Central Bank Digital Currency (CBDC)—Explanation and Usage, Part 2

253 Stories To Learn About Data Analysis

Let's learn about via these 70 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Statistics /Learn Repo 1. The State of Vue 2022: What Has Changed In The Last 12 Months Check the latest statistics on the usage of Vue and learn why this framework is growing in popularity. 2. Over 60% of Writers Already Use AI in Their Writing Workflow Hackernoon polled readers on whether they would use AI tools for their writing/copywriting workflow. Nearly 70% are open to the idea. 3. How to Install RStudio on the WSL System If you are using Windows and don’t want to use the RStudio client on the Windows side, this method will work perfectly. 4. Kannada-MNIST:A new handwritten digits dataset in ML town TLDR: 5. How Many Days of Your Life Have You Spent in an Uber? I've spent 12 days in Ubers over the last 3 years. Read this to find your own lifetime Uber stats. 6. AB Testing For Digital Products An overview of AB testing during the design of digital products like UX, digital marketing advertisements and software development. 7. What LinkedIn Tells Us About Developer Population in the World There are tons of articles about the number of developers in the world. But most of them either lack good methodology or simply borrow statistics from other resources. 8. Calculate Required Rate of Return With the Fama-French Three-Factor Model Investors are always evaluating the amount of risk they are willing to take for a certain expected return. Intuitively, the best investment maximizes the return 9. How to Predict News: Message Sequence Analysis & Digital Intuition We can be warned about upcoming events predominantly occurring under similar circumstances. 10. Granger Causality: Principle of Cause and Effect Explained ... in a world full of data, we can understand the impact with clever methods. Meet Granger causality. 11. How to Use Approximate Leave-one-out Cross-validation to Build Better Models How to use Approximate leave-one-out cross-validation for hyperparameter optimization and outlier detection for logistic regression and ridge regression 12. How to Implement Gaussian Blurs A Gaussian blur is applied by convolving the image with a Gaussian function. We’ll take the Gaussian function and we’ll generate an n x m matrix. 13. Everything you need to know about YCombinator S19 startups It is not a secret to anyone that YCombinator is the most successful accelerator in the world. They have made well over 2,538 investments. 14. Why AI is a Fear-Driven Discipline People are scared of AI. According to Genpact research: 15. Top 5 Tech Startup Trends in 2020 According to Statista, the global startup economy generates nearly $3 trillion. Startups have always been the driving force behind the world market, bringing new ideas, and transforming familiar business systems. Unfortunately, the economic crisis associated with Covid-19 has not spared this area. 16. The Hundred-Page Machine Learning Book [Review] I first ordered The Hundred-Page Machine Learning book back in May and am only just now finishing it up. In COVID-time, that was about 10 years ago. As you might have inferred, this book is NOT a quick read. What it lacks in easy reading, it makes up for in efficiency. This book swallows up the heavyweight mathematics textbooks and spits out a slim product no thicker than the width of my smartphone. From page one all the way to page 136, Andriy Burkov, the author, does not waste a single word in distilling the most practical concepts in machine learning. You read that right. It is MORE than 100 pages! Sounds like the book has some bias. Get it? Now get ready for my hundred-page book review. Just kidding. 17. What if Street Crime Statistics Matched Those of Cybercrime? If street crime statistics matched those of cybercrime, our world would resemble the Wild West. 18. Statistics Cheat Sheet: A Beginner's Guide to Probability and Random Events A beginner’s guide to Probability and Random Events. Understand the key statistics concepts and areas to focus on to ace your next data science interview. 19. Key 2022 Statistics in the No-Code/Low-Code Market We explore a low-code/no-code market poised for exponential growth in the coming years. 20. Size Does Matter: Global Control Group for a Bank Learn how to approach data-driven measurement properly. See what unexpected results we got in a bank and get insights for your own data analytics journey. 21. How Popular is Right-Wing Content on Facebook? Facebook has been criticized in the past because many believe they are allowing right-wing content to run rampant on the site. 22. Outlier Detection with Chi Square This is a simple method for outlier detection, the procedure basically is a Quantile of weibull distribution of the chi square test in python. 23. 3 Interesting NFT Statistics: How NFTs are Being Adopted Globally NFT ownership has increased throughout the globe, with Southeast Asian nations such as the Philippines, Thailand, and Malaysia leading the pack. 24. Correlation vs. Causation: Why Correlation Does Not Imply Causation The terms correlation and causation are quite often confused, however, they are not synonyms and this article specifies the main reasons why. 25. How to Create A  Funnel Chart In R Funnel Chart in R, A funnel chart is mainly used for demonstrates the flow of users through a business or sales process. 26. The Intuition Behind the “LIME” Concept in AI & ML A preambular article describing the fundamental principles & intuition behind the “LIME” concept in Artificial Intelligence & Machine learning. 27. Hinge Loss - A Steadfast Loss Evaluation Function for the SVM Classification Models in AI & ML Researchers use an algebraic acme called “Losses” in order to optimise the machine learning space defined by a specific use case. 28. Digital Workspaces: Hacking the Future of Work A look at how to engage in the future of work through digital workspaces 29. [Infographic] The State of Conversational AI in 2020 Conversational AI was always poised to take off in 2020. In fact, Gartner predicted that 80% of businesses would implement some sort of conversational interface by the end of this year. With the emergence of COVID-19 came compounded growth for the category - and I wanted to capture just how far we’ve come. So for the conversationally curious out there, I created this infographic that offers a clear depiction of where conversational AI stands at this very moment in time. 30. Beyond A/B Testing — Switchbacks and Synthetic Control Group Experimentation designing in the marketplace without AB-Testing using Synthetic Control Groups and Switchbacks. 31. Variance Inflation Factor - A Pertinent Statistical Metric for the Discernment of Multicollinearity 32. E-Commerce as the Cake, Not the Cherry on the Cake E-Commerce has spiraled due to the pandemic environment, leading to irreversibly enhanced online shopping. 33. The Concept Behind "Mean Target Encoding" in AI & ML An introductory article describing the concept & intuition behind “Mean Target Encoding” in AI&ML, its pros, cons and implementation with a real-time example. 34. Console #17: Japanese Pizza Toast and Turtles 01/12/2019 35. Top 7 Most Used Web Frameworks Among Developers Worldwide 2021 [Statistics] Top 7 Most Used Web Frameworks Among Developers Worldwide 2021 [Statistics] 36. A Single Speed Test is Fun — Hundreds of Them, May Actually be More Accurate Releasing the first internal build of the NordVPN apps that included NordLynx – our brand new protocol built on the backbone of WireGuard® – was an exciting moment for the team. Everyone started posting their speed test results on Slack and discussing the variance. While most of the time NordLynx outperformed other protocols, there were some cases with slightly worse speed results. 37. Why I Spent Years Writing a Children’s Book on Data Science I wrote a children's book on data science to inform others who have a hard time understanding data science and machine learning concepts, especially kids! 38. Applying Statistical Analysis to Intraday Forex Trading Using SQL Statistical Analysis, Intraday Forex Trading, Using SQL 39. Variance is More Than Just A Statistician's Buzzword Explaining real world applications and the philosophy of statistical variance through finance, investing, and gambling. 40. CLIP: An Innovative Aqueduct Between Computer Vision and NLP A rudimentary article describing the concept behind the "CLIP" algorithm in deep learning, its approach, implementation, scope & limitations. 41. 👨‍🔬️ Top 10 Data Scientist Skills to Develop to Get Yourself Hired List of Top 10 Data Scientist skills that guaranteed employment. As well as a selection of helpful resources to master these skills 42. How a Data Scientist Sees a Deck of Cards The Data Scientist Creativity Paradox 43. 12 Best Pre-Installed R Datasets Commonly Used for Statistical Analysis R programming is mostly used in statistical analysis and ML.
This article looks at the Best Pre-Installed R Datasets Commonly Used for Statistical Analysis. 44. Radial Basis Functions: Types, Advantages, and Use Cases An introductory article explaining the basic intuition, mathematical idea & scope of radial basis function in the development of predictive machine learning. 45. Use the 80/20 Rule with Moderation The 80/20 rule, a.k.a. Pareto principle, has been perpetuated along the lines: "80% of the effects come from 20% of the causes." Different cases where the rule emerges have been studied, in the last century, by great personalities such as Vilfredo Pareto (land ownership in Italy), George Kingsley Zipf (word frequency in Languages), and Joseph M. Juran (quality management in industries). Working as a Data Scientist, I have seen enough of the 80/20 rule being invoked in business meetings followed by a round of applause 👏👏👏. Also, I have read numerous LinkedIn posts alike. Most times, it is just a reckless stretch of the rule. But what is the danger here, if any? After all, profits matter more than mathematical and statistical rigor. 46. TikTok's Engagement Rate is an Average of 5.96% in 2022 Boost your social media strategies for 2022 by knowing the most important up-and-coming TikTok marketing trends and engagement stats. 47. How to Use Psychological Tricks to Bring Numbers to Life It’s easy to visualize placing 10 chairs in a room, but it’s difficult to visualize 1,000 chairs. In order to acquire an accurate frame-of-reference, most of us would need to start by sketching out a diagram. Understanding this and applying it to business, the last thing you want is for your customer to have to hunt down a pen, paper and calculator in order to understand your numerically-based product features - don’t assign homework. Captivate customer attention by applying these clever ways to convert numbers into things people care about. 48. Use Up-Sampling and Weights to Address Imbalance Data Problem Have you worked on machine learning classification problem in the real world? If so, you probably have some experience with imbalance data problem. Imbalance data means the classes we want to predict are disproportional. Classes that make up a large proportion of the data are called majority classes. Those that make up a smaller portion are minority classes. For example, we want to use machine learning models to capture credit card fraud, and fraudulent activities happens approximately 0.1% out of millions of transactions. The majority of regular transactions will impede the machine learning algorithm to identify patterns for the fraudulent activities. 49. Why Algorithmic Fairness is Elusive In 2016, Google photos classified a picture of two African-Americans as “gorillas.” Two years later, Google had yet to do more than remove the word “gorillas” from its database of classifications. In 2016, it was shown that Amazon was disproportionately offering one-day shipping to European-American consumers. In Florida, algorithms used to recommend detention and parole decisions on the basis of risk of recidivism were shown to have a higher error rate among African-Americans, such that African-Americans were more likely to be incorrectly recommended for detention who would not go on to re-offend. When translating out of a language with gender-neutral pronouns, and into languages with gendered pronouns, Google’s word2vec neural network injects gender stereotypes into translations, such that pronouns become “he” when in conjunction with “doctor” (or “boss,” “financier,” etc.) but become “she” when translated in conjunction with “nurse” (or “homemaker,” or “nanny,” etc.). 50. How to Retrieve Player Stats from the NHL's undocumented REST API It's that time of year again. Summer is coming to an end, Autumn is around the corner, and the NHL season is about to begin. 51. The Importance of Hypothesis Testing Hypothesis tests are significant for evaluating answers to questions concerning samples of data. 52. Detecting Changes in COVID-19 Cases with Bayesian Models Bayesian change point model to estimate the date that the number of new COVID-19 cases starts to flatten in different countries. 53. 3 Data Distributions for Counts in Layman’s Terms Counts are everywhere, so no matter your background, these data distributions will come in handy. 54. How I Broke Into Data Science A software engineer’s journey into data science at Yelp and Uber 55. Who even reads HackerNoon? You, obviously. And millions of people from around the world. What audience are you advertising to? Who are you writing for? Learn more about our readers here. 56. Numbers Don't Lie But They Are Easily Misinterpreted All The Time therefore a working compromise between these 2 extremes should be found on a case-by-case basis 57. Model Calibration in Machine Learning: An Important but Inconspicuous Concept A prelusive article comprehending the concept behind model calibration, its importance and usage in machine learning model development. 58. The Notions behind “Model-Based” and “Instance-Based” Learning in AI & ML A prelude article elucidating the fundamental principles and differences between “Model-based” & “Instance-based” learning in the branches of Artificial Intelligence & Machine learning. 59. Random: Meaning in Everyday Life and in Science “That’s so random!” Common phrase used to express something is unexpected or without a pattern. Does random mean something different in science? 60. 40% Americans Do Not Know What Affects Their Credit Score Your customers’ credit score is one of the most important numbers of their financial lives. This three-digit number is the most used credit scoring model by lenders for evaluating a borrower's creditworthiness or the likelihood that they’ll repay the money. 61. Making Sense of Bitcoin's Volatility Clustering and How To Take Advantage of it In 2019, Bitcoin's (BTC) volatility was erratic. On one day alone, the price of bitcoin changed by over 40%. However, for most days, the change in price hovers around 1%-2%. The purpose of this article is to investigate the nature of BTC's volatility and see what kind of strategies are suited best to take advantage of it. 62. [Dev Update] Hacker Noon + Google Analytics = Happy Authors Austin here, your friendly neighborhood software engineer from Hacker Noon, with some exciting news! We are pleased as punch to announce that contributing writers can now see pageviews and total time reading for all stories published with Hacker Noon since we began in 2016.  How cool is that? Previously our stats page was only accessible for recent story performance. 63. A Programmer's Guide to Crack 'Twice the Work, Half the Time' Code Modern methods of software measurement do not provide a strong base for continuous improvement. This article tries to fill this gap. 64. Statistics Show AI Adoption and Investment has Risen Rapidly in the Past 2 Years The concept of artificial intelligence as a non-human intelligence originated in the 1950s, but no one then could have imagined what success AI would achieve in the 2020s. In just 70 years, we have had bots that can learn, solve complex computational problems, write related texts, generate images and music, and analyze tons of information in seconds. 65. Breaking Down Secretary Problem What did you think of when you had a crush on someone? Did you fantasize about marriage with him/her? When you were in some serious relationship, did you plan marriage with your partner? How did the relationship turn out? Some relationships turn into a marriage, and some don’t. Hearing stories of many friends, I see extremely few people being in a relationship (and later marrying) with only one person whole over their life. 66. Key Application Performance Metrics From the Viewpoint of a Statistician-Turned-Developer You've just released your new app into the wild, live in production. Success! Now what? Your job is done, right? Wrong. Now that you've deployed your code, it's time to monitor it, collect data, and analyze your metrics. 67. #CorrelateThis - The Neural Activity of Mice Indicates Cryptocurrency Price Fluctuations In his research, Gvido reported the discovery of neurons that showed a neural correlation to the price fluctuations of the main cryptocurrencies! 68. These Are The Secret Weapons of Fake News Modern fake news has evolved into a complex organism, carefully designed to hide its deceptive mechanisms from any potential victim. But although the fake news field has grown into every possible digital channel, the truth is that at its core, in order to be effective, it still relies on exploiting our most basic human characteristics. 69. Simple Tips For A Successful Application Performance Monitoring You finally went live, congratulations! Now what? 70. Quantifying Variability: Variance, Standard Deviation, and Coefficient of Variation There are many ways to quantify variability, however, here we will focus on the most common ones: variance, standard deviation, and coefficient of variation. In the field of statistics, we typically use different formulas when working with population data and sample data. Thank you for checking out the 70 most read stories about Statistics on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

70 Stories To Learn About Statistics

Chatbots integrated into news querying serve various crucial purposes. They offer a convenient and conversational approach for users to access news, eliminating the need to navigate websites or apps. Users can simply ask a chatbot for news on a specific topic or event, making information more accessible, especially for those who find traditional methods challenging. Personalization is a key feature of chatbots in the news industry. By learning from users’ past queries and interactions, chatbots can present more relevant and personalized news articles, enhancing the overall user experience. This tailoring of content ensures that users receive information aligned with their preferences. Time efficiency is another significant advantage. Chatbots quickly sift through vast amounts of information, presenting users with the most relevant news articles. This time-saving aspect is particularly beneficial for users who would otherwise have to manually search and filter through numerous sources. Moreover, chatbots contribute to interactive news consumption. They engage users in a conversation, answering follow-up questions and providing additional context or related information. This interactive approach adds depth to the news-reading experience, surpassing the passive nature of traditional methods. Information overload is a common issue in the digital age, and chatbots help mitigate it by filtering out noise. They deliver news that is most relevant to the user’s interests and needs, streamlining the consumption process and enhancing user satisfaction. Visually impaired users benefit significantly from chatbots, especially when integrated with voice technology. This combination provides an invaluable audio-based method for accessing news, promoting inclusivity in news consumption. Integration into commonly used platforms, such as messaging apps, enhances user convenience. Users can receive news updates in the same environment where they communicate with others, streamlining their digital experience. Automated updates and alerts are a proactive feature of chatbots. Programmed to send timely news updates or alerts about breaking news, chatbots ensure that users stay informed in real time, contributing to a more connected and aware user base. Language and regional customization further extend the accessibility of news. Chatbots can be designed to deliver news in multiple languages and tailor content to regional or local interests, catering to diverse demographics and preferences. In summary, chatbots in the news industry elevate user experience through convenient, personalized, and interactive access to news. They address various challenges in traditional news consumption methods while catering to a diverse range of user needs and preferences. In this article, we’ll design an RAG pipeline using OpenAI, Langchain, and Qdrant DB and encase it in an user interface via Streamlit. What Is RAG “RAG” stands for “Retrieval-Augmented Generation.” It’s a technique used in natural language processing and machine learning, particularly in the development of advanced language models like chatbots.In a RAG setup, when a query is input (like a question or a prompt), the retrieval system first searches through its database to find relevant information or documents. This information is then passed on to the generative model, which synthesizes it to create a coherent and contextually appropriate response. Visual of a RAG pipeline A Brief Note on the Components : LangChain is an open-source framework designed to simplify the creation of applications using large language models (LLMs). LangChain’s use-cases largely overlap with those of language models in general, including document analysis and summarization, chatbots, and code analysis. LangChain enables developers to connect LLMs to other data sources, interact with their environment, and build complex applications. It is written in Python and JavaScript and supports a variety of language models, including GPT-3, LLAMA, Hugging Face Jurassic-1 Jumbo, and more. Langchain Qdrant is an open-source vector similarity search engine and vector database written in Rust. It provides a production-ready service with a convenient API to store, search, and manage points — vectors with an additional payload. Qdrant is tailored to extended filtering support, making it useful for various neural network or semantic-based matching, faceted search, and other applications. Qdrant: Setting Up the Environment & Code First, in your directory, create a requirements.txt file with the following content: langchain

streamlit

requests

opeanai

qdrant-client

tiktoken Then run the command to install these dependencies: pip install -r requirements.txt Now create a file name app.py and paste the following code in it, the comments explain their functionality: #importing the needed libraries

import streamlit as st

import requests

from langchain.text_splitter import RecursiveCharacterTextSplitter

from langchain.embeddings.openai import OpenAIEmbeddings

from langchain.vectorstores import Qdrant

from langchain.chat_models import ChatOpenAI

from langchain.chains import RetrievalQA

import os #function to fetch text data from the links of news websites
def fetch_article_content(url):

headers = { 

    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3' 

} 

try: 

    response = requests.get(url, headers=headers) 

    response.raise_for_status() 

    return response.text 

except requests.RequestException as e: 

    st.error(f"Error fetching {url}: {e}") 

    return "" #function to collate all the text from the news website into a single string
def process_links(links):

all_contents = "" 

for link in enumerate(links): 

    content = fetch_article_content(link.strip()) 

    all_contents += content + "\\n\\n" 

return all_contents #function to chunk the articles beofore creating vector embeddings
def get_text_chunks_langchain(text):

text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100) 

texts = text_splitter.split_text(text) 

return texts #creating the streamlit app
def main():

st.title('News Article Fetcher') 

# Initialize state variables 

if 'articles_fetched' not in st.session_state: 

    st.session_state.articles_fetched = False 

if 'chat_history' not in st.session_state: 

    st.session_state.chat_history = "" 



# Model selection 

model_choice = st.radio("Choose your model", \["GPT 3.5", "GPT 4"\], key= "model_choice") 

model = "gpt-3.5-turbo-1106" if st.session_state.model_choice == "GPT 3.5" else "gpt-4-1106-preview" 



#API_KEY 

API_KEY = st.text_input("Enter your OpenAI API key", type="password", key= "API_KEY") 



# Ensure API_KEY is set before proceeding 

if not API_KEY: 

    st.warning("Please enter your OpenAI API key.") 

    st.stop() 



#asking user to upload a text file with links to news articles (1 link per line) 

uploaded_file = st.file_uploader("Upload a file with links", type="txt") 



# Read the file into a list of links 

if uploaded_file: 

    stringio = uploaded_file.getvalue().decode("utf-8") 

    links = stringio.splitlines() 



# Fetch the articles' content 

if st.button("Fetch Articles") and uploaded_file: 

    progress_bar = st.progress(0) 

    with st.spinner('Fetching articles...'): 

        article_contents = process_links(links) 

        progress_bar.progress(0.25)  # Update progress to 25% 



        #Process the article contents 

        texts = get_text_chunks_langchain(article_contents) 

        progress_bar.progress(0.5)  # Update progress to 50% 



        #storing the chunked articles as embeddings in Qdrant 

        os.environ\["OPENAI_API_KEY"\] =  st.session_state.API_KEY 

        embeddings = OpenAIEmbeddings() 

        vector_store = Qdrant.from_texts(texts, embeddings, location=":memory:",) 

        retriever = vector_store.as_retriever() 

        progress_bar.progress(0.75)  # Update progress to 75% 



        #Creating a QA chain against the vectorstore 

        llm = ChatOpenAI(model_name= model) 

        if 'qa' not in st.session_state: 

            st.session_state.qa = RetrievalQA.from_llm(llm= llm, retriever= retriever) 

        progress_bar.progress(1) 



        st.success('Articles fetched successfully!') 

        st.session_state.articles_fetched = True 



#once articles are fetched, take input for user query 



if 'articles_fetched' in st.session_state and st.session_state.articles_fetched: 



    query = st.text_input("Enter your query here:", key="query") 



    if query: 

        # Process the query using your QA model (assuming it's already set up) 

        with st.spinner('Analyzing query...'): 

            qa = st.session_state.qa 

            response = qa.run(st.session_state.query)   

        # Update chat history 

        st.session_state.chat_history += f"> {st.session_state.query}\\n{response}\\n\\n" 



    # Display conversation history 

    st.text_area("Conversation:", st.session_state.chat_history, height=1000, key="conversation_area") 

    # JavaScript to scroll to the bottom of the text area 

    st.markdown( 

        f"<script>document.getElementById('conversation_area').scrollTop = document.getElementById('conversation_area').scrollHeight;</script>", 

        unsafe_allow_html=True 

    ) if name == "main":

main() Then save the app.py and run the following command in your terminal: streamlit run app.py This launches your application at localhost with port number 8051. Conclusion In conclusion, the integration of chatbots into news querying not only addresses the challenges of traditional news consumption but also significantly enhances user experience by providing a convenient, personalized, and interactive access to information. The discussed RAG pipeline, incorporating OpenAI, Langchain, and Qdrant DB, coupled with a Streamlit-based user interface, exemplifies the cutting-edge technological advancements in natural language processing and machine learning. This comprehensive solution not only streamlines the process of fetching and analyzing news articles but also showcases the potential of AI-driven systems in delivering tailored content, mitigating information overload, and ensuring inclusivity for visually impaired users. The outlined code implementation serves as a practical guide for developers interested in building advanced chatbot applications for news retrieval, demonstrating the fusion of language models, vector similarity search engines, and efficient UI design. Ultimately, this innovative approach represents a paradigm shift in news consumption, offering a glimpse into the future of user-centric and technology-driven information access. References https://api.python.langchain.com/en/latest/api_reference.html https://python.langchain.com/docs/integrations/vectorstores/qdrant

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Jurat

jurat.io

11-50 employees

Since 2020

COMPANY RANKING

JURAT

EVERGREEN INDEX #5856

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Jurat WIKI

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