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Topic: 🧠🌐🤖 【ICO】 First Decentralized Machine Learning Blockchain - GNY.IO ��🌐🧠 - page 3. (Read 25437 times)

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GNY featuring in China.


Hackernoon article highlighting GNY.io features in the "China AI society" column of the world's leading wechat public subscription platform.
   
Titled "Top 5 Machine Learning Projects for Beginners"
   


Chinese version https://bit.ly/2GCBn9b

English version https://bit.ly/2UZ3Y0p


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At the beginning of the topic, it was wrote about the migration of LML tokens to Lisk at the beginning 2019. Do you know anything about this?

I saw a change in roadmap the Testnet in May, not March. Are going works well? Because I can't see updates on GitHub.

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GNY tokens now open for trading on two exchanges.


p2pb2b has BTC, USD and ETH pairs with GNY
 
Direct links to each pair below.
 









Exrates also has BTC, USD and ETH pairs with GNY
 
Direct links to each pair below.
 





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Our Operational Machine Learning System In A Blockchain Demo Is Live!

Last week the GNY team met up in London for our All Hands Meeting. It was the first time our developers were able to meet in person - Blockchain and Machine Learning together at last!

It was Spring in London and there was developer love in the air!


Leo Liang and Tom Lorenc pictured above enjoying their bromance in Trafalgar Square
Leo is GNY's Head of blockchain, and Tom is our Chief Technical officer.


By the end of the weekend, Tom and Leo had achieved a true first - a machine learning platform integrated into a Dpos network!

We are excited to share it with you now through the following resources:

· Links to download a Dpos Network and the incorporated ML platform

· 2 Sample sets of data representing two day’s retail sales

· Step by step instructions for how to set up the system on your computer

The video below describes the content of the demo, and and provides some context about how our main chain will be more refined and easier to use.






As mentioned previously, later this Summer, we will launch our main chain, called GNY Centre.

This platform will allow developers to download all the components they need to run their own machine learning applications.


The platform will be “pay-to-play”, powered by purchasing and using GNY tokens.


GNY tokens will be available to purchase on the exchange Exrates later this week.

Exciting stuff, and more to come. Thank you to our amazing community for supporting us along this journey!


A copy of this text can also be found on the official GNY blog..... https://www.gny.io/blog/our-operational-ml-system-in-a-blockchain-demo-is-live
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Soon GNY tokens will be listed at Exrates!.


Exrates is getting one coin richer, and it is GNY token!
 

We will have updates across all our channels when the GNY token becomes available for trading.


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Farewell London.... and Hello big week for GNY on-chain Machine Learning.


Following the London GNY staff conference a series of reveals are upcoming.


⭐️ Codebase Demo


⭐️ Github going live.


⭐️ New exchanges.


⭐️ Press coverage.




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The GNY Project has Now Achieved Machine Learning on Chain.


Thomas Lorenc, Chief Technology Officer/Lead Data Scientist on the project has been testing the blockchain with the GNY Machine Learning algorithm running inside.


Github goes live at end of this month.







Liskサイドチェインプロジェクトは今チェーンに機械学習を実現しました。トーマスLorencは、プロジェクトのリードデータサイエンティストです。彼は内部で実行されている@gny_io機械学習アルゴリズムでblockchainをテストされています。githubのは、今月末に明らかにされています。




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The GNY Project Development Update.


Leo, Head of Blockchain for the GNY Project has presented the coding solution for how information is read by Machine Learning off the chain.


Upcoming will be a demo of how the read function & Machine Learning are running together & moving onto the reply function.






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The GNY Project Team to Review & Strategise in London this March.

The decentralised team members are flying into London for a planned week of intensive development & strategy meetings, as they open up github late March & lay out the rest of 2019




Chief R&D Officer and Co-founder of GNY (bringing Machine Learning to Lisk), Richard Jarritt informed the project's followers on the GNY telegram that there is to be a team meetup in London this March. He said "the GNY team will be flying all members into London during march, which is a planned week of intensive development and strategy meetings, as we open up github later in the month and lay out the rest of 2019". The GNY team members are decentralised currently with work being done in Europe, The US, and Asia. The GNY codebase is operational, and they are currently combining it with the machine learning, which is what the team has been working on daily. Thomas Lorenc, Chief Technology Officer / Lead Data Scientist on the project is this week testing stability of the GNY Machine learning code within Javascript with node.js.

Richard Jarritt, replied to a query from a community member regarding the reason the code migrated from python (common ML coding language) to JavaScript. He said "it was written in python, and we had planned to run a translation inside the chain, but we altered our plans to the more elegant solution of having everything in javascript. This should should in theory add more stability". There are many libraries for ML written in python that modern ML systems used. We have had to also translate the catalog of code we require to run the system. Its been a multi stage task."


Liskサイドチェインプロジェクトは、レビュー&Strategiseします。これは今年3月、ロンドンで発生します。@gny_io分散型チームのメンバーは、集中的な開発&戦略会議の計画の週のためにロンドンへ飛びます。彼らはgithubのを開く前に、彼らはこれを行います。
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The GNY Project Lays Out the Development Stages Recently Met.

(1) Expanding the GNY Neural Networks in JavaScript using NodeJS

(2) The On-chain data handling contract concept was presented to the team; then approved and moved into the coding stage.




Chief R&D Officer and Co-founder of GNY (bringing Machine Learning to Lisk), Richard Jarritt, laid out where the development team are at present in their journey.

1. Expansion.

Thomas Lorenc, Chief Technology Officer / Lead Data Scientist on the project has been carrying out work on expanding the GNY neural net in JavaScript using Node.js. A neural net or neural network could be described simply as a pattern recognition system. This pattern recognition system would be "trained" by feeding it with data. A neural network itself is not an algorithm, but rather a supporting structure for many separate machine learning algorithms to work together and process data inputs. This sort of construction is right up Tom's street as he is an experienced data scientist with a PhD in Computer Science, and one who enjoys architecting and building cutting edge interactive tech including Artificial Intelligence, Machine Learning, Big Data etc. He is an expert in Java, Python, Node.js, MongoDB, JavaScript and web-based and mobile technologies and understands the business ramifications of accurate, reliable, and efficient programming.

2. Approval.

Leo Liang, Head of Blockchain for GNY, has presented the on-chain data handling contract concept to the team. This has has now been approved and is moving into the coding stage.

The GNY universal Beta Api plug-in update is out now and testing is welcomed for commercial developers aiming to work with Machine learning and blockchain. You can visit Github to view the steps to run it here.....  https://github.com/GNYIO/GNYPLUGIN/blob/master/PlugincodeV098


LISKサイドチェイン・プロジェクトは、彼らが最近会ったの開発段階を説明しました。1. Node.js.を使用してJavaScriptでGNYニューラルネットを拡充 2.オンチェーン契約の概念を扱うデータは、チームに提示しました。承認された符号化段階に移動します。
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First preview of the GNY Project's Blockchain up and running.







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GNY Project's Co-Founder Lays Out the Development Plan for the Week Ahead.

Yesterday, Chief R&D Officer and Co-founder of GNY, Richard Jarritt (bringing Machine Learning to Lisk) revealed the tasks for the week to the projects followers on the GNY telegram  

1. Analysis.

The team will undertake a comprehensive analysis of the solutions other Machine Learning developers have offered within the DPOS space. This to ensure their efforts are above current industry standards. Dissection, comparison, and learning for the team this week so.

2. Internal Education.

The heads of team are creating guides to the projects Machine Learning in Python and Javascript. This is to ensure all team members not only understand the concept but also the code. Internal education cannot be underestimated and is vital to keep all team members aligned.  

3. Timeline.

Drafting a completion timeline for the GNY Machine Learning operating within the codebase that Leo Liang (known from the Asch decentralized application platform) has created. This will be made available to the public.

The GNY universal Beta Api plug-in update is out now and testing is welcomed for commercial developers aiming to work with Machine learning and blockchain. You can visit Github to view the steps to run it here....  https://github.com/GNYIO/GNYPLUGIN/blob/master/PlugincodeV098





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Lisk Machine Learning Tokens Have Begun Powering GNY's Beta API.

Yesterday, GNY, the team behind the LML (LISK Machine Learning) project launched the beta version of their universal API system. It is currently in its open test phase, for organisations wishing to use Machine Learning systems utilising blockchain.


Chief R&D Officer and Co-founder of GNY, Richard Jarritt said "Within the next 24 hours, the API plug in details will be open to the public to view on github. I'm very pleased to bring this technology live and look forward to rolling out many products in the coming year! Currently we are looking for several test companies that would like to also join this initial phase, and we welcome any connections the Lisk community may have".

Currently the Beta API is optimized for retail and publishing predictions, so it can recommend specific products for customers, or specific articles based on their user history.  It allows developers to harness the predictive power trapped in historical data, and translate it into secure recommendations stored on the blockchain. Even better, the actions taken (or not taken) by customers are recorded and further refine the accuracy of future recommendations. This predictive recommendation process starts with a clients data.


The GNY API tool works best for users with large volumes of historical data and the GNY team and Lisk community member Lemii have worked together to make that uploading as seamless as possible. Lemii is the original creator of "The Lisk File Manager"; a web UI to send and download files over the Lisk network, so it is no surprise that he was asked to work with the LML and GNY team on data storage on chain.

There are five easy steps to allow you get started and unlock the predictive power in your data. You can view those steps here and start the journey that smart companies will follow to when improving their connection to their customers!





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GNY codebase preview presented by Leo from the @gny_io team. See it here on vimeo https://vimeo.com/309873352

Great progress. This bodes well for 2019.

次の日本語訳。

GNYコードベースプレビュー。@gny_ioチームからレオが提示します。Vimeoのhttps://vimeo.com/309873352 にここでそれを参照してください。大きな進歩。これは2019年のためによく前兆。




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The CTO, CEO, and a section of the development team from @gny_io putting their heads together on a video conference.


Great progress on the GNY codebase, I hear.


A video covering that progress on the codebase will go into production next week.




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The following article was first published by William Ryan on his medium blog account on the 18th December 2018.... https://medium.com/@FinTechWill/gny-machine-learning-a-deeper-look-f92683b9f2b2
William has formally announced his intention to signal as a @gny_io delegate as GNY.US on his personal twitter account here.... https://twitter.com/fly_nryan/status/1058546941708054528



GNY Machine Learning: A Deeper Look.


The world we live in is drastically different than it was years ago. When I was a teenager, I got my first mobile phone: a Nokia 6110. I was thrilled that I could play snake in my history class. Surely, innovation was at its peak. Flash forward nearly twenty years, and the breakthroughs in modern technology are staggering, and I speculate in another twenty, I will look back on these statements and laugh. As humans, we are always looking for new ways to improve. Essentially, the ultimate goal is to improve the quality of life for all people. That is what technology is really about, isn’t it?




Student Surpasses Teacher

The most powerful and awe-inspiring computer on the planet is the human brain. There is no computer that can match the intricacies and impressiveness of what is human consciousness and cognitive ability. However, there are some things that computers can just do…better. We can program the logic, but the machine can execute at speeds that even the brightest minds could not. With the advancement of AI and machine learning, we are now seeing the fruit of this labor. The culmination years of fine-tuning and improving on the technology now allow it to be applied seamlessly and easily to a grand number of different use cases.


Where Patterns Emerge…

Machine learning is a beautiful tool. These tools can analyze data in minutes, find patterns, and apply various scenarios; thus predictions arise. Years of data science has taught us proven methods for the application of data to form information. When information is applied, it births knowledge and wisdom. We discovered that this entire process could be automated, allowing us as humans to focus on more important issues, such as innovation. The amount of time, energy, and money machine learning saves us is staggering. Thus, the student surpasses the teacher. Without getting into all the complexities of how machine learning works, understanding the value it can have on basically aspect of human life is worth noting. We have yet to see the full potential of how revolutionary this technology is, but as time goes forward, it will become undeniably apparent.


GNY.io

We have seen an incredible inflow of interest in permissionless blockchain applications. We have heard all of the amazing things it can do, but we have also seen the struggles of adoption: dApps with no users, growing disinterest, and increasing levels of bad press surrounding this technology. Seeing the value in decentralization, it was never difficult for me to become immersed in this brave new world, but now that the hype has died down, it really has become a critical point where this space needs real users. This is the time where good projects must be born with real, useful products. We all know what it “could” be, but it’s useless if it’s not being actively applied.

Currently, machine learning is heavily proprietary, and is developed and implemented within silos. As effective as it has been, GNY’s strategy is markedly different than legacy machine learning companies. GNY has been developing their machine learning technology since 2015, and great strides have been made since that time. Utilizing blockchain allows any business across the globe to easily and quickly access machine learning to improve their infrastructure, strategy, and application of their business plans, regardless of geographical location, with ease. GNY’s product is already live. In order to achieve full compliance, GNY has taken a “build, then talk” approach, and prior to raising funds, they build their product first. Offering a variety of solutions, GNY is able to consult the particular needs of any company, and apply their machine learning vehicle to basically any aspect of any business. The goal and idea is to create a one-touch solution, vastly reducing the difficulty of accessing and applying this game-changing technology. This is not just talk. GNY is already demonstrating their usefulness. Recently, several demonstrations were made for public institutions, including assisting in catching child sexual predators, and planning city bus and bicycle routes. They’ve already announced their first commercial partner, ReThink, and say several more are on the way.

Not only does this do a great justice to the machine learning world, but it directly impacts the blockchain space as well. The idea is to bring new users into the space, offering legitimacy to blockchain technology. Everyone can use machine learning. Not only that, but with the creation of GNY sidechains, even other blockchains can utilize machine learning features. I believe building products that can be used today is incredibly important to to the viability and direction the space will take. Bringing in new users now to this space is critical, and GNY is hitting all the marks.


Lisk Machine Learning.

In addition to GNY Tokens, the GNY team has created Lisk Machine Learning. Being fans of Javascript and Lisk, it was a no-brainer. Even though the team is blockchain agnostic, they still wanted to utilize Lisk’s ability to create sidechains on their platform. By creating the LML token, projects will be able to build chains on the parent LML chain and directly utilize machine learning within the Lisk ecosystem while accessing the great power of the most prevalent programming language in the world: javascript. This same strategy can be applied to any chain and programming language. This is only first gear.




Tokenomics.

Being blockchain veterans themselves, the GNY team is very cognizant of token economics, and has developed a bright strategy to directly benefit those who hold both GNY and LML tokens. Operating on delegated proof-of-stake (dPoS) has many benefits, including allowing token holders to stake their coins from cold storage. This alone provides a way for holders to earn more tokens. In addition to this, a form of “masternode” is required to run a node, so large numbers of tokens will be kept out of circulation. The token has significant utility as well. GNY/LML tokens will be required to use the platform, thus driving adoption and buy pressure. The network effect will become obvious as more companies utilize the GNY platform. With the earliest backers in mind, GNY has taken a prudent approach to include backers in their thought process. Additionally, since launch about one month ago, LML has been listed on several exchanges.






Community is very important to any project in this space, and GNY is well aware of its importance to have a robust and active community. They are actively taking the steps to build out communities across blockchain. It’s clear they mean business, and since launch, they have done nothing but deliver. As a GNY delegate, I would like to invite you to join the conversation. You can find us in the GNY Telegram. If you are interested in using the platform, becoming a delegate, or just have questions, feel free to come into our cozy community and say hello. I encourage you to join us, and of course, vote for GNY.US

All the best! 💪






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Richard Jarritt, Co-Founder and Chief R&D Officer of the GNY project with development updates via the telegram app over the holiday period.


The tool involving Solidity contracts mentioned in the chat excerpt below is....  https://remix.readthedocs.io/en/latest/


Visit the GNY telegram for chat and up to the minute updates on GNY and LML here ......  https://t.me/GNYioBlockchain







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Richard Jarritt, Co-Founder of GNY & the LML (LISK Machine Learning) project, spoke with MacMac007 about - "Should we fear AI" and  "AI bots and the markets".


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