Topic: 🧠🌐🤖 【ICO】 First Decentralized Machine Learning Blockchain - GNY.IO ��🌐🧠 - page 5. (Read 25437 times)
November 08, 2018, 12:18:44 PM
Wow this is extremely intriguing. extremely pleasant and inviting kind of task which I expectation could succeed, yet for the present i'll simply be cheering from the sidelines since I don't have much information about it.
November 08, 2018, 11:59:00 AM
November 08, 2018, 11:54:39 AM
GNY Eyes Smart City Opportunities, Announces New Ambassador.
San Antonio is one of the fastest growing cities in the US, and like most burgeoning urban centers, it struggles with optimizing transportation, accessibility, and sustainability.
Recently, the GNY team obtained access to city data across a wide range of civic services in the San Antonio, TX area. Our friend and fellow technologist Javier Guerra, from the local San Antonio organization BLOCquarry, facilitated the opportunity. Javier suggested that we partner and make suggestions for the improvement of area transportation, access to services, and sustainability for the local community.
Using GNY AI to empower a more accessible, safe, and sustainable future is a core-value to the entire team, so we were immediately thrilled to start learning how we could apply our machine-learning technology to the smart city movement.
Together, the GNY and BLOCquarry team analyzed data sets and decided to focus the GNY machine-learning system on tackling transportation challenges.
As we analyzed the open-data sets of the bike-share program, bus services, and google traffic information, we uncovered opportunities to positively impact and optimize these key civic service areas.
Our aligned efforts have led to some great insights, fresh partnerships, and a strong appetite to get more involved with the smart-city movement. We’ve packaged up our findings and are preparing them for presentation to civic leadership.
A few of our initial findings are as follows:
•Without an influx of funding for light rail, or another public transit system the next best solution is increasing the “walkability” of the city center, optimization of bus services, and supporting increased bike access for the city.
• Redeploying existing resources more productively will benefit locals with faster bike-share and bus commute times.
•Designing bike and bus networks that allows passengers to access as much of San Antonio as possible with minimal new stops and bike-share hubs added.
•Our initial research highlights key problem areas, and starts to define areas that merit further exploration.
•Without an influx of funding for light rail, or another public transit system the next best solution is increasing the “walkability” of the city center, optimization of bus services, and supporting increased bike access for the city.
• Redeploying existing resources more productively will benefit locals with faster bike-share and bus commute times.
•Designing bike and bus networks that allows passengers to access as much of San Antonio as possible with minimal new stops and bike-share hubs added.
•Our initial research highlights key problem areas, and starts to define areas that merit further exploration.
Emerging from this close working alliance, the GNY team is proud to announce that Javier will be leading the way for myMOBY.io, our new official US Ambassador. Javier recently co-founded myMOBY to address the global need for “Community Driven Delegate Services”. myMOBY is comprised of a solid team of community-focused innovators with a wide range of business and technical expertise. myMOBY will be driving social awareness about the customizable machine-learning power of GNY.io.
November 08, 2018, 11:45:05 AM
Welcome to our new US ambassador!
October 23, 2018, 08:48:18 AM
LISK sidechain project seeking positive delegates!
GNY HQ has issued a call for all contributing and positive Lisk community members to consider becoming a delegate on the LML network (LISK Machine Learning).
GNY are building a typescript based machine learning blockchain project, and if you invest in their ICO with LISK then they will also supply you with tokens on their Javascript-based Lisk Machine Learning blockchain.
The network will match the number of tokens 1:1. You will be receiving two sets of tokens; one set for use in the “Lisk Machine Learning” (L.M.L) blockchain, and the other set for use in the Typescript-based GNY blockchain.
The first Dapp that uses LML tokens has already been announced here.
The number of forging delegates on each chain is expected to be 101.
Delegates will be required to signal ownership of 187500 GNY tokens in their wallet and on confirmation of making voting position the tokens will lock. Delegates can unlock tokens at any time instantly removing themselves from delegate position.
Voting weight on the LML system will only count votes from locked wallets on the system. This reduces voting weight coming from exchange hot wallets.
Future delegates who are interested in receiving delegate information packs about the network can send an email requesting to be on the list. Please send emails to this address: [[email protected]](mailto:[email protected])
GNY HQ has issued a call for all contributing and positive Lisk community members to consider becoming a delegate on the LML network (LISK Machine Learning).
GNY are building a typescript based machine learning blockchain project, and if you invest in their ICO with LISK then they will also supply you with tokens on their Javascript-based Lisk Machine Learning blockchain.
The network will match the number of tokens 1:1. You will be receiving two sets of tokens; one set for use in the “Lisk Machine Learning” (L.M.L) blockchain, and the other set for use in the Typescript-based GNY blockchain.
The first Dapp that uses LML tokens has already been announced here.
The number of forging delegates on each chain is expected to be 101.
Delegates will be required to signal ownership of 187500 GNY tokens in their wallet and on confirmation of making voting position the tokens will lock. Delegates can unlock tokens at any time instantly removing themselves from delegate position.
Voting weight on the LML system will only count votes from locked wallets on the system. This reduces voting weight coming from exchange hot wallets.
Future delegates who are interested in receiving delegate information packs about the network can send an email requesting to be on the list. Please send emails to this address: [[email protected]](mailto:[email protected])
October 16, 2018, 09:23:57 AM
Lisk Machine Learning. Listings update.
Hello folks, just bringing you a minor update to the LISK Machine Learning token listing on behalf of the GNY - LML Team. Although this might actually be a major update for those of you based in the US.
As GNY HQ brings forward a series of announcements on our token trading dates, our first update is for the 'LML' token.
In response to requests from the public, GNY have explored every avenue to allow US entrants to acquire LML and we are pleased to be releasing a temporary ERC20 token to ICO entrants from the 12th November, which will be listed on a series of exchanges from early December 2018.
Details of the swap from ERC20 token to Lisk sidechain will be announced early 2019 and we look forward to bringing the community more enterprise partners prior to that date.
Best Regards, GNY - LML Team.
Handy resources for you.
Our latest Cointelegraph coverage.
Our first Social Impact Partner, Child Rescue Coalition.
Our first Enterprise Partner, La Nova Tile Importers, in collaboration with ReThink! Tile Studio.
Interview with GNY.io CEO Cosmas Wong.
First to Launch a Utility Token ICO In Jersey, Channel Islands.
Some company history.
Lisk blockchain explorer snapshot of our current Lisk holdings to show our commitment to Lisk.
Our latest Cointelegraph coverage.
Our first Social Impact Partner, Child Rescue Coalition.
Our first Enterprise Partner, La Nova Tile Importers, in collaboration with ReThink! Tile Studio.
Interview with GNY.io CEO Cosmas Wong.
First to Launch a Utility Token ICO In Jersey, Channel Islands.
Some company history.
Lisk blockchain explorer snapshot of our current Lisk holdings to show our commitment to Lisk.
October 13, 2018, 07:16:57 AM
Advances in Machine learning and the proliferation of generative design with ReThink! Tile Studio .
The GNY IO team are pleased to bring forward the first enterprise group who will be using the LML Lisk machine learning platform and bring you a statement from the studio.
La Nova Tile Importers (http://www.lanovatile.com), in collaboration with ReThink! Tile Studio (http://lanovatile.com/tag), and GNY LML Machine Learning Platform (http://gny.io) using the LISK blockchain (http://lisk.io) will collaborate on a project that will utilize artificial intelligence technology to algorithmically generate realistic images of common materials such as natural stone, cements, or even wood varieties. The intent is to provide the manufacturing sector a deterministic and non-repeating image base to print onto products such as ceramic tiles, fabrics, vinyl, etc.
Once the computer learning algorithm has been successful in recreating a particular type of product, for example a specific type or marble, the team will utilize Interface Tokens (http://interfacetoken.com) and the Art Blocks platform (http://artblocks.io) to manage the purchasing and ownership of the generated graphical outputs. The entire ecosystem will operate on blockchain technology in a price agnostic ecosystem intended to disrupt legacy manufacturing processes that use pre-defined image sets printed repeatedly over the course of large scale manufacturing cycles.
GNY IO looks forward to bringing you interviews from the Rethink! Studio and welcome the first enterprise group to our ecosystem.
The GNY IO team are pleased to bring forward the first enterprise group who will be using the LML Lisk machine learning platform and bring you a statement from the studio.
La Nova Tile Importers (http://www.lanovatile.com), in collaboration with ReThink! Tile Studio (http://lanovatile.com/tag), and GNY LML Machine Learning Platform (http://gny.io) using the LISK blockchain (http://lisk.io) will collaborate on a project that will utilize artificial intelligence technology to algorithmically generate realistic images of common materials such as natural stone, cements, or even wood varieties. The intent is to provide the manufacturing sector a deterministic and non-repeating image base to print onto products such as ceramic tiles, fabrics, vinyl, etc.
Once the computer learning algorithm has been successful in recreating a particular type of product, for example a specific type or marble, the team will utilize Interface Tokens (http://interfacetoken.com) and the Art Blocks platform (http://artblocks.io) to manage the purchasing and ownership of the generated graphical outputs. The entire ecosystem will operate on blockchain technology in a price agnostic ecosystem intended to disrupt legacy manufacturing processes that use pre-defined image sets printed repeatedly over the course of large scale manufacturing cycles.
GNY IO looks forward to bringing you interviews from the Rethink! Studio and welcome the first enterprise group to our ecosystem.
October 09, 2018, 07:32:16 PM
How Artificial Intelligence Estimates Obesity Levels From Google Map Photos.
The research suggests that the predictive power for obesity rates came from the presence of natural features such as lakes and parks detected by the neural network.
In the study, the features extracted by the neural network were able to explain around 60% of the variation in obesity levels for the ground-truth obesity data from a survey known as Behavioral Risk Factor Surveillance System data.
Analysis of satellite imagery using modern deep learning techniques is an exciting path of research which can help save billions in census expenses. More importantly, it can help sociologists remove implicit biases from sampling techniques which may disadvantage minority communities.
William Falcon.
Read more here.
The research suggests that the predictive power for obesity rates came from the presence of natural features such as lakes and parks detected by the neural network.
In the study, the features extracted by the neural network were able to explain around 60% of the variation in obesity levels for the ground-truth obesity data from a survey known as Behavioral Risk Factor Surveillance System data.
Analysis of satellite imagery using modern deep learning techniques is an exciting path of research which can help save billions in census expenses. More importantly, it can help sociologists remove implicit biases from sampling techniques which may disadvantage minority communities.
William Falcon.
Read more here.
October 07, 2018, 10:42:04 AM
During the duration of Phase 1 (now coming to a close), GNY Tokens are sold for $0.08 (US Dollar, Eight Cents).
This phase 1 of the sale includes a 20% discount on the price per token.
During Phase 2, GNY Tokens will be sold for $0.10 (US Dollar, Ten Cents).
GNY will peg the price of Phase 2 GNY Tokens in BTC, ETH, LISK and XAS immediately prior to the start of Phase 2.
The peg will last for the duration of Phase 2.
The GNY token sale Phase 1 is currently still open.
This phase 1 of the sale includes a 20% discount on the price per token.
During Phase 2, GNY Tokens will be sold for $0.10 (US Dollar, Ten Cents).
GNY will peg the price of Phase 2 GNY Tokens in BTC, ETH, LISK and XAS immediately prior to the start of Phase 2.
The peg will last for the duration of Phase 2.
The GNY token sale Phase 1 is currently still open.
October 02, 2018, 06:44:37 AM
Raport z LISK Londyn - moje osobiste odczucia.
Obejmuje również sidechains MADANA i GNY. LISKロンドンの会合レポート - 私の個人的な気持ち。また、MADANAとGNYのサイドチェーンも含まれています。
LISK London meetup report - my personal feelings. Includes the MADANA & GNY sidechains.
https://medium.com/@LiskHighlights/lisk-london-meetup-report-8ad0c7862469
Obejmuje również sidechains MADANA i GNY. LISKロンドンの会合レポート - 私の個人的な気持ち。また、MADANAとGNYのサイドチェーンも含まれています。
LISK London meetup report - my personal feelings. Includes the MADANA & GNY sidechains.
https://medium.com/@LiskHighlights/lisk-london-meetup-report-8ad0c7862469
October 02, 2018, 01:01:38 AM
Lisk Sidechains were everywhere at our BLOQspace Community event, can you spot the GNY banner?
October 02, 2018, 12:59:19 AM
We even had some community members standing guard over the GNY banner! 
We are members of the Sherwood Pool as a Lisk Delegate and some of our guests were having a great time at out community event!
We are members of the Sherwood Pool as a Lisk Delegate and some of our guests were having a great time at out community event!
October 02, 2018, 12:57:10 AM
GNY swag was on display at the BLOQspace meetup we had tonight!
September 26, 2018, 01:53:21 PM
GNY IN ACTION — GNY has consistently doubled the viewing traffic of website publisher recommendations compared to personal editors curating each article. Since it is possible for an intelligent employee to write "if this, then then this" rules.
September 26, 2018, 12:29:52 PM
It is always paramount to give credit where it is due. I would like to thank the GNY team for being shining example of how to engage with and respect their community. The GNY team sponsored these shirts for some of their early grassroots supporters. We really appreciate it and will proudly wear them as we represent such an admirable platform not only in tech, but culture. GNY to the MOON!
September 24, 2018, 04:35:47 PM
The piece below does not factor in the GNY or LML projects as they are still in development, but nevertheless the piece is an interesting read.
Do you need custom development, API software, or startup acquisition?
Companies can't turn a blind eye to machine learning anymore, as it is so powerful at certain tasks. Answer these four questions to understand whether your business needs machine learning, and, if so, how you can adopt this technology.
AI and machine learning are making a significant impact on multiple industries and changing the landscape of our society. These are not just hot trends; they are here to stay.
Still, machine learning is not a magical solution that applies to every single use case. So often companies embark on an AI development journey without a clear understanding of the value it should bring to their business. As a result, many data science and machine learning projects don’t have clear KPIs and simply drain R&D budgets.
That’s why managers have to ask themselves four key questions to justify the need for machine learning development.
There are three strategies for companies to adopt machine learning.
1. Build a machine learning solution from scratch.
This is probably the riskiest option, as only an estimated 10 percent of machine learning R&D projects succeed. It is still the most viable option for some narrow machine learning cases in specific domains.
2. Explore machine learning with cloud engines from Google, Amazon and the like.
This is the easiest way to gain access to machine learning technology. On the downside, you cannot freely configure system parameters. For instance, Amazon uses only logistic regression models, so is practically useless if you need to use different models for a particular project. That means, more sophisticated machine learning projects require custom solutions development. Furthermore, 80 percent of machine learning development is still about big data engineering. This is something you cannot delegate to Amazon.
3. Buy a machine learning startup.
This is the most expensive option that suits only big companies.
Data science and machine learning often produce unexpected results and give invaluable insights. This technology is here to stay, and it’s going to evolve at an extremely fast pace. Answering the above questions will help you start your machine learning development journey.
Read the full article here.... https://www.business.com/articles/machine-learning-project-questions/?utm_campaign=coschedule&utm_source=twitter&utm_medium=bigthinkingio
September 23, 2018, 11:03:40 AM
What is Machine Learning?.......... ELI5
September 21, 2018, 06:30:23 PM
How Artificial Intelligence Can Influence Governance, Risk, and Compliance.
Artificial Intelligence (AI) offers enormous opportunities to businesses. Given the correlation between risk and an organization's objectives, one could easily extrapolate how AI can help bring insight to Governance, Risk, and Compliance (GRC) activities as well.
But, what is AI? As a working definition, AI is the science and engineering of making intelligent machines and computer programs to achieve a goal. It's about creating a computer mind that can think like a human. It's about machines taking action.
One of the most important technological advances of our time is artificial intelligence, and, in particular, machine learning, which is the ability for a machine to keep improving its performance without human involvement to accomplish tasks. Systems can now be taught to perform activities on their own.
The transformative effects of AI will be felt across nearly all industries. The impact on core processes and business models will be enormous, placing further strain on management and implementation.
Known Unknowns
Probably one of the best cases is fraud detection. Algorithms can be written using various stochastic modeling techniques, coding, and data testing. Of course, for machine learning to be successful, it must have quality data. As a result, there is a premium on structuring risk data in such a way to use it as AI input. Conversely, a challenge implicit in machine learning is substantiating its outcomes. As machines "learn," their conclusions may not always yield the desired result. This conceivably makes it difficult for a risk manager to explain the machine's conclusions to executives or a regulator difficult. For example, there may be issues with multicollinearity, lack of data, as well as how the machine deals with outliers, which is common with many risk data, especially if the organization uses external data.
GRC and AI Image 2 This example applies to a typical risk management anecdote of "driving by looking through the rear-view mirror." The shear amount of data aids in the confidence (not just the statistical significance of a model) of AI's output. This is beneficial to many high inherent, intrinsic risks that organizations experience. Malware is an example.
AI can also be used to substantiate conformance. For example, one large financial services company uses AI to help prevent money laundering, thereby assuring AML/BSA compliance.
Read the full article here.... https://www.nasdaq.com/article/how-artificial-intelligence-can-influence-governance-risk-and-compliance-cm894603
Artificial Intelligence (AI) offers enormous opportunities to businesses. Given the correlation between risk and an organization's objectives, one could easily extrapolate how AI can help bring insight to Governance, Risk, and Compliance (GRC) activities as well.
But, what is AI? As a working definition, AI is the science and engineering of making intelligent machines and computer programs to achieve a goal. It's about creating a computer mind that can think like a human. It's about machines taking action.
One of the most important technological advances of our time is artificial intelligence, and, in particular, machine learning, which is the ability for a machine to keep improving its performance without human involvement to accomplish tasks. Systems can now be taught to perform activities on their own.
The transformative effects of AI will be felt across nearly all industries. The impact on core processes and business models will be enormous, placing further strain on management and implementation.
Known Unknowns
Probably one of the best cases is fraud detection. Algorithms can be written using various stochastic modeling techniques, coding, and data testing. Of course, for machine learning to be successful, it must have quality data. As a result, there is a premium on structuring risk data in such a way to use it as AI input. Conversely, a challenge implicit in machine learning is substantiating its outcomes. As machines "learn," their conclusions may not always yield the desired result. This conceivably makes it difficult for a risk manager to explain the machine's conclusions to executives or a regulator difficult. For example, there may be issues with multicollinearity, lack of data, as well as how the machine deals with outliers, which is common with many risk data, especially if the organization uses external data.
GRC and AI Image 2 This example applies to a typical risk management anecdote of "driving by looking through the rear-view mirror." The shear amount of data aids in the confidence (not just the statistical significance of a model) of AI's output. This is beneficial to many high inherent, intrinsic risks that organizations experience. Malware is an example.
AI can also be used to substantiate conformance. For example, one large financial services company uses AI to help prevent money laundering, thereby assuring AML/BSA compliance.
Read the full article here.... https://www.nasdaq.com/article/how-artificial-intelligence-can-influence-governance-risk-and-compliance-cm894603
September 21, 2018, 08:59:39 AM
I'm a sucker for everything related to machine learning, I really do believe that's the way of the future. Honestly, your project sounds incredibly interesting to me, what's the best way to learn more details about it?
The GNY.io telegram group would be a good place to start, wattson
................. https://t.me/GNYioBlockchain September 21, 2018, 08:58:09 AM
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