Streaming music and royalties for Akon on stellar blockchain
Blockchain being used for music and streaming and payments . Grammy-nominated singer Akon will launch his forthcoming token using Stellar’s blockchain.
According to an update , the Akoin cryptocurrency ecosystem will use Stellar’s network as its basis. Akoin co-founder and president Jon Karas said the decision was made in part due to the shared values between his project and Stellar.
“ Akoin selected Stellar’s distributed, hybrid blockchain due to a shared vision for creating global financial inclusion, particularly in areas such as Africa.”
The update claims Akoin will be compatible with Stellar wallets and interoperable with all digital assets and currencies currently supported by the Stellar network. Karas highlighted the Stellar Network’s “efficient cross-asset transfers of value” as a benefit for Akoin and said users will be able to instantly swap from one currency to another.
In addition, users will be able to exchange mobile phone minutes, which have become a popular means for exchange in certain parts of Africa, for Akoin and other currencies on Stellar’s network.
According to a spokesperson for the project, Akoin will not be a stablecoin and instead have a fluctuating price. He said,
“ Akoin is not a stablecoin, but we will provide access to other leading stablecoin offerings within our eco-system.”
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The NHL has a new franchise called the Seattle Kraken. They don’t start playing till next season and there are no players drafted or signed yet.
Their team will be built or selected via The Expansion Draft, which going by prior expansion drafts , will wind up filling the majority of roster positions. Then comes the NHL Entry Draft, which is more for young prospects. And finally free agency where they can sign various players.
Not sure how many free agents get signed by expansion teams. Would a Taylor Hall have signed with the Seattle Kraken and not the Buffalo Sabres this year ? Who knows. And too bad they missed out on all the goaltenders changing teams.
Analytics and data science are here to stay in the NHL
NHL teams are at the edge of artificial intelligence and can utilize a number of technological and analytical tools to narrow the search for prospects and the right player fit or to break a tie between a few players whose potential may appear even at first glance.
There is the traditional video and scouting reports, but now advanced analytics and data science departments on some NHL teams are changing the way they find and sign and build teams. Ron Francis and the Seattle Kraken are at the forefront of building an analytics driven organization.
So over the next year the Seattle Kraken will be analyzing data and building models to help them build a team.
Machine learning models are known to amplify the biases present in the data. These data biases frequently do not become apparent until after the models are long deployed. And sometimes not at all until something goes viral on social media and a company or model gets heat.
To tackle this issue and to enable the preemptive analysis of large-scale dataset, REVISE (REvealing VIsual biaSEs) is a tool that assists in the investigation of a visual dataset, surfacing potential biases currently along three dimensions:
1 – Object-based :
Object-based biases relate to size, context, or diversity of object representation
2 – Gender-based :
Gender-based metrics aim to reveal the stereotypical portrayal of people of different genders
3 – Geography-based
Geography-based analyses consider the representation of different geographic locations
REVISE is a open source tool that automatically detects possible forms of bias in a visual dataset along the axes of object-based, gender-based, and geography-based patterns, and from which next steps for mitigation are suggested.
Once you know how to analyze everything, your information can be gold. The value and importance of leveraging machine learning to create a true competitive advantage is known to most successful startups & businesses. Artificial intelligence provides actionable insights, which lead to a stronger business strategy and plan of action. It is essential to have usable data and actionable predictive analytics to make the most of your analytics and data science pipelines, and for a business person to understand the typical product life-cycle flow.
Artificial Intelligence Drives Innovation
Retail Case Study
For several years, inventory and sales information has been obtained by a network of integrated but silo department store systems. Mergers, Acquisitions, various consultancies and data warehouses turned this business intelligence and ‘big data’ platform into a data swamp, not a data lake. Nobody wanted to use it because it was like a scary swamp monster.
This system acted as the analytic and AI center of excellence used to train a machine learning model by the Pagarba Solutions data science team. The team helped the founders, directors and C-Level executives identify strategic actionable priorities and act on them in a timely manner. The current system was the method used to create a demand forecasting model to make future predictable trends and forecasts.
By analyzing data from the past five years of retail sales, an adaptive selective model approximated demand for the coming month. Vector auto-regression models coupled with external data sources are often used in machine learning. That made it possible at any time to forecast demand for any particular product. The company was able to maximize inventory and warehouse management by using this solution to provide alternatives available.
BioTech Smart Medicine Inventory Management Hardware Case Study
The Pagarba Solutions client was advised to use multi-modal bio-metric authentication object recognition system that required a user to pass both a facial and voice recognition to gain appropriate authorized access.
Pagarba Solutions Artificial Intelligence experts suggested introducing enhanced security features to the system, such as image and object recognition technology like optical character recognition (OCR). This was for driver’s license or passport ID authentication. Another strategy and plan was to reduce outliers and overfitting by using deep learning algorithms to prevent fake or biased facial recognition. And finally using Natural Language Processing technology( NLP) for security based written quiz like challenges. These changes also culminated in the development of an event-as-a-Service (EVaaS) platform.
Artificial Intelligence Process Flow & Pipeline
Executives, Directors, & Business owners want to know how to use AI and emerging technology to achieve actionable and realistic business goals. What’s the Return on Investment? They are less interested in the details of how the technology works.
Steps of a Artificial Intelligence Strategy & Development project
1> User Experience (UX) is Key
How will your customers and end users use this Artificial Intelligence (AI) product ? Think of the “old school” recommendation engines we see on Amazon, Wal-mart, any ecommerce site or even youtube and netflix or Hulu. A recommendation engine is really a subclass of information filtering systems that predict the “rating” or “preference” a end user would give to an item.
Recommendation systems work best with unbiased explicit user feedback like Amazon and shopping cart purchases or watching a video on Youtube or Netflix or listening to a song on Pandora or Spotify or Apple Music. But in many use cases the application and platform offers mostly implicit feedback scenarios. Think click through rates, page views, google search queires, like or dislikes, up or down voting and so on. This data is heavily biased and we’ve seen people cheat these systems or bully younger users. Click-through rate is heavily dependent on the position of content on a page. Poor UX/UI and web design and it’s like putting Milk at the front of a grocery store. Not ideal if you think with a data-heavy analytics scope. Implicit feedback also tends to perform worse. Google search results are heavily sponsored and ad based on page one now, so a lot of the content may show sponsored results and/or clickbait headlines. And then the site is all spam. This results in high bounce rates after initially high click through rates. This may not matter to a spam site, but a legit business might not be getting the results they desired.
The user experience (UX) can be broken down into three parts:
- Initial Impression
- What is the user trying to achieve?
- How does the user arrive at this experience?
- Where do they go?
- What should they expect?
- Interacting Stage
- What should they see ?
- Is it clear what to do next?
- How are they guided through errors and processes ?
- Feedback Loop
- Did the end user achieve their goal?
- Is there a clear “end” to the experience? Call to Action ?
- What are the follow-up steps (if any)?
Knowing what an end user should see during the start, into the interacting stage and feedback after of your app or software will ensure the data science and AI team pick the right features and variables for the models and the data science team trains and continuously improves and updates the AI models on accurate proper data from the start. And it helps the engineering and devops teams to automate the MLOps process flow. And they can build and design valuable automated feedback loops for analytics that offers the best solution for end users.