Seattle kraken deep diving into analytics

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.

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Artificial intelligence bias

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.


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From an idea to Artificial intelligent product gold
artificial intelligence company
artificial intelligence company

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:
  1. Initial Impression
    1. What is the user trying to achieve?
    2. How does the user arrive at this experience?
    3. Where do they go?
    4. What should they expect?
  2. Interacting Stage
    1. What should they see ?
    2. Is it clear what to do next?
    3. How are they guided through errors and processes ?
  3. Feedback Loop
    1. Did the end user achieve their goal?
    2. Is there a clear “end” to the experience? Call to Action ?
    3. 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.

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Kraken becoming a bank in Wyoming …

The Wyoming Banking Board just voted to approve Kraken, a San Francisco-based cryptocurrency exchange, as a special purpose depository institution (SPDI).

Technically it’s the first crypto exchange in the United States to become a bank. “By becoming a bank we get direct access to federal payments infrastructure, and we can more seamlessly integrate banking and funding options for customers,” David Kinitsky, a managing director at Kraken and the CEO of the newly formed Kraken Financial, told CoinDesk.

If this plays out as it seems , good and bad in a sense , Kraken Financial will no longer have to deal with a variety of different rules for regulating digital assets, depending on which state.

SPDI banks in Wyoming, started in November 2019, are still different than regular national banks. They never actually hold full legal ownership over any digital assets, but are legally allowed to hold them. Any assets following a bankruptcy must be returned to customers.

What about crypto debit ?

Kraken has plans now that it has been approved. “We would expect to offer a host of new products as we get established,” Kinitsky told CoinDesk. “Those will range from things like qualified custody for institutions, digital-asset debit cards and savings accounts all the way to new types of asset classes.”

In the wake of a July letter from the U.S. Office of the Comptroller of the Currency giving national banks the go-ahead to custody crypto, the Division of Banking also announced it has been working with Promontory Financial Group, a prominent Washington, D.C.-based consulting firm made up of lawyers and former government regulators. In October, the division along with Promontory will publish the first manual for banks regarding procedures and policies for handling digital assets, Land said.

In addition to more products, Kraken Financial will give Kraken the ability to operate in more jurisdictions, Kinitsky said. As a state-chartered bank, Kraken now has a regulatory passport into other states without having to deal with a patchwork state-by-state compliance plan.

“By becoming a bank we get direct access to federal payments infrastructure, and we can more seamlessly integrate banking and funding options for customers,” David Kinitsky, a managing director at Kraken and the CEO of the newly formed Kraken Financial, told CoinDesk.

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Oculus Quest 2 starting at $299

Oculus Quest 2 is lighter, supposed to be more powerful, and a better screen. And it starts at $299 with 64 GB of built-in storage, and the $399 model comes with 256 GB. 

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Oculus quest 2 introduced

Oculus quest 2 introduced.

6GB ram , close to 4K , better controls and Options for 256 GB storage

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