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HiveIO Launches Hive Fabric 7.3 to Deliver Data Center Intelligence without Infrastructure Overhaul

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New operational capabilities include graphics acceleration, NVMe
caching, and configurable in-memory storage to deliver an intelligent
virtualization solution

HOBOKEN, N.J.–(BUSINESS WIRE)–lt;a href=”https://twitter.com/hashtag/DataCenterManagement?src=hash” target=”_blank”gt;#DataCenterManagementlt;/agt;–HiveIO,
Inc.
, a company that transforms commodity data center equipment into
an intelligent virtualization platform, today released version 7.3 of
Hive Fabric™, an Artificial Intelligence-ready fabric solution that
enables organizations to deploy virtualization technology without the
need for vendor complexity or specialists. The latest software release
provides Hive Fabric users with increased operational capabilities to
further reduce the time needed to support a virtualization environment
while also maximizing the performance, capacity, and spend on existing
infrastructure.

“Hive Fabric was developed with IT professionals in mind, helping them
withstand common industry pain points like flexibility and usability,”
said Dan Newton, CEO of HiveIO. “The solution has helped IT in a variety
of industries exceed their business goals by creating a virtualization
solution that works with users, not against them. We’re continuing to
grow with a user-first mindset, and the launch of 7.3 delivers the new
capabilities based directly on feedback and needs of current Hive Fabric
users.”

Hive Fabric combines KVM hypervisor, software-defined storage (SDS) and
networking, and virtual desktop management, into an all-in-one
virtualization solution, eliminating the need for a multi-vendor,
multi-contract approach. The new features within the 7.3 solution
include:

  • Graphics Acceleration: The rise in augmented and virtual
    reality has increased the need for graphics acceleration. To
    seamlessly improve the performance of virtual machines (VMs),
    administrators can now install graphics processing units (GPUs) inside
    of Hive Fabric-enabled servers and then simply turn the acceleration
    on or off with a single click. Graphics acceleration is available via
    GPU Sharing or GPU Passthrough and supports NVIDIA, ATI, and Intel.
  • Software-Defined Networking (SDN): Flexible networking is key
    to delivering a fully virtualized data center. With ethernet
    consolidating and the speed increasing, a need for IT Administrators
    to separate traffic and guarantee bandwidth for desktops and
    applications is becoming a necessity. Administrators can now add
    multiple physical and virtual SDNs giving them the flexibility to fit
    with any network architecture.
  • Configurable In-Memory Storage: Balancing business requirements
    and the cost of infrastructure is challenging for any IT team. Memory
    is the most scarce, highest-cost resource in the data center and a key
    to meeting competing business objectives. The SDS capability extends
    to managing server memory, allowing it to be allocated to either
    storage or memory for virtual machines, with differing allocations
    possible on every server.
  • Hive Sense: The comprehensive simplicity of setting up and
    running Hive Fabric extends to HiveIO Support. Introduced in 7.3, Hive
    Sense will allow HiveIO to proactively support customers by sending
    logs, metrics, and configuration information back to the company. This
    reduces the time needed to collect logs or understand how the
    infrastructure is deployed, so support engineers can resolve issues
    faster and remove the burden from your IT administrators.

Unlike legacy platforms that require specialists to operate overly
complicated systems, Hive Fabric utilizes an Intelligent Message Bus and
intuitive user interface (UI) to show an all-encompassing view of a data
center and its connected components in real time. This makes it easy for
administrators to find and act upon vital information and reduce
downtime.

“The Hive Fabric UI, coupled with the easy-to-use enhancements in 7.3,
empowers administrators of all skill levels to manage the entire data
center,” said Toby Coleridge, Vice President of Product at HiveIO.
“Organizations can reallocate their highly-skilled specialists to other
areas of the business to drive innovation rather than be bogged down
with daily administrative tasks.”

Hive Fabric 7.3 is available now. Sign up for a demo and get more
information at www.hiveio.com/hive-fabric.

About HiveIO Inc.

Based in Hoboken, New Jersey, HiveIO transforms commodity data center
infrastructure into an Intelligent Virtualization platform delivering
virtual desktops, virtual servers, and software-defined storage in a
single Hive Fabric install on any x86 commodity infrastructure. No
specialists required. The simplicity, performance, and security of our
AI-ready Hive Fabric solution removes complexity from the data center
while providing scalable power for the workloads of tomorrow. For more
information, visit www.hiveio.com
and follow @HiveIOInc
on Twitter.

© 2018 HiveIO Inc. All rights reserved. HiveIO, Hive USX, Hive Fabric,
and the HiveIO logo are trademarks of HiveIO Inc.

Contacts

For HiveIO
Liza McIntosh
407-376-5886
HiveIO@ink-co.com

Cannabis

Strainprint™ Technologies Welcomes Organic Medical Cannabis Producer to Growing List of Subscribers

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Strainprint™ Technologies Ltd, the leader in cannabis data and analytics is pleased to expand their nation-wide coverage with the addition of Stewart Farms.

Stewart Farms is a late-stage applicant headquartered in Alberta that is building a 100,000 sq. ft. vertical aquaponics farm in Saint StephensNew Brunswick. They will be utilizing automation, vertical farming, and land-based aquaculture to produce medical grade organic cannabis for both recreational and medical markets in Canada. All of their products will be free of herbicides, pesticides and synthetic nutrients. At full capacity, they will reach more than 10,000 kgs per year of organically farmed cannabis and more than 200,000 kgs of organic tilapia.

Stewart Farms’ long term vision is to deliver tools and products to support  future customers and patients during their health and wellness journeys. “Through our partnership with Strainprint we gain direct access to their incomparable data and analytics tools. This will aid us in educating a wider audience on the medical benefits and best practices of cannabis-based medicine,” said Tanner Stewart, Co-founder & CEO of Stewart Farms. “We know our customers are looking to consume more than dried cannabis. We know Canadians of all legal ages are trying to sort through their personal engagement with cannabis as a medicine. What we want to know, on an ongoing basis, is what is and is not working for people. Strainprint’s patient-led data services will give us the insight needed to create custom products and further understand the benefits of our existing products. Finally, our goal is to partner with companies and teams that truly have people’s best interest at heart. Caring about a patient’s success is what makes up the core of the Strainprint team. We couldn’t be happier to move forward with them at our side.”

Stewart Farms will access the Strainprint Analytics web platform, the most sophisticated cannabis analytics platform available to improve product development. Strainprint Analytics is built on top of the largest and most granular scientific data set of its kind in the world, with more than 1.3 million anonymized, patient reported medical cannabis outcomes and more than 65 million data points on strain efficacy.

“We’re thrilled to provide our real-time, crowd sourced data to an environmentally conscious company that uses innovative, cutting edge farming technology to produce top-tier medical cannabis,” said Strainprint CEO, Andrew Muroff. “Our organizations are equally committed to improving lives using cannabis therapy, and our shared core motivation is to provide guidance and support to help cannabis patients achieve their health goals.”

 

SOURCE Strainprint Technologies Ltd.

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Field Trip Ventures Inc. Retains KCSA Strategic Communications as Public Relations Counsel

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Greenery Map, the world’s first and only cannabis search engine to allow users to search for cannabis products based on their desired mood, medicinal use, and method of consumption, is now offering users – both businesses and consumers – the opportunity to register and connect to the app in an entirely mobile way. Previously, businesses were required to access GreeneryMap.com from a desktop in order to manage accounts and connect to the inventory API system, but that road block has been removed, allowing for an entirely mobile experience through the Greenery Map app.

App users turn to Greenery Map to help them decide on the strain of Cannabis and product that is right for them depending on their desired mood or medicinal effect. Following the matching, consumers are given a detailed history and description of the strain so that they can make educated purchases, cutting down on time that budtenders need to spend with patrons and speeding up the purchase process. Consumers can then see all of the ways to purchase said product, including the nearby dispensaries that have it in stock, online options, and even delivery options through the app. Greenery Map fully integrates with a dispensary’s inventory API system to update in real-time and give consumers real-time information on dispensary stock for the item they are looking for. The Greenery Map system also provides cannabis businesses in both the B2B and B2C sectors a private database to share and sell information and products. For Cannabis businesses, Greenery Map also offers the opportunity to open bank and merchant accounts through their partnership with PayHouse Consulting Group.

 

SOURCE Greenery Map

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WeBank, IBM and Other Organizations Jointly Held the 1st International Workshop on Federated Machine Learning in conjunction with IJCAI 2019

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Once a concept, AI is now ushering in a key stage of application. What’s the solution to the data silos among businesses? Given the enhanced regulation on data at home and abroad, what’s the solution to data privacy and security concerns? What’s the status quo of Federated Machine Learning and how to establish an ecosystem for FML in the future?

WeBank, IBM and other organizations jointly held the 1st International Workshop on Federated Machine Learning for User Privacy and Data Confidentiality (FML’19) in conjunction with the 28th International Joint Conference on Artificial Intelligence (IJCAI-19) on Aug. 12, 2019, to further discussion on these issues.

President of IJCAI, Chair of FML Steering Committee, Chief AI Officer of WeBank Professor Qiang Yang delivered opening remarks at the workshop. Dr. Shahrokh Daijavad from IBM and Dr. Jakub Konečný from Google presented keynote addresses. In the panel discussion, top scholars from WeBank, Bar-Ilan University, IBM, Squirrel AI, Google, Huawei, Clustar, Sinovation Ventures and many other renowned enterprises and universities shared and discussed their findings and experience in FML as an emerging AI technology.

This workshop received 40 papers, of which 12 were presented during the workshop, 19 presented via poster. Awards include Best Theory Paper Award, Best Application Paper Award, Best Student Paper Award, Best Presentation Award. Selected high quality papers will be invited for publication in a special issue in the IEEE Intelligent Systems journal. All these attracted numerous scholars to engage in discussions and join efforts for building the FML ecosystem.

Experts from IBM and Google Share Groundbreaking Findings with a Focus on the Theory and Application of FML

Privacy and security are becoming a key concern in our digital age. On 25th May last year, the implementation of General Data Protection Regulation (GDPR) by the EU, the toughest Act on data privacy protection, stressed that user data collection must be open and transparent. A series of laws and regulations from China and overseas also pose new challenges to the traditional way of handling data and model for cooperation. Seeking ways for AI to adapt to this new reality became top priority, a demand that led to this workshop on FML.

A wealth of solutions and breakthroughs were shared by Dr. Shahrokh Daijavad from IBM and Dr. Jakub Konečný from Google in their speech on FML.

Besides how FML can help tackle challenges in the business world, Dr. Shahrokh Daijavad also shared the concept of Fusion AI, which means to train models on widely distributed data sets, but fuse them to produce one equivalent to what centralized training would yield. “Unlike traditional machine learning, in Fusion AI, model parameters are shared and data is not transferred, which makes Fusion AI model better than models that moving data centrally.” Given the widely distributed data, the development of Fusion AI and FML became ever important and imminent.

“FML enables machine learning engineers and data scientists to work productively with decentralized data with privacy by default,” said Dr. Jakub Konečný from Google. He also shared with us how FML works and its use cases at Google. In the case of Gboard, as on-device data is privacy sensitive or large or is more relevant than server-side proxy data, and labels can be inferred naturally via user interaction, the application of Federated RNN compared to prior n-gram model can increase the accuracy of next-word prediction by 24%, and the click rate of prediction strip by 10%.

Major Figure Panelists Discuss the Way Ahead for FML

The moderator of the panel discussion, AI Principal Scientist of WeBank Dr. Lixin Fan joined panelists including Professor Benny Pinkas from Bar-Ilan University, Dr. Shahrokh Daijavad of IBM Academy of Technology, Chief Architect of Squirrel AI Dr. Richard Tong, Research Scientist of Google Dr. Jakub Konečný, Dr. Baofeng Zhang from CTO Office of CBG Software in Huawei, Executive VP of Clustar Dr. Junxue Zhang, VP of AI Institute in Sinovation Ventures Dr. Ji Feng and other experts in a host of in-depth exchanges with attendees, to shed light on the way ahead for FML.

Experts shared thoughts in the panel discussion on questions including but not limited to: How to meet the security and compliance requirements? Is there a way to extend the value of data while observing user privacy and data security? Given the classic trade-off between data regulation and development of AI, how to achieve the long-term goal of establishing a stable and win-win business ecosystem?

List of Award-Winners

Best Theory Paper Award, Best Application Paper Award, Best Student Paper Award and Best Presentation Award selected by all attendees were announced at the closing of the workshop.

Best Theory Paper Award: 
Preserving User Privacy for Machine Learning: Local Differential Privacy or Federated Machine Learning? By Huadi Zheng, Haibo Hu & Ziyang Han;

Best Application Paper Award: 
FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare. By Yiqiang ChenJindong WangChaohui YuWen Gao & Xin Qin;

Best Student Paper Award: 
Quantifying the Performance of Federated Transfer Learning. By Qinghe JingWeiyan Wang, Junxue Zhang, Han Tian & Kai Chen;

Best Presentation Award: 
Federated Generative Privacy. By Aleksei Triastcyn and Boi Faltings.

President of IJCAI, Chief AI Officer of WeBank Professor Qiang Yang, Chief Architect of Squirrel AI Dr. Richard TongandVP of AI Institute in Sinovation Ventures Dr. Ji Feng presented the awards.

“The mission of this International Federated Machine Learning Workshop is to facilitate further understanding in the academia, business community as well as legal and regulatory institutions by promoting the establishment of FML ecosystem in the hope that more businesses will join and build a platform for students aspired to work in FML to find research teams that suit them,” said Professor Qiang Yang.

Held Aug. 10-16, 2019 in Macao, China, IJCAI-19 is one of the leading International Academic Conference on AI, attracting over 3000 AI research personnel and experts. The 1st International Workshop on Federated Machine Learning (FML’19) was a highlight for experts joining this event. Visionaries in the academia and industrial sector expressed the willingness to be part of the effort for academic research, application of FML in the future, and the development and boom of AI ecosystem.

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