Geminare, a leader in delivering data curation, resiliency and IT orchestration solutions, announced today the availability of mezzanine.ai as a standalone platform. Mezzanine.ai facilitates impactful, curated business outcomes through the use of readily available pre-trained machine learning models from key industry AI leaders including Google Cloud, IBM Watson, AWS and Microsoft Azure.
Mezzanine.ai is a new approach for businesses looking to engage with machine learning. It empowers your data to be seamlessly and securely evaluated through multiple pre-trained machine learning models through a sophisticated, wizard-driven, orchestration and automation platform. Mezzanine.ai delivers on the promise of bringing AI and machine learning capabilities directly to businesses, with absolutely no need for in-house AI or machine learning knowledge, specialized technology or data scientists. By leveraging a multiplicity of machine learning models simultaneously, the detail and volume of data insights increase exponentially. Through its comprehensive application integration library, mezzanine.ai’s underlying patented platform establishes secure synchronizations between business applications and data, whether hosted or on-premises, allowing companies to instantly leverage the power of machine learning.
Joshua Geist, Geminare’s CEO, commented, “Machine learning is a transformational force for businesses, but like so many other technologies before it, access is limited to those with significant budgets and expert knowhow. Mezzanine.ai changes that by enabling businesses to access whichever models are best suited for their needs from the platforms of their choice, whether it’s just a single pre-trained model or all of them at the same time. With mezzanine.ai, businesses can test and evaluate the results of their data as it’s run through pre-trained ML models, almost instantaneously. Results with customers such as MovieComm have been outstanding and exactly what we had hoped for when launching mezzanine.ai – immediate access to machine learning – quickly and easily.”
Customer Case Study: Mezzanine.ai powers MovieComm’s exponential growth in data capture and analytics within the media and entertainment industry.
MovieComm helps thought leaders inspire and engage others by using Hollywood movie clips to make communications come alive. Customers work with MovieComm to obtain clips with specific references or themes for projects ranging from embedding high-value content into multiple communications platforms including: internal communications platforms, presentations, email and text messages. With a published goal of over 1,000,000 documented clips to provide to the market, leveraging the power of AI and machine learning was a clear necessity and represented a potentially big win for MovieComm. However, the required machine learning models were dispersed across different vendor platforms including Amazon Rekognition, Google Video Intelligence and Microsoft Media Services, creating a barrier to entry for MovieComm – one that mezzanine.ai was able to resolve with ease.
Through mezzanine.ai, multiple machine learning models were used in parallel to extract data from movie clips. Insights gleaned include Sentiment, Unsafe Content, Objects, and Celebrity Recognition – items that customers use in their searches for the ideal clip, as well as audio transcripts allowing for translation and specific search terms. Machine learning models were orchestrated to be leveraged in sequence in order to gain the most insight from the tools available. Extracted data was then automatically populated back into the MovieComm platform.
“All machine learning models are not created equal,” Scott DiGiammarino, CEO of MovieComm stated. “Using mezzanine.ai and its machine learning orchestration opened up new insight into our data and has been a game-changer for us. MovieComm is now able to deliver a vastly superior end-user experience, and fully leverage the value of our films’ assets. Overnight, we’ve gone from a usable database of tens of thousands of searchable items to millions, and from a cataloging time of weeks to just minutes. Our experience with mezzanine.ai capabilities has been invaluable.”
IJCAI 2019 in the Spotlight: WeBank AI Group Shared Remarkable Academic Innovation
The 28th International Joint Conferences on Artificial Intelligence (IJCAI) was held from August 10th – 16th, in Macao, SAR, China. WeBank, the first private and digital bank in China, contributed to the event with multiple academic research findings, and demonstrated great engagement in IJCAI by organizing a heavy-weight workshop FML’19 (the 1st International Workshop on Federated Machine Learning for User Privacy and Data Confidentiality).
100+ World-leading Scholars Discussed Academic Frontier at the 1st International Workshop on Federated Machine Learning
The idea of adopting FML in AI for data confidentiality and user privacy was coined by WeBank in China. In a bid to promote this emerging AI technology, WeBank, IBM and other organizations jointly held the 1st International Workshop on Federated Machine Learning in conjunction with IJCAI 2019. 100+ leading scholars with insight in FML from home and abroad were invited to share cutting-edge academic findings and most advanced applications. President of IJCAI, Chief AI Officer of WeBank Professor Qiang Yang delivered opening remarks. Dr. Shahrokh Daijavad from IBM and Dr. Jakub Konečný from Google delivered keynote addresses. The moderator of 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 exchanging thoughts on the way ahead for FML.
Take Stock on 40 Years’ Achievement of AI in China – Panel Discussion Featuring Chinese Characteristics
Elements of previous conferences including Traditional AI Session, Industry Day focused on industrial application and Best Paper Awards Session were inherited in this year’s IJCAI. IJCAI-19 also opened panels and workshops under the new agenda with a focus on most-discussed topics e.g., data privacy and AI universality. Chief AI Officer of WeBank Professor Qiang Yang engaged in multiple agendas as the President of IJCAI-19 and chair of two panel discussions namely AI in China, AI and User Privacy.
Panelists of “AI in China” include Academician of Chinese Academy of Sciences Bo Zhang, Academician of Chinese Academy of Engineering Wen Gao, Dean of School of AI of Nanjing University Professor Zhihua Zhou, Professor Pascale Fung of Department of Electronic and Computer Engineering of HKUST, Professor Tong Zhang of HKUST, CEO of 4Paradigm Wenyuan Dai. These leading figures in China’s AI sector shared stories within the industry. This theme, with its unique historic significance, added weight to IJCAI-19.
IJCAI-19 also witnessed the founding of the Guangdong–Hong Kong–Macao Greater Bay Area on AI and Robotics Federation. Announced at “AI in China” Panel, the establishment of this new Federation requires the three academic societies to pool leading scholars and experts in AI and robotics within their localities. The merge was widely supported, acclaimed and recognized across the industrial sector and the government as it will further promote cohesion of talents, spawning of scientific innovation, R&D and application of key technologies in China.
“AI and User Privacy” Panel was joined by Director of IEEE Standards Association Victoria Wang, Professor Pedro Domingos of University of Washington, Director of Swiss Re Institute Jeffrey Bohn, Senior Research Scientist of WeBank AI Dr. Yang Liu and a host of experts and scholars to further discussions on how to promote technology development and legislation process simultaneously, which is helpful to addressing the user privacy issue in the current stage of AI development.
WeBank Shared New Insight on AI Safety Workshop
Besides user privacy as discussed in the panel discussions, data confidentiality also represents a common concern in the age of big data. At the AI Safety 2019 Workshop, Senior Research Scientist of WeBank AI Dr. Yang Liu delivered a speech themed Federated Machine Learning (FML), and shared in-depth insight on how to safeguard user privacy and AI safety as well as a number of technologies for privacy protection. She also elaborated on three categories of FML, namely Horizontal Federated Learning, Vertical Federated Learning, Federated Transfer Learning. According to Dr. Liu, data confidentiality and user privacy are the two major challenges in the age of big data, particularly challenging for financial, medical, legal and other data sensitive industries, whereas FML is a great solution to both challenges.
AI Enabling Contextualized Application in Finance – WeBank Shared Insight on Digital Innovative Transformation
While FML serves as the theoretical basis, the application of FML represents a common concern for all walks of life. Challenges before the highly digitalized financial sector manifest even greater complexity and risk. At Industry Day in IJCAI 2019, Deputy Managing Director of WeBank AI Tianjian Chen shared in-depth insight on digital banking business in the financial sector under the theme “AI in Digital Banking”, and elaborated on the important role that AI plays in digital banking. Given the challenges of AI application in the financial sector, he pointed the way ahead for the new generation of AI. “Safety, fairness, data protection are major challenges in the application of AI in the banking sector,” he said. “FML is potentially the new path to take for addressing these challenges.” So far, WeBank AI Group has developed a series of pioneering technologies including FML, which are proven to be contributive to joint modeling for credit risk control and anti-money laundering, etc.
Demonstration of FML Visualization – WeBank Introduced Best Practice
WeBank AI Group is dedicated to promoting widespread application of FML across the industry, sharing capabilities, enabling multi-win results. Videos submitted by WeBank AI Group, including Multi-Agent Visualization for Explaining Federated AI, Learning Federated Learning, were accepted by Demonstration Track and AI Video Competition of IJCAI-19, of which, the latter was awarded Most Educational Video. The videos provide straightforward illustration of cases to attendees on how FML and FedAI system works, all designed for more partners within the industry to enhance understanding of FML technologies and become promoters.
As an endeavor to explore “AI + Art” fusion, the man-computer car racing game demo developed by WeBank AI Group based on FML technology will be displayed under the invitation of China Central Academy of Fine Arts, and is expected to be showcased in the Shenzhen and Shanghai Art Exhibition scheduled in late October and early November respectively.
Among 35 papers submitted in Demonstration Track, the research paper on AI empowering flexible staffing by WeBank, HKUST, NTU and BBK Group, titled Fair and Explainable Dynamic Engagement of Crowd Workers, won the Innovation Award.
WeBank “AI+X” Innovation Debut – Exploration of Future AI Ecosystem
In addition to academic research findings, WeBank AI Group also exhibited industry-leading innovations in four main areas namely “AI + Service”, “AI + Marketing”, “AI + Big Data”, “AI + Asset Management”, which drew the attention of government officials from Macao SAR, professors and scholars of universities and research institutions in China and abroad.
In the area of “AI + Big Data”, WeBank established FedAI ecosystem for cooperation, the world’s first industrial-grade framework for Federated Learning (FATE), AI scenario-based rapid modeling platform (QML). In “AI + Service”, WeBank explored new approaches and scenarios for human-computer interaction, developed ubiquitous robots focused on financial services, integrated core technologies such as NLP, TTS, OCR with scenarios, and expanded to a series of business contextualized applications. The robot developed independently by WeBank became a spotlight in the exhibition. In “AI + Marketing”, WeBank AI Group played a leading role in trust marketing development, aimed to promote long-link and long-term effective marketing conversion of high-value products. In “AI + Asset Management”, WeBank used new alternative big data and machine learning technology helped forge a new generation of AI-driven intelligent asset management system.
China’s scientific research capability is among the top in the world thanks to advances in big data, AI and other frontier technologies. As an internet bank dedicated to innovation in fintech, WeBank AI Group demonstrates its commitment in global collaboration for scientific research and sparks technological advances by coupling theory and application. Looking ahead, WeBank will further leverage its strength in AI technology and platform, integrate top-notch resource worldwide, forge high-level network for knowledge and research, and lead the way for global tech innovation.
Galaxy Digital to Host a Shareholder Update Conference Call on Wednesday, August 28, 2019 at 9:00AM EDT
Galaxy Digital Holdings Ltd. (TSXV: GLXY; Frankfurt: 7LX) (“Galaxy Digital” or the “Company”) is pleased to announce that it will report second quarter 2019 financial results before the opening of the TSX-Venture Exchange on Wednesday, August 28, 2019.
Michael Novogratz, CEO and Founder of Galaxy Digital, and members of management will host a conference call to provide a general update to shareholders on the Company’s activities and results on the same day at 9:00 am Eastern Daylight Time (EDT).
The dial-in number for callers in the United States or Canada is 800-289-0459. The dial-in number for callers in Germanyis 0800-589-4608. Callers who reside outside of the United States, Canada or Germany should dial +1-323-794-2558. The passcode for all participants is 877692. For those unable to participate, an audio recording of the call will be available on the Company’s website until at least 5:00 pm EDT on September 17, 2019.
SOURCE Galaxy Digital Holdings Ltd
Crypto Earn: Now Earn 8% p.a. on DAI & MKR Deposits
Crypto.com, the pioneering payments and cryptocurrency platform, announced today that it has listed Maker (MKR) and added DAI & MKR to Crypto Earn, allowing users to enjoy up to 8% p.a. on their deposits.
Dai (DAI) DAI is a USD-pegged stablecoin running on the Ethereum developed by the MakerDAO team. Its $1 USDequivalent is maintained through a dynamic system of Collateralized Debt Positions (CDPs), autonomous feedback mechanisms, and appropriately incentivized external actors. DAI can be used in the same manner as any other cryptocurrency: It can be freely sent to others, used as payments for goods and services, or held as long term savings.
Maker (MKR) is an ERC-20 token on the Ethereum blockchain that backs and stabilizes the value of stablecoin DAI through automatic pricing mechanisms built into smart contracts. MKR tokens are created or destroyed in accordance with price fluctuations of the DAI coin in order to keep it as close to $1 USD as possible and is part of a fully inspectable system on the Ethereum blockchain. MKR tokens are also used to pay transaction fees on the Maker system, and provide holders with voting rights within Maker’s continuous approval voting system.
Crypto Earn now supports 13 coins for holders to maximize their returns, including BTC, ETH, LTC, XRP, BNB, TUSD, PAX, USDC, MCO, BAT, LINK with the addition of DAI and MKR. Interest is paid out weekly in the coin deposited with flexible, 1-month or 3-month terms available. Users earn more by staking at least 50 MCO.
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