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Sabio’s App Science® Wins “Best Behavioral Targeting Platform” in 2019 MarTech Breakthrough Awards Program

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Sabio, the media and technology company behind App Science®, today announced it has been selected as the winner of the “Best Behavioral Targeting Platform” award by MarTech Breakthrough, a leading market intelligence organization that recognizes the top companies, technologies and products in the global marketing, sales and advertising technology industry today.

“Sabio is a shining example of what the MarTech Breakthrough Awards program aims to highlight, with “breakthrough” machine-learning technology as well as a creative approach to push forward a higher standard in marketing,” said James Johnson, Managing Director at MarTech Breakthrough. “We applaud Sabio on their innovative approach to mobile marketing and advertising and we look forward to seeing continued success from the Company. Congratulations to Sabio on the well-deserved 2019 MarTech Breakthrough Award.”

The mission of the MarTech Breakthrough Awards is to honor excellence and recognize the innovation, hard work and success in a range of marketing, sales and advertising technology related categories, including marketing automation, market research and customer experience, AdTech, SalesTech, marketing analytics, content and social marketing, mobile marketing and many more. This year’s program attracted more than 2,500 nominations from over 15 different countries throughout the world.

Sabio’s App Science® is a proprietary machine learning platform that pairs observations of consumer behavior with corresponding data to inform marketing decisions. Sabio’s unique approach uses AI to examine how key customer behavioral data points work in combination with mobile data such as location, device and App ID information to formulate predictive clusters and the valuable customer insights that they glean.

“Our holistic approach means we don’t just look at the consumer in a vacuum,” said Joe Camacho, CMO, Sabio. “We take several important behavioral factors into consideration and utilize them to collectively identify the ideal consumer who will engage with a brand’s advertising content. We are firmly committed to providing our clients and partners with a platform that delivers smarter mobile advertising and insights, and we are thrilled to receive this 2019 MarTech Breakthrough Award in recognition of our commitment and success in doing so.”

 

SOURCE Sabio

Artificial Intelligence

IJCAI 2019 in the Spotlight: WeBank AI Group Shared Remarkable Academic Innovation

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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 GuangdongHong KongMacao 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.

 

SOURCE WeBank

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Population Size and Immigration Policy Could Define the Outcome of US-China Tech Race, Says Ctrip Executive Chairman James Liang

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James Liang, Co-founder and Executive Chairman of Ctrip, recently attended the 2019 Yabuli China Entrepreneurs Forum Summer Summit, where he discussed how population and demographic management could be the determining factors in the US-China tech race.

During the forum, Liang spoke about how each nation’s approach to demographics and immigration will play a pivotal role in determining their future innovation and development potential. According to Liang, although China is well-placed to outpace the US in the short term, it will need to tackle the core challenges of population size and demographic make-up to ensure sustainable growth in the long run.

“In simple terms, the more people you have, the more research scientists and engineers will be available to develop world-leading artificial intelligence technologies to overtake your competitors,” said Liang.

Liang observes that China’s current momentum in the tech race stems from both its mammoth population size and the size of its market. With a population of 1.4 billion, China has more human resources to invest in research and development. Coupled with the fact that China is home to the world’s largest e-commerce market, this means domestic technology giants have access to more user application scenarios and innovation opportunities.

In addition, the number of undergraduates produced by China is more than triple that of the US, placing China at a definite competitive advantage for R&D over the next two decades.

Despite this, Liang warned that diminishing fertility rates, an aging population and a ‘brain drain’ of talent would put China’s advantage at risk in the future. The average Chinese family has 1.2 children, effectively halving the population every generation. Lingering effects from China’s one-child policy mean that the population above age 65 will continue to grow exponentially, reaching 330 million by 2050, further impeding the pace of innovation. Chinese students are also lured by the prospect of studying overseas, forming almost a third of American international students.

When asked about China’s radical solutions to address population and demographic issues, Liang responded that proactive policies would play the most prominent role in maintaining its competitive edge.

“In the long run, the outcome will come down to foresighted management of population policy,” said Liang. “It’s not wise to wait for the latest technologies to solve severe demographic problems. For China, this would require significant reform by putting in place policies to encourage childbearing, relaxing immigration and visa laws, and reforming the education system.”

Contrary to China’s potential innovation deficit in the future, Liang views the US’ attitude to immigration as the nation’s most significant hurdle in the future. Liang noted that at present, the US population more than doubles when taking migration into account and, at its current pace, is set to surpass China’s by 2100.

In addition, more than half of all US tech companies are built by immigrants, and non-US citizens make up 45% of all Ph.D. candidates in key innovation fields like engineering, and computer sciences are non-US citizens, as well as 35% in mathematics. Ultimately, Liang says, this demonstrates a strong correlation between a diverse population and innovation.

“An open door to the brightest minds of the world means that US technology companies can be assured of their ability to remain at the forefront of innovation,” said Liang. “The restrictive immigration policies pursued by the incumbent US president run the genuine risk of throwing away the strongest advantage the US holds – an openness to overseas talent. If this protectionism persists, it could have devastating effects on America’s ability to innovate technologically.”

In the end, Liang says the future will be decided by the nation that can best tackle their challenges and adopt new policies to maintain their edge in terms of population and demographics.

“This is where I believe the battle for the top will be won or lost – a young, dynamic population with an openness to seeking the best talent from overseas,” says Liang.

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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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