Companies like Mastercard are implementing AI strategies that are transforming how customer experience is done. Join this VB Live event for insights on why AI is essential for fintech companies, plus how to implement it, how to make it perfom, and more. AI has been around for a long time -- it's only in the last three or four years that people have been paying attention to it in the fintech space, says Dr. Steve Flinter, VP of artificial intelligence and machine learning at Mastercard Labs. "A huge driver of innovation is that small startups, fintechs, and non-technology corporates are able to get access to this technology that five or 10 years ago would have been locked away in university research labs and the big corporate R&D labs," Flinter says. The amount of data now available, and the ability to store and process that at scale, combined with open source technology, compute power, and breakthroughs in technologies like computer vision and NLP are all part of this AI democratization.

Learning embeddings from large-scale networks is an open challenge. Despite the overwhelming number of existing methods, is is unclear how to exploit network structure in a way that generalizes easily to unseen nodes, edges or graphs. In this work, we look at the problem of finding inductive network embeddings in large networks without domain-dependent node/edge attributes. We propose to use a set of basic predefined local encodings as the basis of a learning algorithm. In particular, we consider the degree frequencies at different distances from a node, which can be computed efficiently for relatively short distances and a large number of nodes. Interestingly, the resulting embeddings generalize well across unseen or distant regions in the network, both in unsupervised settings, when combined with language model learning, as well as in supervised tasks, when used as additional features in a neural network. Despite its simplicity, this method achieves state-of-the-art performance in tasks such as role detection, link prediction and node classification, and represents an inductive network embedding method directly applicable to large unattributed networks.

Microsoft Flight Simulator is a triumph, one that fully captures the meditative experience of soaring through the clouds. But to bring the game to life, Microsoft and developer Asobo Studio needed more than an upgraded graphics engine to make its planes look more realistic. They needed a way to let you believably fly anywhere on the planet, with true-to-life topography and 3D models for almost everything you see, something that's especially difficult in dense cities. A task like that would be practically impossible to accomplish by hand. But it's the sort of large-scale data processing that Microsoft's Azure AI was built for.

As more and more industries take on artificial intelligence to solve some of their biggest challenges, can machines help us understand and fix climate change issues? So your phone recognises your face, and your bank can block any transaction unlike your spending habits. And your online supermarket nudges you with their vegan products just because you've bought that oat milk once, while your online movie platform keeps throwing B-movies at you after you watched that soap opera last month. A growing number of our devices and services are relying on artificial intelligence (AI), a technology that continues to branch out and pop up in more and more areas of our lives. Scientists, entrepreneurs, and governments are leveraging AI to explore solutions for some of society's biggest challenges.

Event forecasting is a challenging, yet consequential task, as humans seek to constantly plan for the future. Existing automated forecasting approaches rely mostly on structured data, such as time-series or event-based knowledge graphs, to help predict future events. In this work, we formulate the forecasting problem as a restricted-domain, multiple-choice, question-answering (QA) task that simulates the forecasting scenario. To showcase the usefulness of this task formulation, we introduce a dataset ForecastQA, a question-answering dataset consisting of 10,392 event forecasting questions, which have been collected and verified via crowdsourcing efforts. We also present our experiments on ForecastQA using BERT-based models and find that our best model achieves 61.0\% accuracy on the dataset, which is still far behind human performance by about 18%. We hope ForecastQA will support future research efforts in bridging this gap.

The African Development Bank held a workshop to convey the continent's immense investment and partnership opportunities to Asian business leaders, particularly as the continent seems poised to return to economic growth in 2021 following the impact of the COVID-19 pandemic. The two-hour virtual event, held in English, Korean, and Chinese, offered participants an opportunity to learn more about the Bank and its operations. The webinar comes on the heels of the recently launched African Economic Outlook 2020 -Asia Supplement, which revised growth projections and outlook for Africa for 2020 and 2021. "I take this opportunity to strongly encourage Asian private sector entities gathered here today, to partner with the Bank to take advantage of the multiple investment opportunities that exist on the continent," said Samuel Higenyi Mugoya, the Bank's Director for Syndication, Co-financing and Client Solutions Department, which co-organized the event, together with the Bank's Asia External Representation Office. In introducing the Bank, Takashi Hanajiri, Head of the Asia External Representation Office, provided an overview of the Bank and its history and components before providing a summary of its flagship Africa Investment Forum (AfricaInvestmentForum.com) initiative and the opportunities it offers.

Held virtually today on the sidelines of the 64th IAEA General Conference, the first ever IAEA meeting discussing the use of artificial intelligence (AI) for nuclear applications showcased the ways in which AI-based approaches in nuclear science can benefit human health, water resource management and nuclear fusion research. Open to the public, the event gathered over 300 people from 43 countries and launched a global dialogue on the potential of AI for nuclear science and the related implications of its use, including ethics and transparency. AI refers to a collection of technologies that combine numerical data, process algorithms and continuously increasing computing power to develop systems capable of tracking complex problems in ways similar to human logic and reasoning. AI technologies can analyse large amounts of data to "learn" how to complete a particular task, a technique called machine learning. "Artificial Intelligence is advancing exponentially," said Najat Mokhtar, IAEA Deputy Director General and Head of the Department of Nuclear Sciences and Applications.

As per the records of theWorld Health Organization, the first formally reported incidence of Zika virus occurred in Brazil in May 2015. The disease then rapidly spread to other countries in Americas and East Asia, affecting more than 1,000,000 people. Zika virus is primarily transmitted through bites of infected mosquitoes of the species Aedes (Aedes aegypti and Aedes albopictus). The abundance of mosquitoes and, as a result, the prevalence of Zika virus infections are common in areas which have high precipitation, high temperature, and high population density.Nonlinear spatio-temporal dependency of such data and lack of historical public health records make prediction of the virus spread particularly challenging. In this article, we enhance Zika forecasting by introducing the concepts of topological data analysis and, specifically, persistent homology of atmospheric variables, into the virus spread modeling. The topological summaries allow for capturing higher order dependencies among atmospheric variables that otherwise might be unassessable via conventional spatio-temporal modeling approaches based on geographical proximity assessed via Euclidean distance. We introduce a new concept of cumulative Betti numbers and then integrate the cumulative Betti numbers as topological descriptors into three predictive machine learning models: random forest, generalized boosted regression, and deep neural network. Furthermore, to better quantify for various sources of uncertainties, we combine the resulting individual model forecasts into an ensemble of the Zika spread predictions using Bayesian model averaging. The proposed methodology is illustrated in application to forecasting of the Zika space-time spread in Brazil in the year 2018.

This paper deals with belief base revision that is a form of belief change consisting of the incorporation of new facts into an agent's beliefs represented by a finite set of propositional formulas. In the aim to guarantee more reliability and rationality for real applications while performing revision, we propose the idea of credible belief base revision yielding to define two new formula-based revision operators using the suitable tools offered by evidence theory. These operators, uniformly presented in the same spirit of others in [9], stem from consistent subbases maximal with respect to credibility instead of set inclusion and cardinality. Moreover, in between these two extremes operators, evidence theory let us shed some light on a compromise operator avoiding losing initial beliefs to the maximum extent possible. Its idea captures maximal consistent sets stemming from all possible intersections of maximal consistent subbases. An illustration of all these operators and a comparison with others are inverstigated by examples.

Equipping machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing goal of AI. Over the last decade, large-scale knowledge bases, also known as knowledge graphs, have been automatically constructed from web contents and text sources, and have become a key asset for search engines. This machine knowledge can be harnessed to semantically interpret textual phrases in news, social media and web tables, and contributes to question answering, natural language processing and data analytics. This article surveys fundamental concepts and practical methods for creating and curating large knowledge bases. It covers models and methods for discovering and canonicalizing entities and their semantic types and organizing them into clean taxonomies. On top of this, the article discusses the automatic extraction of entity-centric properties. To support the long-term life-cycle and the quality assurance of machine knowledge, the article presents methods for constructing open schemas and for knowledge curation. Case studies on academic projects and industrial knowledge graphs complement the survey of concepts and methods.