As part of Lufthansa's ongoing global #SayYesToTheWorld brand campaign, it has become the first airline to launch a Watson Ads campaign, joining other brands such as Lego, Behr Paint, State Farm, Best Western, and TruGreen Lufthansa's AI-powered ad helps consumers plan their next international adventure in Europe, showcasing a specific location and helping a consumer explore the city by providing local travel facts, and tailored image galleries or videos. The campaign, which will run from 10/1 - 11/25, will help Lufthansa to discover new insights about consumers and their travel planning needs, which can inform future marketing efforts. The AI-powered ad is available to US consumers on weather.com, Featured cities include: Athens, Barcelona, Berlin, Budapest, Copenhagen, Florence, Frankfurt, Krakow, Milan, Munich, Oslo, Paris, Prague, Rome, and Stockholm. The campaign focuses on exploring a world of new possibilities and saying yes to the unknown.
The field of probabilistic numerics (PN), loosely speaking, attempts to provide a statistical treatment of the errors and/or approximations that are made en route to the output of a deterministic numerical method, e.g. the approximation of an integral by quadrature, or the discretised solution of an ordinary or partial differential equation. This decade has seen a surge of activity in this field. In comparison with historical developments that can be traced back over more than a hundred years, the most recent developments are particularly interesting because they have been characterised by simultaneous input from multiple scientific disciplines: mathematics, statistics, machine learning, and computer science. The field has, therefore, advanced on a broad front, with contributions ranging from the building of overarching generaltheory to practical implementations in specific problems of interest. Over the same period of time, and because of increased interaction among researchers coming from different communities, the extent to which these developments were -- or were not -- presaged by twentieth-century researchers has also come to be better appreciated. Thus, the time appears to be ripe for an update of the 2014 Tübingen Manifesto on probabilistic numerics[Hennig, 2014, Osborne, 2014d,c,b,a] and the position paper[Hennig et al., 2015] to take account of the developments between 2014 and 2019, an improved awareness of the history of this field, and a clearer sense of its future directions. In this article, we aim to summarise some of the history of probabilistic perspectives on numerics (Section 2), to place more recent developments into context (Section 3), and to articulate a vision for future research in, and use of, probabilistic numerics (Section 4).
If you'd like to learn how to run R within Azure Machine Learning and SQL Server, you may be interested in these upcoming 4-day Practical Data Science courses, presented by Rafal Lukawiecki from Project Botticelli. In this classroom-based course, you will learn machine learning, data mining, some statistics, data preparation, and how to interpret the results. You will also learn how to formulate business questions in terms of data science hypotheses and experiments, and how to prepare inputs to answer those questions. Rafal will share his decade of hands-on experience while teaching you about Azure Machine Learning (Azure ML) which is the foundation of Cortana Analytics Suite, and its highly-visual, on-premise companion, the SQL Server Analysis Services Data Mining engine, supplemented with the free Microsoft R Open and Microsoft R Server software. By the end of this course you will be able to plan and run data science projects.
Professor Bart Baesens is a professor at KU Leuven (Belgium), and a lecturer at the University of Southampton (United Kingdom). He has done extensive research on analytics, customer relationship management, web analytics, fraud detection, and credit risk management. His findings have been published in well-known international journals (e.g. Machine Learning, Management Science, IEEE Transactions on Neural Networks, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Evolutionary Computation, Journal of Machine Learning Research, …) and presented at international top conferences. He is also author of the books Credit Risk Management: Basic Concepts, published by Oxford University Press in 2008; and Analytics in a Big Data World published by Wiley in 2014.
We already knew that the city of Moscow is saturated with CCTV cameras, but we've only just learned the extent that the city is able to conduct surveillance on its citizens. NTechLab is a bold Russian company that is at the forefront of the most talked about technology around, facial recognition. Their app, FindFace, which can track everyone on VKontakte, the Russian equivalent of Twitter, based on their profile, caused an outcry in and outside Russia after it was used to to identify and harass sex workers and porn actresses through their personal profiles. Later, the company launched an emotion-reading recognition system, re-igniting concerns over the citizens' privacy and personal data. Despite rumours, nobody really knew who's using this state-of-the-art technology as NTechLab doesn't disclose the identity of their clients.