T ensors have a rich history, stretching over almost a century, and touching upon numerous disciplines; but they have only recently become ubiquitous in signal and data analytics at the confluence of signal processing, statistics, data mining and machine learning. This overview article aims to provide a good starting point for researchers and practitioners interested in learning about and working with tensors. As such, it focuses on fundamentals and motivation (using various application examples), aiming to strike an appropriate balance of breadth and depth that will enable someone having taken first graduate courses in matrix algebra and probability to get started doing research and/or developing tensor algorithms and software. Some background in applied optimization is useful but not strictly required. The material covered includes tensor rank and rank decomposition; basic tensor factorization models and their relationships and properties (including fairly good coverage of identifiability); broad coverage of algorithms ranging from alternating optimization to stochastic gradient; statistical performance analysis; and applications ranging from source separation to collaborative filtering, mixture and topic modeling, classification, and multilinear subspace learning. Index Terms --T ensor decomposition, tensor factorization, rank, canonical polyadic decomposition (CPD), parallel factor analysis (PARAF AC), T ucker model, higher-order singular value decomposition (HOSVD), multilinear singular value decomposition (MLSVD), uniqueness, NPhard problems, alternating optimization, alternating direction method of multipliers, gradient descent, Gauss-Newton, stochastic gradient, Cram er-Rao bound, communications, source separation, harmonic retrieval, speech separation, collaborative filtering, mixture modeling, topic modeling, classification, subspace learning. N.D. Sidiropoulos, X. Fu, and K. Huang are with the ECE Department, University of Minnesota, Minneapolis, USA; email: (nikos,xfu,huang663)@umn.edu .

This feature could be used to provide an overview of a recently enacted financial services regulation or a news event.

Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag. This formulation is gaining interest because it naturally fits various problems and allows to leverage weakly labeled data. Consequently, it has been used in diverse application fields such as computer vision and document classification. However, learning from bags raises important challenges that are unique to MIL. This paper provides a comprehensive survey of the characteristics which define and differentiate the types of MIL problems. Until now, these problem characteristics have not been formally identified and described. As a result, the variations in performance of MIL algorithms from one data set to another are difficult to explain. In this paper, MIL problem characteristics are grouped into four broad categories: the composition of the bags, the types of data distribution, the ambiguity of instance labels, and the task to be performed. Methods specialized to address each category are reviewed. Then, the extent to which these characteristics manifest themselves in key MIL application areas are described. Finally, experiments are conducted to compare the performance of 16 state-of-the-art MIL methods on selected problem characteristics. This paper provides insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research.

Bottom line: Machine learning is providing the needed algorithms, applications, and frameworks to bring greater predictive accuracy and value to enterprises' data, leading to diverse company-wide strategies succeeding faster and more profitably than before. The good news for businesses is that all the data they have been saving for years can now be turned into a competitive advantage and lead to strategic goals being accomplished. Revenue teams are using machine learning to optimize promotions, compensation and rebates drive the desired behavior across selling channels. Predicting propensity to buy across all channels, making personalized recommendations to customers, forecasting long-term customer loyalty and anticipating potential credit risks of suppliers and buyers are Figure 1 provides an overview of machine learning applications by industry. Unlike advanced analytics techniques that seek out causality first, machine learning techniques are designed to seek out opportunities to optimize decisions based on the predictive value of large-scale data sets.

This feature could be used to provide an overview of a recently enacted financial services regulation or a news event.

Artificial intelligence (AI), deep learning, and neural networks represent incredibly exciting and powerful machine learning-based techniques used to solve many real-world problems. For a primer on machine learning, you may want to read this five-part series that I wrote. While human-like deductive reasoning, inference, and decision-making by a computer is still a long time away, there have been remarkable gains in the application of AI techniques and associated algorithms. The concepts discussed here are extremely technical, complex, and based on mathematics, statistics, probability theory, physics, signal processing, machine learning, computer science, psychology, linguistics, and neuroscience. That said, this article is not meant to provide such a technical treatment, but rather to explain these concepts at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.

From Amazon and Facebook to Google and Microsoft, leaders of the world's most influential technology firms are highlighting their enthusiasm for Artificial Intelligence (AI). While there is growing interest in AI, the field is understood mainly by specialists. Our goal for this primer is to make this important field accessible to a broader audience. We'll begin by explaining the meaning of'AI' and key terms including'machine learning'. We'll illustrate how one of the most productive areas of AI, called'deep learning', works.

Websays is the result of 15 years of scientific investigations in Web Crawling, Automatic Learning and Text Analytics. Dr. Hugo Zaragoza, Websays' founder, is a worldwide expert in those technologies. He has worked more than 10 years as a lead researcher in Microsoft and Yahoo! in the United States, England and Spain. In 2010 Dr. Zaragoza founded Websays with the objective of applying the most cutting edge technology in information retrieval and data analytics, including various new patent pending technologies developed by Websays. Websays services focus on online reputation monitoring and social media marketing.

Founders, designers and executives bet on AI, chatbots and VR. Last year, I wrote about the top web design trends to watch in 2016. My predictions received a lot of attention and questions about what's next not only in the web design but UX design world. I reached out to 10 UX design experts and asked them to share their opinion on what's going to be the biggest UX trend in 2017. Read on to find out why founders, designers and executives bet on AI, chatbots and VR. Noam Alloush is the founder of SITE123, a free website builder empowering people to make a website using ready-made styles and layouts.

As in the global survey, we are concerned with platform business models and the design choices that allow these business models to grow. We find the term platform, which is well-established in economic and management literature, offers a more useful and accurate term than some of the terms that have been used such as "share economy companies," "internet companies" or, even more broadly, "tech companies." Network effects are a key characteristic that distinguish platforms from other business models. As more users engage with a platform, the more attractive the platform becomes to potential new users. When more users attract more users, a dynamic is created that in turn triggers a self-reinforcing cycle of growth.