Digital transformation may be the promised land for many forward-looking enterprises, but few are actually ready for it. There are many organizational and cultural issues to address -- getting executive and employee buy-in, determining what processes need to be digitized, and getting people to rethink their roles. If that isn't enough, technical issues also appear to be getting in the way as well. Scalability issues, resource issues, and application backlogs are quickly getting in the way of making progress on the journey. That's the latest revelation of a survey of 463 business and IT leaders released by Appian and conducted by DevOps.com,

Deep learning has emerged as a powerful machine learning technique that learns multiple layers of representations or features of the data and produces state-of-the-art prediction results. Along with the success of deep learning in many other application domains, deep learning is also popularly used in sentiment analysis in recent years. This paper first gives an overview of deep learning and then provides a comprehensive survey of its current applications in sentiment analysis.

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.

In recent years, deep neural networks have yielded state-of-the-art performance on several tasks. Although some recent works have focused on combining deep learning with recommendation, we highlight three issues of existing works. First, most works perform deep content feature learning and resort to matrix factorization, which cannot effectively model the highly complex user-item interaction function. Second, due to the difficulty on training deep neural networks, existing models utilize a shallow architecture, and thus limit the expressive potential of deep learning. Third, neural network models are easy to overfit on the implicit setting, because negative interactions are not taken into account. To tackle these issues, we present a generic recommender framework called Neural Collaborative Autoencoder (NCAE) to perform collaborative filtering, which works well for both explicit feedback and implicit feedback. NCAE can effectively capture the relationship between interactions via a non-linear matrix factorization process. To optimize the deep architecture of NCAE, we develop a three-stage pre-training mechanism that combines supervised and unsupervised feature learning. Moreover, to prevent overfitting on the implicit setting, we propose an error reweighting module and a sparsity-aware data-augmentation strategy. Extensive experiments on three real-world datasets demonstrate that NCAE can significantly advance the state-of-the-art.

Marketing analytics is a diverse field, with both academic researchers and practitioners coming from a range of backgrounds including marketing, operations research, statistics, and computer science. This paper provides an integrative review at the boundary of these three areas. The topics of visualization, segmentation, and class prediction are featured. Links between the disciplines are emphasized. For each of these topics, a historical overview is given, starting with initial work in the 1960s and carrying through to the present day. Recent innovations for modern large and complex "big data" sets are described. Practical implementation advice is given, along with a directory of open source R routines for implementing marketing analytics techniques.

Insurers will replace multiple policies (with often-overlapping or gapped coverage) with a single risk-mitigation and claim-adjudication solution. Though use of these technologies in insurance applications is still in the early days, it is clear they will have profound impact on the industry. This paper will explore how these three transformative technologies might be woven together to create a single platform, enabling insurers to mitigate claim events, slash operating costs and improve the customer experience. Successfully implementing such a platform will require a significant change to the insurance business model. Insurers will expand their role beyond just that of a counterparty to whom risk is transferred and become a critical business partner providing operational, logistical, and business process services to their clients. IoT and sensor data provide granular data in real time on processes and conditions that were previously detectable only through post-production Q/A processes or manual checks.

Former wealth management CTO Doug Fritz breaks down AI and explains how advisors can use it now and what to expect in the future.

A recent MIT Technology Review article titled "The Dark Secret at the Heart of AI" warned: "No one really knows how the most advanced algorithms do what they do. That could be a problem." Thanks to this uncertainty and lack of accountability, a report by the AI Now Institute recommended that public agencies responsible for criminal justice, health care, welfare and education shouldn't use such technology. Given these types of concerns, the unseeable space between where data goes in and answers come out is often referred to as a "black box" -- seemingly a reference to the hardy (and in fact orange, not black) data recorders mandated on aircraft and often examined after accidents. In the context of A.I., the term more broadly suggests an image of being in the "dark" about how the technology works: We put in and provide the data and models and architectures, and then computers provide us answers while continuing to learn on their own, in a way that's seemingly impossible -- and certainly too complicated -- for us to understand.

To compete in today's fierce retail environment, Wayfair Inc. has to think and act like a tech company, investing deeply in artificial intelligence technologies and other emerging tech to power its business. This complimentary document comprehensively details the elements of a strategic IT plan that are common across the board – from identifying technology gaps and risks to allocating IT resources and capabilities. You forgot to provide an Email Address. This email address doesn't appear to be valid. This email address is already registered.

Impulse response estimation has for a long time been at the core of system identification. Up until some five to seven years ago, the generally held belief in the field was indeed that we knew all there was to know about this topic. However, the enlightening work by Pillonetto and De Nicolao [2010] changed this by showing that the estimate can in fact be improved significantly by assuming a Gaussian Process (GP) prior over the impulse response, which acts as a regularizer. This model-driven approach has since then been further refined [Pillonetto et al., 2011, Chen et al., 2012, Pillonetto et al., 2014], where the prior in this case could be interpreted to encode not only smoothness information, but also information about the exponential decay of the impulse response. In this paper we employ deep leaning (DL) to find a suitable regularizer via a method that is driven by data. Deep learning is a fairly new area of research that continues the work on neural networks from the 1990's. To get a brief, but informative, overview of the field of deep learning we recommend the paper by LeCun et al. [2015] and for a more complete snapshot of the field we refer to the monograph by Goodfel-low et al. [2016]. Deep learning has recently revolutionized several fields, including image recognition (e.g.