Today, most enterprises using ML have only a handful of deployments and pilots under way, but, according to Deloitte Global, progress in five key areas should make it easier and faster to develop ML solutions. In response, technology vendors are creating compact ML software models to undertake tasks such as image recognition and language translation on portable devices. Semiconductor vendors are developing their own power-efficient AI chips to bring ML to mobile devices. With smartphones an increasingly viable deployment option for ML, the number of potential applications is growing. Collectively, the five vectors of ML progress should double the intensity with which enterprises are using this technology by the end of 2018.

Toronto-based Cyclica President and CEO Naheed Kurji acknowledges that artificial intelligence (AI) is a transformative technology, but he contends it is not the "silver bullet" for drug discovery and development. Instead, he says that AI together with cloud-based computing could serve as a catalyst for a new approach to drug development. Kurji emphasizes that it is important to create "a virtual drug discovery ecosystem where a number of companies who are expert in their space come together and present a more holistic solution than any individual one could do itself because there is no one silver bullet to this problem. The market is so big and there are so many issues, one company can't do it alone." Kurji leads a five-year-old company that has developed and validated a cloud-based platform, called Ligand Express, which uses biophysics, bioinformatics and AI to help pharmaceutical companies navigate the drug discovery pipeline by assessing the safety and efficacy of drugs. The integrated platform enables companies to screen potential small-molecule drugs against repositories of structurally-characterized proteins or'proteomes' to identify significant protein targets. The platform then leverages AI to determine the biological relevance of these targets, and systems biology data to link this information to particular biological pathways or diseases. Kurji says Cyclica's platform, broadly launched in November 2017 already is being used by some of the top 50 pharma companies globally.

Literature reviews can be time-consuming and tedious to complete. By cataloging and refactoring three state-of-the-art active learning techniques from evidence-based medicine and legal electronic discovery, this paper finds and implements FASTREAD, a faster technique for studying a large corpus of documents. This paper assesses FASTREAD using datasets generated from existing SE literature reviews (Hall, Wahono, Radjenovi\'c, Kitchenham et al.). Compared to manual methods, FASTREAD lets researchers find 95% relevant studies after reviewing an order of magnitude fewer papers. Compared to other state-of-the-art automatic methods, FASTREAD reviews 20-50% fewer studies while finding same number of relevant primary studies in a systematic literature review.

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.