There's a reason it's called "big data." The growth in the volume of structured and unstructured information is exploding, literally exponentially, just as Moore's Law predicted. In fact, by 2025 there will be more than 180 zettabytes of data created and consumed worldwide, per Statista, helping to catapult the global data market to $103 billion by 2027. Since most banking products and services have become commodities, financial services executives are anxious to analyze even a tiny sliver of those zettabytes in order to differentiate themselves from competitors. As Capgemini puts it, banks and credit unions must evolve from capturing and managing data to using data to deliver hyper-relevant content, products and customized pricing based on customer and member behaviors, lifestyle, personality and preferences. Artificial intelligence (AI) applied to big data provides this range of insights.

As Artificial Intelligence (AI) is gaining in popularity, it is now clear that its evolution also complements the growth of cloud computing. Using AI within the cloud can then enhance the performance and efficiency of the cloud as well as drive the digital transformation of organizations. AI capabilities within the cloud computing environment are a strategic key to make businesses more efficient, strategic, and insight-driven, all the while giving them more flexibility, agility, and cost savings by hosting data and applications in the cloud. Hence, we have asked experts in the industry to explore the ever-growing role of AI in cloud computing. AI Cloud computing essentially means combining artificial intelligence (AI) with cloud computing.

Risk adjustment has become an increasingly important tool in healthcare. It has been extensively applied to payment adjustment for health plans to reflect the expected cost of providing coverage for members. Risk adjustment models are typically estimated using linear regression, which does not fully exploit the information in claims data. Moreover, the development of such linear regression models requires substantial domain expert knowledge and computational effort for data preprocessing. In this paper, we propose a novel approach for risk adjustment that uses semantic embeddings to represent patient medical histories. Embeddings efficiently represent medical concepts learned from diagnostic, procedure, and prescription codes in patients' medical histories. This approach substantially reduces the need for feature engineering. Our results show that models using embeddings had better performance than a commercial risk adjustment model on the task of prospective risk score prediction.

Channelnomics catches up with Pure Storage to talk about its success and what the channel means to the company. Pure has always been committed to a 100 percent channel business model and we work closely with a select group of partners who lead their businesses with Pure. This model has resulted in accelerated growth and increased profitability for our partners and for our business. This year we launched a new Pure Partner Program, which includes strategic investments in the tools and resources needed to achieve a partner program that attracts, enables and retains top partners. The program includes new training, certifications, support and incentives, and rewards partners who build a Pure practice and lead with Pure in the market.

Artificial Intelligence (AI), machine learning and the internet of things (IoT) lead emerging technology conversations across the world. Companies recognise that these technologies are ready to be used to drive real business benefits. The Asia Pacific and Japan (APJ) region is set to pick up the pace on these two fronts. According to a recent cloud survey by MIT Technology Review Custom and VMware, more than 70% of non-users of AI in APJ said their organisations will adopt the technology within five years. IDC forecasted global IoT spending to surpass $1 trillion USD in 2020, with APJ leading the way.