HumanaPharmacy is a leader committed to the health and wellbeing of members through mail-order delivery of maintenance and specialty medicines as well as diabetic supplies. The Senior Cloud Architect leads the planning, design, and engineering of enterprise-level infrastructure and platforms related to cloud computing. The Senior Cloud Architect work assignments involve moderately complex to complex issues where the analysis of situations or data requires an in-depth evaluation of variable factors. The Senior Cloud Architect performs technical planning, architecture development and modification of specifications for cloud computing environments. Develops specifications for new IT cloud computing products and service offerings. Assesses the compatibility and integration of products/services proposed as standards in order to ensure an integrated architecture across interdependent technologies. Begins to influence department's strategy. Makes decisions on moderately complex to complex issues regarding technical approach for project components, and work is performed without direction. Responsibilities • Advocate and define architecture vision from a strategic perspective, including internal and external platforms, tools, and systems. Required Qualifications • Bachelor's degree • 5 or more years of technical experience • Must be passionate about contributing to an organization focused on continuously improving consumer experiences Preferred Qualifications • Experience in SFCC Additional Information Humana and its subsidiaries require vaccinated associates who work outside of their home to submit proof of vaccination, including COVID-19 boosters. Associates who remain unvaccinated must either undergo weekly negative COVID testing OR wear a mask at all times while in a Humana facility or while working in the field.

The old way of working is over and has given way to new approach. The pandemic has made it crystal clear. In order to stay competitive, companies need to transform to a special normal. You're at the forefront of this technology transformation which can be driven by three major shifts: modernization of critical workloads, the orchestration of applications flexibly across environments, and the growing use of operational AI and edge applications -- be it Cloud, AI/ML, Automation, Cybersecurity or game-changing technology. Leading companies are aligning their business transformation efforts with the adoption of cloud platforms.

Mobile edge computing (MEC) is considered a novel paradigm for computation-intensive and delay-sensitive tasks in fifth generation (5G) networks and beyond. However, its uncertainty, referred to as dynamic and randomness, from the mobile device, wireless channel, and edge network sides, results in high-dimensional, nonconvex, nonlinear, and NP-hard optimization problems. Thanks to the evolved reinforcement learning (RL), upon iteratively interacting with the dynamic and random environment, its trained agent can intelligently obtain the optimal policy in MEC. Furthermore, its evolved versions, such as deep RL (DRL), can achieve higher convergence speed efficiency and learning accuracy based on the parametric approximation for the large-scale state-action space. This paper provides a comprehensive research review on RL-enabled MEC and offers insight for development in this area. More importantly, associated with free mobility, dynamic channels, and distributed services, the MEC challenges that can be solved by different kinds of RL algorithms are identified, followed by how they can be solved by RL solutions in diverse mobile applications. Finally, the open challenges are discussed to provide helpful guidance for future research in RL training and learning MEC.

The Cloud paradigm is at a critical point in which the existing energy-efficiency techniques are reaching a plateau, while the computing resources demand at Data Center facilities continues to increase exponentially. The main challenge in achieving a global energy efficiency strategy based on Artificial Intelligence is that we need massive amounts of data to feed the algorithms. Nowadays, any optimization strategy must begin with data. However, companies with access to these large amounts of data decide not to share them because it could compromise their security. This paper proposes a time-series data augmentation methodology based on synthetic scenario forecasting within the Data Center. For this purpose, we will implement a powerful generative algorithm: Generative Adversarial Networks (GANs). The use of GANs will allow us to handle multivariate data and data from different natures (e.g., categorical). On the other hand, adapting Data Centers' operational management to the occurrence of sporadic anomalies is complicated due to the reduced frequency of failures in the system. Therefore, we also propose a methodology to increase the generated data variability by introducing on-demand anomalies. We validated our approach using real data collected from an operating Data Center, successfully obtaining forecasts of random scenarios with several hours of prediction. Our research will help to optimize the energy consumed in Data Centers, although the proposed methodology can be employed in any similar time-series-like problem.

Disruptive technology is the technology that affects the normal operation of a market or an industry. Digital disruption entails established companies and start-ups alike enlisting new technologies in the fight to dislodge incumbents, protect entrenched positions, or to re-invent entire industries and business activities. And to remain disruptive in the market, it is really important to keep innovating. This is crucial because, innovations occur now and then in every industry, however, to be truly disruptive, and innovation must entirely transform a product or solution that historically was so complicated only a few could access it. On a minimum level, digital transformation enables an organization to address the needs of its customers more simply and directly. But through disruptive innovation, companies can offer a far better way to users of doing things that current incumbents simply cannot compete with. Artificial intelligence (AI), E-Commerce, cloud, social networking, Internet of Things, 5G, blockchain and other emerging technologies are being leveraged to blur the lines between industries, creating new business models and converging sectors. A company that disrupts its market is in a great position to take advantage of new opportunities. Sometimes offering something different can change the whole market for the better. Most of the top disruptive companies get this label by offering highly innovative products and services and here are 100 such top disruptive companies listed below. The company provides innovative, managed cloud services to help its customers succeed. With best-in-class service and technology, 403Tech protects companies against cybercrimes while enabling greater efficiency and productivity. Some of its popular services include desktop support, server support, wired and wireless networking, virus removal, data recovery, and backup and hosted cloud services. Aegeus Technologies aims to design and develop robotic technologies and solutions.

What AI and data analytics trends are taking the industry by storm this year? This comprehensive review highlights upcoming directions in AI to carefully watch and consider implementing in your personal work or organization.

The pivotal role that the digital infrastructure plays in delivering critical societal, economic and governmental functions has become clearer than ever before as a result of the COVID-19 pandemic. There is now a high level of awareness in both business and society that availability, reliability, affordability and sustainability are all essential aspects of the digital infrastructure that must be ensured in both the short and long term. At the same time, the cyberphysical convergence is picking up speed, highlighting the need for advanced network technologies to support use cases that blur the boundaries between physical and digital realities. The rapid acceleration in the adoption rate of digitalization during the pandemic would not have been possible without the existing capabilities of both the mobile and the fixed communications infrastructure. Going forward, 5G will be the main digital infrastructure for consumers with mobile and fixed wireless residential access supporting augmented/virtual reality and artificial intelligence (AI) based services.

Edge technology aims to bring Cloud resources (specifically, the compute, storage, and network) to the closed proximity of the Edge devices, i.e., smart devices where the data are produced and consumed. Embedding computing and application in Edge devices lead to emerging of two new concepts in Edge technology, namely, Edge computing and Edge analytics. Edge analytics uses some techniques or algorithms to analyze the data generated by the Edge devices. With the emerging of Edge analytics, the Edge devices have become a complete set. Currently, Edge analytics is unable to provide full support for the execution of the analytic techniques. The Edge devices cannot execute advanced and sophisticated analytic algorithms following various constraints such as limited power supply, small memory size, limited resources, etc. This article aims to provide a detailed discussion on Edge analytics. A clear explanation to distinguish between the three concepts of Edge technology, namely, Edge devices, Edge computing, and Edge analytics, along with their issues. Furthermore, the article discusses the implementation of Edge analytics to solve many problems in various areas such as retail, agriculture, industry, and healthcare. In addition, the research papers of the state-of-the-art edge analytics are rigorously reviewed in this article to explore the existing issues, emerging challenges, research opportunities and their directions, and applications.

Deep Neural Network (DNN), one of the most powerful machine learning algorithms, is increasingly leveraged to overcome the bottleneck of effectively exploring and analyzing massive data to boost advanced scientific development. It is not a surprise that cloud computing providers offer the cloud-based DNN as an out-of-the-box service. Though there are some benefits from the cloud-based DNN, the interaction mechanism among two or multiple entities in the cloud inevitably induces new privacy risks. This survey presents the most recent findings of privacy attacks and defenses appeared in cloud-based neural network services. We systematically and thoroughly review privacy attacks and defenses in the pipeline of cloud-based DNN service, i.e., data manipulation, training, and prediction. In particular, a new theory, called cloud-based ML privacy game, is extracted from the recently published literature to provide a deep understanding of state-of-the-art research. Finally, the challenges and future work are presented to help researchers to continue to push forward the competitions between privacy attackers and defenders.

NUREMBERG, Germany and SUNNYVALE, CA, USA, May 5, 2021 – Google Cloud and Siemens, an innovation and technology leader in industrial automation and software, today announced a new cooperation to optimize factory processes and improve productivity on the shop floor. Siemens intends to integrate Google Cloud's leading data cloud and artificial intelligence/machine learning (AI/ML) technologies with its factory automation solutions to help manufacturers innovate for the future. Siemens and Google Cloud to cooperate to transform manufacturing by enabling scaled deployment of artificial intelligence. Data drives today's industrial processes, but many manufacturers continue to use legacy software and multiple systems to analyze plant information, which is resource-intensive and requires frequent manual updates to ensure accuracy. In addition, while AI projects have been deployed by many companies in "islands" across the plant floor, manufacturers have struggled to implement AI at scale across their global operations.