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Fog Computing Test Bed: Cutting Costs and Latency in Data Transmission

#artificialintelligence

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5 Reasons Why Azure IoT Edge Is Industry's Most Promising Edge Computing Platform

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Last week, Microsoft announced the general availability of Azure IoT Edge, the edge computing platform that has been in works for more than a year. Out of the top 5 public cloud platforms – AWS, Azure, Google Cloud Platform, IBM Cloud and Alibaba Cloud – only Microsoft and Amazon have a sophisticated edge computing strategy. Other players are yet to figure out their story for edge computing. Amazon's edge platform is delivered through AWS Greengrass – a service that was announced at re:Invent event in 2016 and became generally available in June 2017. AWS recently added the ability to perform inferencing of machine learning models.


5 Reasons Why Azure IoT Edge Is Industry's Most Promising Edge Computing Platform

Forbes Technology

Last week, Microsoft announced the general availability of Azure IoT Edge, the edge computing platform that has been in works for more than a year. Out of the top 5 public cloud platforms – AWS, Azure, Google Cloud Platform, IBM Cloud and Alibaba Cloud – only Microsoft and Amazon have a sophisticated edge computing strategy. Other players are yet to figure out their story for edge computing. Amazon's edge platform is delivered through AWS Greengrass – a service that was announced at re:Invent event in 2016 and became generally available in June 2017. AWS recently added the ability to perform inferencing of machine learning models.


Cloud Computing and AI transform the Banking sector

#artificialintelligence

With the adoption and development of cognitive computing capabilities, the way customers interact with their banks will ultimately change for good. Artificial intelligence and cloud computing will empower banks to efficiently redefine the workflow, create innovative products and services, and transform customer experiences. Many banks have adopted AI, infusing it into their customer experience. This development is witness to AI's role in banking becoming increasingly crucial and visible over the next few years. The introduction of cloud computing and AI will permit the banking workforce to discard repetitive, process driven tasks towards the more strategic and innovative kinds of work that will ultimately drive the industry forward.


Cloud Computing: Microsoft Azure announces major AI, IoT moves

#artificialintelligence

Microsoft is making another huge play, but this time it's in the world of IoT (Internet of Things) and AI (artificial intelligence). Microsoft Azure, the company's cloud computing service, is making a huge splash into the fast-growing IoT enterprise. In getting into IoT space, the Redmond-based company [VIDEO] has teamed with C3, a Silicon Valley-based IoT company. The two tech companies plan to work on technologies to help enterprise customers accelerate their IoT and AI development, according to SDX Central. Reports by SDX Central and Business Wire presented most of the information used in this article.


Intel to Streamline Computer Vision With Its OpenVINO Toolkit

@machinelearnbot

Many people primarily think of Intel as a computer chip manufacturer, but it's on the cutting edge of technology in other ways for its role in building PCs, software, 5G connectivity solutions and much more. Recently, Intel announced plans for its OpenVINO (Open Visual Inference & Neural Network Optimization) toolkit, which seeks to promote faster development for computer vision applications associated with edge computing. Not long ago, people were solely familiar with traditional cloud computing, which requires sending data to distant locations for processing. However, there are numerous issues with that approach, including those associated with latency. The explosion in popularity of new smartphone technology and innovative IoT devices means more data than ever gets stored in the cloud, and there are no signs of the amount reducing anytime soon.


The Artificial Intelligence Journey

#artificialintelligence

Although AI hype today exceeds adoption and usage, best-in-class companies are piloting cloud computing AI projects to discover the most relevant use cases which provide the most beneficial financial and business outcomes. Bots are micro-services or apps that can operate on other bots, applications or services in response to event triggers or user requests in many cloud computing applications Often emulating a human, they automate tasks based on predefined rules or via more sophisticated algorithms which may involve machine learning. Robotic process automation (RPA) bots automate mundane yet complex human tasks primarily related to form-driven workflows such as data collection, sorting, filtering, searching and categorizing. Chatbots and virtual assistants (bots with simple natural language query capability) assist with high-volume, low-value interactions with customers, employees, suppliers, partners and other roles. Chatbots and VAs (virtual customer assistants [VCAs]) are predominantly used for customer service and support.


Innovations in Artificial intelligence, Machine Learning, Cloud, and Blockchain

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This edition of ITCC TOE provides a snapshot of the emerging ICT led innovations in machine learning, blockchain, cloud computing, and artificial intelligence. This issue focuses on the application of information and communication technologies in alleviating the challenges faced across industry sectors in areas such as brick & mortar retail, e-commerce, data labelling, 5G, photo and video editing, manufacturing, talent and business intelligence, amongst others. ITCC TechVision Opportunity Engine (TOE)'s mission is to investigate emerging wireless communication and computing technology areas including 3G, 4G, Wi-Fi, Bluetooth, Big Data, cloud computing, augmented reality, virtual reality, artificial intelligence, virtualization and the Internet of Things and their new applications; unearth new products and service offerings; highlight trends in the wireless networking, data management and computing spaces; provide updates on technology funding; evaluate intellectual property; follow technology transfer and solution deployment/integration; track development of standards and software; and report on legislative and policy issues and many more. The Information & Communication Technology cluster provides global industry analysis, technology competitive analysis, and insights into game-changing technologies in the wireless communication and computing space. Innovations in ICT have deeply permeated various applications and markets.


Optimized Computation Offloading Performance in Virtual Edge Computing Systems via Deep Reinforcement Learning

arXiv.org Artificial Intelligence

To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network (RAN), which supports both traditional communication and MEC services. Nevertheless, the design of computation offloading policies for a virtual MEC system remains challenging. Specifically, whether to execute a computation task at the mobile device or to offload it for MEC server execution should adapt to the time-varying network dynamics. In this paper, we consider MEC for a representative mobile user in an ultra-dense sliced RAN, where multiple base stations (BSs) are available to be selected for computation offloading. The problem of solving an optimal computation offloading policy is modelled as a Markov decision process, where our objective is to maximize the long-term utility performance whereby an offloading decision is made based on the task queue state, the energy queue state as well as the channel qualities between MU and BSs. To break the curse of high dimensionality in state space, we first propose a double deep Q-network (DQN) based strategic computation offloading algorithm to learn the optimal policy without knowing a priori knowledge of network dynamics. Then motivated by the additive structure of the utility function, a Q-function decomposition technique is combined with the double DQN, which leads to novel learning algorithm for the solving of stochastic computation offloading. Numerical experiments show that our proposed learning algorithms achieve a significant improvement in computation offloading performance compared with the baseline policies.


Secure Mobile Edge Computing in IoT via Collaborative Online Learning

arXiv.org Machine Learning

To accommodate heterogeneous tasks in Internet of Things (IoT), a new communication and computing paradigm termed mobile edge computing emerges that extends computing services from the cloud to edge, but at the same time exposes new challenges on security. The present paper studies online security-aware edge computing under jamming attacks. Leveraging online learning tools, novel algorithms abbreviated as SAVE-S and SAVE-A are developed to cope with the stochastic and adversarial forms of jamming, respectively. Without utilizing extra resources such as spectrum and transmission power to evade jamming attacks, SAVE-S and SAVE-A can select the most reliable server to offload computing tasks with minimal privacy and security concerns. It is analytically established that without any prior information on future jamming and server security risks, the proposed schemes can achieve ${\cal O}\big(\sqrt{T}\big)$ regret. Information sharing among devices can accelerate the security-aware computing tasks. Incorporating the information shared by other devices, SAVE-S and SAVE-A offer impressive improvements on the sublinear regret, which is guaranteed by what is termed "value of cooperation." Effectiveness of the proposed schemes is tested on both synthetic and real datasets.