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Oracle leverages machine learning to manage, secure enterprise systems

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The new Oracle Management Cloud suite combines Oracle Management Cloud, Oracle Application Performance Monitoring Service, and Oracle Infrastructure Monitoring Cloud Service. The new Oracle Management Cloud suite includes the Standard Edition services, as well as Oracle IT Analytics Cloud Service and the new Oracle Orchestration Cloud Service. The Oracle Management Cloud has an analytics engine that is constantly updated with real-world data, providing it with evolving analytics. Oracle has also expanded its Oracle Log Analytics Cloud Service to monitor and analyze security and operational logs from a wide variety of both on-premises and cloud technologies, providing unified monitoring.


Blog Review: Oct. 11

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Intel's Necati Canpolat argues that 5G and Wi-Fi will see increasing impacts on each other and require integration to make the best use of each technology. Cadence's Paul McLellan shares highlights from EDPS, where machine learning in EDA tools was a hot topic, plus a look at the challenges facing test. Editor in Chief Ed Sperling examines what happens if enough people can't afford or don't want driverless cars. Editor in Chief Ed Sperling finds more companies assessing pre-built and pre-verified circuits as a way of reducing time to market.


Microsoft launches 'Project Brainwave' for real-time AI

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With the help of ultra-low latency, the system processes requests as fast as it receives them. He added that the system architecture reduces latency, since the CPU does not need to process incoming requests, and allows very high throughput, with the FPGA processing requests as fast as the network can stream them. Microsoft is also planning to bring the real-time AI system to users in Azure. "With the'Project Brainwave' system incorporated at scale and available to our customers, Microsoft Azure will have industry-leading capabilities for real-time AI," Burger noted.


IBM cooks up a hardware architecture for tastier cloud-based services

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The new technologies could reach IBM's 56 data centers late this year or early next year. Three years ago, IBM sunk $1 billion to set up its Watson business unit in the New York City borough Manhattan. IBM CEO Ginni Rometty has often cited lofty goals for the unit when claiming Watson would reach 1 billion consumers by the end of 2017, $1 billion in revenues by the end of 2018 and, eventually, $10 billion in revenue by an unnamed date. IBM executives remain confident, given the technical advancements in AI and machine learning capabilities built into Watson and a strict focus on corporate business users, while competitors -- most notably Amazon -- pursue consumer markets.


Microsoft launches Project Brainwave for real-time artificial intelligence

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With the help of ultra-low latency, the system processes requests as fast as it receives them. He added that the system architecture reduces latency, since the CPU does not need to process incoming requests, and allows very high throughput, with the FPGA processing requests as fast as the network can stream them. Microsoft is also planning to bring the real-time AI system to users in Azure. "With the'Project Brainwave' system incorporated at scale and available to our customers, Microsoft Azure will have industry-leading capabilities for real-time AI," Burger noted.


Microsoft unveils Project Brainwave for real-time AI - Microsoft Research

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First, we have defined highly customized, narrow-precision data types that increase performance without real losses in model accuracy. Third, Project Brainwave incorporates a software stack designed to support the wide range of popular deep learning frameworks. Companies and researchers building DNN accelerators often show performance demos using convolutional neural networks (CNNs). Running on Stratix 10, Project Brainwave thus achieves unprecedented levels of demonstrated real-time AI performance on extremely challenging models.


4 ways to use AI for better cloud ops efficiency TechBeacon

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The increasing adoption of cloud (soon, 80% of all IT budgets will be committed to cloud solutions) and the emergence of artificial intelligence (AI) and machine-learning (ML) technologies are allowing companies to use intelligent software automation to make decisions on known problems, predict issues, and provide diagnostic information to reduce the operational overhead for engineers. With AI Ops, machine intelligence and AI technologies can detect cost spikes, provide deep visibility into who used what, and help companies deploy intelligent automation to address these issues. In addition, AI and ML on AI Ops can intelligently automate other areas of operations including deployment (with cluster management and auto-healing tooling), application performance management (not just what's happening but why it's happening due to what), log management (real-time streaming of log events and auto detection of relevant anomaly events based on application stack), and incident management (by suppressing noise from different alerting systems and providing diagnostics for engineers to get to the root cause faster). Companies must leverage AI Ops and technologies such as artificial intelligence and machine learning to disrupt cloud operations and ease infrastructure management.


AI: the new frontier for optimization

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Software control of both IT equipment and the supporting data center infrastructure has long been a fundamental tool for monitoring and managing both operational and energy efficiency. As financial pressures, data regulations and corporate social responsibility (CSR) demand data center operators to stay focused on improving efficiency, such tools will become more important and increasingly integrated. Separate from systems management software suites, data center infrastructure management (DCIM) tools assist in the management of supporting infrastructure such as cooling and backup power supply systems. Facilities management software, like building and energy management systems (BMS), assist in the monitoring of building or site-level functions including air conditioning and electricity supply.


AI-powered IoT: How artificial intelligence works at the industrial edge

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Powered by a host of technologies, including low-cost sensors, IP and wireless networks, private and public clouds, and powerful edge infrastructure, industrial IoT promises to transform the way companies provide products and services and interact with customers and partners. A new generation of mining technology uses AI, GIS, and GPS data and programmable logic controllers, which enable driverless vehicles and loaders to operate autonomously and determine optimal routes and positioning. Moreover, as Ethernet replaces proprietary networks in mining environments and industrial IoT is extended to mining sites and processing facilities on the edge of the network, new types of sensors, controllers, and intelligent instruments can further boost operations. An AI-enabled manufacturing robot will similarly need to crunch real-time sensor data from its own inputs as well as data from nearby equipment to optimize performance, carry out specific tasks, and avoid causing injury to nearby workers.


Flipboard on Flipboard

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ML algorithms will be embedded right into the source of data including operating systems, databases, and application software. It's a matter of time before the public cloud providers add an intelligent VM recommendation engine for each running workload. With Machine Learning, IT admins can configure predictive scaling that learns from the previous load conditions and usage patterns. By applying Machine Learning to the power management, data center administrators can dramatically reduce the energy usage.