Windows 10 after two years: Was the upgrade worth it? After a little more than two years, Microsoft has finally settled into a rhythm with its new, fast-paced development cadence for Windows 10. Check Settings System About to see full details about the current Windows 10 installation. What Microsoft's marketers are calling the Fall Creators Update (officially version 1709) begins arriving on desktop PCs today via Windows Update and will soon be available for download at all the usual places. The final build number for this release is 16299.
Machine Learning helps make complex systems more efficient. By applying advanced Machine Learning techniques such as Cognitive Fingerprinting, wind project operators can utilize these tools to learn from collected data, detect regular patterns, and optimize their own operations. In his session at 18th Cloud Expo, Stuart Gillen, Director of Business Development at SparkCognition, discussed how research has demonstrated the value of Machine Learning in delivering next generation analytics to improve safety, performance, and reliability in today's modern wind turbines. Speaker Bio Stuart Gillen is the Director of Business Development at SparkCognition. In this role, he is responsible for driving business engagements, partner development, marketing activities, and go-to market strategy.
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.
Since 2012, the common exit for an AI company has been acquihire--acquired for talent or technology, not business performance--with many companies selling for below 50M. These domain experts give AI teams a leg up in making their products relevant, practical, and indispensable to their target markets. So you have unique data and you built a team with both domain and technical acumen, but have you built a system that fits within the target users' daily or regular work flow? Over next five years, AI will continue to expand as a layer across every enterprise business process--from sales to marketing to customer service to product development to finance to operations.
Hiring and Managing Field Workforce: The bot's usage can be used as a tool to hire the right person based on the questions the person is asking the bot for performing a drill of a real-life scenario. BA Teams: Dependence on BA teams to make sense of various KPI from production forecast to sales forecast and everything in between which is needed by sales and management teams is a huge bottleneck especially in fast moving industries. At Acuvate, we are working on integrating all the systems or all the relevant systems using Acuvate AIP/BOT Core through microservices and azure platform with aggregator bot. In the process of unifying, bots need to be built for each department / practice / operation and once this is done any employee can just ping the aggregator bot and enquire anything from ESS or the intranet and the aggregator bot checks for permissions levels and then identifies the area of query and passes it on to the relevant bot where the SME bot quickly checks the access levels of the employee for the query posed respond accordingly.
In the supply chain, AI can analyze large data sets and recommend customer service and operations improvements while supporting better working capital management. As corporate systems become more interconnected, providing access to a wider breadth of supply chain data, the opportunity to leverage AI increases. Let's look at the potential benefits of using AI to link transportation data with order data: A logistics enterprise ensures the delivery of a product within two days. This information supplies customer service and supply chain professionals with proactive alerts of potential fulfillment challenges.
DevOps at Cloud Expo taking place October 31 - November 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA, will feature technical sessions from a rock star conference faculty and the leading industry players in the world. With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend 21st Cloud Expo, October 31 - November 2, 2017, at the Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation. The upcoming 21st International @CloudExpo @ThingsExpo, October 31 - November 2, 2017, Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY announces that its Call For Papers for speaking opportunities is open. With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend @CloudExpo @ThingsExpo, October 31 - November 2, 2017, at the Santa Clara Convention Center, CA, and June 12-4, 2018, at the Javits Center in New York City, NY, and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation.
Unlike past advancements that largely brought more speed and performance to well-established infrastructure, cognitive computing promises an entirely new data ecosystem. By an almost two-to-one margin, CIOs at digitally enabled organizations are leading the development of new business strategies, and these companies are four times more likely to invest in cognitive automation than organizations where the CIO is not leading the transition process. For example, its ability to manage unstructured data analysis can vastly improve IT support services and daily infrastructure management by enhancing efficiency and delivering successful outcomes to users. Cognitive computing will elevate the CIO's role from what is essentially a technical support position to a core business asset.
While these factors are critical to achieving the desired performance of enterprise applications, a new processor started to gain attention – Graphics Processing Unit or GPU. Like most of the ML algorithms, deep learning relies on sophisticated mathematical and statistical computations. Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are some of the modern implementations of deep learning. Irrespective of the type of neural network used, all the deep learning algorithms perform complex statistical computations.
To bring machine learning into data management, DataSphere collects metadata of a client's data access and how it experiences the IOPS, latency, bandwidth and availability provided by storage. Intelligent analytics are then applied against the metadata, Smith said, enabling DataSphere to match business requirements for performance, cost and reliability, make real-time automated decisions, move data without application disruption to overcome or prevent outages, and maintain compliance of service level agreements or objectives. Snapshot enhancements deliver the ability to move or copy data to the cloud to protect data without impacting enterprise capacity. Snapshot enhancements deliver the ability to move or copy data to the cloud to protect data without impacting enterprise capacity.