Results


1001 Startup Ideas AI Based Performance Management System

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Some organisations may not be comfortable with changing their email systems, and hence, the startup may consider tying up with big email service provider companies and provide the software as an integration to their email software that is already being offered in the market. The integration of task management and artificial intelligence part with third party email service providers will come in handy as that gives access to the bulk clients. Investors are funding artificial intelligence startups more than ever. Some of the top investors in the artificial intelligence industry are Khosla Ventures, Intel Capital, Data Collective, Google Ventures, New Enterprise Associates and Andreessen Horowitz.


Dynatrace Drives Digital Innovation With AI Virtual Assistant

Forbes

Innovation in the white-hot digital performance management (DPM) market continues to accelerate, and it was clear from this week's Perform conference in Las Vegas that Dynatrace is setting the pace. In fact, Coop's mobile application is state-of-the-art, featuring digital payments, couponing, and e-receipts, with in-store location tracking and streaming video content on the way. "We're using davis for everything we can," says Jeppe Lindberg, Application Performance Manager at Coop Denmark. "Coop is working with Dynatrace to deliver relevant data to relevant people inside Slack," Lindberg explains.


ZeroStack uses machine learning to create self-driving clouds

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There is so much data growth today that businesses have to invest in both public clouds and private data centers, hence the high adoption rate of "hybrid" environments. Turnkey private clouds are becoming increasingly popular because they give businesses an Amazon-like experience but in a private cloud model, so the data and infrastructure stays in the company data center. ZeroStack 3.0 will use the AI capabilities to build cloud optimization capabilities that can determine the best cloud to run specific workloads based on cost and performance. As the complexity of data centers continues to increase and the business demands become more challenging to meet, ZeroStack's AI-driven approach will become a key requirement of running a private cloud.


[slides] @Dyn's Cloud #APM and #NPM at @CloudExpo #AI #ML #Monitoring

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With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend @CloudExpo @ThingsExpo, June 6-8, 2017, at the Javits Center in New York City, NY and October 31 - November 2, 2017, Santa Clara Convention Center, CA. Join Cloud Expo / @ThingsExpo conference chair Roger Strukhoff (@IoT2040), June 6-8, 2017, at the Javits Center in New York City, NY and October 31 - November 2, 2017, Santa Clara Convention Center, CA for three days of intense Enterprise Cloud and'Digital Transformation' discussion and focus, including Big Data's indispensable role in IoT, Smart Grids and (IIoT) Industrial Internet of Things, Wearables and Consumer IoT, as well as (new) Digital Transformation in Vertical Markets. Accordingly, attendees at the upcoming 20th Cloud Expo / @ThingsExpo June 6-8, 2017, at the Javits Center in New York City, NY and October 31 - November 2, 2017, Santa Clara Convention Center, CA will find fresh new content in a new track called FinTech, which will incorporate machine learning, artificial intelligence, deep learning, and blockchain into one track. The upcoming 20th International @CloudExpo @ThingsExpo, June 6-8, 2017, at the Javits Center in New York City, NY and October 31 - November 2, 2017, Santa Clara Convention Center, CA announces that its Call For Papers for speaking opportunities is open.


The 4 Stages of B2B Marketing Performance Management Maturity

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Strategy: Place marketing bets and optimize based on down-funnel performance metrics. Measurement: In addition to top-of-the-funnel metrics like leads, the marketing department can now measure their performance with down-funnel metrics like opportunities, closed deals, and revenue. The ability to make decisions based on predictive insights allows marketers to place smarter bets, as well as modify bets in real time. Strategy: Place marketing bets based on down-funnel metrics and allow the predictive engine to deliver insights and proactively optimize budget allocation.


Cloud 3.0: The Rise of Big Compute

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Furthermore, the existing category leaders driving billions of dollars of compute heavy workload revenue in the legacy on-premise high performance computing (HPC) market are facing the innovator's dilemma needing to reinvent their entire business to provide effective Big Compute solutions in the space – providing a unique opportunity for the most innovative companies to become category leaders. Just like Big Data removed constraints on data and transformed major enterprise software categories, Big Compute eliminates constraints on compute hardware and provides the ability to scale computational workloads seamlessly on workload-optimized infrastructure configurations without sacrificing performance. A comprehensive Big Compute stack now enables frictionless scaling, application-centric compute hardware specialization, and performance-optimized workloads in a seamless way for both software developers and end-users. Specifically, Big Compute transforms a broad set of full-stack software services on top of specialty hardware into a software-defined layer, which enables programmatic high performance computing capabilities at your fingertips, or more likely, as back-end function evaluations part of software you touch every day.


PIQ, Everlast team up on AI that helps boxers train, deliver a knockout punch

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Artificial intelligence has been making its way into sports wearables thanks to PIQ Sport Intelligence. Together, the two companies will launch an app and the world's first artificial intelligence wearable device designed for boxers. More: Track your ski runs easily with PIQ's new app and multisport sensor Using the PIQ ROBOT device, boxers can tap into the hive mind of thousands of boxers and millions of motions. "We're excited to work with PIQ to bring wearable technology to the sport of boxing.


Hello 2017, and Recap of Top 10 Posts of 2016

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As we kick off what will surely be another very exciting year of progress in artificial intelligence, machine learning and data science, we start with a quick recap of our "Top 10" most popular posts (based on aggregate readership) from the year just concluded. We also show how Microsoft R Server can harness the deep learning capabilities of MXNet and Azure GPUs using simple R scripts. Few things in life can beat "free", and that was certainly true about our free eBook on creating intelligent apps using SQL Server and R. You can now embed intelligent analytics and data transformations right in your database, and make transactions intelligent in real time. We also announced that, on Windows, Microsoft R Server (MRS) would be included in SQL Server 2016.


Intel To Duke It Out With Nvidia In The Coprocessor Market

Forbes

In our previous analysis, we discussed how Intel is competing with Nvidia in the data center coprocessor market. These computational capabilities make GPUs ideally suited for use as coprocessors in High Performance Computing environments. It is worth noting that GPUs have a parallel architecture with hundreds of cores, making it highly suited for matrix and vector operations in both deep learning and 3D computer graphics. Currently, it is debatable as to which one – Intel's Xeon Phi processor family (formerly code-named Knightsbridge) or Nvidia's Tesla processors – is better in terms of performance.


Using Machine Learning Algorithms to Improve Your Business Workflows

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For example, when you apply machine learning algorithms to a sales workflow process, the technology is constantly learning from its mistakes and reprogramming itself to improve performance. The next generation of productivity software and machine learning might also include more intelligent document creation tools and processes. There's also the prospect of machine learning that complements traditional customer relationship management and collaboration platforms, helping users better capture and interact with customer data and internal content and saving them the time of searching for content across platforms. Applying machine learning to customer service enables organizations to offer a layer of proactive self-help tools that can provide customers with options to resolve their issues without having to call into the actual customer service department.