it software


The coming together of SD-WAN and AIOps

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Software-defined wide-area networking (SD-WAN) and AIOps are both red-hot technologies. SD-WANs increase application availability, reduce costs and in some cases improve performance. AIOps infuses machine learning into IT operations to increase the level of automation. This reduces errors and enables businesses to make changes at digital speeds. Most think of these as separate technologies, but the two are on a collision course and will give rise to what I'm calling the AI-WAN.


Who's Eyeing Quantum Clouds - News Analysis

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There has been a lot of talk, as of late, that the cloud has become a resource to turn to when a company needs large amounts of digital storage, constantly updated SaaS (software-as-a-service), or high-performance computing capabilities. Couple this with the expansion of AI (artificial intelligence) in applications, even smaller companies can realize the full computing power available in the cloud. And while the cloud has been highly publicized as an option for everything digital, an even more powerful environment exists that hasn't gotten nearly as much coverage, at least not yet. Lookout world here comes quantum computing. A recent IDC (Intl Data Corp.) survey of IT (information technology) and business personnel responsible for quantum computing adoption has revealed that improved AI capabilities, accelerated BI (business intelligence), and increased productivity and efficiency are quickly proving to be the top expectations of organizations currently investing in cloud-based quantum computing.


Two years late, but upgrade wave finally washes a billion folk onto Windows 10 as its Android phone waits in the wings

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Roundup While Azure wobbled and Windows was updated, the Microsoft gang continued toiling away with Python, Portuguese and Private Link for its cloud and an altogether more down to earth way uninstalling .NET. Corporate veep of Modern Life, Search & Devices at Microsoft, Yusuf Mehdi was hopping about his office with delight today as the software behemoth announced that, with NASA-esque levels of delay, Windows 10 had finally broken the billion barrier. That means, according to Mehdi, the "One in every seven people on the planet are ... doing great things with Windows 10." Or possibly looking up the price of Macs at the Apple Store. Mehdi also revealed that there were 17.8 million Windows Insiders - quite the army of unpaid testers. It has taken a long, long time to get to this point.


Artificial Intelligence Software Market to Reach $89.8 Billion in Annual Worldwide Revenue by 2025 Omdia

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Compared to a few years ago, the artificial intelligence (AI) market is starting to solidify around real-world applications with the pace of change being faster than it ever has been before, as startups and technology providers rush to create platforms and targeted niche solutions for solving specific enterprise problems. According to a new report from Tractica, the rising tide of AI adoption across multiple industries will drive significant growth during the next decade, and the market intelligence firm forecasts that annual worldwide AI software revenue will increase from $3.2 billion in 2016 in 2016 to $89.8 billion by 2025. This forecast is a significant upgrade of Tractica's previous projection for AI market revenue, which was published in 2Q17, due to an improved outlook for a number of specific use cases across multiple industries. "Artificial intelligence is already key to how consumer internet companies operate today, allowing them to roll out hyper-personalized services by following an'AI first' strategy," says research director Aditya Kaul. "The rest of the market in the enterprise and government sectors is still catching up on adopting AI and has yet to fully understand its value, including the breadth and depth of use cases, the technology choices surrounding AI, and the implementation strategies."


A Massive Opportunity Exists To Build 'Picks And Shovels' For Machine Learning

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Many multi-billion-dollar companies have been built by providing tools to make software development easier and more productive. Venture capitalists like to refer to businesses like these as "pick and shovel" opportunities, a reference to Mark Twain's famous line: "When everyone is looking for gold, it's a good time to be in the pick and shovel business." Atlassian, which offers a suite of software development and collaboration tools, has a public market capitalization above $30B. GitHub, a code repository, was acquired for $7.5B by Microsoft in 2018. Pivotal, which accelerates app development and deployment, was valued at $2.7B in VMWare's acquisition last year.


Promising artificial intelligence startup ideas for 2020

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AI startups are an area that has been growing for the past several years. This tech has applications in dozens of professions and niches the world over. As reported by Statista, the market research firm Tractica stated that in 2019, the global AI software market was expected to increase 154 per cent, with a forecast worth approximately 14.7 billion US dollars. This is just one of many stats indicating that an AI startup would be a smart enterprise in which you might invest. If you're wondering about the benefits of AI companies, there are many.


Google's TensorFlow is ready for quantum, but is AI ready for quantum?

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Spoiler alert: Quantum computers may not make your cats and dogs classifiers go any faster. Here's how you can still get a free Windows 10 upgrade You can still use Microsoft's free upgrade tools to install Windows 10 on an old PC running Windows 7 or Windows 8.1. No product key is required, and the digital license says you're activated and ready to go. Google this week announced a new version of its TensorFlow framework for building machine learning models, a kind of mash-up between TensorFlow and Cinq, another framework developed at Google that's designed for building quantum computing algorithms. Together, they could let you build a deep learning model to run on a future quantum computer with no more than a bunch of lines of Python.


The Emergence of AI-as-a-Service

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Software-as-a-service (SaaS) has become part of the tech lexicon since emerging as a delivery model, shifting how enterprises purchase and implement technology. A new "_" as a service model is aspiring to become just as widely adopted based on its potential to drive business outcomes with unmatched efficiency: artificial intelligence as a service (AIaaS). According to recent research, AI-based software revenue is expected to climb from $9.5 billion in 2018 to $118.6 billion in 2025 as companies seek insights into their respective businesses that can give them a competitive edge. Organizations recognize that their systems hold virtual treasure troves of data but don't know what to do with it or how to harness it. They do understand, however, that machines can complete a level of analysis in seconds that teams of dedicated researchers couldn't attain even over weeks.


Today's technology trends that will still matter a decade from now ZDNet

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Here's how you can still get a free Windows 10 upgrade You can still use Microsoft's free upgrade tools to install Windows 10 on an old PC running Windows 7 or Windows 8.1. No product key is required, and the digital license says you're activated and ready to go. Like previous years, 2018 featured a bevy of buzzword-laden technologies, but we at ZDNet are fatigued by the never-ending stream of acronyms. With that fatigue in mind, we put together a simple test for the year in technology. What technologies talked about today will actually matter in a decade?


Scaling Tensorflow data processing with tf.data

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This talk presents tf.data tools for scaling TensorFlow data processing. As model training becomes more distributed in nature, tf.data has evolved to be more distribution aware and performant. In particular: tf.data service that allows your tf.data pipeline to run on a cluster of machines, and tf.data.snapshot As model training becomes more distributed in nature, tf.data has evolved to be more distribution aware and performant. This talk presents tf.data tools for scaling TensorFlow data processing.