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Predictive analytics: Your key to preventing network failures

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"In the case of network performance and quality issues, forecasting algorithms help manage multiple dimensions of the analysis and decide which events have the most impact on results," Noya says. Predictive analytics enables security analytics platforms to recognize anomalous behavior from systems, devices and/or users. Predictive analytics is a proactive forecasting technology with the platform allowing enterprises visibility of what network usage, performance and quality will look like months and even a few years into the future. "In turn, this helps the enterprise prepare and forecast for network upgrades, new devices and personnel," he notes As a prerequisite for network cost prediction, it is necessary to build network allocation cost foundations that enable the attribution of cost, both capital expenditures (CapEx) and operating expenditures (OpEx), to specific technical services or end customer products, Noya notes.


Another Example Of How Artificial Intelligence Will Transform News And Journalism

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The technology relies on natural language generation (NLG), a cornerstone of much of the progress which has been made in recent years thanks to artificial intelligence and automation. The PA project is known as RADAR – Reporters and Data and Robots – and relies on open data sets from government, local authorities and public services. Urbs Media editor-in-chief Gary Rogers told me that they initially started looking at the possibilities of generating stories for national media using open data sources, but soon realized that its highly geographically-segmented nature meant it was very well suited for local stories. "So instead of writing one story about a dataset – a national story – you could write 10 regional stories or 200 local authority-based stories.


Super Robot Wars: China Wants to Wrest AI Dominance from the US by 2030

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The plan, which has been approved by the highest levels of Beijing authority, outlines a 13-year plan to bring China to the forefront of the burgeoning field of AI. "Artificial intelligence has become a new engine of economic development," the document reads, translated to English. By 2025, they intend to quintuple the industry's value to 5 trillion yuan with world-leading technological breakthroughs, and by 2030 they intend to be the world leader in AI technology and economics with a 10 trillion yuan AI industry. The initiative in artificial intelligence is the latest step in Chinese President Xi Jinping's efforts to bring China to the forefront of technological innovations.


After Mobile App Success, Rock My World Introduces Fitness Chatbot Xconomy

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Adam Riggs-Zeigen has come a long way since 2013, when he co-founded San Diego's Rock My World and introduced the mobile app RockMyRun to provide upbeat music playlists to motivate smartphone-equipped runners. Rock My World developed Jolt.ai for Slack, the software that enables corporate teams to share workplace messages, ideas, and other information through a variety of channels that can be customized for a variety of projects and purposes. The artificial intelligence-based system incorporates sensor data from fitness devices in its messages to Slack user groups, he said. For example, Jolt.ai may send some users a mid-afternoon message that they have not yet met their daily activity goal of walking 10,000 steps.



FINE LIST OF 20 TOP WORLD AI & BIG DATA EXPERTS TO FOLLOW IN 2017 [WITH MOZ SOCIAL SCORE]

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Important Note: Because it's hard to compare enormous structures with other companies or individual influencers, we have excluded, for more visibility, The Biggest AI / Big Data Companies, like IBM, DELL, Microsoft, Nvidia, Google, Amazon, Elon Musk/SpaceX… These are, in each case, in the « Hall of fame of AI ». And, we'll publish a HALL OF FAMES AI RANKING soon. Tip: You can check your Social Authority there: MySocialAuthority.com (Moz and Klout scores)


AI Meets CRM: Transforming Customer Experiences

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Artificial intelligence (AI) embedded into commerce and customer relationship management (CRM) platforms creates new possibilities for customer experience and beyond. The business world is now entering a golden age of AI, and both customers and employees can expect to reap the benefits.


AI for security: Microsoft Security Risk Detection makes debut - Next at Microsoft

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The Microsoft Security Risk Detection service is unique in that it uses artificial intelligence to ask a series of "what if" questions to try to root out what might trigger a crash and signal a security concern. Heasman said DocuSign used Microsoft Security Risk Detection to look for bugs and vulnerabilities in software it had bought or licensed and wanted to incorporate into its particularly software involved with handling documents uploaded to the platform, which could contain malicious content. Roots in Microsoft's own security testing Microsoft itself has been using a key component of Microsoft Security Risk Detection, called SAGE, since the mid-2000s, starting with versions of Windows, Office and other products. Developers can sign up to learn more about the Windows version or Linux preview on the Microsoft Security Risk Detection website.


P&G Bought A Bad Batch Of Data; Jobs Open At Twitter For Machine Learning Expertise AdExchanger

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Twitter's actively recruiting, which is no surprise for a tech company. But it's clearly hopping on the machine learning train, including with regard to ad targeting. Based on the job descriptions, which can be read here, it looks as if Twitter wants talent that can help develop algos around image and video classification; identify context around text, video and images; and predict consumer engagement and conversion propensity. As CEO Jack Dorsey mentioned at Cannes [AdExchanger coverage], Twitter wants to be more strategic about its ad tech bets.


Autonomous to Smart: Importance of Artificial Intelligence – InFocus Blog Dell EMC Services

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This incident highlights two significant challenges with respect to the application of artificial intelligence in the world of the Internet of Things (IoT), edge analytics and creating "smart" devices: The challenge for any autonomous device (car, truck, drone, washer, wind turbine, pace maker) is how to manage challenge #1 within the computational and storage limitations of #2. And I'm not even sure where to begin with how an autonomous car might handle something like this (I hope your autonomous car hasn't been watching any Transformer movies…) We don't need autonomous devices as much as we need "smart" devices; devices smart enough to do what my daughter did when faced with an unexpected situation requiring a real-time decision with only a limited amount of historical data and experience. Historically, the rapidly declining storage costs and rapidly increasing CPU processing capabilities have allowed technologists to wait for the technology to advance in order to address the problem for them. Unfortunately, the growth in the sensor data and complexity of "smart" decisions at the edges is increasing faster than Moore's Law can cover (see Figure 2).