{"id":17860,"date":"2017-05-15T06:30:26","date_gmt":"2017-05-15T13:30:26","guid":{"rendered":"http:\/\/insidebigdata.com\/?p=17860"},"modified":"2017-05-16T08:27:26","modified_gmt":"2017-05-16T15:27:26","slug":"how-traditional-industries-are-using-machine-learning-and-deep-learning-to-gain-strategic-business-insights","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2017\/05\/15\/how-traditional-industries-are-using-machine-learning-and-deep-learning-to-gain-strategic-business-insights\/","title":{"rendered":"How Traditional Industries Are Using Machine Learning and Deep Learning to Gain Strategic Business Insights"},"content":{"rendered":"
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This article is part of a\u00a0special insideHPC\u00a0report that explores trends in machine learning and deep learning.\u00a0The complete report,\u00a0available here<\/a>, covers how businesses are using machine learning and deep learning, differentiating between AI, machine learning and deep learning, what it takes to get started and more.\u00a0<\/em><\/p>\n

The impact of AI on business functions will\u00a0play out differently in different industry sectors. In financial services firms, AI\u2019s impact is expected to be felt most strongly in the area of customer interaction, and in manufacturing organizations\u2014 product development. Health and life sciences, meanwhile, anticipate the AI impact will be greatest in management decision-making. In general, early AI implementations may help some organizations to boost revenue, their operating efficiency or their margins.<\/p>\n

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Download the full report here.<\/a><\/p><\/div>\n

Many enterprises are now embracing an accelerated path for accessing, and processing, and taking advantage of data by using AI \u2014 machine learning or deep learning, in other words\u2014becoming an \u201cAI enterprise.\u201d If you\u2019re still on the fence, it\u2019s clear that the time for action is now. Why? Because this is the year we\u2019re going to cross into the zettabyte regime in terms of data volume. So, with the amount of data that\u2019s at our disposal, coupled with the intended applications of the data, enterprises are looking for new ways to manage it all. The whole idea of drowning in your \u201cdata deluge,\u201d predicates that enterprises look for new solutions. The motivation is to do more with valuable data assets \u2014 start using AI, machine learning and deep learning. The whole mindset is changing from being overwhelmed by the data deluge, to actually\u00a0being data hungry. AI is opening an insatiable desire for data, and this is happening now.<\/p>\n

[clickToTweet tweet=”Enterprises are now embracing an accelerated path for accessing,processing,and taking advantage of data by using AI. ” quote=”Enterprises are now embracing an accelerated path for accessing, processing, and taking advantage of data by using AI. “]<\/p>\n

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One area that appears to be taking off is how enterprises are now seriously looking at\u00a0\u201caccelerated analytics\u201d and \u201cAI driven analytics\u201d as a solution. \u201cAccelerated analytics\u201d involves graphics processing unit (GPU)-accelerated solutions, like Kinetica, MapD, SQream DB, BlazingDB, Graphistry, and BlazeGraph.\u00a0\u201cAI driven analytics\u201d addresses the needs of\u00a0the enterprise\u2019s digital business and of getting access to information. Accelerating the analytics component is one side of the equation, the other side is doing it at a fraction of the cost.<\/p>\n

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The industry is in a dynamic feedback loop state where a continuing stream of successful proof-of-concept projects drive more and more enterprises to consider AI solutions.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/blockquote>\n<\/div>\n<\/div>\n

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There is a striking importance of evaluating\u00a0and adopting AI, machine learning and deep learning technologies now or continuing to lose ground to competitors that already are in the process of doing so. The number of examples of AI successfully solving complex problems grows every day. This means that the industry is in a dynamic feedback loop state where a continuing stream of successful proof-of-concept projects drive more and more enterprises to consider\u00a0AI solutions.<\/p>\n<\/div>\n<\/div>\n<\/div>\n

\u00a0Below, we\u2019ll highlight a couple of compelling use case examples focusing on the successful use of AI, machine learning and deep learning. The examples also illustrate how GPUs can be effectively combined with AI technology.<\/div>\n