{"id":17865,"date":"2017-05-22T06:30:13","date_gmt":"2017-05-22T13:30:13","guid":{"rendered":"http:\/\/insidebigdata.com\/?p=17865"},"modified":"2017-05-23T09:09:59","modified_gmt":"2017-05-23T16:09:59","slug":"differentiating-between-ai-machine-learning-and-deep-learning","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2017\/05\/22\/differentiating-between-ai-machine-learning-and-deep-learning\/","title":{"rendered":"Differentiating between AI, Machine Learning and Deep Learning"},"content":{"rendered":"

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

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

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With all the quickly evolving jargon in the industry today, it\u2019s important to be able to differentiate between AI, machine learning and deep learning. The easiest way to think of their relationship is to visualize them as a concentric model, as depicted in the figure to the right, with each term defined. Here, AI\u2014the idea that came first\u2014has the largest area, followed by machine learning\u2014which blossomed later and is shown as a subset of AI. Finally, deep learning\u2014which is driving today\u2019s\u00a0AI explosion\u2014fits inside both.<\/p>\n

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Machine learning takes some of the core ideas\u00a0of AI and focuses them on solving real-world problems with neural networks designed to mimic our own decision-making. Deep learning focuses even more narrowly on a subset of machine learning tools and techniques, and applies them to solving just about any problem which requires \u201cthought\u201d\u2014human or artificial.<\/p>\n

Machine learning takes some of the core ideas\u00a0of AI and focuses them on solving real-world problems with neural networks designed to mimic our own decision-making.<\/p><\/blockquote>\n

\"AI\"<\/a>

Credit: Fortune<\/p><\/div>\n

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Machine learning is well-suited for problem domains typically found in the enterprise, like making predictions with supervised learning methods (e.g. regression and classification), and knowledge discovery with unsupervised methods (e.g. clustering). Deep learning is an area of machine learning that has achieved significant progress in certain application areas that include pattern recognition, image\u00a0classification, natural language processing (NLP), autonomous driving, and so on. Machine learning techniques like random forests<\/a> and gradient\u00a0boosting<\/a> often perform\u00a0better in the enterprise problem space than deep learning.<\/p>\n

[clickToTweet tweet=”Machine learning is well-suited for problem domains typically found in the enterprise. #AI” quote=”Machine learning is well-suited for problem domains typically found in the enterprise. #AI”]<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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Deep learning attempts\u00a0to learn multiple levels\u00a0of features of large data sets with multi-layer neural networks and make predictive decisions for the new data. This indicates two phases in deep learning: first, the neural network is \u201ctrained\u201d with a large number of input data; second, the trained neural network is used for \u201cinference\u201d designed to make predictions with new data. Due to the large number of parameters and training set size, the training phase requires tremendous amounts of computation power.<\/p>\n

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The figure below summarizes the roles of training versus inferencing in deep learning.<\/p>\n

\"AI\"<\/a>

Inference is where capabilities learned during deep learning training are put to work. (Photo: Nvidia)<\/p><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

You can download the complete report,\u00a0\u201cinsideHPC Research Report on Riding the Wave of Machine Learning & Deep Learning,\u201d<\/a>\u00a0courtesy of Dell EMC and Nvidia.\u00a0<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

With all the quickly evolving jargon in the industry today, it\u2019s important to be able to differentiate between AI, machine learning and deep learning. This article is part of a special insideHPC report that explores trends in machine learning and deep learning. <\/p>\n","protected":false},"author":37,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[87,180,67,58],"tags":[437,264,547,564,277,263,96],"acf":[],"yoast_head":"\nDifferentiating between AI, machine learning and deep learning<\/title>\n<meta name=\"description\" content=\"With all the quickly evolving jargon in the industry today, it\u2019s important to be able to differentiate between AI, machine learning and deep learning.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2017\/05\/22\/differentiating-between-ai-machine-learning-and-deep-learning\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" 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