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The Difference between AI, Machine Learning and Deep Learning

The insideBIGDATA Guide to Deep Learning & Artificial Intelligence is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting area of technology. In this guide, we take a high-level view of AI and deep learning in terms of how it’s being used and what technological advances have made it possible. We also explain the difference between AI, machine learning and deep learning, and examine the intersection of AI and HPC. We present the results of a recent insideBIGDATA survey, “insideHPC / insideBIGDATA AI/Deep Learning Survey 2016,” to see how well these new technologies are being received. Finally, we take a look at a number of high-profile use case examples showing the effective use of AI in a variety of problem domains. The complete insideBIGDATA Guide to Deep Learning & Artificial Intelligence is available for download from the insideBIGDATA White Paper Library.

The Difference between AI, Machine Learning and Deep Learning

With all the quickly evolving nomenclature in the industry today, it’s important to be able to differentiate between AI, machine learning and deep learning. The simplest way to think of their relationship is to visualize them as a concentric model as depicted in the figure below. Here, AI— the idea that came first—has the largest area, followed by machine learning—which blossomed later and is shown as a subset of AI. Finally deep learning—which is driving today’s AI explosion— fits inside both.

AI has been part of our thoughts and slowly evolving in academic research labs since a group of computer scientists first defined the term at the  Dartmouth Conferences in 1956 and provided the genesis of the field of AI. In the long decades since, AI has alternately been heralded as an  all-encompassing holy grail, and thrown on technology’s bit bucket as a mad conception of overactive academic imaginations. Candidly, until around  2012, it was a bit of both.

Over the past few years, especially since 2015, AI has exploded on the scene. Much of that enthusiasm has to do with the wide availability of GPUs that make parallel processing ever faster, cheaper, and more powerful. It also has to do with the simultaneous one-two punch of practically infinite storage and a flood of data of every stripe including images, video, text, transactions, geospatial data, etc.

On the same trajectory, deep learning has enabled many practical applications of machine learning and by extension the overall field of AI. Deep learning breaks down tasks in ways that make all kinds of machine assists seem possible, even likely. Driverless cars, better preventive healthcare,  even better movie recommendations, are all here today or on the horizon. AI is the foundation for the present and the future.

If you prefer, the complete insideBIGDATA Guide to Deep Learning & Artificial Intelligence is available for download in PDF from the insideBIGDATA White Paper Library, courtesy of NVIDIA.

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