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| Photo: Rich Brueckner |
We also explain the difference between AI, machine learning and deep learning, and examine the intersection of AI and HPC. We also present the results of a recent insideBIGDATA survey 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 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...
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.
Download the insideBIGDATA Guide to Deep Learning & Artificial Intelligence, courtesy of NVIDIA.
Source: insideHPC
