Data Storage Appears to be Artificial Intelligence’s New Frontier

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Scanrail1 | Shutterstock.com

Data storage is often overlooked when people talk about AI technology, but without advanced storage tech, you can’t have advanced AI. As AI functionality expands, so will data storage capacity.

It seems like every time there is a new development in AI technology, we cannot explain it without first explaining the basics of what modern AI can do and how they work. For my part, I’ve got it down to a sweet science.

It goes something like this: Mention the words ‘deep learning neural network‘, then explain that AI can crunch enormous amounts of data to learn and self-improve.

Only now, however, I’m noticing that I tend to build on the latter part of that statement, specifically the part where the AI learns and improves itself. And while it’s really cool how they can do that, it’s high time that we start talking about the ‘enormous amounts of data’ part of the equation, because without a keen focus on storage the AI revolution won’t happen.

Why Storage is Critical

You might be wondering why storage is such a big deal.

Let’s use an analogy to explain: Imagine you have two children with an equal potential for intelligence, and that they live in completely different environments. One child is born with a world of information at their fingertips, with access to the best libraries and education one can imagine, while the other child simply has none of those things save for the most basic education possible.

IBM's cloud and Baidu Cloud gave their oceans of data for NVIDIA's AI kitClick To Tweet

Now imagine you met the adult that those children will become. Which one will seem more intelligent to you? Which one would you rather hire, or confide in as a doctor, psychiatrist, or expert in nuclear physics? My money is on the one with the better education.

And for AI, storage is roughly equivalent to an education. We talk about how neural networks learn all the time, but perhaps we should be focusing more on the materials they are using to learn.

If an AI is being trained to visually recognize the style of a particular artist, for example, then they need to crunch data that includes pictures of that artist’s work, specifically labeled, as well as pictures of other artists so the machine can draw contrast between what it notices between the work of one artist and another. The more pictures it has in its database to draw information from, the better educated the AI will be.

So, better storage is equivalent to a better education for AI, and in a world that is becoming increasingly dependent on what AI can do for us, that makes effective storage solutions a worthwhile venture, to say the least.

NVIDIA: How Cloud Data Storage is Ahead of the gap

Let’s talk practicality for a moment. How is advanced storage helping us now?

One good example of this comes from NVIDIA. They may be famous for their graphic acceleration hardware, but they are also the creator of one of the most prolific deep learning platforms on the market. Give their system enough storage and it will enable your business to harness the power of AI to help handle the immense amount of data that you are producing.

 

data storage
NVIDIA Tesla P100 GPU | Nvidia.com

NVIDIA found that storage, by the way. By using the extra space provided by Baidu Cloud, they produced the Tesla P40 GPU available for companies that want to start using

 

AI to handle their data, and they didn’t stop there. IBM followed suit, opening up their cloud storage platform to give NVIDIA the ability to make the more advanced Tesla P100 GPU.

 

The combination of NVIDIA’s deep learning platform and these accelerators are poised to be the king of AI development kits, so expect to see more advanced AI systems in the hands of civil engineers, transportation systems, and businesses around the world.

We’re amazed every day at how advanced AI are becoming, but always remember that every fancy new ability for AI is built on a foundation of data storage, and more storage is always better. Who knows, maybe soon we’ll know how much data we’ll need to crunch to finally get self-driving cars out on the street.

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