Artificial intelligence (AI) has come a long way over the last few decades. The history of AI development actually goes back farther than many people might think, although it didn’t really jump into the public consciousness until the 1990s with high profile media events like IBM’s Deep Blue defeating World Chess Champion Garry Kasparov in 1997. Today’s AI, however, has become an almost ubiquitous part of everyday life, making many of the technology services that are taken for granted possible.
For data centers, AI has proven to be an asset and an opportunity. While the two may not seem to have much in common with one another at a glance, their future success will very likely be interconnected for a few key reasons. In order to appreciate that relationship, it helps to know what people mean when they use the term “artificial intelligence” in today’s technology context.
AI, Algorithms, and Machine Learning
As the popularity of HBO’s Westworld has demonstrated, people are still fascinated by the notion of machines becoming self-aware enough to form their own thoughts and personalities. Unfortunately (or fortunately if science fiction is any guide), modern AI bears little resemblance to this concept. When people talk about AI today, they’re actually referring to complex mathematical algorithms that analyze data to produce increasingly refined results.
Sometimes called machine learning, these algorithms are based on models developed by humans. Once this model is in place, it’s used to analyze raw data in search of patterns and other insights. Over time, a program driven by machine learning will refine the model based on the results it produces, allowing it to generate increasingly nuanced and thinly sliced results.
This basic principle can be used for something as simple as determining what type of programs a Netflix user prefers to a task as complex as adapting a self-driving automobile to its primary user. Machine learning algorithms are especially important for financial services, marketing, and healthcare industries because they deliver insights about opportunities, customers, and risk areas.
Basic AI Requirements
Part of the reason the biggest AI advancements have come in the last several years is that computer processors simply weren’t powerful enough in the past to accommodate the huge workload demands of these programs. As processors have gotten smaller and more energy efficient, it has become more feasible (and less expensive) for more organizations to develop their own AI research programs.
Another challenge is the high volume of data algorithms needed to generate meaningful and actionable insights. Even the most well-designed algorithm is only as good as the data being fed into it. For many years, this data simply wasn’t readily available. With the explosion of the internet and mobile technology, however, people are providing organizations with massive amounts of personalized data, much of it unstructured.
The combined need for computing power and data makes the data center a perfect asset for organizations developing AI machine learning. Data centers are uniquely situated to provide the massive processing power these programs require, and the virtualization of servers has made it possible for them to parcel out that computing muscle accordingly. As the development of internet of things (IoT) devices has brought even more customer information into the data center, organizations are increasingly turning to edge computing solutions to analyze that data.
Data Centers as AI Hubs
As AI programs become more ubiquitous and sophisticated in the coming years, data centers will likely continue to serve as “AI hubs” that serve both as a repository for data and a warehouse for analyzing it. While the largest tech companies operate their own massive data centers that accumulate data and run it through AI programs driven by custom built, liquid cooled processors, most companies lack the resources needed to establish such a system.
Data centers can still provide many of these benefits, however. By storing their vital information in a data center, they can leverage machine learning services from either the data center itself or other providers as part of a hybrid cloud architecture to gain many of the same benefits. With their ability to provide connections to so many other providers, data centers make it possible for even the smallest companies to gain access to some of the same machine learning tools used by the largest tech companies. As more data flows in from IoT devices through a network of edge data centers, organizations will have more opportunities than ever before to capitalize on the potential insights found within that data.
While artificial intelligence doesn’t yet resemble the self-functioning machines of science fiction, the forms it has taken are incredibly valuable for organizations looking to find a competitive advantage in increasingly crowded markets. With more data being generated every year, their ability to analyze that data using AI-driven machine learning will be a critical criterion for sustained business success. Data centers are uniquely positioned to provide the computing power these programs need while also storing the valuable information they were designed to capitalize upon. Whatever developments AI researchers continue to make in the coming years, data centers will surely be crucial to their successful implementation.
About Blair Felter
As the Marketing Director at vXchnge, Blair is responsible for managing every aspect of the growth marketing objective and inbound strategy to grow the brand. Her passion is to find the topics that generate the most conversations.
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