No longer confined to the realm of science fiction, artificial intelligence (AI) technology has found its way into so many applications that it can almost be considered a ubiquitous aspect of modern life. Organizations are increasingly turning to AI solutions to make their existing processes more efficient, harvest better insights from big data, and expand the capabilities of automated systems. Whether it’s a digital media algorithm that serves up just the right content to keep consumers engaged or a fully autonomous vehicle capable of making split second decisions in traffic, AI is already making good on its potential to transform the way technology interacts with the world.
Key Elements of the AI Revolution
Until about twenty years ago, AI remained an enticing concept that was beyond the reach of most organizations. While there was no shortage of ideas about how the technology might be implemented, the high development costs and lack of affordable third-party solutions ensured that AI innovation remained confined mainly to large enterprises and government-funded research institutions. Two factors driving these high costs were the limitations in computer processor technology and difficulty gathering enough data to accommodate machine learning.
Innovations in Processing
When looking at all the potential applications of AI, it’s often easy to forget that it’s fundamentally based on the same principles as any other computer program and is therefore bound by the physical limitations of the hardware running it. The complex algorithms that allow programs to analyze data, identify trends and patterns, and then put new rules in place to react to future situations requires tremendous processing resources. For many years, that meant buying, deploying, and maintaining huge numbers of servers, which was far beyond the means of most companies.
In recent decades, however, innovations in computing technology have made processors smaller and more energy efficient, which has allowed more organizations to conduct their own AI research and build software that allows them to deploy AI solutions on a broad scale. For companies that don’t want to manage their own servers, the development of cloud computing has allowed them to access scalable, virtualized resources capable of powering their AI development.
Explosion of Data
If sophisticated algorithms are the engine driving AI solutions, data provides the fuel that keeps them running and pushing into unexplored territory. Even the most well-designed algorithm is only as good as the information being fed into it. If the data provided is incomplete, biased, or irrelevant, it’s almost impossible to draw meaningful insights from it and train programs to respond to future situations. Gathering sufficient volumes of data was incredibly time consuming and difficult in previous decades because few organizations had the networking infrastructure in place to capture, store, and manage it.
Over the last two decades, however, the expansion of the internet and the proliferation of mobile devices have generated massive quantities of data across almost every aspect of everyday life. Much of this information is unstructured, making it difficult to manage with existing technology systems. Conveniently, this is the very sort of data that AI solutions need in order to learn most effectively. The need to analyze all of that unstructured data prompted new investments in AI research, which in turn led to the development of innovative new solutions that needed even more data.
Challenges of AI Development
Despite these developments, organizations still face challenges as they explore the potential of AI and deploy solutions that make use of it. The actual infrastructure that makes AI possible remains a significant barrier to entry. While innovative startups and smaller companies can often outsource their processing and data requirements, the larger enterprises that provide those resources while also pursuing their own AI developments must find ways to overcome these challenges.
Thanks to innovations in processing technology, developers now have the capability to create and train incredibly complex AI systems. Unfortunately, the energy costs of managing that infrastructure are quite high. That’s because cutting-edge AI still requires a lot of raw processing power. A recent study found that the computing power needed for noteworthy milestones (like DeepMind’s AlphaZero, which was developed to beat the world’s best chess, shogi, and go players) increased a staggering 300,000 times between 2012 and 2018, roughly doubling every 3.4 months. In order to scale development effectively, the AI enterprises managing the tech stacks hosting these workloads need to find ways to reduce their overall energy costs.
Data Access and Management
While the quantity of data required to train and deploy an AI system can vary greatly depending upon its complexity, most of them need access to quite a lot of information to generate meaningful results. A general (but still debated) rule of thumb is that a model needs about ten times more data than it has degrees of freedom, which is the number of parameters being considered in the calculation. For advanced AI systems like Google’s latest language model, which features around 1.6 trillion parameters, the amount of data that must be gathered and managed is truly vast and requires extensive connectivity to multiple sources of information. Without ready access to diverse data sets, training and deploying useful AI solutions takes much too long for organizations operating in a competitive market.
Why Colocation Can Benefit AI Enterprises
Colocation data centers provide an ideal solution for AI enterprises looking to reduce their energy costs and expand their access to data. Migrating high-density servers from outdated on-premises facilities and into a colocation environment not only eliminates the burdensome capital costs of building and maintaining infrastructure, but also provides major efficiency benefits. That’s because colocation data centers often deploy their own AI-driven environmental systems to control cooling costs. Not only are those operational savings passed on to customers, but more efficient cooling also delivers performance benefits by extending the life of IT equipment.
A carrier-neutral data center is also a hub of connectivity with access to vast data resources. Plugging AI models into curated data lakes or accessing networks bursting at the seams with incoming data from customers and a multitude of online systems allows developers to train them quickly and get them to market faster. For enterprises looking to market their AI solutions, colocation data centers can serve as a hub for hosting and deploying these applications to customers around the world. With so many connectivity options, these facilities make it easy to connect AI systems to the cloud or make them directly accessible over cross-connections.
Unlock Your AI Potential with Evoque Data Center Solutions
Whether your AI enterprise is looking for a colocation provider with proven uptime reliability or a hybrid IT partner capable of supporting your digital transformation efforts, Evoque Data Center Solutions has the power capacity and experience to meet your evolving needs. With multiple colocation data centers positioned in key global markets and industry-leading cloud consulting services, our team can help your organization develop a hybrid cloud approach that keeps your IT costs low and provides true platform flexibility for the future.
To learn more about how our data center and cloud services can unlock the potential of your latest AI project, talk to one of our colocation experts today.