Artificial Intelligence in Data Centers​

With artificial intelligence (AI) comes greater efficiency, reliability, and scalability, helping organizations meet the evolving demands of modern IT infrastructures. In terms of artificial intelligence in data centers, we at Faulkner Haynes are proud to be your Local Vertiv Office, offering industry-leading products and services to help your company maximize performance and profitability. Below we’ve outlined a few of the ways AI helps us achieve these initiatives, as well as the overall timeline of artificial intelligence.

How Our Vertiv Products Support Artificial Intelligence in Data Centers

Vertiv’s use of artificial intelligence in data centers is ever-expanding. In the United States, the largest push has not been the implementation of AI specifically, but rather liquid cooling and direct-to-chip cooling technologies used to support AI high compute devices, which output immense amounts of heat.

In November, Vertiv announced a collaboration with Intel to support its next-generation AI accelerators with liquid-cooling technology. For more information about liquid cooling and AI, read Vertiv’s blog, “Powering and Cooling AI and Accelerated Computing in the Data Room.”

Several other ways Vertiv-based data centers utilize AI have not yet been adopted in the United States, but the following examples paint of picture of the possibilities that could lie ahead.

Predictive Maintenance

Vertiv employs AI algorithms to analyze vast amounts of data collected from sensors installed throughout the data center infrastructure. These algorithms can detect patterns indicative of potential equipment failures or performance degradation before they occur. An example of this is Vertiv’s LIFE Services remote monitoring, which relies on AI and machine learning to provide real-time visibility, analysis, and diagnostics of critical services for a constant preventive assessment of the network.

By predicting maintenance needs in advance, data center operators can schedule repairs or replacements proactively, minimizing downtime and optimizing equipment lifespan.

Get real-time insights in your data center with our Monitoring and Management Solutions.

Energy Efficiency Optimization

According to a Vertiv blog, “AI Technology: A Key Part of the Continuity of Data Centers,” a McKinsey report highlights that AI expands the potential for energy savings across the grid, by its “ability to analyze large amounts of data related to traffic patterns, real-time demand, and availability of network resources, enabling fast, automated decisions about which parts of the system can be put into sleep or shutdown mode.”

AI algorithms are used to optimize energy usage within data centers. By analyzing data on power consumption, cooling systems efficiency, and workload patterns, AI can identify opportunities for energy savings without compromising performance or reliability. This includes dynamically adjusting cooling settings, workload distribution, and power usage to achieve the optimal balance between efficiency and performance.

Anomaly Detection and Capacity Planning

AI-powered monitoring solutions like the ones mentioned above continuously analyze data streams to identify abnormal behavior or potential security threats in real time, allowing data center operators to respond promptly to mitigate risks and ensure the integrity of their infrastructure. These AI algorithms can also provide recommendations for optimizing resource allocation and scaling infrastructure to meet evolving demands effectively.AI-powered monitoring solutions like the ones mentioned above continuously analyze data streams to identify abnormal behavior or potential security threats in real time, allowing data center operators to respond promptly to mitigate risks and ensure the integrity of their infrastructure. These AI algorithms can also provide recommendations for optimizing resource allocation and scaling infrastructure to meet evolving demands effectively.

Optimization of Cooling Systems

AI algorithms are used to optimize the operation of precision cooling systems within data centers. By analyzing temperature and humidity data collected from sensors distributed throughout the facility, AI can adjust cooling settings in real-time to maintain optimal environmental conditions while minimizing energy consumption and operational costs.

Timeline of Artificial Intelligence

When did artificial intelligence in data centers first appear? The development and evolution of artificial intelligence can be traced through a timeline that spans several decades. Here is a concise overview of key milestones and events in the history of AI:

1950s: The Birth of AI

• 1950: Alan Turing introduces the Turing Test, a measure of a machine’s ability to exhibit intelligent behavior indistinguishable from that of a human.
• 1956: The term “Artificial Intelligence” is coined at the Dartmouth Conference, marking the official birth of AI.

1960s-1970s: Early AI Research

• 1961: The first industrial robot, Unimate, is introduced, marking the beginning of robotics in AI. It was created by inventor George Devol.
• 1969: Shakey, the first mobile robot with reasoning abilities, is developed at the Stanford Research Institute.
• 1973: The Lighthill Report criticizes the progress of AI research, leading to a decrease in funding, known as the “AI Winter.”

1980s-1990s: Expert Systems and Neural Networks

• 1980s: Expert systems, rule-based programs that mimic human expertise, gain popularity.
• 1986: Geoffrey Hinton, David Rumelhart, and Ronald Williams publish a paper on backpropagation, a critical development in training neural networks.
• 1997: IBM’s Deep Blue defeats world chess champion Garry Kasparov in a chess match. Deep Blue was a chess-playing supercomputer.

2000s: Machine Learning Resurgence

• 2006: Geoffrey Hinton and his team publish a paper on deep learning, reigniting interest in neural networks.
• 2011: IBM’s Watson wins Jeopardy!, showcasing the potential of AI for natural language processing and knowledge representation.
• 2012: AlexNet, a deep convolutional neural network, achieves a breakthrough in image classification at the ImageNet competition.

2010s: Rise of Deep Learning and AI Applications

• 2014: Facebook AI Research (FAIR) is established, contributing to advancements in deep learning. Simply put, deep learning is a method of AI that teaches computers to process data in a way that is inspired by the human brain.
• 2016: AlphaGo, developed by DeepMind, defeats world champion Go player Lee Sedol, demonstrating AI mastery in complex games.
• 2018: OpenAI’s GPT-3 (Generative Pre-trained Transformer 3) is introduced, showcasing the power of large-scale, pre-trained language models.

2020s: Continued Advancements and Ethical Considerations’

• 2020s: Ongoing advancements in natural language processing, computer vision, reinforcement learning, and AI applications in various industries.
• 2024: Anticipated advancements in AI technologies, including increased integration with emerging technologies like quantum computing and continued focus on ethical AI development.

Artificial Intelligence and Data Centers Like Yours – Contact Us Today!

To learn more about artificial intelligence in data centers, visit our blog, Data Center Artificial Intelligence in Your Network.

We hope this information about artificial intelligence in data centers was both helpful and inspiring. Ready to leverage AI in your facility? Contact Faulkner Haynes today to get started!