Artificial Intelligence (AI) plays an increasingly integral role in our everyday lives. Companies in every industry, from healthcare to manufacturing to banking, are building applications powered by AI and Machine Learning (ML). Their efforts are creating efficiencies for consumers and increasing productivity for businesses, helping doctors, financial advisors and retailers make more accurate, data-driven decisions.

While the release of ChatGPT makes it seem as if AI exploded onto the scene recently, it has actually been with us for quite some time. Neural networks, which form the basis for generative AI applications, were first introduced in 1965. Businesses have been leveraging large data sets and predictive algorithms for the last decade. Only recently, however, have numerous technological innovations come together to facilitate the amazing breakthroughs we now see in AI and data science, bringing them to our collective conscience.

Here are some of the technologies driving the AI revolution:

Advanced Computing Architecture

In the last 10 years, according to an Open AI study, the computational power used for AI training has grown at an astronomical rate, doubling every 3.4 months. The processors powering today’s AI advancements include:

Graphics Processing Units (GPUs) Formerly used for high-end gaming PCs and workstations, GPUs have thousands of cores that speed up the ML training process. They are used for training everything from cloud-based virtual machines to consumer devices.

Tensor Processing Units (TPUs)- Designed for high-volume, low-precision computations, TPUs are used to accelerate ML workloads.

Field Programming Gate Arrays (FPGAs)- A programmable processor, FPGAs are customized for specific types of workloads, such as training ML models. Unlike traditional Central Processing Units (CPUs), FPGAs can be programmed in the field after they are manufactured.

These next-generation processors are optimized for neural network workloads and geared for parallel processing, which enables AI developers to train larger, more complex models in shorter amounts of time. By making AI programming more efficient, these processors have reduced the cost of training AI models by up to 99.5%, which makes AI more affordable and accessible to all types of businesses.

Availability of Global Data

According to the International Data Corporation (IDC), the total volume of global data is set to reach 175 zettabytes (175 trillion gigabytes) by 2025. By analyzing data, AI can extract meaningful patterns, identify trends and make predictions.

Data scientists use large historical data sets to teach ML models for various industries. From these data sets, machines learn how to detect various diseases, understand traffic patterns, recognize market trends and forecast weather. The larger and more accurate the data set, the more efficient the ML model will be.

Cloud Storage and Public Cloud AI Platforms

Of course, all this data means nothing unless you have someplace to store it and make it readily available to those who need it. Cloud deployments store huge amounts of structured and unstructured data, and network connectivity provides easy, low-cost access to the rich data sets that enterprises, research institutes and government agencies need to train AI models. 

AI infrastructure, tools and scalability are available through public cloud platforms and application marketplaces, making it easier and more cost-effective for all types of businesses to develop, deploy and manage their own AI/ML applications.

Deep Neural Networks

AI systems use deep learning to analyze data, learn patterns and improve their experience. Artificial neural networks replicate the human brain, using thousands of cores for training to speed up the process of generalizing learning models.

Convolutional Neural Networks (CNN) such as Single Shot Multibox Detector (SSD) and Generative Adversarial Networks (GAN) employ deep learning for computer vision and image processing. ML techniques such as Capsule Neural Networks (CapsNet) and transfer learning accumulate precise data for problem-solving and analysis, enabling AI applications to give accurate results and predictions.

Advanced Algorithms

In recent years, AI developers have created complex ML algorithms for domains such as natural language processing, computer vision and reinforcement learning. Transfer learning and ensemble approach strategies have increased the effectiveness of ML model deployment and training, which in turn have increased the accessibility and usability of AI solutions.

Open-Source Collaboration

Through platforms like GitHub, AI/ML developers can share code, data sets, and research papers, allowing them to build on each other’s work. The open-source movement has sparked a global knowledge exchange and a sense of community among AI experts and researchers, further accelerating AI innovation.

Investments in AI Technology

The final element in the AI technology revolution is money. AI/ML would never have advanced as far as it has if someone hadn’t paid for it.

For most companies, AI investments take three forms: workforce development, research and technology hardware. Hardware investments haven’t changed in theory—servers for compute and storage—but the High-Performance Computing (HPC) server deployments for developing and hosting AI applications and storing datasets for large language models are considerably larger and more dense than previous IT demands.

As they always have, data centers provide critical infrastructure to support this hardware with the essential elements of space, power, cooling, connectivity, and other mission-critical support systems. As power, size, and density have skyrocketed in recent years, data center providers have met the new demands with emerging technologies like liquid cooling solutions.

Thanks to the AI boom, data center demand is outpacing supply, and companies are preleasing space and power in future facilities to meet their substantial and exacting requirements. Call Sabey Data Centers today to ask about preleasing agreements!