How AI at the Edge is Revolutionizing Real-Time Decision Making

By Anthony Mares, Director of Infrastructure Engineering, DataBank

AI at the Edge

As AI’s popularity grows and is proving to deliver innovative new business benefits, we’re seeing many cases where it can be combined with other technologies. AI-powered analytics. AI-driven virtual assistants. AI in industry-specific technologies in healthcare, finance, insurance, and virtually every other sector.

What about AI at the edge, though? Just what is this combination, and what does it mean for companies looking to take advantage of all it can offer?

AI at the edge refers to the deployment of artificial intelligence algorithms on edge devices, servers, or systems directly at the “edge” of a network instead of relying solely on centralized processing. This approach enables real-time data analysis and decision-making closer to where the data is generated, helping to reduce latency and improve application performance.

Initially, AI models required extensive resources and were run on powerful servers in centralized locations. However, as devices became more capable and the demand for immediate insights grew, the shift to edge computing emerged, enabling AI to operate independently in more diverse and dynamic edge environments.

This has opened new possibilities for industries where real-time responses are critical such as technology, healthcare, finance, automotive, and utilities. Some companies are already utilizing AI to screen resumes and provide HR support to their employees.

The Benefits of AI at the Edge

AI in edge computing enhances the efficiency, speed, and intelligence of localized data processing. These AI algorithms can perform various tasks such as data analysis, pattern recognition, decision-making, and predictive modeling – all at the source of the data.

Here’s a closer look at how AI functions within edge computing in various industries and its overall benefits:

• Real-time data processing: AI at the edge allows for the immediate processing of data where it is generated. For example, in a smart manufacturing plant, AI algorithms embedded in edge devices can analyze data from machinery in real time, detecting anomalies or predicting equipment failures. This rapid processing enables immediate corrective actions, minimizing downtime and maintaining production efficiency.
• Reducing latency: By processing data locally, AI at the edge significantly reduces the time it takes to transmit data to a central server or cloud for analysis and back. This is crucial in examples such as autonomous vehicles, where split-second decisions are necessary.
• Optimizing bandwidth use: AI at the edge helps to optimize network bandwidth by processing and filtering data locally, sending only relevant or summarized information to the cloud when necessary. For example, in a video surveillance system, AI can analyze footage at the edge to detect unusual activity, sending alerts and relevant clips to the central system rather than streaming all footage continuously.
• Enabling more intelligent decision-making: Edge AI enables devices to make intelligent decisions autonomously without relying on cloud-based instructions. For instance, in agriculture, AI-equipped drones can fly over fields, using edge computing to analyze soil conditions and plant health in real time. Based on the analysis, the drone can make immediate decisions, such as where to apply fertilizer or water, optimizing resource use and improving crop yields.
• Enhancing security and privacy: Processing sensitive data locally on edge devices reduces the need to transmit it to external servers, thus minimizing the risk of data breaches. In healthcare, for example, AI at the edge can analyze patient data directly on medical devices, providing immediate insights to physicians and clinicians while keeping the data within the hospital’s secure environment.

How Data Centers Can Provide AI at the Edge

Data centers are now doing all they can to offer AI at the edge to meet the growing demand for low-latency, high-performance applications across various industries. As more businesses adopt IoT, autonomous systems, and real-time analytics, the need for immediate data processing close to the source has become critical.

Offering edge AI capabilities enables data center companies to differentiate themselves in a competitive market. By integrating AI at the edge into their services, data center companies can provide customers with the ability to process and analyze data locally, reducing the time it takes to make decisions and improving the responsiveness of their applications. Utilizing high performance computing (HPC) clusters, which are made up of multiple servers connected through high-speed networks, allows for parallel processing. For their customers, this means enhanced performance where milliseconds can make a significant difference.

AI at the Edge Use Cases

Gaming

Consider the case of a popular online gaming company that has millions of players across the globe, including in regions with varying levels of internet infrastructure. To ensure that every player, regardless of location, experiences smooth gameplay with minimal latency, the company leverages AI at the edge for its game hosting.

For example, in a densely populated city on the west coast, where internet speeds can fluctuate due to high user demand, the company deploys edge servers closer to this specific geographic area. These servers host the game locally, allowing players to connect to a nearby data center instead of a distant central server. The edge AI continuously monitors network conditions, predicting and adjusting for potential slowdowns in real-time. This ensures that players enjoy fast, responsive gameplay with reduced lag, even during peak hours.

By hosting the application closer to consumers, the gaming company enhances the overall player experience, providing a competitive edge in markets where internet connectivity may not be as robust. This approach also allows the company to roll out updates and new features rapidly in specific regions, tailoring the experience to local preferences and maintaining high performance globally.

Smart Cities

Or imagine the example of a smart city where the energy grid is optimized using edge AI. Smart meters in homes and businesses continuously monitor energy consumption and weather conditions, making real-time adjustments to energy distribution.

During a sudden heatwave, the edge AI in these meters can quickly decide to balance the load, diverting excess energy from non-essential areas to critical cooling systems in hospitals or other vital facilities. By processing this information locally, the city ensures a stable energy supply, even in rapidly changing conditions, demonstrating the transformative power of AI at the edge.

AI at the Edge Delivers Better Results

By bringing AI closer to where data is generated, edge computing becomes faster, more responsive, and more efficient. This combination not only improves performance in applications requiring real-time processing but also enables smarter, more autonomous systems across various industries.