AI and Edge Computing: A Symbiotic Relationship

Nature can teach us a lot about technology. Bees and flowers, for example, have evolved one of nature’s purest symbiotic relationships over millions of years.

At the most basic level, flowers provide bees with nectar for food, and bees spread pollen that helps flowers reproduce. Seeing how this mutually beneficial partnership has evolved unlocks some insights into the current state of technology and how two of the fastest-growing sectors—artificial intelligence and edge computing—complement and amplify the other.

AI & Edge Computing

A great example of this evolution is vision AI or computer vision. Edge computing is all about processing data as efficiently as possible, but analyzing visual data was originally a manually intensive process.

A camera sensor captures vast data, and developers write code to identify patterns within it. Insights from the process could drive new strategies or operational changes based on the camera's data.

Today, AI has flipped that original model. People can use an AI model specifically developed to find the patterns they are looking for instead of writing code.

AI now enables average video cameras to detect designated objects accurately without requiring hand-coded solutions. As AI and machine learning improved, edge computing for remote visual data also advanced.

Benefits for the Entire Ecosystem

As in nature, the symbiotic connection between AI and edge computing augments the benefits to the entire ecosystem that improves efficiency and lowers costs, including:

• Placing compute and storage near data reduces latency, enabling AI to make split-second decisions for critical services.
• Processing data locally on edge devices prevents the transmission of private information, and AI models can still work on the information at the edge without fear of exposing it to the cloud or the public.
• Federated learning—an approach to training machine learning models—can be done with data on edge devices. The AI model gets better in real time rather than waiting for information to be transmitted to a central location.
• The tremendous computational power needed to build AI models can strain network traffic and transmission costs as large volumes of data go to centralized data centers or the cloud. Executing an AI model on an edge device reduces the burden on network bandwidth and data transfer costs.

Real-World Impacts

Edge computing and AI are transforming industries by processing, analyzing, and sharing vast data efficiently. Edge technology enhances inventory insights, enabling AI-driven demand forecasting for better management and profitability in retail.

Practical AI-at-the-edge uses in automotive include maintaining in-vehicle software and delivering service packages to dealerships.

It may seem like AI and edge computing have stepped into the spotlight only recently, but the two have been working together for years and evolving alongside each other. Just as “the edge” isn’t one location, “AI” isn’t only one thing. But together, they have the potential to transform our world where anything that can get connected will be connected.