Data drives modern technology in both medical and industrial sectors. In the medical sector alone, we’ll have roughly 25 billion Internet of Things devices in service by 2025. But how and where do we process the data these devices collect?
One proposed solution is edge computing, enhanced by AI-powered analysis. Properly implemented, edge AI computing can deliver faster and more accurate results than cloud computing, leading to better patient outcomes and safer business operations.
Cloud Computing vs. Edge Computing: What’s the Difference?
Cloud computing: In a cloud computing environment, data from various sources, such as sensors, cameras, and wearable devices, is transmitted to a cloud computing server, which is typically off-site from the data’s source and provided by a third-party source. This data is processed, analyzed, and stored on this server for future access.
While cloud computing reduces the workload for on-site IT teams and helps prevent data silos from forming, it is also slower than other forms of computing. Data being transferred far from the source to an off-site server or back to the end-user must pass through several firewalls and layers of encryption, which adds time to the process. While this time is usually only a matter of seconds, seconds matter in critical environments like healthcare or managing vehicles.
Edge computing: In an edge computing environment, data collection and processing occur on the “edge” of a system, as close to the source as possible (meaning sensors and IoT devices in most cases). Frequently, this means devices are physically connected, such as an EKG machine plugged directly into a medical computer or an industrial PC connected to assembly machines or sensors on an oil rig. This proximity makes data collection and processing much faster, which is extremely important in sectors where speed is critical.
How Does AI Interact With Edge Computing?
AI algorithms can process and analyze data far faster than conventional methods, making them perfect for a role that already prioritizes speed. In a dedicated edge AI computing role, they can do so with or without an Internet connection, making them perfect for mobile roles or areas where connectivity can be spotty. Compared to the seconds cloud computing takes to get results, edge AI computing can achieve the same outcomes in milliseconds.
Edge AI computing is being explored for high-demand roles like:
- Patient monitoring: By connecting a medical AI box PC to equipment monitoring a patient’s vital signs, the PC can detect the first sign of abnormal activity in the patient’s body, such as falling heart rates or oxygen levels. The AI can then alert healthcare providers, improving response times and preventing harm to the patient.
- Machine vision: Using visual data from cameras and AI algorithms for machine vision is a natural fit. By allowing computers to “see,” businesses in both industrial and medical sectors can reap benefits. In industry, edge computing-enabled vision is used for self-driving vehicles; by linking multiple cameras to a single industrial AI box PC, the car can see its surroundings and react accordingly. In healthcare, machine vision is used to enhance diagnostic tools, with AI able to detect visual signs of disease that providers might otherwise miss.
- Machine control: By monitoring sensor data for multiple manufacturing machines, edge AI computers can detect imperfections or faults early on, preventing equipment breakdown and ensuring quality control on all products.
Benefits of Edge AI Computing
Implementing edge AI computing comes with numerous benefits, the most significant being:
- Speed: The first and most obvious benefit that edge computing delivers is its speed. The difference between seconds and milliseconds may not sound like much, but it means everything if it helps a self-driving vehicle avoid a collision or helps save a patient’s life.
- Security: Because edge AI performs its calculations locally, it doesn’t need to transfer data to the cloud or other external locations. This helps prevent data from being intercepted or mishandled and can be further enhanced with measures like data encryption and TPM chips.
- Less Internet Reliance: Tied to the previous point, edge computing is not reliant on a constant connection to the Internet like cloud computing. This is ideal for any role where Internet connection is tenuous, such as on a self-driving vehicle.
- Lower Power Requirements: Edge AI computers are designed for efficient power consumption, making integrating them into different roles easier and contributing to a lower electricity bill.
Embrace AI-Powered Edge Computing With Cybernet
By implementing edge computing enhanced with AI algorithms, companies in both medical and industrial fields can work faster, more efficiently, and rely less on cloud computing services. All it takes is the proper computer hardware to support these AI algorithms.
If your healthcare group or industrial business is looking for AI computers that can support edge processes, contact the team at Cybernet Manufacturing. We’d be happy to explain how our industrial and medical AI box PCs can be used in edge computing roles.