Edge AI is the deployment of artificial intelligence on edge devices – IoT and other connected devices that are part of the local network, rather than cloud or remotely located devices.

While cloud computing has become popular for its links to Software as a Service, edge computing is gaining traction because it reduces lag and encourages lean systems and processes, which can be especially beneficial in artificial intelligence applications, where lightning-fast decisions need to be made, and in manufacturing, where those decisions need to be deployed quickly.  

Cloud Computing

In cloud computing, data is sent to remote servers to be analysed and manipulated. This can be especially beneficial for businesses with multiple locations or for some online casinos. For example, iGaming’s legal status in Florida means that players from the Sunshine State play casino games at offshore sites – sites whose servers are located in other states or overseas.

Data, including transactions as well as account and even individual game data, is sent from the user’s computer to the server, where it is processed, analyzed, and used before results are returned to the individual. According to online casino expert Matt Bastock, this remote data handling will continue until Florida changes its online gambling regulations.

How Edge Computing Differs

Edge computing differs from cloud computing, in particular in where data is handled. Rather than sending data to remote servers, edge devices, which can include connected IoT devices as well as manufacturing devices and plant machinery, directly handle the data themselves, or are connected directly to processors that do so.

The data doesn’t have to be transmitted before it is analyzed, which reduces lag and can help prevent data loss.

Adding AI To The Data Mix

AI can be used to collect local data from multiple sources, analyze and manipulate the data quickly, and return calculations and requirements to the original and other connected devices.

Artificial intelligence can operate more quickly than humans and traditional systems. It is adaptive, which means it not only makes changes to data on the fly, but it can use those changes and any new data it collects to formulate new responses and update its own algorithms.

AI’s Use in Manufacturing

Edge AI is becoming increasingly common in manufacturing processes. It is lean, quick, responsive, and adaptive. It is less likely to make mistakes than using human analysis, and its adaptability means that it can manage more complex equations than traditional manufacturing systems.

Edge Device Deployment

Edge devices in manufacturing include sensors and connected manufacturing machinery. In a lot of cases, these devices tend to be connected via IoT bridges, which are also connected to local computers and systems. It is on these computers where AI is typically deployed, although its algorithms may also work directly on the bridges or even on some devices.

Quality Control

Quality control is a critical part of the manufacturing process. Data from assembly lines, which includes manufacturing and product data, as well as data collected by sensors, cameras, and other devices, can be used by AI algorithms and using image recognition and other analytical software to help identify defects.

Anomalies are detected in real time, enabling work to stop and defective products to be recovered. AI algorithms can also determine the best path to prevent further defects, feeding this information back to the manufacturing floor, effectively fixing the problem in real time.

Enhanced Safety Decisions

Safety is another critical component of manufacturing. Where defects occur, these can potentially cause accidents in the manufacturing plant, or even after the fact, when defective products have been delivered.

Because of how quickly edge AI identifies and rectifies problems, it can prevent potentially dangerous products from being released, and it can also help prevent accidents on the manufacturing floor. Because humans aren’t required to step in, check for defects, or make corrections themselves, there is less risk of injury.

Predictive Maintenance

Sensors and systems constantly monitor the machinery and edge devices on the network. This makes it possible to accurately predict when maintenance might be required, even before it is needed.

This pre-emptive maintenance typically demands less downtime and costs less money than in cases where machinery breaks down or stops operating completely, not least because it is possible to schedule maintenance for quiet periods or operational downtime.

Improved Human-Machine Collaboration

It isn’t just machinery that sensors monitor and AI reacts to. They can gather data on human actions and, therefore, improve interactions and collaboration between employees and edge devices.

This level of interaction is a critical part of industry 5.0, which means the use of edge AI in manufacturing can help future-proof manufacturing processes and businesses.