What is Ai at the edge?
Ai analysis has become a mainstream topic of conversation within the business world and the general public. We have all been touched by Ai in some way, with or without knowing it through chatbots, video analysis, music playlist generation and many more. Our thirst for an easier, safer and more efficient life drives the usage of these tech solutions.
Industry in general is always looking for solutions that will serve their customer base and Ai help this cause, for good or for bad. In order to satisfy customers' and users’ demand for an easier, safer and more efficient life industry-leading companies and organisations need to find ways to serve customer needs at a cost-effective rate and Ai has proven to provide that solution.
For industries and organisations, Ai can be implemented in a number of ways, in the cloud, on the edge or in a hybrid form. Each of these formats has its benefits and flaws, however, in this article, we will look at Ai at the Edge and its benefits.
What does edge mean?
Simply put the “edge” means as close to the user as possible for example your smartphone is an edge device. It contains computing power that can process complex Ai algorithms and return insightful data.
In the case of industry and organisations, there are many devices in the market that have the capability of processing complex Ai algorithms, all designed to improve the speed of insights, and data generation and reduce cost. Some good examples are street cameras, large machinery temperature gauges, robots in manufacturing plants and autonomous vehicles.
All that is needed for an edge device is adequate computing capability to process the incoming information with an Ai algorithm/s and extract required insights. Naturally, the more complex the Ai algorithm or required insight the more computing power is required.
How can it be deployed?
Edge Ai computing is deployed through the IoT device that is being used to analyse the activity and situation or through a processing unit close to the IoT device, allowing all devices to connect to one processing unit.
As mentioned before there are a couple of critical requirements for the successful deployment of Ai on the edge:
Sensor Device: these are devices such as cameras, heat sensors, robots and the list goes on. It’s critical that these sensors have the capacity to capture adequate data for the Ai algorithm to do its job well. For example, cameras need adequate frame speed and pixelation for computer vision to be accurate.
Computing Power: this is usually in the form of Graphical Processing Units (GPUs), to run AI applications, naturally the heavier the Ai algorithm/s the more processing power is required. Nowadays these GPU units are smaller and more powerful so they can easily be attached to a sensor.
Network Connection: To shift the data to the end-user or cloud the edge sensor needs to be connected to a network such as Wifi. Obviously the more data or the more complex the data the more bandwidth is required to ship the data. 5G offers great advancements in this field.
Naturally it goes without say, these critical requirements are also the restricting factors for edge Ai. If the capturing sensor isn’t adequate the Ai processing will have a reduced accuracy, if the processor is limited then the complexity of the Ai algorithm is reduced and if the network connection is inadequate the data will not flow easily.
What are the benefits of edge Ai?
So let’s look at the advantages of Ai on the Edge in comparison to Cloud processing.
One of the most significant advantages of Edge Computing over Cloud Computing is reduced latency. In edge computing, the data is processed locally, which means that the response time is much faster compared to cloud computing, where the data has to be sent to a centralised location for processing. This reduced latency is particularly important in applications such as autonomous vehicles, where real-time decisions need to be made based on the data received from sensors.
With Edge Computing, the data is processed locally, which means that sensitive data does not need to be transmitted over the network, reducing the risk of data breaches. This is particularly important in applications such as traffic monitoring, where citizen data needs to be kept secure and confidential.
Edge Computing can be more cost-effective than Cloud Computing, particularly in applications where large amounts of data need to be processed. With Edge Computing, the data is processed locally, which means that the cost of transmitting data over the network is reduced. Additionally, Edge Computing can reduce the need for expensive data storage and processing equipment, as the processing power and storage capacity can be distributed across multiple devices.
Edge Computing can be more reliable than Cloud Computing, particularly in applications where the network connection may be unstable or unreliable. The fact that the data is processed locally, means that the processing can continue even if the network connection is lost. This is particularly important in applications such as industrial automation, where the loss of network connectivity could lead to costly downtime.
Edge Computing can enhance privacy, particularly in applications where data privacy is critical. The sensitive data can be kept on the device and not transmitted over the network, reducing the risk of data breaches. This is particularly important in applications such as video surveillance, where privacy concerns are a major issue.
With Ai growing in its capability to analyse more use cases, increased accuracy and the combination of powerful edge sensor devices, we will see more applications of Ai in our day-to-day world. And Ai at the Edge will provide a growing channel for more reliable insights, faster, with better privacy and security.