If you are slightly confused, we get it. For more than a decade, IT architects and software giants alike have been advocating the heavenly advantages of computing in the Cloud. Today, we are here to tell you that there is a lot to be said for a more down-to-earth approach as well. We want to describe the advantages of Edge AI—which essentially combines the advantages of local computing with the benefits of cloud computing for your AI setup.
If you are wondering how Edge AI can improve your business—or how devices such as Robovision Edge can help manufacturers and other companies scale faster—read on: this article is for you!
What is Edge AI?
Edge AI is a combination of two concepts that mesh very well with each other: edge computing and artificial intelligence (AI).
Put simply, edge computing refers to processing and storing data locally in a device that sits between two networks (typically a local network and the internet), instead of in some form of centralised cloud infrastructure. The edge device can still periodically use the Cloud to transfer data to and from it, but does not require a constant connection. Edge computing is therefore indispensable for applications that cannot rely on 24/7 connectivity and need real-time data processing.
Edge computing is indispensable for real-time and low-latency applications.
AI models are trained algorithms that output a prediction based on an input. These models still need to be integrated into applications that can actually act on these predictions. Traditionally, AI applications are developed and run on the cloud in what is called ‘cloud AI’. Cloud AI uses a centralised platform environment to do everything, from training to deployment. This works very well for many use cases. However, there are obvious limits to cloud-based AI. The two most important downsides include the need for constant connectivity and the high latency, with noticeable response delays to and from the cloud.
The two most drawbacks of cloud-based AI are the need for constant connectivity and the high latency.
Moving the AI algorithm from the cloud into an edge device solves these two problems at once. Called Edge AI, it is the logical solution for AI-enabled smart applications that need to respond in real time and with low latency. This approach comes with its own fair share of benefits for manufacturers.
In fact, you have probably already encountered Edge AI in some shape or form: a smart traffic camera, a self-driving cars or even your smartphone’s virtual-voice assistant.
Benefits of Edge AI
Low Latency
With Edge AI, the processing happens close to the source where the data is generated. The resulting low latency has made many smart applications possible that would otherwise have stayed firmly in the realm of science fiction. Self-driving cars are a classic example. Edge computing makes it possible to process camera input in real time, which is vital since any delay in reaction time can lead to fatal consequences in traffic. Other applications, such as face recognition or fingerprint verification, would be running unbearably slowly if it were not for Edge AI.
Less Bandwidth and Storage
By processing the data locally, away from the cloud, edge applications hold much less bandwidth. The intelligent data capturing capability in edge devices will only select the input samples that are interesting to improve AI models further. As a result, the device only needs to transfer the training and retraining data to the cloud while discarding the rest, reducing storage space and decreasing business costs.
Increased Reliability
For consumers, connectivity issues are an inconvenience. For manufacturers, downtime often translates to significant losses. By removing the need to talk to the cloud 24/7, edge devices rely less on internet connection to perform their duties. This way, they minimise the impact of cloud, bandwidth and network issues for manufacturers when they do pop up. And thanks to the introduction of 5G mobile networks, edge devices have become faster and more reliable than ever before.
Data Privacy and Security
Data privacy and security are a key issue for businesses that deal with consumer data. But as AI applications become more mainstream, sensitive user data is increasingly at risk of being breached or lost. Privacy and security have therefore been a long-standing concern for cloud computing. By storing the data on the edge device itself, user information remains local and decentralised. With edge AI, manufacturers can assuage privacy concerns and minimise the risk of data leaks and security breaches.
Lower Power Consumption
Training AI is computationally very intensive. Running the actual trained AI model is not. Edge AI enjoys the best of both worlds. It runs AI models locally on an edge device from near the source where the input data is generated, while the heavy-duty computations needed to train these models are done on the cloud. As a result, intelligent edge devices consume little power when operating, which reduces power costs and removes the need for a continuous power supply in some cases. This energy efficiency is crucial for battery-powered applications such as autonomous drones.
Continuous Improvement
Operators and managers need to maintain and improve AI models over time with representative training data if they want to keep their AI applications well-oiled. Having a structured approach to do so is vital to the success of any smart automation project: the inability to maintain AI models in production is the leading cause of failure in AI projects. That’s why some edge devices like our proprietary Robovision Edge device provide a very handy, streamlined way of capturing production data and uploading them to a central platform to retrain the AI models. This enables smart applications to perform their task with consistently high accuracy and handle changing production data.
Some edge devices like our Robovision Edge device provide a streamlined way of retraining the AI models.
Robovision: the Safe Bet
Of course Cloud AI is here to stay: its benefits for training and retraining huge datasets are evident. However, in production environments where speed, data security and low latency are key and when connectivity is not always guaranteed, moving part of the AI to the Edge will give you the best of both worlds.
When it comes to AI, living at the edge may actually be the safest bet you can make.