edge-artificial-intelligence

What Is Edge Artificial Intelligence?

Edge artificial intelligence (edge AI) combines artificial intelligence and edge computing to create AI workflows that span from centralized data centers to the edge of the network.

Understanding edge artificial intelligence

While most AI applications are developed and run entirely within the cloud, edge AI advocates for workflows that span from centralized data centers in the cloud to endpoints which can include various user devices. 

Edge AI combines edge computing and artificial intelligence to enable computation and data storage to be as near the point of request as possible. This results in numerous benefits: 

  • Reduced bandwidth consumption.
  • Lower latency. 
  • Fewer weight and size constraints.
  • High availability.
  • Improved security.
  • Improved model accuracy.
  • Real-time analytics, and
  • Reduced costs (compared to cloud-based AI).

To deliver these benefits, edge AI runs machine learning algorithms at the edge of the network so that information and data can be processed in IoT devices directly. Edge AI does not require a private data center or central cloud computing facility and can even be run on existing CPUs and less capable microcontrollers (MCUs).

How does edge AI technology function?

AI utilizes deep neural network (DNN) structures to replicate human cognition and intelligence. These networks have been trained to answer specific questions by being exposed to variations of the question and the correct answers. 

Training a model in this way requires vast amounts of data that are often stored in a data center or the cloud, and the process of training and configuring the model sometimes requires collaboration between data scientists. Once the model has been trained, it becomes an inference engine that can answer real-world questions.

With edge AI, the interference engine runs on an IoT device. When artificial intelligence identifies a problem, data is uploaded to the cloud to further train the model. The model then replaces the less-refined inference engine at the edge, creating a feedback loop where the edge AI model (and thus the device) becomes smarter over time.

As this process occurs, there is no human involvement. 

Edge AI use cases

Edge AI can be found in almost any industry, but here are a few common use cases.

Manufacturing

Edge AI is used in manufacturing to allow for better control over critical assets and also to incorporate predictive maintenance into operations. In the case of the latter, sensor data can predict when a machine will fail and can alert management to the fact.

Autonomous vehicles

The ability of edge AI to process data in real time is critical to the viability of autonomous vehicles. These vehicles cannot rely on cloud-based AI since it can often take seconds for the data to be processed.

On the road and especially in terms of collision avoidance, these few seconds may be the difference between life and death for the passenger.

Entertainment

Edge AI is also useful in the context of VR, AR, and mixed reality. The size of VR glasses that stream video content can be reduced by transferring computational power to edge servers located near the device.

Microsoft’s HoloLens 2 is an AR headset with a holographic computer that is currently being used by clients in manufacturing, engineering, construction, education, and healthcare to increase efficiency and reduce costs.

Edge Artificial Intelligence and Decentralized AI

The edge artificial intelligence paradigm might help the development of AI in a more decentralized manner.

Indeed, the primary risk of a large AI industry is the development of a system that is too centralized. This happens, especially if AI models can access the users’ data, at any time, with the justification of delivering a real-time, hyper-personalized experience.

Instead, with Edge Artificial intelligence, the hyper-personalized experience can be delivered on the edge of the network, as the AI model, only accesses the data of the user on the fly, through the device, with the data that never leaves the device, thus enabling the AI model to deliver highly personalized, contextual experiences.

The user enjoys these experiences, while the data never leave the user’s device, thus being more privacy-focused.

With this kind of network, the central players will need to take care only of the pre-training of the large generative model and a system of identity verification, which is privacy-oriented.

Key takeaways

  • Edge artificial intelligence (edge AI) combines artificial intelligence and edge computing to craft AI workflows that span from centralized data centers to the edge of the network.
  • While most AI applications are developed and run entirely within the cloud, edge AI advocates for workflows that span from centralized data centers to endpoints which include various user devices.
  • Edge AI runs machine learning algorithms at the edge of the network so that information and data can be processed in IoT devices directly. This creates several benefits like reduced latency, enhanced privacy, and reduced bandwidth consumption.

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