Edge AI: Five trends to watch

Artificial intelligence at the edge is constantly evolving and has countless applications, including self-driving cars, art, healthcare, personalized advertising, and customer service. Ideally, edge architecture provides lower latency due to being closer to requests.

It is predicted that the edge artificial intelligence market will grow from US$1.4 million in 2021 to US$8 million in 2027, with a compound annual growth rate of 29.8%. Much of this growth will come from factors such as artificial intelligence for the Internet of Things, wearable consumer devices, and the need for faster computing in 5G networks. These bring opportunities and retention, as edge AI’s real-time data is vulnerable to cyberattacks.

Let’s take a look at five trends that are likely to shape the field of edge AI in the next year.

Decoupling AI from the Cloud

One of the big changes today is the ability to run AI processing without a cloud connection. For example, two new chip designs recently released can increase the processing power of IoT devices to the extreme, skipping remote servers or cloud computing. Their current Cortex-M processor can handle object recognition, while other features like gesture or voice recognition come into play with the addition of ARM's Ethos-U55. Google's Coral, a toolkit for building products using native AI, also promises to handle large amounts of AI "offline."

Machine Learning in Action

Best practices for machine learning operations will prove edge AI is a valuable business process. IT production needs a new life cycle - or, at least, that's the speculation during the development of MLOps. MLOps can help enterprises stream and push data to the edge. As more enterprises discover what works best for them when it comes to edge AI, a continued refresh cycle may prove effective.

Specialized Chips

To do more processing at the edge, companies need custom chips to deliver enough power. An example is an AI accelerator chip paired with a software suite that essentially converts AI models into computational graphs. IBM released their first accelerator hardware in 2021 aimed at fighting fraud.

New use cases and capabilities for computer vision

Computer vision continues to be one of the top uses of edge AI. A major development in this field is multimodal artificial intelligence, which pulls data from multiple data sources, goes beyond natural language understanding, analyzes gestures and performs inspection and visualization. This could come in handy for AI that interacts seamlessly with people, such as shopping assistants.

Higher-order vision algorithms can now classify objects by using finer-grained features. It goes deeper to determine the make and model than it does to identify the car.

Training a model to identify granular features unique to each object is difficult. However, methods such as feature representation using fine-grained information, segmentation to extract specific features, algorithms to normalize object poses, and multi-layer convolutional neural networks are all current ways to achieve this goal.

Initial enterprise use cases include quality control, real-time supply chain tracking, using snapshots to identify internal locations and detecting deepfakes.

The growth of artificial intelligence on 5G accelerates

5G and more advanced technologies are coming. Satellite networks and 6G await telecom providers. For the rest of us, it will take some time to transition between the 4G core network that is compatible with some 5G services before fully moving into next-generation networks.

What does this have to do with edge artificial intelligence? AI on 5G can bring better performance and security to AI applications. It can provide some of the low-latency benefits required for artificial intelligence and unlock new applications such as factory automation, toll collection and vehicle telemetry, as well as smart supply chain projects.

There are more emerging trends in edge AI than we can list. In particular, its development may require some changes on the human side. Edge AI management will become the job of IT departments, and costs can be optimized by using IT resources rather than letting lines of business manage edge solutions.

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