Edge AI

As artificial intelligence (AI) evolves and adapts to become a more integrated part of our everyday lives, edge AI enables and furthers its growth. Integrated into smart objects and internet of things (IoT) devices, edge AI has spread within certain industries to make tasks more efficient and to speed business operations and processes.

What is edge AI?

Edge AI definition: Edge AI combines edge computing, artificial intelligence and machine learning to carry out tasks on connected devices.

Edge AI, also called AI at the edge, is a technology that performs tasks directly on devices that support local processing. That is, edge AI processes data closer to where it is generated, on smart devices themselves. This local processing reduces latency and enhances privacy, differentiating edge AI from traditional AI, which relies on centralized data centers. Since the term “the edge” is vague and does not convey any specific location, it provides context to the fluidity and flexibility of edge AI.

Edge AI provides real-time data to these smart devices, boosting their productivity and user assistance capabilities in applications such as intelligent vehicles, smart home appliances, wearable devices, smartphones, industrial IoT devices, healthcare equipment and retail systems. Given this diverse ecosystem, edge AI can deliver faster, more efficient and context-aware services across various domains.

This technology is intertwined with our day-to-day lives, including traffic lights, devices in hospitals and stores, and phones. Essentially, it can be used by any device that is connected to the internet and has local processing capability.

How does edge AI work?

Edge AI, like many artificial intelligence technologies, aims to produce humanlike intelligence to perform its functions properly. It uses neural networks within the data structure to replicate human cognition.

A crucial component of edge AI is deep learning, which involves training data to ensure accuracy. This training is especially important for edge AI since it operates with real-time data where errors can be especially significant. To avoid such errors, the model must undergo extensive training.

Once trained, the model becomes an inference engine, capable of running on local devices where edge AI is deployed. When the model encounters issues, it sends these back to the original AI model via the cloud for retraining. This continuous learning loop helps edge AI achieve and maintain high.

What is the history of edge AI?

The history of edge AI is closely linked to the development of edge computing, which emerged in the 1990s. As wireless networks grew in prominence and mobile devices became more integral to our daily lives, the need for interconnected and highly usable devices grew. This demand led to the development of edge computing.

Simultaneously, AI technology was advancing and being increasingly integrated into our devices. In the 2010s, edge AI brought these two emerging technologies together. Their integration has led to devices that use AI to operate efficiently and provide users with the best possible experience through real-time data processing and decision-making.

What are key types of edge AI?

While edge AI is a subset of artificial intelligence, a number of AI models are used to make up edge AI. Machine learning, neural networks and deep learning are all vital to the edge AI process.

• For smart devices that use edge AI to work effectively and to the best of their ability, machine learning teaches devices to learn from data that it has processed. Additionally, machine learning that works with edge AI enables it to analyze large amounts of data easily.
• Neural networks help devices with integrated AI know how to answer a particular type of question. Edge AI needs neural networks to function correctly because certain questions posed to smart devices require answers that include real-time data and information.
• Deep learning is essential to a multitude of complex applications, including edge AI. Without deep learning, it would be harder for defects in the data to be found, so smart devices would fail to work effectively.

How is edge AI used?

Edge AI intersperses technology seamlessly within our daily lives, allowing us to stay better connected in more intuitive ways. Beyond mobile smartphones, smart devices at home are one of the most prominent uses of edge AI. Increasingly, smart home appliances and devices are available to the public to simplify tasks and make existing technology simpler to use. Semiautonomous vehicles are another growing use case for edge AI. Edge AI helps improve the accuracy and efficiency of these vehicles and reduces the risk of safety and security issues. Since AI devices do not need to access a remote server to provide any information, homeowners’ and drivers’ data and privacy are kept safe.

The healthcare industry has also become more efficient and effective through edge AI. Patient data is a major security concern, with large amounts of sensitive, private data accessed remotely. Smarter edge AI technology bolsters this security. Additionally, with wearable devices being worn by more patients, edge AI technology alerts healthcare workers more quickly in emergencies.