What is Edge AI?
Edge artificial intelligence (AI) is an extension of edge computing that enables the processing of data and algorithms directly from an endpoint device.
What does edge AI look like today?
Edge AI combines two emergent technologies: edge computing and artificial intelligence (AI). Whereas edge computing stems from the same general premise, in that data is generated, collected, stored, processed, and managed from a local location rather than a remote data center, edge AI further evolves the concept to the device level, using machine learning (ML) that mimics human reasoning to reach points of user interaction, such as a computer, edge server, or Internet of Things (IoT) device. In general, these devices don’t require an Internet connection to operate and can make decisions independently.
A widespread example of edge AI technology is a virtual assistant like Google Assistant, Apple’s Siri, or Amazon Alexa. From the moment a user says, “Hey,” these tools listen and learn from a user’s words (i.e., machine learning), interact with a cloud-based application programming interface (API), and store what they learn locally.
What might edge AI look like in the future?
While smart home appliances, smartphones, and wearables are among the most common examples, other, less consumer-grade examples include self-driving vehicles, drones, robots, and surveillance cameras enabled with some form of video analytics. In each instance, data is captured and used to make decisions in real- or near-real-time. Self-driving cars, for example, use visual data and other types of sensors alongside cloud computing to determine road conditions and make decisions quickly. These conditions could include nearby cars and inanimate objects, pedestrians, and inclement weather conditions simultaneously.
Overall, the field of edge AI has significant growth potential in the near future. Industry research estimates the global edge computing market size to grow to $61.14 billion USD by 2028.
What are the benefits of edge AI?
Compared to other forms of data processing, where data is sent to remote data centers or the cloud for processing, edge AI is more nimble and agile, offering faster, localized processing with less latency than traditional forms of cloud computing. Without as many bandwidth and data transmission limitations, quick response times can lead to improved user experience (UX), especially with wearable and mobile technologies where speed is essential. The ability to find useful answers, generate insights, and expedite transactions in mere seconds (or less) can lead to consumer preference and other competitive advantages.
Installing devices with pre-loaded algorithms reduces the need for a complex, Internet-enabled infrastructure, which can be costly and time-consuming to build and deploy. And without the need for massive amounts of data streaming at all times, the cost of data communication also goes down. Additionally, the autonomous nature of edge AI lessens the need for constant monitoring by data scientists. While human interpretation will always be a key factor in determining the ultimate value of data and the innovation it generates, edge AI platforms take over some of that responsibility, thereby reducing a company’s bottom line.
Less expensive edge infrastructures also make this technology more accessible and versatile. By eliminating several Internet-dependent prerequisites, edge AI can operate in areas with little to no network coverage.
With edge AI, data is no longer transmitted and shared through the cloud or remote servers; all processing and storage happens locally, making it more secure and private. This added level of protection is invaluable—even mission-critical—for specific workloads that require compliance with strict regulatory or compliance laws such as the Health Information Portability and Accountability Act (HIPAA).
How does edge AI work?
Edge AI is based on the tenets of standard ML architectures, in which AI algorithms are used to process data and generate responses based on certain factors. In the past, this involved sending data to a centralized data center via a cloud-based API, where it could be analyzed for insights. Often, transferred data capacity would be limited, making high-definition content like photo and video a significant obstacle.
But the rising prominence of IoT and smart devices has decentralized where data analysis can happen. Made with embedded microprocessors that contain the necessary algorithms, endpoint devices can now collect, interpret, and make decisions based on its intended programming in real time, regardless of how robust the data may be.
Edge AI and HPE
The edge is where the action is, and HPE is on the forefront of edge AI platforms and edge infrastructures. As demonstrated by being named by Compass Intelligence as the 2021 Industrial IoT Company of the Year, HPE is dedicated to helping enterprises and institutions unlock their full data potential at the edge, wherever that edge may be, and accelerate innovation. This innovation can take any form: remotely monitoring wildlife in order to better understand animal behavior, analyzing manufacturing performance for efficiency and proactively identifying potential risks before they occur, or even delivering a mobile-optimized stadium experience for fans of one of the English Premier League’s top football clubs.
Finding innovation at the intelligent edge begins with the right infrastructure, and HPE offers a wide portfolio of edge platforms. For instance, hardware like HPE Edgeline Converged Systems enable companies to shift to a distributed converged compute model, letting them gain access to the real-time, local decision-making that enables autonomous data processing and analysis, along with world-class security at all times.
Beyond hardware, services like Aruba Edge Services Platform (ESP) deliver the industry’s first scalable AI-powered, cloud-native platform to simplify challenges at the edge. Offering multiple management tools and increased visibility into all assets and edge locations, Aruba ESP lets users rapidly respond to changing business needs.