Since its discovery, artificial intelligence (AI) has evolved to cater to the ever-growing demand of users. And one of the notable developments in the field is edge AI.
Note that like any new technology such as cloud computing in its infancy, AI also faces tons of challenges related to overloading and data security. That is what edge AI aims to address. It hopes to allow users to run AI processes without worrying about data transmission slowdown and privacy. Edge AI is enabling self-driving cars and smart devices to rapidly act upon data inputs. Find out how in this post.
To better understand what edge AI is, though, we first need to define one of its underlying technologies—edge computing.
What is Edge Computing?
Edge computing is the process of making computation and data storage more accessible to users. It runs operations on local devices such as a computer, an Internet of Things (IoT) device, or a dedicated edge server. Unlike the cloud, it is unaffected by latency and bandwidth issues that can hamper performance.
What is Edge AI?
Edge AI is the combination of edge computing and AI. The concept pertains to running AI algorithms on a local device that has edge computing capacity. Since edge AI does not require systems to connect to others, it allows users to process data in real-time.
At present, most AI processes are carried out using cloud-based centers that need substantial computing capacity and so are prone to downtime. Edge AI makes these processes part of an edge computing device’s workflow, allowing users to curate the data first before it gets sent to a different location, saving time.
Now that you know what edge AI is, you may be asking what it is for. Read on to find out.
What are Current Applications of Edge AI?
Here’s a list of some real-life applications of edge artificial intelligence.
Self-driving vehicles use edge AI devices that can process data within the same hardware. An autonomous car requires immediate data processing, such as recognizing oncoming vehicles, identifying traffic signs, and looking out for pedestrians and other road hazards to ensure the safety of both the people in and outside it. Edge AI allows autonomous vehicles to collect and process all necessary inputs in real-time.
Surveillance and Monitoring
Edge AI is also beneficial for security cameras as these no longer have to upload raw video signals to a cloud server for processing. Edge AI-capable security cameras can use machine learning (ML) algorithms to process captured images and videos locally. This process allows the devices to track and monitor several people and items directly. Footage would only be transmitted to a cloud server when necessary, thus reducing remote processing and memory consumption.
Industrial Internet of Things
The Industrial IoT (IIoT) highly depends on automating manufacturing and operational processes to increase productivity. Using edge AI allows IIoT devices to do visual inspections and carry out robotic control faster and at lower costs.
What Benefits does Edge AI Provide?
Here’s a list of the benefits that edge artificial intelligence brings its users.
Data charges depend on bandwidth use. By keeping AI processing to a local machine, users can reduce data communication costs since they no longer have to transmit data to another device for analysis. Users get results faster, too.
Cloud users are often afraid of losing transmitted data along the way. Edge AI lessens the chances of data leakage or loss because the processing occurs locally. Users can thus control or limit who has access to information better.
Real-time processing is one of the most robust features of edge AI. It allows users to collate, process, and analyze data then implement solutions in the fastest way possible, making devices highly useful for time-dependent applications.
Edge AI has made notable contributions to some industries so far but it still has room to grow. At its infancy, edge AI-capable machines still cost a lot to manufacture and so they remain on the pricey side. In the future though, we may see edge AI system prices fall but for now we have to wait.