Edge AI is the abbreviation of edge artificial intelligence. It is the next development frontier of the Internet of Things system. So, what is edge artificial intelligence? How to realize edge artificial intelligence? Follow the editor to read below Let’s read this article!
Simply put, edge artificial intelligence refers to the application of artificial intelligence directly on edge devices Artificial intelligence is used in the form of machine learning algorithms that run. Machine learning is a broad field that has made tremendous progress in recent years. It is based on the principle that computers can autonomously improve their performance on a given task by learning from data, sometimes even beyond human capabilities.
Today, machine learning can perform many advanced tasks, including but not limited to:
● Computer vision: image classification, object detection, semantic segmentation.
● Speech recognition, natural language processing, chatbots, translation.
● Weather and stock market prediction and recommendation system.
● Anomaly detection and predictive maintenance.
So machine learning has been around for so long, what makes edge AI suddenly so special? To explain this better, let’s first look at what edge in edge AI actually means .
Essentially, edge computing and cloud computing are designed to do the same thing, which is to process data, run algorithms, etc. However, the fundamental difference between edge computing and cloud computing is where the computing actually occurs.
In edge computing, information processing occurs on-site and on distributed IoT devices actively deployed or at the edge. Some examples of edge devices are smartphones, and various microcontrollers. However, in cloud computing, the same information processing occurs in a centralized location, such as a data center.
Traditionally, cloud computing has dominated the IoT space. Since it is powered by data centers that naturally have greater computing power, IoT devices at the edge can simply transmit local data and maintain their key characteristics of low power consumption and affordability. While cloud computing remains a very important and powerful tool for the IoT, edge computing has received increasing attention recently for two important reasons.
● Hardware on edge devices becomes more powerful while remaining cost competitive.
● Software is increasingly optimized for edge devices.
This trend is making huge headway, and it is now possible to run machine learning on edge computing devices, whereas machine learning has long been reserved for cloud computing due to high computing requirements! So, edge computing Artificial intelligence was born.
With edge artificial intelligence, IoT devices are becoming increasingly smarter. Through machine learning, cutting-edge devices are now able to make decisions, make predictions, process complex data, and manage solutions.
For example, edge IoT devices can process operating conditions and predict whether a machine will malfunction, which allows companies to perform predictive maintenance and avoid greater losses and costs in the event of a complete failure.
On the other hand, security cameras equipped with edge artificial intelligence will no longer just capture video, but will be able to recognize humans and calculate foot traffic. Or, with facial recognition, it's even possible to identify exactly who passed through a certain area and when.
As machine learning advances, many exciting possibilities will now extend to edge devices as well. But the key to this paradigm shift is clear, and there are reasons why cloud computing capabilities are more marginalized than ever.
The most direct benefit of processing information at the edge is that there is no need to transmit data between clouds. Therefore, delays in data processing can be significantly reduced.
In the previous example of preventive maintenance, edge AI-enabled devices will be able to respond immediately, such as shutting down damaged machines. If we use cloud computing to execute machine learning algorithms, we will lose at least one second in the transmission of data to the cloud. While this may not sound important, when it comes to operating critical equipment, every achievable margin of safety is worth pursuing!
In Less data is transmitted between edge IoT devices, resulting in lower network bandwidth requirements and therefore lower costs.
Take the image classification task as an example. Due to reliance on cloud computing, the entire image must be sent for online processing. But if you use edge computing instead, you no longer need to send that data. Instead, we can simply send the processed result, which is typically several orders of magnitude smaller than the original image. If we multiply this effect by the number of IoT devices in the network, it could be as many as thousands or more.
Reducing data transmission to external locations also means fewer open connections and fewer opportunities for cyber attacks. This allows edge devices to operate securely, avoiding potential interception or data leakage. Additionally, since data is no longer stored in a centralized cloud, the consequences of a single breach are greatly mitigated.
Due to the distributed nature of edge artificial intelligence and edge computing, operational risks can also be distributed throughout the network. Essentially, even if a centralized cloud computer or cluster fails, individual edge devices are able to maintain their functionality because the computing process is now independent of the cloud! This is especially important for critical IoT applications, such as healthcare.
While the practical benefits of edge AI are clear, its intrinsic impact may be more elusive.
Edge artificial intelligence represents the first wave of truly integrating artificial intelligence into daily life. While AI and machine learning research has been around for decades, we are only now starting to see their practical applications in consumer products. Self-driving cars, for example, are the product of advances in edge artificial intelligence. Edge AI is slowly but surely changing the way we interact with our environment in many ways.
The use and development of artificial intelligence is no longer exclusive to research institutions and large enterprises. Because edge AI is designed to run on relatively affordable edge devices, it’s easier than ever for anyone to understand how AI works and develop one that works for them.
More importantly, edge AI enables educators around the world to bring artificial intelligence and machine learning into classroom learning in tangible ways. For example, by providing students with hands-on experience using edge devices.
It is often said that the potential of artificial intelligence and machine learning is only limited by human creativity and imagination, but as machine learning becomes more and more As we become more advanced, many tasks that were once only possible by humans will become automated, and our inherent concepts of productivity and purpose will be seriously challenged.
While I can’t be sure what the future will bring, I am generally optimistic about what edge AI will bring because I believe it will push us to find more creative and fulfilling jobs. For example, AI-embedded edge devices can monitor PPE in real time, including hard hat compliance in the work environment, and send safety and maintenance signals to anyone who violates PPE. Computer vision combined with machine learning can automate the PPE compliance monitoring process.
Another example is that AI-integrated cameras can alleviate bottlenecks and choke points in cities that often hinder traffic. Traffic jams occur mainly due to ignoring certain factors, such as the distance between two moving vehicles, traffic lights, street signs, pedestrians at intersections, etc. Intelligent transportation systems are the main application areas of computer vision, including vehicle classification, traffic violation detection, traffic flow analysis, parking lot detection, license plate recognition, pedestrian detection, traffic sign detection, collision avoidance, road condition monitoring, etc., as well as in-car driver attention force detection.
Through this article, I believe everyone has understood what edge artificial intelligence is and what it means for the Internet of Things and even the future of mankind. I hope that the above can give you some help.
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