No matter how powerful AI is, it still relies on remote cloud servers for all the complex processing. It is not capable enough for situations and issues that need instant responses. So what to do? Developers introduced Edge AI as a wonderful solution to this concern. It is artificial intelligence combined with edge computing that runs directly on the device rather than in the cloud.
Edge AI, also called artificial intelligence at the edge, involves running machine learning (ML) and AI algorithms locally on physical devices like smartphones, wearables, sensors, or smart cameras rather than depending on a centralized cloud server or remote data center.
Edge AI is where a developer deploys AI models and algorithms directly on edge devices. These are devices at the network’s periphery, adjacent to where data is generated and actions need to be taken. The tech range in edge is vast, from IoT sensors to powerful servers. Example devices include:
Sounds familiar? That’s because they are.
Recent advancements in AI, like smaller, more efficient language models such as GPT-4o Mini, have sped up this shift to Edge AI. This is because such devices lower the computational, memory, and power needs to run AI at the edge. Overall, there are many benefits of Edge AI post-implementation. I will cover that in a later section.
Read Also: What is Generative AI?
As I mentioned before, wherever you look, there is an application of Edge AI. This advancement in artificial intelligence has multiple applications and benefits across industries and everyday life. Some of them include:
You may have noticed from the above examples that Edge AI is making life and professions more efficient, productive, accurate, and faster. That’s why I think Edge AI is relevant now.
Edge AI comprises 3 key components. They are edge devices, AI models, and communication simultaneously. Let me simplify them first before taking you through the behind-the-scenes of Edge AI.
This is the hardware part of AI on the edge. The vast range of Edge AI devices can have powerful servers that can handle large data loads. There are also resource-constrained IoT sensors for particular tasks, on the other hand. Let me remind you of these devices again:
These devices run locally for real-time data processing that boosts the speed a lot (more on that in another section).
Developers implement different AI models on the edge. These include deep learning, machine learning, and computer vision algorithms.
Communication protocols like MQTT and REST APIs bridge the gap between the Cloud and Edge devices. Here’s some useful information about these protocols:
Also Explore: What is AI Infrastructure?
Just like it has 3 key components, Edge AI works in 3 simple phases. These are data collection, data processing, and finally, real-time action.
What the Edge devices do is simply and continuously collect data from multiple sources, steadying the stream of information. These can be IoT sensors, cameras, or anything that understands the language of 0s and 1s. The collected data for real-time data analysis can be in the form of:
An everyday example of this is how your smartwatch collects the number of steps you walk.
The next step is for the edge AI models to locally process the collected data. The model analyzes the data to extract:
This ensures quick decision-making. For instance:
The last step is for the edge devices to take the right action based on the output from the AI models. Here are a few real-life examples for clarity.
This ability to act fast is quite handy in situations where instant responses can be the difference between life and death. For example, security breaches and medical emergencies.
The powerful and advanced technology that goes into it has made it very convenient for our professional and everyday life. Here’s what I think can be the benefits of Edge AI.
The power usage decreases since data is processed locally instead of on the Cloud. This saves organizations large expenses, which they can use to implement new technologies, renovate their offices, and grow.
Local data processing on the edge has one more benefit. It reduces the data transmitted over the internet, which frees up the bandwidth. The network can manage more data traffic this way.
Organizations combine smart-integrated hardware with cloud-based platforms with the help of Edge intelligence to scale AI workloads. An advantage of this is that companies can add devices and expand operations without hindering the network. That’s not all. The edge devices run independently even when the network is down.
Users get faster responses since all the processing is happening on the devices themselves. That’s because there is no need to fetch the results from a server every time the user enters a query.
Real-time actions and processes accelerate the speed a lot. This is mandatory because for example, a self-driving car must identify and avoid an obstacle instantly. This is exactly the same as human reflexes in certain situations.
I can conclude so far that Edge AI does not depend on a constant internet connection. It will also work fine even when your internet connection is unstable, which makes it more reliable.
Cloud-based AI services are heavy on your pockets. Especially for workloads needing high computing power. However, you have the option to use cloud resources as repositories for data that doesn't require immediate action when it comes to Edge AI. What it does is reduce the load on the cloud platforms and network, ultimately decreasing costs.
Everything happens locally, according to how Edge AI works. The system controls the devices in real time. This takes automation to new heights. For example, driverless cars in food delivery and checkout-free counters at shops.
Read Also: Gemini vs. ChatGPT: Which AI is Better?
AI on the edge is everywhere around us. It is making our personal and professional lives more convenient and modular, and is saving us a lot of time in our busy lives. Here are some real-world uses of Edge AI.
Healthcare services such as hospitals have become more responsive, efficient, and quicker in attending to emergencies with Edge AI. Here are a few examples for a better understanding.
Manufacturing units become more efficient due to implementing AI on the edge. Sensors and AI models will perform predictive analytics to detect if a machine will become faulty. This ensures that a solution is found before it’s too late.
IoT sensors combined with Edge AI make your house automated and slightly self-sufficient. It’s because of Edge AI that you are able to unlock doors with your voice, change screens with hand gestures, and control your AC from anywhere.
Intelligent cities consist of smart homes, buildings, and traffic management. Here are a few examples to make my point clear.
Automation in retail reduces unnecessary hard work, confusion, and effort. Checkout-free counters, automated stock management, and automated thief alarms are all examples of Edge AI uses in this sector.
Edge AI enables automated organization of vehicles and traffic. Driverless Cars and modern AI-powered cars can automatically detect obstacles on the road and decrease the speed, switch lanes, or even brake the vehicle to a halt. Here are a few more real-life examples:
Also Explore: Windsurf AI: Agentic Code Editor
Edge AI can not be said to be perfect for many reasons. It also comes with many limitations and challenges that might make you struggle.
For example, a retail chain owner managing multiple stores must manage multiple Edge AI devices simultaneously and remotely. This gets frustrating and time-consuming for one person because edge computing is already advanced. Plus, you are adding the complexity of AI to it.
Another concern is implementing Edge AI in different environments. For example, the system must work efficiently in challenging temperature conditions of construction sites. That means that the edge device can’t falter in extreme heat. However, given that it has a high processing load, there is a higher chance of that happening.
One of the biggest challenges of Edge AI is running complex AI models on devices with limited processing power, memory and storage. Complex optimization solutions are needed to sort this problem out. This will cost you resources and money because it’s a high-level skill and the developers and engineers will surely ask for a higher pay for their work.
It ensures long battery life while maintaining performance is crucial for implementing Edge AI. However, that’s one of the biggest limitations and challenges of Edge AI. Creating power efficient devices that don’t hinder execution are also expensive.
Data security and privacy concerns have always been there since the implementation of the first AI tool. Edge AI is not different. Here, the concern is even bigger because sensitive data is processed directly on the edge device. The risk of data breach is greater unless there are strict security measures.
Traditional intelligent tools such as generative AI models collect data from servers and then show the output to the user. These AI systems are slightly slower compared to Edge AI because the latter collects data, processes it, and takes action in real-time. Another difference is that Edge AI also implements edge computing techniques and principles, making it more advanced.
As you already know, Edge AI enables the offline collection and processing of data on the spot to take action in real-time. Edge AI is very fast because there is no need to transmit data to and from servers. However, it can not handle extreme data workloads and becomes complex when scaling across multiple systems.
That’s where Distributed AI comes in. Also known as DAI, it works together with Edge AI. While the former helps scale the system across multiple devices, the latter executes faster data collection and real-time processing.
Cloud computing combined with Edge computing and AI gives many benefits. Here are a few real world examples for you.
Read Also: What is an AI Engineer?
What I have discussed so far about Edge AI is amazing. However, that’s just the surface level. With advancements in AI and technology, Edge can completely revolutionize how the world works. Despite its limitations, Edge AI won’t just stop at driverless cars and automated doors. The future where robots and humans work together creatively is still far, but it is surely going to happen. It’s only up to you whether you will embrace it or be embarrassed by it.
That wraps up the article for today. Edge AI, which combines edge computing with artificial intelligence, takes machine smartness to the peak. Whether it's driverless cars, automated traffic light management, or remote patient data monitoring, Edge AI has applications all around us.
This technology has modernized personal and professional lives, adding comfort, convenience, and modularity to it. Hard work has been replaced with smart work and so on. But, on the other side of the wall, Edge AI might steal our jobs in the future. What do you think?
Ans. I have mentioned the 7 types of AI below.
Ans. Here are a few real world examples of Edge AI.
Ans. Edge AI implementation will cost a hobbyist only a few 100$. However, single-device setups can cost around $30000. Overall, the most expensive Edge AI setup is the one that is implemented in enterprises for around $500000.
Ans. These 3 companies lead the market in Edge AI hardware and silicon.
