What is Edge AI Computing – Applications | Use Cases | Examples W

 

Recent advancements in AI, IoT devices, and edge computing are unlocking the potential of Edge AI, enabling breakthroughs like helping doctors detect diseases, powering self-driving cars, and even aiding plant pollination. Edge AI computing, which originated in the 1990s with content delivery networks, is now widely explored by experts and companies for its ability to process data faster using servers closer to users.

What is Edge AI Computing?

Edge AI refers to using artificial intelligence (AI) directly on devices like sensors or Internet of Things (IoT) devices instead of relying on cloud computing. This allows data to be processed and analyzed in real-time without sending everything to the cloud. Using AI on local devices speeds up the process and reduces the need for constant internet access.

 

With edge AI computing, data is stored closer to the device where it's created, and AI algorithms work right on the device. In other words, data can be processed in milliseconds, providing instant feedback. Whether or not there’s an internet connection, edge AI helps make decisions faster.

 

Technologies like self-driving cars, wearable devices, security cameras, and smart home appliances all use edge AI to provide real-time information to users. As more industries adopt this technology, they are finding new ways to improve efficiency, automate processes, and drive innovation, while also addressing concerns like security, latency, and costs.

AI Edge Computing Benefits 

In 2022, the global Edge AI market was valued at USD 14.8 billion and is expected to grow to USD 66.5 billion by 2023. This rapid growth is driven by the increasing demand for IoT-based edge computing services and the many benefits of Edge AI. Some of the key advantages of Edge AI include:

 

• Reduced Latency: With edge AI computing, data is processed directly on the device, eliminating the need to wait for data to travel to a distant server. This results in faster responses and no delays.
• Less Bandwidth Use: Edge AI reduces the amount of data sent over the internet. This saves bandwidth and helps networks handle more data simultaneously, making everything run more smoothly.
• Real-Time Analytics: Edge AI computing allows for instant data processing on devices, even without an internet connection. In other words, decisions can be made quickly, without needing to rely on remote systems.
• Better Data Privacy: Since data is processed locally, it doesn't need to be sent to other servers, which reduces the risk of data breaches. However, this is especially important for industries with strict data privacy rules.
• Scalability: Edge AI can easily grow by using cloud platforms and edge technology. Companies are integrating edge capabilities directly into their equipment, which makes it simpler to expand systems without losing efficiency.
• Lower Costs: Cloud AI services can be expensive. With Edge AI, the heavy lifting is done by local devices, reducing the need for costly cloud resources. In addition, this helps lower both hardware and operational costs.

 

Unlike traditional cloud computing, where all the work is done in a central location, Edge AI reduces the strain on networks and servers. This results in a smoother experience with less traffic and lower system demands. Plus, AI and edge computing can operate with less human intervention, which means fewer resources are needed to manage the system, leading to even more cost savings for businesses.

Edge AI Use Cases

Edge AI is becoming common in many areas. You can find it in smartphones, smartwatches, autonomous vehicles, and connected home devices. Many industries use edge AI computing to cut costs, automate processes, make better decisions, and improve operations.

 

• Healthcare: Edge AI is changing healthcare. It helps build smarter systems and keeps patient data private. For example, wearable devices like smartwatches can check heart rate, blood pressure, glucose, and more. They can also detect falls and alert caregivers. In emergencies, paramedics can use edge AI to get quick health data from wearable devices. This helps them get advice from doctors and prepare for treatment even before reaching the hospital.
• Manufacturing: Manufacturers are using edge AI technology to improve production. It helps monitor machines and predict when they might fail called predictive maintenance. Equipment sensors can detect issues and send alerts for repairs, preventing downtime. Edge AI also helps with quality control, worker safety, optimizing production, and supply chain management.
• Retail: With the rise of online shopping, physical stores are using new technologies. “Pick-and-go” stores, smart shopping carts, and automatic checkouts are some examples. These technologies use AI to improve the in-store shopping experience and make it faster for customers.
• Smart Homes: Smart homes are full of devices like doorbells, thermostats, fridges, and light bulbs. These devices use edge AI to make life easier. For example, you can check who's at the door or adjust your home temperature without sending data to the cloud, keeping your data safe and private.
• Security and Surveillance: In security, speed is key. Traditional systems often send video to the cloud for processing, which can be slow. Edge AI solves this by processing video locally, avoiding delays. Additionally, it can detect suspicious activity, alert users, and trigger alarms in real-time, making homes safer.

AI Edge Applications

Edge AI computing is used in many devices and systems. Some examples are facial recognition, real-time traffic updates in semi-autonomous vehicles, connected devices, and smartphones. It is also used in video games, robots, smart speakers, drones, wearable health devices, and security cameras.

 

Here are a few areas where Edge AI is growing:

 

1. Security Camera Detection: Traditional security cameras record footage and store it for later use. With Edge AI, cameras can process the footage in real-time. However, this allows them to detect and react to suspicious activity instantly, making security faster and cheaper.
2. Image and Video Analysis: Edge AI can help robots respond to images or sounds automatically. It can also be used to recognize and understand spaces or scenes in real time, like identifying objects or areas.
3. Industrial Internet of Things (IIoT): In manufacturing, Edge AI helps monitor machines for defects or issues. Furthermore, it can make quick changes to the production process to prevent problems and improve efficiency.
4. Real-Time Solutions in Healthcare: During the COVID-19 pandemic, AI was used to deliver accurate, real-time information. In healthcare, AI in medical devices helps monitor, test, and treat patients more effectively.

Edge AI Companies

Many companies are working on edge AI to create innovative solutions for businesses. Some of the biggest names include Google, Amazon, Microsoft, Facebook, Apple, Alibaba, Baidu, Salesforce, Intel, and IBM. These companies are helping push the limits of edge AI technology.

 

• Google has invested a lot in edge AI with its Edge TPU chips and ML Kit platform.
• Amazon offers AWS DeepLens, which helps developers build and deploy computer vision apps easily.
• Microsoft is a major player, providing Windows ML, which helps deploy AI models on devices with Windows 10.
• Facebook offers tools like PyTorch Mobile, a library of pre-trained models, and easy APIs to use machine learning on mobile devices.
• Apple is making progress with Core ML, one of the edge AI devices to build intelligent apps.
• Alibaba has the X-Brain platform for edge computing.
• Baidu created DeepBench to measure AI performance on different devices.
• Salesforce uses edge AI to improve customer engagement platforms.
• Intel has an AI developer kit to help developers build powerful edge applications.
• IBM offers the PowerAI Edge platform for deploying deep learning apps at the edge.

Edge AI Examples

Here, we’ll elaborate on the examples of Edge AI Computing that will help you to sum up the entire blog.

 

• Autonomous Vehicles: These smart cars process data locally and stay connected to the cloud. They can still work even if they lose their internet connection.
• Smart Traffic Lights: It is part of the Internet of Vehicles (IoV). These traffic lights work with cars and emergency services to create safe, quick routes during emergencies.
• Health Monitors: Edge devices track a patient’s vital signs, helping with remote surgeries and diagnostics.
• Smartphones: Smartphones use edge AI to process data quickly right on the device.
• Wearable Health Devices: Smartwatches are a popular example of edge AI, monitoring health data on the spot.
• Smart Appliances: Devices like smart refrigerators and thermostats use edge AI to work more efficiently.

Wrapping Up!

Edge AI computing transforms how data is processed by enabling real-time decision-making on devices, without relying on cloud servers. In addition, this makes systems smarter, faster, and more efficient in areas like healthcare, manufacturing, and transportation. It reduces delays, protects privacy, and boosts performance. Devices like self-driving cars, smart appliances, and health trackers can make quick, accurate decisions on their own. As demand for faster, safer solutions grows, Edge AI will become increasingly important in our daily use of technology.

 

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