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Edge AI: A Step Towards Next Gen AI Applications

Ashesh Anand
Feb 15, 2023

What Is Edge AI?

Edge AI (also known as "edge computing") refers to the use of artificial intelligence (AI) and machine learning (ML) algorithms at the edge of a network, rather than in the cloud or a data center. Edge AI involves running AI and ML models on devices that are located close to the data source, rather than sending the data to a remote server for processing. This can enable real-time analysis and decision-making, as well as reduce latency and the amount of data that needs to be transmitted over the network.

Edge AI has often been used in IoT (Internet of Things) applications, where devices are distributed across a wide area and need to operate independently without a constant connection to the cloud. Examples of edge AI applications include self-driving cars, drones, industrial automation systems, and smart home devices. By running AI and ML models on these devices, they can make decisions and take action without the need for a constant connection to the cloud.

Edge AI can also be used in environments where data privacy and security are a concern, as it allows data to be processed locally rather than being transmitted to a remote server. This can help to protect sensitive data and reduce the risk of data breaches.

Also Read | Ransomware-as-a-Service (RaaS): A Serious Cyber Threat

How Does Edge AI Technology Work?

Edge AI technology allows devices to process and analyze data locally, rather than sending the data to a remote server for processing. This can be accomplished using various techniques, including the following:

Pre-processing: Some devices may have limited processing power or bandwidth, so it may be necessary to pre-process the data to reduce its size and complexity before sending it to the edge AI model. Pre-processing techniques can include filtering, sampling, and feature extraction.

Model deployment: Once the data has been pre-processed, the edge AI model can be deployed to the device. This can be done using various approaches, such as transferring the model to the device via a wired or wireless connection or installing the model on the device as part of the firmware.

Data analysis: The edge AI model can then analyze the data and make decisions or take actions based on the results. For example, a self-driving car might use an edge AI model to analyze sensor data in real-time to make driving decisions, such as when to turn or brake.

Post-processing: In some cases, the results of the edge AI model may need to be post-processed before they can be used by the device. For example, the results may need to be aggregated, filtered, or transformed in some way before they can be used by the device's actuators or other components.

Edge AI technology can enable real-time analysis and decision-making, as well as reduce latency and the amount of data that needs to be transmitted over the network. It is often used in IoT applications and other scenarios where low latency and data privacy are important.

What is Edge AI Hardware?

Edge AI hardware refers to the physical devices and components that are used to implement edge AI systems. These can include:

Sensors:

Edge AI devices often have sensors that collect data from the environment, such as cameras, microphones, and temperature sensors.

Processors:

Edge AI devices need processors to run the AI and ML models and perform data analysis. These processors can range from simple microcontrollers to more powerful processors such as GPUs (graphics processing units) or specialized AI processors.

Storage:

Edge AI devices may have onboard storage to store data and models, as well as software and other resources.

Networking:

Edge AI devices may have networking capabilities to allow them to communicate with other devices or send data to the cloud. This can include Wi-Fi, Bluetooth, or cellular connectivity.

Actuators:

In some cases, edge AI devices may have actuators that allow them to take actions based on the results of the AI and ML models. For example, a self-driving car might have actuators that control the steering, acceleration, and braking.

Edge AI hardware can vary widely depending on the specific application and requirements. In general, the hardware should be designed to be efficient, reliable, and able to operate in a wide range of environments.

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Use cases of Edge AI

Advantages of Edge AI:

Here are some key advantages of edge AI:

Low latency:

One of the main advantages of edge AI is that it can enable low-latency decision-making and actions. This is because the data is processed locally on the device, rather than being transmitted to a remote server for processing. This can be especially important in applications where real-time responses are critical, such as self-driving cars, drones, and industrial automation systems.

Reduced data transmission:

By processing data locally on the device, edge AI can also reduce the amount of data that needs to be transmitted over the network. This can help to reduce the load on the network and improve overall performance. In addition, it can also help to reduce data transmission costs, particularly in applications where data is transmitted over long distances or through cellular networks.

Improved data privacy:

Another advantage of edge AI is that it allows data to be processed locally, rather than being transmitted to a remote server. This can help to protect sensitive data and reduce the risk of data breaches. This can be particularly important in applications where data privacy is a concern, such as in the healthcare or financial industries.

Better reliability:

Edge AI can also improve the reliability of systems by allowing them to operate independently without a constant connection to the cloud. This can be important in applications where a consistent connection is not always available, such as in remote or rural areas.

Increased scalability:

Edge AI can also help to improve the scalability of systems by allowing them to process and analyze data locally. This can help to reduce the load on the cloud and improve overall performance, particularly in applications where there are a large number of devices generating data.

Improved cost efficiency:

In addition to reducing data transmission costs, edge AI can also help to improve the cost efficiency of systems by allowing them to operate independently without a constant connection to the cloud. This can help to reduce the need for expensive infrastructure and maintenance costs.

Enhanced security:

Edge AI can also improve the security of systems by allowing data to be processed locally rather than transmitted over the network. This can help to reduce the risk of data breaches and protect sensitive information.

In summary, edge AI can provide a number of advantages over traditional cloud-based approaches, including low latency, reduced data transmission, improved data privacy, better reliability, increased scalability, improved cost efficiency, and enhanced security. These advantages make edge AI an attractive solution for a wide range of applications, particularly in the Internet of Things (IoT) and other scenarios where low latency, data privacy, and reliability are important.

Also Read | Top AI Technologies Used in 2022

Use cases of Edge AI:

Edge AI is used in a wide range of applications and industries, including the following:

Internet of Things (IoT):

Edge AI is often used in IoT applications, where devices are distributed across a wide area and need to operate independently without a constant connection to the cloud. Examples of edge AI applications in the IoT include smart home devices, industrial automation systems, and wearable devices.

Transportation:

Edge AI is also used in transportation applications, such as self-driving cars and drones. In these applications, edge AI is used to analyze sensor data in real-time to make driving decisions and control the vehicle.

Healthcare:

Edge AI is also used in the healthcare industry to enable real-time analysis of patient data and make decisions about treatment. For example, an edge AI system might be used to monitor vital signs and alert healthcare providers if there are any abnormalities.

Retail:

Edge AI is also used in retail applications, such as automated checkout systems and inventory management systems. In these applications, edge AI is used to analyze data from sensors and cameras to identify and track products.

Manufacturing:

Edge AI is also used in manufacturing applications, such as predictive maintenance systems and quality control systems. In these applications, edge AI is used to analyze data from sensors and cameras to identify potential problems and improve efficiency.

Agriculture:

Edge AI is also used in the agriculture industry to optimize crop yields and reduce the use of pesticides and other chemicals. For example, an edge AI system might be used to monitor soil moisture levels and optimize irrigation schedules.

These are just a few examples of the many ways that edge AI is being used in a variety of industries. As the technology continues to advance, it is likely that edge AI will be used in an even wider range of applications.

Also Read | Types of Intelligent Agents in Artificial Intelligence

Edge AI vs. Cloud AI: Which One is Better for Security and Privacy?

Both Edge AI and Cloud AI can offer advantages in terms of security and privacy, depending on the specific application and requirements.

Here are some factors to consider when deciding which approach is better for security and privacy:

Data transmission:

Edge AI can offer improved security and privacy by allowing data to be processed locally, rather than being transmitted over the network to a remote server. This can help to reduce the risk of data breaches and protect sensitive information. On the other hand, cloud AI can offer improved security by storing data in secure, centralized servers that are managed by experienced IT professionals.

Data access:

Edge AI can offer improved security and privacy by limiting data access to the device on which it is processed. This can help to reduce the risk of unauthorized access to sensitive data. On the other hand, cloud AI can offer improved security and privacy by allowing data access to be controlled and managed through role-based access controls and other security measures.

Data storage:

Edge AI can offer improved security and privacy by storing data locally on the device, rather than on a remote server. This can help to reduce the risk of data breaches and protect sensitive information. On the other hand, cloud AI can offer improved security and privacy by storing data in secure, centralized servers that are managed by experienced IT professionals.

In general, both edge AI and cloud AI can offer advantages in terms of security and privacy, depending on the specific application and requirements. It is important to carefully consider these and other factors when deciding which approach is best for a given scenario.

End Note:

Edge AI is swiftly moving from being a preference to being a requirement for new goods and services that are entering the market, such as self-driving cars and smart home appliances. Edge computing enables algorithms to run and computer vision tasks like image segmentation to be processed locally on an IoT device rather than over the cloud.

Currently, edge AI offers considerable improvements including improved security, cost-effectiveness, and reliability on the system it is linked to, outweighing the drawbacks. Edge artificial intelligence offers a fail-safe computing method that is unaffected by network irregularities and data breaches that are more likely to occur in the cloud.

Those who aspire to develop extraordinary and new items or software today and in the future appear to prefer living life on the edge. You are now fully aware of what edge AI is and how it is revolutionizing business.

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Edge AI: A Step Towards Next Gen AI Applications

What Is Edge AI?

How Does Edge AI Technology Work?

What is Edge AI Hardware?

Sensors:

Processors:

Storage:

Networking:

Actuators:

Advantages of Edge AI:

Low latency:

Reduced data transmission:

Improved data privacy:

Better reliability:

Increased scalability:

Improved cost efficiency:

Enhanced security:

Use cases of Edge AI:

Internet of Things (IoT):

Transportation:

Healthcare:

Retail:

Manufacturing:

Agriculture:

Edge AI vs. Cloud AI: Which One is Better for Security and Privacy?

Data transmission:

Data access:

Data storage:

End Note:

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