The transformative power and capabilities of artificial intelligence have improved the convenience of business operations and the organization's return on investment. Now, edge artificial intelligence represents the next stage of technological development. Artificial intelligence (AI) has been stable in companies around the world for a long time.

The transformative power and capabilities of artificial intelligence have improved the convenience of business operations and the organization's return on investment. Now, edge artificial intelligence represents the next stage of technological development. Artificial intelligence (AI) has been stable in companies around the world for a long time.

Although artificial intelligence has some weaknesses, it continues to have a positive impact on the work quality and productivity of many organizations around the world. Artificial intelligence systems in any industry can be automated on a large scale, whether it is healthcare, national defense or e-commerce.

What is artificial intelligence at the edge?

  Edge artificial intelligence consists of AI models and algorithms that process data locally on independent hardware devices. In short, artificial intelligence technology is localized, smaller in scale, and easier to use by ordinary people. The AI algorithms in such devices use locally generated data for real-time machine learning. The data to be processed locally is sent and received through transmission signals and sensors on such devices. This "endpoint" artificial intelligence system does not need to be digitally connected to the cloud to perform tasks and operations locally. Instead, they have the ability to independently process data and make decisions. As mentioned earlier, edge computing brings the power of artificial intelligence to your personal device, requiring a built-in microprocessor and receiver to obtain processable data.

The benefits of edge AI

   First of all, we must understand the difference between edge computing and decentralized computing.

   Whether edge AI is better than its conventional counterparts is debatable, because they all perform slightly different tasks with the seamless efficiency and speed that we associate with AI. Therefore, comparing them may not be a simple task. Most importantly, edge artificial intelligence is an evolved version of its predecessors. Here, we will see some of the main qualities of edge AI.

 A) Reduce costs and bandwidth requirements

   Cloud-based artificial intelligence systems use large amounts of data to operate and require large bandwidth to operate normally. Therefore, for organizations that rely heavily on artificial intelligence for daily operations, the costs associated with data and bandwidth usage are usually high. Edge AI keeps data processing locally on the device. Therefore, the bandwidth usage of edge artificial intelligence devices will not be as high as that of devices using traditional cloud artificial intelligence. Therefore, the bandwidth cost can be controlled. More importantly, edge AI users can also get results faster because their networks and devices have very low network traffic.

b) Greater autonomy and performance of terminal equipment

  One of the main characteristics of edge AI is that it provides higher independence for all endpoint devices. As mentioned earlier, such devices do not need to be connected to a central server to operate. Therefore, the speed and efficiency of such equipment is always high. An example of this quality is the autonomous driving system in cars on busy roads. The artificial intelligence in this system is highly automated and can be corrected and adjusted in real time when driving an unmanned vehicle through any type of road, without being affected by external factors. Machine learning in edge AI devices is usually implemented in real time.

In addition, compared to devices driven by standard AI, devices that support edge AI show a higher level of responsiveness and performance. As we now know, edge AI computers process data locally, eliminating the delay of sending data back and forth from cloud-based infrastructure. Therefore, the endpoint performance is stronger and the delay is minimal.

c) More data privacy

   Needless to say, data privacy and security are important parameters in modern computing. The possibility of data loss transmitted through various communication channels in the cloud computing network always exists. In this case, the main cause of data leakage is the absolute distance between two or more data points. Therefore, organizations that use cloud computing and artificial intelligence solutions need to make every effort to ensure that their data is effectively protected. Generally speaking, edge computing reduces the chance of data leakage or leakage due to local processing of data. In addition, users can also set restrictions on who can access the data stored in their personal devices. Therefore, edge AI is a safer choice for user data processing.

Application of edge artificial intelligence

   Now that we have seen the advantages of edge AI solutions compared to traditional or cloud-based computing systems, the following are some common practical applications of edge AI today:

  1) Audio analysis system

Recognizing the audio input and processing the data in it are two key requirements for many devices today. Audio analysis can be used for various purposes, such as recognition and access management (IAM) or voice recognition of driving commands in mobile phones or luxury cars. Deep learning and edge AI are applied to noise reduction equipment to help the system carefully analyze various sound triggers and eliminate them.

Another example of artificial intelligence affecting audio analysis is an accident prevention system installed in a car, which can detect approaching vehicles through visual effects and sounds (even in severe interference and background noise) based on computer vision, and take preventive measures Protect people in the car. In addition, human speech analysis is an important part of audio analysis. Artificial neural networks and natural language processing (NLP) tools can be configured to train edge-based AI models in language and keyword recognition. This function can be used to execute voice command requests issued on these devices.

   In addition, applications such as text-to-speech conversion can also be implemented in edge artificial intelligence systems, and vice versa. Finally, the audio analysis of edge AI is also used in AI-driven chatbots. Essentially, the localized data processing capabilities of edge AI make it possible to implement these functions in independent devices in the real world.

2) Smart energy system

   Applications such as interconnected wind farms can be conceptualized and seamlessly implemented through edge AI. Generally, if a pure cloud system is used for this purpose, the cost of running such a system will be high. In contrast, even if a combined cloud edge system is used for computing operations, the cost of data procurement, management, and processing can be controlled. Wind farms require endpoint-based solutions because they use multiple surveillance cameras, access sensors, and biometric security sensors for employees working near wind turbines. These devices and sensors must operate efficiently and process data at lightning speed in order for other wind farms to operate successfully. Therefore, edge AI solutions can be used to reduce the cost of wind power generation systems and reduce overall processing time and the amount of bandwidth used.

 3) Visual entertainment system

  Edge AI is widely used in modern visual entertainment systems, including augmented reality, virtual reality and mixed reality. For these types of systems, data processing and AI analysis must be performed locally to save time and cost. AR systems require users to wear virtual reality or 3D glasses to fully enjoy the visual immersive experience. Since the computer system is processed offline through a dedicated edge server, edge computing and artificial intelligence can reduce the size of the glasses.

4) Smart speakers and home assistants

   Smart home assistants such as Amazon's Alexa and Google Home are very popular in today's world that relies on artificial intelligence. As you might expect, such devices and systems use edge AI enhanced speed and data mobility to make the smart home concept more feasible and implementable.