AI Autonomous vehicle self-driving cars.
AI Autonomous vehicle self-driving cars.

What is Edge AI?

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Edge AI is artificial intelligence (AI) that performs data processing and analysis close to, or at, the data source. Edge AI is used in Internet of Things (IoT) devices and other resources with limited computational power or bandwidth.

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Edge AI aims to provide real-time insights and actionable intelligence without relying on the cloud or a data center. By using Edge AI, organizations can improve the responsiveness of their systems and decrease their reliance on cloud infrastructure.

How Edge AI Works

Edge AI usually relies on machine learning algorithms to learn from data. These algorithms can be trained on data sets either in the cloud or on-premises. Once the algorithm has been trained, it can be deployed to an Edge device, which will run autonomously.

The benefits of using Edge AI include reduced latency, increased privacy, and improved reliability. Edge AI can also help organizations reduce their costs by reducing their dependency on cloud infrastructure.

Types of Edge AI

There are three main types of Edge AI:

  • Visual
  • Audio
  • Text

Visual Edge AI

Includes applications that analyze images or video footage. Everyday use cases for visual Edge AI include security surveillance, facial recognition, traffic monitoring, and agricultural yield analysis.

Audio Edge AI

Includes applications that analyze sounds or speech. Everyday use cases for audio Edge AI include voice search assistants, speaker identification, and noise cancellation.

Text Edge AI

Includes applications that analyze text data. Everyday use cases for text Edge AI include sentiment analysis, optical character recognition (OCR), and chatbots.

Conclusion:

Edge AI is a type of artificial intelligence (AI) that processes data locally instead of relying on the cloud. Edge AI has numerous benefits over traditional cloud-based AI, including reduced latency, increased privacy, improved reliability, and lowered costs. Edge AI can be used for various applications, including security surveillance, facial recognition, traffic monitoring, agricultural yield analysis, voice search assistants, speaker identification, noise cancellation, sentiment analysis, optical character recognition (OCR), and chatbots.

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