Artificial Intelligence
What is Artificial Intelligence?
Artificial Intelligence (AI) is the capability of machines to perform tasks that typically require human intelligence—such as learning, reasoning, and problem-solving. In Edge AI, these intelligent computations occur directly on devices (sensors, cameras, IoT gateways) rather than relying solely on cloud servers, enabling faster, safer, and more efficient decision-making.
Why is it Used?
AI at the edge is used to process data locally—reducing latency, enhancing privacy, and allowing real-time analytics even in environments with limited connectivity. This makes Edge AI ideal for industries like manufacturing, autonomous vehicles, healthcare, and smart cities, where instant insights drive performance and safety.
How is it Used?
AI models are deployed on edge devices that use embedded processors or microchips to analyze data instantly. Instead of sending all information to the cloud, Edge AI systems use machine learning (ML) and deep learning algorithms locally for applications like predictive maintenance, video analytics, voice recognition, and intelligent automation.
Types of Artificial Intelligence
Reactive Machines – Perform specific tasks without memory (e.g., image recognition).
Limited Memory AI – Learns from recent data for adaptive responses (e.g., autonomous navigation).
Edge Machine Learning – Lightweight ML models optimized for device-level execution.
Neural Network Accelerators – Specialized AI chips designed for edge inference.
Benefits of Artificial Intelligence
Reduced Latency: Real-time decision-making without cloud dependency.
Enhanced Privacy: Sensitive data stays on local devices.
Cost Efficiency: Lower bandwidth and cloud storage usage.
Reliability: Continuous operation, even offline.
Scalability: Deploy AI across thousands of distributed endpoints seamlessly.