AI Glossary
The complete dictionary of Artificial Intelligence
Edge AI Processing
Artificial intelligence processing performed directly on edge devices, enabling local decisions without depending on the cloud for minimal latency.
Embedded Intelligence
Integration of machine learning capabilities directly into embedded systems, enabling autonomous intelligent processing without external infrastructure.
Low-latency Processing
Processing of sensor data with minimal delay between capture and analysis, essential for real-time critical applications.
Sensor Analytics
Advanced analysis of data from sensors to extract relevant information, detect trends, and support automated decision-making.
Edge Optimization
Adaptation of AI models and algorithms to function efficiently within the computational and energy constraints of embedded devices.
Continuous Learning
Ability of edge systems to continuously improve by adapting to new sensor data patterns without requiring complete retraining.
Edge-to-Cloud Architecture
Hybrid architecture combining fast local edge processing with centralized analysis and storage in the cloud for distributed intelligence.
Edge Model Compression
Techniques for reducing the size of AI models to optimize their deployment on edge devices with limited resources.
Sensor Data Pipeline
Structured processing flow from sensor data capture to their analysis and exploitation, including preprocessing, inference and post-processing.
Edge Intelligence
Capability of edge systems to perform complex cognitive operations locally, including perception, reasoning and autonomous decision-making.
Real-time Decision Making
Automatic generation of decisions based on instantaneous analysis of sensor data, allowing immediate reactions to environmental changes.