AI Glossary
The complete dictionary of Artificial Intelligence
Edge AI
AI processing directly on IoT devices to reduce latency and cloud dependency.
TinyML
Deployment of machine learning models on microcontrollers with extremely limited memory and computational resources.
IoT Anomaly Detection
Automatic identification of unusual behaviors or failures in real-time sensor data streams.
Multisensory fusion
Intelligent combination of data from multiple sensors to enhance the accuracy and robustness of analyses.
Federated IoT Learning
Distributed training of ML models on IoT devices without centralizing raw data to preserve privacy.
AI Energy Optimization
Techniques to minimize energy consumption of AI models on battery-powered IoT devices.
Intelligent signal processing
Application of AI to filter, denoise, and extract relevant information from raw sensor signals.
Predictive IoT Maintenance
Using AI to anticipate equipment failures by analyzing data from connected sensors.
Cognitive IoT
IoT systems capable of learning, reasoning, and automatically adapting to their environment without human intervention.
Quantification of IoT Models
Compression of deep learning models by reducing numerical precision to fit IoT device constraints.
Online IoT Learning
Continuous adaptation of ML models directly on IoT devices as new data arrives.
AI IoT Security
Protection of AI models and data in IoT ecosystems against attacks and breaches.
Intelligent data aggregation
Compression techniques preserving critical information during sensor data transmission to the cloud.
Real-time stream processing
Continuous and instant analysis of sensor data to make real-time decisions on IoT devices.
Continuous IoT model updates
Automated and progressive deployment of new ML model versions across distributed IoT device networks.