Artificial intelligence explained and applied: AI, machine learning, LLMs, generative AI and inference in real operations.
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AI accident reconstruction turns the raw accelerometer data that a vehicle's GPS tracker sends after an impact into a clear, objective impact report available in real time. Mative Cloud isolates a ±5-minute window around the event, a Machine Learning model classifies the side of the impact (front, rear, left or right) with an accuracy exceeding 95%, and an LLM turns that result into a readable narrative for car rental and fleet operators.
AI inference is the process through which an artificial intelligence model applies what it has learned during training to make predictions, classifications or decisions based on new input data. Unlike training, which is computationally heavy, takes hours or days and runs in data centers, inference is lighter, takes milliseconds and runs in the cloud or on edge devices such as smartphones and IoT sensors.
Artificial intelligence (AI) is the broadest field: any technology that lets machines simulate human intelligence. Machine learning (ML) is a subset of AI in which algorithms learn from data without being explicitly programmed, large language models (LLMs) are AI models trained on huge amounts of text to understand and generate natural language, and generative AI is the branch of AI that creates original content such as text, images, music or video.
Machine learning pays back by cutting operating and management costs. It automates manual operations, predicts machine failures before they occur, optimizes energy use through sensor data and intelligent HVAC systems, and lowers transport and storage costs by optimizing routes and forecasting demand accurately.