AI FUNDAMENTALS

Multimodal

AI Fundamentals

Definition

A multimodal AI model can process and generate different types of data: text, images, audio, video. For example, it can analyze a photo of a defective component and describe the problem in text, or read a scanned document and extract structured information. Multimodality opens scenarios such as automated visual quality control in factories.

Related terms

Computer Vision

Computer vision is the field of AI that enables computers to interpret images and video. It includes object recognition, quality inspection, license plate reading, and security video analysis. In Italian manufacturing, computer vision automates visual quality control, reducing defects reaching the customer and speeding up production lines.

NLP & Language

Speech-to-Text (STT)

Speech-to-text is the technology that converts spoken language into written text. Modern systems achieve accuracy above 95% even in noisy environments. It is used to transcribe meetings, support calls, document dictation, and voice commands in factories. Combined with NLP, it enables automatic analysis of conversation content.

NLP & Language

OCR (Optical Character Recognition)

OCR is the technology that converts images of text (scanned documents, photos of invoices, delivery notes) into editable and searchable digital text. Modern deep learning-based OCR systems handle complex layouts, tables, and handwritten text. It is the first step to digitizing and automating document processes in SMEs.

EXPLORE

More terms in AI Fundamentals

Artificial Intelligence

Artificial intelligence is the computer science discipline that develops systems capable of performing tasks that normally require human intelligence: understanding language, recognizing images, making decisions. For Italian SMEs, AI represents a concrete opportunity to automate repetitive processes and achieve measurable competitive advantages.

Machine Learning

Machine learning is a subfield of AI in which systems automatically learn from data without being explicitly programmed for every scenario. ML algorithms analyze patterns in historical data to make predictions on new data. In business practice, it is used to forecast demand, classify documents, and optimize production processes.

Deep Learning

Deep learning is an advanced machine learning technique that uses neural networks with many layers (hence 'deep') to learn complex data representations. It underpins modern speech recognition, machine translation, and text generation systems. It requires large amounts of data but produces extraordinarily accurate results.

Neural Network

An artificial neural network is a computational model inspired by the workings of the human brain, composed of nodes (neurons) organized in interconnected layers. Each connection has a weight that is adjusted during training. Neural networks underpin nearly all modern AI systems, from image recognition to text generation.

Language Model

A language model is an AI system trained on enormous amounts of text to understand and generate natural language. Large language models (LLMs) such as GPT and Claude can write texts, answer questions, translate, and reason about complex problems. For businesses, they represent versatile assistants for any text-based task.

GPT

GPT (Generative Pre-trained Transformer) is the architecture behind ChatGPT, developed by OpenAI. It is a language model pre-trained on vast text corpora that can be used to generate content, answer questions, and perform complex text tasks. GPT-4 and subsequent versions have demonstrated increasingly advanced reasoning capabilities.

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