AI FUNDAMENTALS

Machine Learning

AI Fundamentals

Definition

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.

Related terms

AI Fundamentals

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.

Machine Learning

Supervised Learning

Supervised learning is the ML technique in which the model learns from labeled examples: for each input, the correct output is provided. For example, by showing thousands of emails labeled as 'spam' or 'not spam', the model learns to classify new emails. It is the most common approach in business applications because it produces reliable and interpretable results.

Machine Learning

Unsupervised Learning

Unsupervised learning is the ML technique in which the model finds patterns in data without predefined labels. The model autonomously discovers hidden structures such as groups of similar customers (clustering) or data anomalies. It is useful when you don't know what to look for: AI can reveal market segments or anomalous behaviors you hadn't considered.

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.

Deep Learning

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.

Transformer

The Transformer is the neural network architecture introduced by Google in 2017 that revolutionized AI. It uses a mechanism called 'attention' to process data sequences in parallel rather than sequentially. Virtually all modern language models (GPT, Claude, Gemini, LLaMA) are based on this architecture.

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