AI FUNDAMENTALS

Neural Network

AI Fundamentals

Definition

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.

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.

AI Fundamentals

Weight

In a neural network, weights are the numerical values associated with connections between neurons that determine the importance of each input. During training, weights are continuously adjusted to minimize the model's error. A model's 'weights' are essentially its learned knowledge and are saved in files that can be gigabytes in size.

AI Fundamentals

Parameter

Parameters are the internal numerical values that an AI model adjusts during training to improve its performance. A model with more parameters is generally more capable but also more expensive to run. GPT-4 has hundreds of billions of parameters, while smaller and more efficient models can be run on local hardware.

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

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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