AI FUNDAMENTALS

Parameter

AI Fundamentals

Definition

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.

Related terms

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

Training

Training is the phase in which an AI model learns from data, adjusting its parameters to improve performance on a specific task. It can require enormous computational resources and large amounts of data. SMEs rarely train models from scratch: they use pre-trained models via fine-tuning or prompt engineering, drastically reducing costs and time.

AI Fundamentals

Foundation Model

A foundation model is a large-scale AI model trained on vast and diverse data that can be adapted to multiple specific tasks. GPT-4, Claude, and Gemini are foundation models. Instead of building a model for every need, companies can start from a foundation model and specialize it for their requirements through fine-tuning or prompt engineering.

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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