AI Glossary

Application Programming Interface - a way to interact with AI models programmatically.

The simulation of human intelligence in machines programmed to think and learn like humans.

A technique that allows models to focus on relevant parts of the input when producing output.

Systematic errors in AI predictions, often reflecting biases present in training data.

The maximum number of tokens a language model can process in a single interaction.

A subset of machine learning based on artificial neural networks with multiple layers.

A dense vector representation of words or tokens that captures semantic meaning.

The process of further training a pre-trained model on a specific dataset for a particular task.

An optimization algorithm used to minimize the loss function by iteratively adjusting model parameters.

When an AI model generates false or nonsensical information that appears plausible.

The process of using a trained model to make predictions on new, unseen data.

AI models trained on vast amounts of text data to understand and generate human-like text.

A subset of AI that enables systems to learn and improve from experience without being explicitly programmed.

The branch of AI focused on enabling computers to understand, interpret, and generate human language.

A computing system inspired by biological neural networks, consisting of interconnected nodes (neurons).

When a model learns training data too well, including noise, reducing its ability to generalize.

The practice of designing and optimizing input prompts to get desired outputs from AI models.

A parameter that controls the randomness of model outputs; higher values mean more creative/random results.

The basic unit of text that language models process, typically words or subwords.

A neural network architecture that uses self-attention mechanisms, foundational for modern LLMs.