AI Glossary•اے آئی لغت

A category of artificial intelligence that can create new, original content such as text, images, video, music, or code based on patterns learned from training data.

An AI model trained on massive amounts of text data to understand, generate, predict, and translate human-like language in a contextual and coherent manner.

A branch of AI focused on building systems that learn from data, identify patterns, and make decisions with minimal human intervention.

A specialized subset of machine learning based on multi-layered artificial neural networks designed to mimic the neural processing power of the human brain.

A computational system inspired by the structure and function of biological brains, consisting of interconnected nodes (neurons) that process data in layers.

The art and science of structuring, phrasing, and optimizing inputs (prompts) to guide Generative AI models into producing highly accurate and relevant outputs.

Taking an already pre-trained AI model and training it further on a smaller, specialized dataset to optimize its performance for a specific task.

A field of AI that trains computers to interpret and understand visual information from the world, including digital images and real-time videos.

The ability of a computer program to understand, interpret, and manipulate human language as it is spoken and written.

A hypothetical form of AI where a machine possesses the ability to learn, understand, and apply intelligence across any intellectual task at a level equivalent to or exceeding a human.

A phenomenon where an AI model generates highly confident but factually incorrect, false, or completely fabricated information in its response.

A breakthrough deep learning architecture introduced in 2017 that processes words simultaneously, allowing models to understand context and relationships in text much faster.

A machine learning method where the model is trained on a labeled dataset, meaning each training example is paired with its correct output label.

An area of machine learning where an AI agent learns to make decisions by performing actions in an environment and receiving rewards or penalties in return.

A modeling error in AI training where a model learns the training data and noise too perfectly, making it perform poorly on new, unseen datasets.