Artificial Intelligence

A subset of AI in which algorithms improve their performance on a task through experience, without being explicitly programmed for every scenario. Machine learning systems identify patterns in data and use statistical models to make predictions or decisions.

Computing systems loosely inspired by the structure of biological neural networks, consisting of layers of interconnected nodes (neurons) that process information. Each connection has a weight that is adjusted during training to minimize prediction errors.

A subfield of machine learning that uses neural networks with many layers (deep architectures) to learn hierarchical representations of data. Deep learning excels at automatically extracting features from raw data without manual feature engineering.

The branch of AI concerned with enabling computers to understand, interpret, and generate human language. NLP combines computational linguistics with machine learning to process text and speech data at scale.

The field of AI that trains computers to interpret and understand visual information from the world, including images and video. Computer vision systems can detect objects, recognize faces, read text, and analyze scenes.

A type of machine learning where an agent learns to make decisions by performing actions in an environment and receiving rewards or penalties. The agent aims to maximize cumulative reward over time through exploration and exploitation.

A machine learning approach where the model is trained on labeled data, meaning each training example includes both the input and the correct output. The model learns to map inputs to outputs and generalize to unseen data.

A machine learning approach where the model is trained on data without labeled outputs, and must discover hidden patterns, groupings, or structures on its own. Common techniques include clustering and dimensionality reduction.

AI systems that can create new content such as text, images, music, code, or video by learning patterns from training data. These models generate novel outputs that resemble the data they were trained on, using architectures like transformers and diffusion models.

The study of moral principles and guidelines governing the design, development, and deployment of AI systems. AI ethics addresses concerns such as algorithmic bias, transparency, privacy, accountability, and the societal impact of autonomous systems.