The use of technology to monitor, control, and execute tasks and processes automatically with minimal human input.

When an AI model's internal workings and decision-making processes are not easily understood or explained, even by its creators.

The process of generating new AI models by combining existing models or algorithms to create more advanced systems.

An AI model's act of identifying patterns in input data to predict categories for new, unseen data points.

An AI model's act of identifying groupings and patterns in data without explicitly defined criteria.

An AI model's ability to analyze and interpret visual data from cameras or sensors.

The capability of an AI system's hardware (e.g., GPUs and TPUs) to perform complex computations required for training and running the model efficiently. Crucial for the performance of machine learning algorithms, especially deep learning models.

The capability of an AI system's hardware (e.g., GPUs and TPUs) to perform complex computations required for training and running the model efficiently.

A way of training an AI model to self-critique and revise its responses to align with a predefined set of rules or principles based on human values.

A way of training an AI model to self-critique and revise its responses to align with a predefined set of rules or principles based on human values. Aims for the model to be harmless and self-improve without human labeling of harmful outputs.