Frameworks and guidelines for the ethical development, deployment, and oversight of AI technologies.
Prejudice or favoritism towards certain outcomes, often reflecting societal, historical, or data biases.
An approach to machine learning where the model performs well on tasks with very limited labeled training data.
Large-scale models that serve as a base for building specific applications without starting from scratch. These models are pre-trained on extensive data and can be fine-tuned for particular tasks.
A subset of AI technologies that can generate new content, including text, images, and videos, that resemble human-like creations.
AI models that generate new data with the same statistics as the training set.
A type of machine learning model designed to understand, generate, and interact with human language at scale.
The skill of crafting inputs (prompts) to effectively communicate with AI models, particularly LLMs, to achieve desired outputs.
The practice of reusing a pre-trained model on a new, related problem. It allows for leveraging knowledge from one task to improve performance on another.
Used for generating complex data structures like images by learning a compressed representation of the data.
The ability of a model to correctly perform tasks it has never seen during training. It involves understanding tasks from descriptions and applying previously learned knowledge.
