AI alignment is the critical field of research aimed at ensuring artificial intelligence systems behave in accordance with human intentions and values. As AI capabilities rapidly advance, alignment efforts seek to address the fundamental challenge of creating powerful AI systems that reliably pursue intended goals while avoiding unintended or harmful outcomes.

AI annotation is the process of labeling and categorizing data to train machine learning models, enabling them to understand and interpret various forms of information such as text, images, and audio. According to Moveworks, this crucial step provides context for AI systems, allowing them to extract valuable insights and perform complex tasks like object recognition, sentiment analysis, and natural language understanding.

Regularization in AI is a set of techniques used to prevent machine learning models from overfitting, improving their ability to generalize to new data. According to IBM, regularization typically trades a marginal decrease in training accuracy for an increase in the model's performance on unseen datasets.

Hyperparameter tuning is a crucial process in machine learning that involves selecting the optimal set of external configuration variables, known as hyperparameters, to enhance a model's performance and accuracy. As reported by AWS, this iterative process requires experimenting with different combinations of hyperparameters to find the best configuration for training machine learning models on specific datasets.