• Machine Learning Model: An algorithmic framework that allows a computer to learn from data and make predictions or decisions without being explicitly programmed for the specific task.

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• Training Data: The dataset used to train a machine learning model includes examples the model learns from to generate new content.

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• Neural Network: A series of algorithms modeled loosely after the human brain, designed to recognize patterns and interpret sensory data through machine perception, labeling, and clustering raw input.

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• Deep Learning: A subset of machine learning involving neural networks with many layers, allowing the model to learn complex patterns through large amounts of data.

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• Natural Language Processing (NLP): A field of AI that focuses on the interaction between computers and humans through natural language, enabling computers to understand and generate human language.

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• Predictive Model: A model that predicts unknown future events based on known data.

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• Creativity Algorithms: Algorithms specifically aim to generate creative and novel outputs that may include originality, non-obviousness, and value.

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• Transfer Learning: A machine learning method where a model developed for one task is reused as the starting point for a model on a second task, which is particularly useful in Generative AI for adapting a pre-trained model to generate new kinds of content.

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