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These terms relate to different forms of AI models, learning methodologies, and specific techniques used in AI development.

• AGI: Artificial General Intelligence. A type of AI with the ability to understand, learn, and apply knowledge across a broad range of tasks at a level comparable to human intelligence.
• Deep Learning: A subset of machine learning that uses neural networks with many layers to model and understand complex data patterns.
• Embeddings: Numeric representations of data that capture meaning and relationships.
• Fine-Tuning: The process of training a pre-trained AI model on a specific dataset to adapt it to a particular task or domain.
• GAN: Generative Adversarial Network. A type of neural network architecture where two networks compete, enabling the generation of realistic data.
• LLM: Large Language Model. Massive AI models trained on extensive text datasets, capable of generating, understanding, and reasoning with human-like text.
• Machine Learning: A branch of AI that enables systems to learn patterns and make predictions from data without explicit programming for each task.
• Multimodal AI: AI systems capable of processing and integrating information from multiple data types, such as text, images, and audio.
• Neural Networks: Computational models inspired by the human brain, consisting of layers of interconnected nodes that process data in a structured way.
• Parameters: The adjustable values within an AI model that the system learns during training to make predictions or decisions.
• Prompt: A text input or instruction given to an AI model to guide its response or behavior.
• Prompt Engineering: The process of crafting inputs to guide AI systems to produce desired outputs effectively.
• RAG: Retrieval-Augmented Generation. An AI approach combining information retrieval from external sources with text generation, enhancing accuracy and relevance.
• Reinforcement Learning: A type of machine learning where an agent learns to make decisions by performing actions in an environment and receiving rewards or penalties.
• Self-Supervised Learning: A method where AI learns by teaching itself, using data that is not fully annotated (labeled). It finds patterns and relationships in the data to create its own clues for training.
• Semi-Supervised Learning: Combines supervised and unsupervised learning by using a small amount of labeled data alongside a larger set of unlabeled data for training.
• Supervised Learning: A machine learning approach where models are trained on labeled data, learning to map inputs to known outputs.
• Transformers: Advanced neural network architectures designed to process sequences of data and excel in tasks like language translation and summarization.
• Unsupervised Learning: A learning approach where models analyze and find patterns in data without labeled outputs.
• Zero-Shot Learning: The ability of an AI model to perform tasks it has never explicitly been trained on, using generalized knowledge.