• Artificial Intelligence (AI) – Technology that enables machines to mimic human intelligence.
• Machine Learning (ML) – A method where computers learn from data to make decisions or predictions.
• Neural Network – A computer system modeled after the human brain, used in AI to recognize patterns.
• Deep Learning – A type of ML using complex neural networks for tasks like image and speech recognition.
• Model – A program trained to perform specific tasks, like generating text or images.
• Training Data – Information used to teach an AI model how to perform tasks.
• Prompt – A user’s input or question that guides the AI’s response.
• Token – A piece of text (like a word or part of a word) used in AI processing.
• Natural Language Processing (NLP) – AI that understands and generates human language.
• Text Generation – Creating written content using AI based on a prompt.
• Image Generation – Creating pictures or artwork using AI.
• Chatbot – An AI program that simulates conversation with users.
• Large Language Model (LLM) – A powerful AI trained on vast amounts of text to understand and generate language.
• Fine-Tuning – Adjusting an AI model to perform better on specific tasks.
• Bias – Unfair or inaccurate results caused by flawed training data.
• Hallucination – When AI generates false or misleading information.
• Ethics – Moral principles guiding responsible use of AI.
• OpenAI – A leading company in generative AI research and development.
• Inference – The process of an AI model making predictions or generating content.
• Multimodal AI – AI that can understand and generate across different types of data (e.g., text, images, audio).

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