AI glossary
- Artificial Intelligence (AI)
- Technology that enables computers to perform tasks that normally require human intelligence, such as understanding language, recognising patterns, or making decisions. Generative AI is a type of AI.
- AI dubbing
- Technology takes the original dialogue from a video and replaces it with a generated audio track in a different language, making it sound as if the characters are naturally speaking that language.
- AI literacy
- The skills and knowledge needed to understand and use AI tools effectively.
- AI music
- Technology where computers, not humans, use algorithms and artificial intelligence to create melodies, rhythms, and even entire songs. It can mimic various music styles, from classical to pop.
- AI slop
- Low-quality content created by generative AI which often contains errors and is not requested by the user
- AI text-to-speech
- Technology that converts written words into spoken words. This tool takes the text from websites, documents, or any digital text and turns it into audio.
- AI voice cloning
- Technology that can mimic a person's voice. By analysing recordings of someone's speech, AI learns to reproduce their way of speaking - their tone, accent, and even emotional inflections.
- AI voice generator
- This technology harnesses advanced algorithms and machine learning techniques to generates and synthesises human-like voices, acting as a digital vocal artist. It can generate voices of different pitches, accents, and even emotional tones.
- Algorithm
- A set of step-by-step rules or instructions a computer follows to solve a problem or complete a task.
- Bias
- Discriminatory or slanted responses that favour certain groups, ideas or types of information, mainly stemming from either skewed training data or societal norms that influence the AI's output.
- Critical AI literacy
- The ability to question and evaluate how AI works, including its biases, limitations, and social impacts. It’s about asking why the AI gave a certain result, what data it was trained on, who benefits, and what might be missing.
- Datasets
- Collections of information—such as text, images, or sounds—that generative AI learns from. They provide the foundation for what the AI can create. The scope and diversity of a dataset directly influence the quality and variety of the outputs.
- Deep learning
- A type of machine learning that uses large, layered neural networks to process complex patterns—especially in speech, images, or text.
- GenAI or Generative AI
- AI that creates new content, such as images, music, or text. Examples include DALL·E and ChatGPT.
- Hallucinations
- Instances where an AI system generates incorrect or nonsensical information.
- Heuristic
- A mental shortcut we use to make decisions quickly. In AI, we often apply heuristics without realising it—like trusting results with citations, assuming a polished output is correct, or going with the first answer a chatbot gives. Heuristics save time but can lead to blind spots, especially when dealing with complex or uncertain information.
- Inaccuracies
- Errors, distortions, fabrications or omissions in genAI outputs.
- Large Language Model (LLM)
- Models that generate human-like text by predicting language patterns based on massive datasets.
- Machine learning
- A form of AI where computers learn from examples rather than being given exact instructions. By finding patterns in data, machine learning systems can improve their performance on tasks over time.
- Malicious purpose
- Intentional use of artificial intelligence to create harmful content, going beyond unintentional errors, including generating deceptive information, manipulating data, or crafting content with the explicit aim of causing damage.
- Model versioning
- Keeping track of the different updates or releases of an AI model, like knowing which version you’re using.
- Natural Language Processing (NLP)
- Allows computers to understand and work with human language.
- Neural networks
- A type of artificial intelligence inspired by the way the human brain works, made up of layers of connected “nodes” (like artificial neurons) that process information and learn patterns, allowing computers to recognise things like images, speech, or text.
- Outputs
- The content an AI tool provides based on our prompts or inputs.
- Prompts or input
- Questions or instructions given to an AI system, guiding what it should focus on or generate.
- Prompt engineering
- The practice of designing and refining the instructions (prompts) we give to AI systems to get more accurate, useful, or creative responses.
- Retrieval-Augmented Generation (RAG)
- Combines Large Language Models (LLMs) with external sources of information to provide more accurate, current, and context-specific responses.
- Rules-based AI
- A system that makes decisions or solves problems by following a set of predefined rules written by humans.
- Semantic search
- An AI search tool that looks for results based on the meaning of a question, not just matching exact words.
- Synthetic data
- Data that's artificially created rather than collected from the real world.
- Training data
- The dataset used to teach an AI system.
- Transformer model
- A type of AI model designed to process language and other data by focusing on the relationships between words in context, enabling more accurate understanding and generation of text.
- Token
- A chunk of text, like a word or part of a word, that AI systems use to process language.
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