|
Word |
Definition |
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Hallucinations |
When AI generates information or content that appears plausible but is entirely fabricated and incorrect. Always verify AI outputs for accuracy. |
|
Prompts |
The instructions or questions you provide to an AI tool to generate a response. Clear and specific prompts lead to better and more relevant outputs. |
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Bias |
The tendency of AI systems to produce skewed results based on the data they were trained on. Bias can result from unrepresentative training data and can affect the fairness and accuracy of AI outputs. |
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Training Data |
The dataset used to teach an AI model how to perform tasks. The quality and diversity of the training data significantly impact the AI’s performance and bias. |
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Large Language Models (LLMs) |
A type of AI that uses machine learning techniques to understand and generate human language. Examples include GPT-4 and BERT. They are trained on vast amounts of text data to predict and generate coherent language. |
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Generative AI |
AI systems that can create new content, such as text, images, or music. These systems use patterns learned from training data to generate original outputs. |
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Machine Learning |
A subset of AI that involves training algorithms to learn from data and make predictions or decisions without being explicitly programmed for every task. |
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Natural Language Processing (NLP) |
A field of AI that focuses on the interaction between computers and human language. It enables machines to understand, interpret, and generate human language. |
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Neural Networks |
A series of algorithms that mimic the operations of a human brain to recognize patterns and solve common problems in AI, particularly in image and speech recognition. |
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Inference |
The process by which an AI model makes predictions or generates outputs based on new input data, using the knowledge it has learned during training. |
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Overfitting |
A modelling error in machine learning where a model learns the training data too well, including noise and outliers, which negatively impacts its performance on new, unseen data. |
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Deep Learning |
A subset of machine learning that uses neural networks with many layers (hence “deep”) to analyze various factors of data. It is particularly effective in tasks like image and speech recognition. |
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Ethical AI |
The study and practice of designing and deploying AI systems in ways that ensure they are fair, accountable, transparent, and respect user privacy and other ethical considerations. |