Semi-supervised learning combines a small amount of labelled data with a large amount of unlabelled data. The model first learns from the few examples with known answers and then uses patterns in the remaining data to improve – for instance by adopting confident predictions as additional training examples. This pays off when labelling is expensive, such as with medical images or thousands of support tickets.
In supervised learning, a model learns from examples for which the correct answer is already known, such as emails labelled “spam” or “not spam”. From these it derives rules to classify new, unseen cases or to predict values. Typical tasks are classification and regression; most of the effort goes into creating cleanly labelled data.
In unsupervised learning, a model receives data without predefined correct answers and searches for structures on its own. Typical methods are clustering, which groups similar records, and dimensionality reduction, which condenses many features into a few essential ones. Organisations use it to discover customer segments or spot unusual transactions; the groups found must then be interpreted by domain experts.
Data scarcity occurs when there is too little suitable or labelled data for an AI task to train or test a model reliably. It affects, for example, rare defect types in production, niche languages or new products without a sales history. Remedies include transfer learning, synthetic data, data augmentation or pre-trained language models that deliver useful results from just a few examples.
Training data are the examples from which an AI model learns patterns: large text collections for language models, labelled images for image models. Their quality, diversity and recency largely determine how reliable and fair a model is and how up to date its knowledge will be. For organisations it also matters whether a provider uses the customer data they enter for further training.
