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.
Synthetic data is generated artificially rather than collected in the real world, but mimics the patterns and properties of real data. It helps when genuine data is scarce, expensive or sensitive from a data protection perspective – for example for testing software, training models or checking a chatbot with realistic sample queries. It is important that it reflects reality closely enough and does not allow conclusions about real people.
In transfer learning, a model that has already learnt a large general task serves as the starting point for a new, specialised one. Instead of starting from scratch, it is adapted, for instance through fine-tuning, using comparatively little data of your own. This saves computing time and data, for example when a general image model is meant to learn to spot production defects in photos.
Few-shot learning is a model’s ability to solve a task from only a handful of examples. With language models this usually happens directly in the input: you show two or three pairs of question and desired answer, and the model applies the same format and style to new cases – without retraining. Example: three correctly categorised customer enquiries are often enough for further enquiries to be sorted the same way.
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.
