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      "text": "Model: once a ML algorithm has been trained on data, the output of the process is known as the model. This can then be used to make predictions.\nSelf-supervised learning (SSL): a form of unsupervised learning, where manually labeled data is not needed. Raw data is instead modified in an automated way to create artificial labels to learn from. An example of SSL is learning to complete text by masking random words in a sentence and trying to predict the missing ones.\n(Large) Language model (LM, LLM): a model trained on textual data. The most common use case of a LM is text generation. The term “LLM” is used to designate multi-billion parameter LMs, but this is a moving definition.\nComputer vision (CV): enabling machines to analyse, understand and manipulate images and video.\nTransformer: a model architecture at the core of most state of the art (SOTA) ML research. It is composed of multiple “attention” layers which learn which parts of the input data are the most important for a given task. Transformers started in language modeling, then expanded into computer vision, audio, and other modalities.",
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