Machine Learning
Machine learning is the practice of building software that learns patterns from data instead of following rules a programmer wrote by hand. Modern AI, including large language models, is built with it.
In traditional software, a programmer writes the rules: if the transaction is over this amount and from a new country, flag it. In machine learning, you show a model many examples, such as transactions labeled fraud or not fraud, and a training process adjusts the model until its predictions match the examples. The rules end up encoded in the model’s learned parameters rather than written down anywhere.
Deep learning is the branch of machine learning that uses neural networks with many layers, and it is behind almost all recent AI progress. Large language models are deep learning models trained on text. The practical consequence for anyone using them is the same as for any machine learning system: the model is only as good as the data it learned from, and it can be confidently wrong on cases unlike its training data.
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