AI Vocabulary · 01 The Model
What is Machine Learning?
Software that learns patterns from examples instead of following rules written by hand.
Traditional software follows rules a programmer writes: if the invoice is over 10,000, ask for approval. Machine learning works the other way round. You show the system many examples, and training adjusts it until it can make good predictions on new cases it has never seen.
Machine learning has been in everyday products for years: spam filters, product recommendations, fraud detection, route planning. Today's generative AI is a newer, much larger form of it, usually built on neural networks.
Why it matters for you
Machine learning is only as good as its examples. If the data is thin, outdated or one-sided, the system learns those flaws, which is one source of bias. Asking "what was it trained on?" is often the most useful question about any AI product.
Own it
Your own historical data, such as decisions, tickets, documents and outcomes, is the raw material for machine learning. Keeping it clean, documented and under your control is what lets you use it later, with whichever tool you choose.
Where it comes from
IBM researcher Arthur Samuel coined "machine learning" in 1959, in a paper about teaching a computer to play checkers. His program improved by remembering board positions and, in later versions, by playing thousands of games against itself, until it reached a respectable amateur level. Learning from experience instead of following hand-written rules is still the core idea. Arthur Samuel (Wikipedia)
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