Artificial Intelligence
Artificial intelligence is software that performs tasks which normally need human judgement: understanding language, recognising images, planning, deciding. Modern AI is built by training models on data rather than hand-writing rules, which is why it copes with messy input a rigid program cannot.
No jargon, no math
In plain words, what is it?
AI is not one technology, it is a family. The member doing the work today is the model: a huge mathematical function tuned on examples. During training the model adjusts its internal numbers until its outputs match the right answers on the data you gave it. Afterwards you freeze it and use it: question in, answer out. Everything else you will meet in this glossary (retrieval, agents, guardrails) is plumbing around this core. The boundary matters: a model predicts patterns, it does not understand them. Where its training data is thin it can be confidently wrong, and it never knows what it does not know.
Terms that live nearby.
Machine Learning
Machine learning is a way of building software where the algorithm tunes its own internal numbers from examples, instead of a programmer writing every rule. You supply data and correct answers; the finished product is a model that predicts on inputs it has never seen.
ReadAlgorithm
An algorithm is a finite, unambiguous sequence of steps that solves a class of problems: a recipe that takes an input, applies rules, and always produces an output. Every piece of software, AI included, is algorithms all the way down.
ReadAI Model
An AI model is the finished artefact that comes out of training: a file of tuned numbers that maps inputs to predictions. You build it once and run it as many times as you like; running it is called inference.
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