Algorithm
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.
No jargon, no math
In plain words, what is it?
An algorithm is a recipe with zero judgement. You give it an input, it runs the steps in order, hits a decision, follows the branch, and hands back an output. For the same input it always returns the same output, and that is exactly the point. A route sorter, a checksum, a search: all fixed rules written by a human in advance. Machine learning flips the deal: there a human writes the algorithm that learns, and the steps inside get shaped by the data. Classic algorithms fail when the problem changes shape and the written steps no longer cover it. They never improvise.
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Input. You hand over the data the recipe expects: a list, a number, an image.
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Steps. It executes the rules in order. No improvisation, no judgement.
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Decision. A condition is checked and the path splits: yes or no.
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Output. A result comes back, identical for identical input.
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Done. It halts, or loops back to take the next input.
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.
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.
ReadTraining
Training is the process that turns an untrained model into a working AI: you feed it thousands or millions of examples, compare its guesses with the correct answers, and nudge its internal settings until the guesses get good.
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