At its most basic, an algorithm simply tells a computer what to do next with an “and,” “or,” or “not” statement. Think of it like math: it starts off pretty simple but becomes infinitely complex when expanded.
It’s important to point out that not all algorithms are related to AI or machine learning specifically, but for the purposes of this article we’ll focus on those that are.
When chained together, algorithms – like lines of code – become more robust. They’re combined to build AI systems like neural networks. Since algorithms can tell computers to find an answer or perform a task, they’re useful for situations where we’re not sure of the answer to a question or for speeding up data analysis.
As an example, imagine you have to sort through a million files for the word “blue.” Even if it only took you one second per file, you’d have to sort for over 11 days straight without stopping to sleep, eat, or use the loo. But, if you taught a computer to recognize the word “blue” using an algorithm, it could do the work for you – and given enough processing power and proper algorithmic-tuning, it could probably accomplish the task in a few seconds....read more>>>...