A clear and finite set of step-by-step instructions used by computers to solve problems, make decisions, or process data is called an algorithm.
Suppose you want to bake a cake. You cannot just throw eggs and flour into an oven and hope for the best. You need to follow a recipe: Step 1, mix ingredients; Step 2, preheat the oven; Step 3, bake for 30 minutes and so on. This list of steps is an algorithm.
A computer too needs very clear, ordered instructions. An algorithm is simply a recipe for thinking, written so a machine can follow it perfectly.
An algorithm should ideally satisfy the below conditions.
An algorithm is the idea. A program is its implementation.
For example:
Algorithm: Check each number and swap if it’s bigger than the next one.
Program: The Python, JavaScript, or C++ code that performs that logic.
Different programs can implement the same algorithm in different ways.
In traditional software, algorithms are explicitly written by humans.
In AI and machine learning, humans design the learning algorithm. The system learns the rules from data and identifies patterns. They ingest labeled or unlabeled inputs, adjust internal parameters (like weights in neural networks), and output predictions or classifications.
Modern AI models still rely on algorithms that are far more complex and data-driven. Algorithms also determine how fast and how efficiently the AI runs, affecting compute cost and latency.
From search engines to AI models, washing machines to customer support bots, everything ultimately depends on the underlying algorithm doing its job correctly.
Understanding this helps demystify software and AI. Most of what looks intelligent is really the result of well-designed algorithms working at scale.
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