What Is an Algorithm, and How Does an AI Algorithm Actually Work?

 

If you've ever followed a recipe, you've already executed an algorithm. This post breaks the idea down in simple terms — starting with what an algorithm is in general, then explaining how that idea evolves into something called "AI."

What Is an Algorithm?

An algorithm is simply a set of step-by-step instructions for solving a problem or completing a task. It doesn't need a computer to exist — a recipe, a set of driving directions, or the steps you follow to do laundry are all algorithms in everyday life.

In computing, an algorithm is a precise sequence of instructions a computer follows to go from an input to an output. For example:

• Input: A list of unsorted numbers
• Algorithm: A set of steps that compares and rearranges the numbers
• Output: The same numbers, now sorted from smallest to largest

Traditional software is built almost entirely on algorithms like this: explicit, rule-based instructions written by a programmer. If X happens, do Y. The computer doesn't "decide" anything — it just follows the rules it was given, exactly as written.

So What Makes an AI Algorithm Different?

This is where things shift. A traditional algorithm is told exactly what to do. An AI algorithm, by contrast, is given data and a goal, and it works out the rules itself.

Instead of a programmer writing "if the email contains the word 'lottery', mark it as spam," an AI algorithm is shown thousands of examples of emails already labeled "spam" or "not spam." It studies the patterns in that data — word frequency, sender behavior, formatting — and gradually builds its own internal rules for telling the two apart. Once trained, it can apply that pattern to new emails it has never seen before.

This is the core idea behind machine learning, the most common type of AI algorithm used in business today.

How an AI Algorithm Works, Step by Step

Most AI algorithms follow a similar basic process:

1. Collect data — Gather a large set of examples relevant to the task (past customer purchases, images of products, historical sales figures, etc.).
2. Train the model — Feed that data into the algorithm. It looks for patterns and relationships, gradually adjusting itself to get better at predicting the right answer.
3. Test and evaluate — Check the trained model against new data it hasn't seen before, to see how accurately it performs.
4. Deploy — Once it performs well enough, the model is put to use on new, real-world inputs.
5. Improve over time — As more data comes in, the model can be retrained or fine-tuned to stay accurate and adapt to changing patterns.

The key difference from traditional software is step 2. A traditional program's logic is written by hand. An AI model's logic is learned from examples — which is also why AI systems can improve with more data and experience, while a traditional program stays exactly the same until a human rewrites it.

A Simple Analogy

Think of the difference this way:

• Traditional algorithm = a recipe someone wrote for you. Follow it exactly, and you'll get the same result every time.
• AI algorithm = someone who has tasted thousands of dishes and, over time, has learned to recognize what makes a dish taste good — even for a dish they've never tried before.

The first approach is precise and predictable but rigid. The second is more flexible and can generalize to new situations, but it depends heavily on the quality and quantity of the examples it learned from.

Why This Matters

Understanding this distinction helps explain both AI's strengths and its limits:

• AI algorithms are powerful because they can find patterns in data too complex or too large for a human to spot manually.
• They are only as good as the data they're trained on — biased or incomplete data leads to biased or inaccurate results.
• Unlike traditional software, their reasoning isn't always fully transparent, which is why "explainability" is such an important topic in AI discussions today.

At its heart, an AI algorithm isn't magic — it's a structured process of learning from examples instead of following fixed instructions. That single shift is what separates a calculator from a recommendation engine, and it's the foundation for almost everything we now call "artificial intelligence."