What is an Algorithm?

An algorithm is a defined set of instructions or rules that a computer follows to perform a specific task or solve a problem. In its simplest form, it is a logical sequence of steps that turns input data into a desired output. Every process in Artificial Intelligence (AI), from sorting data to predicting outcomes, relies on algorithms.

In traditional computing, algorithms are written by programmers to follow exact instructions. In Machine Learning (ML), algorithms are designed to learn from data and improve their performance over time without being explicitly reprogrammed.

How algorithms work

At their core, algorithms follow a structured process: take input, process it using logical or mathematical operations, and produce output. In AI systems, algorithms analyse patterns, adjust their internal parameters, and make predictions or classifications based on the data they receive.

  • Input: The data or information fed into the algorithm.
  • Processing: The operations or calculations the algorithm performs.
  • Output: The result, decision, or prediction produced.

Algorithms in Artificial Intelligence

AI algorithms are used to power systems that can recognise images, understand speech, recommend products, or predict outcomes. They work within machine learning models to identify relationships and patterns in large datasets, helping systems learn from experience rather than manual programming.

  • Supervised Learning: Algorithms learn from labelled data to make predictions, such as recognising spam emails.
  • Unsupervised Learning: Algorithms find hidden patterns in unlabelled data, such as customer segmentation.
  • Reinforcement Learning: Algorithms learn by trial and error, receiving feedback from their actions to improve decisions.

Examples of AI algorithms

  • Decision Trees: Break data into branches to reach a final decision or classification.
  • Neural Networks: Mimic human brain structure to process complex information in layers.
  • Support Vector Machines: Classify data points by finding the optimal boundary between categories.
  • K-Means Clustering: Group data points into clusters based on similarity.
  • Linear Regression: Model relationships between variables to predict outcomes.

Algorithm vs Model

Algorithms and models are closely related but not the same. An algorithm is the method or process for learning, while a model is the result of running that algorithm on data. For example, an algorithm might define how to learn from data, and the trained model applies that knowledge to new information.

Why algorithms matter in AI

Algorithms determine the performance, fairness, and reliability of AI systems. A well-designed algorithm improves accuracy, reduces bias, and adapts efficiently to new data. Poorly designed or untested algorithms can lead to errors or unethical outcomes, highlighting the importance of model validation and feature engineering.

  • Efficiency: Optimised algorithms process data faster and more accurately.
  • Scalability: Algorithms allow AI systems to handle larger, more complex datasets.
  • Ethical impact: Algorithms directly affect fairness and bias in decision-making.

Algorithms form the foundation of modern AI and machine learning systems. They are closely related to Deep Learning architectures, Feature Engineering, and Model Validation, which together ensure that AI behaves predictably and reliably in real-world scenarios.

Learn more: Explore how Shipshape Data helps businesses design, test, and deploy AI algorithms responsibly, ensuring performance, fairness, and long-term reliability across all applications.