Machine learning algorithms enable an AI system to learn from data and improve its performance over time.
Understanding Algorithms in AI
An algorithm, at its core, is a systematic set of instructions designed to perform a specific task or solve a particular problem. Think of it as a recipe in cooking: it provides step-by-step directions to achieve a desired result. In the realm of artificial intelligence, algorithms process data to produce specific outcomes. These can be broadly categorised into rule-based algorithms and machine learning algorithms.
Rule-Based Algorithms:These algorithms operate based on a predefined set of rules and conditions. They don't "learn" in the way humans do but rather follow their programming to the letter.
Example: Consider the classic game of Rock-Paper-Scissors. A rule-based algorithm for this game would have explicit instructions for every possible scenario:
- If Player A chooses rock and Player B chooses scissors, Player A wins.
- If both players choose paper, it's a draw.... and so on. The algorithm uses a series of conditional statements (like if-else) to determine the outcome based on the game's established rules.
Machine Learning Algorithms:Unlike rule-based algorithms, machine learning algorithms have the ability to learn from data. They adjust and refine their operations based on the information they're exposed to, improving their performance over time.
Example: Imagine training an algorithm to differentiate between images of cats and dogs. Initially, the algorithm might not know the difference. However, by feeding it a large dataset of labelled cat and dog images, it begins to recognise patterns, shapes, and textures unique to each. A cat might have pointy ears, while a dog might have a broader snout. Over time, and with enough data, the algorithm becomes proficient at distinguishing between the two. When presented with a new, unlabelled image, it can confidently predict whether the image is of a cat or a dog based on the patterns it has learned.