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Demystifying AI: A Beginner’s Glossary (Part one)

In an age where technology evolves at breakneck speed, Artificial Intelligence (AI) stands at the forefront, leading the charge. It’s a term that fills pages of science fiction and splashes across news headlines, but what does it all really mean? Let’s unlock the vocabulary of AI, focusing on some of the most pivotal terms that will give you a handle on what AI is all about. Here’s part one of our AI glossary, covering significant terms from A to L.


Artificial Intelligence (AI)

AI is the overarching term for machines designed to engage in human-like thought processes such as learning, reasoning, and self-correction. The simplest AI can perform basic tasks, while more advanced AI can drive cars or even beat grandmasters at chess. It’s the science and engineering of making intelligent machines, especially intelligent computer programs.



Algorithms are the heart of AI. Think of them as complex recipes a computer follows to achieve a goal. These step-by-step instructions guide the AI to process information, make decisions, and solve problems. They can be as straightforward as sorting a list or as intricate as predicting your next online purchase.


Big Data

In the realm of AI, “Big Data” refers to the colossal and complex datasets that are used for training sophisticated models, including Large Language Models (LLMs) like ChatGPT. These systems require a gargantuan volume of text data—sourced from a plethora of books, articles, websites, and other digital content—to learn the intricacies of human language.



Chatbots are AI systems that we interact with via a text or voice interface. They’re programmed to mimic human conversation and are often used in customer service to answer questions or guide users through a website.


Deep Learning

Deep learning is a type of machine learning that uses neural networks with many layers (hence “deep”) to analyse data. It’s the technology behind voice control in devices like phones, tablets, and TVs, and it’s getting smarter all the time.


Ethics in AI

As AI becomes more integrated into daily life, ethical concerns are raised about privacy, bias, accountability, and transparency. Developers and users must consider how AI decisions affect real people and how to ensure AI behaves in morally acceptable ways.



Heuristics is a problem-solving approach that uses a practical method, not necessarily perfect, but sufficient for reaching an immediate goal. In AI, it can refer to ‘rules of thumb’ that help in making decisions or judgements quickly.


Learning Algorithms

Learning algorithms are the part of AI that gives it the ability to learn from data and, crucially, improve over time. Machine learning utilises algorithms that analyse data, learn from it, and then apply what they’ve learned to make informed decisions.


Machine Learning

Machine learning is a field of artificial intelligence (AI) that focuses on enabling machines to learn from data and make decisions or predictions based on that data. It operates on the principle that systems can learn from patterns, identify trends, and make decisions with minimal human intervention. It’s what enables AI systems to improve at tasks through experience—much like a human learning a new skill by doing.


Natural Language Processing (NLP)

NLP is a technology that allows computers to understand, interpret, and respond to human language in a valuable way. Through NLP, machines can read text, hear speech, interpret it, measure emotion, and determine which parts are important.

Understanding these terms is just the start of the journey into the fast-moving world of AI. Stay tuned for the next instalment, where we’ll continue to decode the language of artificial intelligence.

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