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Many of us have interacted with some kind of AI, especially after the boom of Generative AI and Large Language Models (LLMs) like chatGPT, Gemini, Copilot, etc. But what do all these acronyms mean?

Artificial Intelligence (AI) is an umbrella term for a range of algorithm-based technologies that perform tasks under varying and unpredictable circumstances without human oversight or that can learn from experience and improve performance when exposed to data sets (NASA, 2019; ICO, 2024).

Machine Learning (ML) is a subset of AI defined as the field of study that gives computers the ability to learn without explicitly being programmed. The function of a machine learning system can be descriptive, meaning that the system uses the data to explain what happened; predictive, meaning the system uses the data to predict what will happen; or prescriptive, meaning the system will use the data to make suggestions about what action to take (MIT, 2024).

Generative AI (GenAI) refers to machine learning models that have the ability to generate high-quality text, images, and other content based on the data they were trained on (IBM, 2024). These types of algorithms, which includes large language models (LLMs), caused the so-called AI hype in recent years when developers deployed them in the form of accessible chatbots that generate text, images, sounds, videos, and even code.