Artificial Intelligence (AI) encompasses a wide range of technologies that enable machines to perform tasks that typically require human intelligence. AI is an umbrella term, and its various subfields, like machine learning, are defined by the specific techniques used to achieve intelligent behavior in machines. 

Below are a few common terms and their definitions that may be used in articles, videos and courses about AI.

Artificial Intelligence (AI):​ Involves techniques that equip computers to emulate human behavior, enabling them to learn, make decisions, recognize patterns, and solve complex problems in a manner akin to human intelligence.​ 

Machine Learning (ML):​ A subset of AI that uses advanced algorithms to detect patterns in large data sets, allowing machines to learn and adapt. Machine learning algorithms use supervised or unsupervised learning methods.​ 

Deep Learning (DL):​ A subset of machine learning that uses neural networks for in-depth data processing and analytical tasks. It leverages multiple layers of artificial neural networks to extract high-level features from raw input data, simulating the way human brains perceive and understand the world.​ 

Generative AI:​ Generative AI models are deep learning models that generate new content, like text, images, audio, and code. They differ from general AI models because they generate original output based on patterns learned from data the model was trained on, as opposed to analyzing existing data and making predictions.​

Algorithm: An algorithm is a step-by-step method for solving a problem or carrying out a task. It provides a clear set of instructions that guide specific actions, whether implemented in software or hardware. ​​ 

Bias in AI: Bias refers to systematic favoritism, distortion, or unfairness in the outputs produced by Generative AI tools—often reflecting imbalances, stereotypes, or exclusions present in the data on which the models were trained. 

Hallucinations: Hallucinations occur when a generative AI system produces false, misleading, or entirely fabricated content—even if it appears accurate or convincing. This can happen in both text and images.

Large Language Model (LLM): Large Language Models (LLMs) are a type of artificial intelligence that can generate human-like text and perform related tasks by being trained on massive datasets, learning patterns, and rules of language.  

Neural networks: Neural networks are machine learning systems that mimic the human brain to perform tasks such as image recognition, speech recognition, and decision making. 

Prompt: A prompt is the input a user gives the AI model in order for it to provide a specific output. They can range from simple questions to very complex instructions. The art and science of writing prompts in order to have the model produce a more accurate and relevant response is known as prompt engineering.

 Supervised learning: Supervised learning uses labeled data to train models that can predict outcomes or classify data. 

Unsupervised learning: Unsupervised learning uses unlabeled data to discover patterns, relationships, or anomalies within the data.