Glossary: Some general AI terms
Across this website, there are many terms which may be new to some of you. We’ve defined a few below.
Algorithm: A sequence of rules given to an AI machine to perform a task or solve a problem. Common algorithms include classification, regression, and clustering.
AI Ethics: Refers to the issues that AI stakeholders (engineers, government officials, etc.) must consider to ensure responsible development and use of AI.
Artificial Intelligence (AI): refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions.
Chatbot: A software application designed to imitate human conversation through text or voice commands.
Generative AI: a subset of artificial intelligence that leverages machine learning techniques to generate content. It can create new content that is similar to the input data it has been trained on. Examples include creating images, writing text, and composing music.
Hallucinations: In the context of AI, they are the generation of information that isn’t present in the input data. This can occur when AI models, such as language models, generate outputs that include false or misleading information.
Large Language Model (LLM): a type of AI model designed to generate human-like text. It’s trained on a large amount of text data and can generate sentences by predicting the likelihood of a word given the previous words used in the text.
Machine Learning (ML): a subset of AI, providing systems the ability to automatically learn and improve from experience without being explicitly programmed.
Prompt Engineering: the practice of designing and optimizing prompts to effectively interact with AI models, particularly language models, to produce desired outputs.
If you want to look at a more complete list of AI terms please access the glossary on the Coursera website.