Common AI Terms

1. Artificial Intelligence: An umbrella term that encompasses several different subcategories including generative AI.
2. Machine Learning: A subfield of AI that uses algorithms trained on data sets to create self-learning models. Example: Product suggestions based on past purchases.
3. Neural Networks: A type of Machine Learning that is designed to act like a human brain. Example: perform computations and transform input into something the network can use to make a decision.
4. Deep Learning: Involves neural networks with multiple layers. Capable of recognizing patterns, classifying data and making predictions.
5. Natural Language Processing: Capable of understanding and generating human language.
6. Generative AI: A type of AI that uses natural language processing to generate new content. It focuses on classifying or identifying content based on preexisting data
7. Conversational AI: a type of generative AI where human interaction is most important. This is used for chatbots and virtual assistants. For example Amazon’s Alexa.
8. GPT: stands for Generative Pre-trained Transformer. It’s a type of artificial intelligence designed to generate human-like text by predicting the next word in a sequence given all the previous words within some text. GPT models are trained on a diverse range of internet text and can perform a variety of language tasks such as translation, question-answering, and content creation.
9. ChatGPT: an AI chatbot developed by OpenAI (a company) that can engage in human-like conversations. It’s based on the GPT (Generative Pre-trained Transformer) architecture and is trained to understand and generate text in a conversational manner.
10. Prompts: a specific input or instruction given to an AI system to generate a desired output. It acts as a guiding mechanism to direct the AI’s creativity toward a specific goal or outcome, enhancing the user’s control over what the AI produces.