» AI Key Terms


Term	Definition
Artificial Intelligence (AI)	The ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. This includes learning from experience, reasoning, understanding language, recognizing patterns, and problem-solving.
Generative Models	These are a type of machine learning models that creates new content by learning from training data and then generating new output that are based on, or similar to, that specific training set. These generative models learn patterns, structures, and features from the training data and can create content with similar characteristics.
Generative Pre-trained Transformer (GPT)	A language model that uses deep learning to create realistic text. It is used in many applications, such as translation, question-answering, and text generation. This represents the ‘GPT’ of ChatGPT.
Machine Learning (ML)	A subset of AI that involves the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.
Deep Learning (DL)	A subset of machine learning that's based on artificial neural networks with representation learning. It can be supervised, semi-supervised, or unsupervised and aims to model high-level abstractions in data by using multiple processing layers.
Natural Language Processing (NLP)	A subfield of AI that focuses on the interaction between computers and humans through natural language. The ultimate objective of NLP is to read, decipher, understand, and make sense of human language in a valuable way.
Language Model (LM)	A type of model in NLP that predicts the next word or character in a sequence. These models are used in speech recognition, text generation, and other NLP tasks.
Token	In the context of NLP, a token is a single unit that is a building block for a sentence or document, such as a word, a character, or a subword.
Fine-Tuning	A process in machine learning where a pre-trained model (like GPT) is further trained on a new dataset with a smaller amount of data. The purpose of fine-tuning is to adopt the general knowledge of the pre-trained model to a specific task.
Text Classification	This involves assigning categories or labels to text. For example, sorting emails into "spam" and "not spam" is a form of text classification.
Transfer Learning	The application of knowledge gained while solving one problem to a different but related problem. For example, knowledge gained while learning to recognize cars could apply when trying to recognize trucks.
Prompt	In the context of AI, a prompt is an input given to a language model that it uses to generate a response or output.
Training	This is the process of feeding data into AI software so that it begins the machine learning process.