STUDENTS: See disclaimer and note below
Some important terms to become familiar with (adapted and used with permission from Chapman University AI 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. |
| 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. |
| 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. |
| Generative Models | These are a type of machine learning models that create 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. |
| 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. |
| Large Language Model (LLM) | A large language model (LLM) is a type of artificial intelligence (AI) algorithm that uses deep learning techniques and massively large data sets to understand, summarize, generate and predict new content. The term generative AI also is closely connected with LLMs, which are, in fact, a type of generative AI that has been specifically architected to help generate text-based content. |
| 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. |
| Models | AI models or artificial intelligence models are programs that detect specific patterns using a collection of data sets. It is an illustration of a system that can receive data inputs and draw conclusions or conduct actions depending on those conclusions. |
| 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. |
| 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. |
| 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. |
| 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. |
| 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. |
Disclaimer: This website does not endorse the use of AI in teaching and learning at LMU. Faculty decide their policies related to technology in their courses and reserve the right to consider the unauthorized use of AI to be a breach of the university’s Academic Honesty Policy. Students should always consult their faculty for the specific course policies related to the use of AI. Sanctions for violations of the Academic Honesty Policy may include failure for the assignment, the course, academic probation, suspension, or dismissal from the university. For more information, visit LMU's Academic Honesty website.
NOTE: This webpage has not been reviewed by any standing university committees for alignment with university policy, academic freedom, or the faculty role in shared governance. It will undergo a comprehensive review in Fall 2024.