LibGuides: Artificial Intelligence: OpenAI, ChatGPT, LLMs, and More: Glossary of AI Terms

Glossary of AI Terms

Below is a list of common terms related artificial intelligence that may be of interest to educators. Click to expand each box to learn more about each term.

Artificial Intelligence (AI)

The simulation of human intelligence in machines. The goals of artificial intelligence include learning, reasoning, and perception. AI is being used across different industries, including finance, education, and healthcare.

Artificial General Intelligence (AGI): Artificial general intelligence has not yet been realized and would be when an AI system can learn, understand, and solve any problem that a human can.
Artificial Narrow Intelligence (ANI): AI can solve narrow problems and this is called artificial narrow intelligence. For example, a smartphone can use facial recognition to identify photos of an individual in the Photos app, but that same system cannot identify sounds.

Citation: Glossary of artificial intelligence terms for educators. (n.d.). Engage AI Institute. Retrieved April 26, 2023, from https://sites.google.com/ncsu.edu/ai-engage/nexus-conversations/nexus-blog/glossary-of-artificial-intelligence-terms-for-educators

AI Literacy

AI literacy refers to a broad set of skills, attitudes, beliefs, and competencies related to AI technology. There are many facets to AI literacy, ranging from understandings of how technologies work to critical and reflective dispositions about ethics. As AI technologies continue to influence our society, culture, and educational systems, it will be important for users to develop and refine their AI literacies.

Algorithm

Algorithms are the “brains” of an AI system and what determines decisions in other words, algorithms are the rules for what actions the AI system takes. Machine learning algorithms can discover their own rules (see Machine learning for more) or be rule-based where human programmers give the rules.

Citation: Glossary of artificial intelligence terms for educators. (n.d.). Engage AI Institute. Retrieved April 26, 2023, from https://sites.google.com/ncsu.edu/ai-engage/nexus-conversations/nexus-blog/glossary-of-artificial-intelligence-terms-for-educators

ChatGPT

Chat-based generative pre-trained transformer (ChatGPT): A system built with a neural network transformer type of AI model that works well in natural language processing tasks (see definitions for neural networks and Natural Language Processing below). In this case, the model: (1) can generate responses to questions (Generative); (2) was trained in advance on a large amount of the written material available on the web (Pre-trained); (3) and can process sentences differently than other types of models (Transformer).

Transformer models: Used in ChatGPT (the T stands for Transformer), transformer models are a type of language model. They are neural networks and also classified as deep learning models. They give AI systems the ability to determine and focus on important parts of the input and output using something called a self-attention mechanism to help.
Self-attention mechanism: These mechanisms, also referred to as attention help systems determine the important aspects of input in different ways. There are several types and were inspired by how humans can direct their attention to important features in the world, understand ambiguity, and encode information.

Citation: Glossary of artificial intelligence terms for educators. (n.d.). Engage AI Institute. Retrieved April 26, 2023, from https://sites.google.com/ncsu.edu/ai-engage/nexus-conversations/nexus-blog/glossary-of-artificial-intelligence-terms-for-educators

Critical AI

Critical AI is an approach to examining AI from a perspective that focuses on reflective assessment and critique as a way of understanding and challenging existing and historical structures within AI.

Citation: Glossary of artificial intelligence terms for educators. (n.d.). Engage AI Institute. Retrieved April 26, 2023, from https://sites.google.com/ncsu.edu/ai-engage/nexus-conversations/nexus-blog/glossary-of-artificial-intelligence-terms-for-educators

Deep Learning

Deep learning models are a subset of neural networks. With multiple hidden layers, deep learning algorithms are potentially able to recognize more subtle and complex patterns. Like neural networks, deep learning algorithms involve interconnected nodes where weights are adjusted, but as mentioned earlier there are more layers and more calculations that can make adjustments to the output to determine each decision. The decisions by deep learning models are often very difficult to interpret as there are so many hidden layers doing different calculations that are not easily translatable into English rules (or another human-readable language).

Generative AI

Generative AI refers to a category of artificial intelligence (AI) algorithms that generate new outputs based on the data they have been trained on. Unlike traditional AI systems that are designed to recognize patterns and make predictions, generative AI creates new content in the form of images, text, audio, and more.

Generative AI uses a type of deep learning called generative adversarial networks (GANs) to create new content. A GAN consists of two neural networks: a generator that creates new data and a discriminator that evaluates the data. The generator and discriminator work together, with the generator improving its outputs based on the feedback it receives from the discriminator until it generates content that is indistinguishable from real data.

Generative AI has a wide range of applications, including:

Images: Generative AI can create new images based on existing ones, such as creating a new portrait based on a person’s face or a new landscape based on existing scenery
Text: Generative AI can be used to write news articles, poetry, and even scripts. It can also be used to translate text from one language to another
Audio: Generative AI can generate new music tracks, sound effects, and even voice acting

Citation: What is generative AI? An AI explains. (2023, February 6). World Economic Forum. https://www.weforum.org/agenda/2023/02/generative-ai-explain-algorithms-work/

Intelligence Augmentation

Augmenting means making something greater; in some cases, perhaps it means making it possible to do the same task with less effort. Maybe it means letting a human (perhaps teacher) choose to not do all the redundant tasks in a classroom but automate some of them so they can do more things that only a human can do. It may mean other things. There’s a fine line between augmenting and replacing and technologies should be designed so that humans can choose what a system does and when it does it.

Citation: Glossary of artificial intelligence terms for educators. (n.d.). Engage AI Institute. Retrieved April 26, 2023, from https://sites.google.com/ncsu.edu/ai-engage/nexus-conversations/nexus-blog/glossary-of-artificial-intelligence-terms-for-educators

Intelligent Tutoring Systems

A computer system or digital learning environment that gives instant and custom feedback to students. An Intelligent Tutoring System may use rule-based AI (rules provided by a human) or use machine learning under the hood. By under the hood we mean the underlying algorithms and code that an ITS is built with. ITSs can support adaptive learning.

Adaptive Learning: Subject or course material is adjusted based on the performance of the learner. The difficulty of material, the pacing, sequence, type of help given, or other features can be adapted based on the learner’s prior responses.

Interpretable Machine Learning

Interpretable machine learning, sometimes also called interpretable AI, describes the creation of models that are inherently interpretable in that they provide their own explanations for their decisions. This approach is preferable to that of explainable machine learning (see definition below) for many reasons including the fact that we should understand what is happening from the beginning in our systems, rather than try to “explain” black boxes after the fact.

Black Boxes: We call things we don’t understand, “black boxes” because what happens inside the box cannot be seen. Many machine learning algorithms are “black boxes” meaning that we don’t have an understanding of how a system is using features of the data when making their decisions (generally, we do know what features are used but not how they are used). There are currently two primary ways to pull back the curtain on the black boxes of AI algorithms: interpretable machine learning (see definition above) and explainable machine learning (see definition below).

Glossary of artificial intelligence terms for educators. (n.d.). Engage AI Institute. Retrieved April 26, 2023, from https://sites.google.com/ncsu.edu/ai-engage/nexus-conversations/nexus-blog/glossary-of-artificial-intelligence-terms-for-educators

Machine Learning

Machine learning is a field of study with a range of approaches to developing algorithms that can be used in AI systems. AI is a more general term. In ML, an algorithm will identify rules and patterns in the data without a human specifying those rules and patterns. These algorithms build a model for decision making as they go through data (you will sometimes hear the term machine learning model). Because they discover their own rules in the data they are given, ML systems can perpetuate biases. Algorithms used in machine learning require massive amounts of data to be trained to make decisions.

It’s important to note that in machine learning, the algorithm is doing the work to improve and does not have the help of a human programmer. It is also important to note three more things. One, in most cases the algorithm is learning an association (when X occurs, it usually means Y) from training data that is from the past. Two, since the data is historical, it may contain biases and assumptions that we do not want to perpetuate. Three, there are many questions about involving humans in the loop with AI systems; when using ML to solve AI problems, a human may not be able to understand the rules the algorithm is creating and using to make decisions. This could be especially problematic if a human learner was harmed by a decision a machine made and there was no way to appeal the decision.