AI is the acronym for "artificial intelligence". The term AI might be the current buzz word, but the phrase "artificial intelligence" has been around for much longer than that. It has been in use for nearly 70 years!
AI can mean a lot of things. It could mean the autocomplete on your phone or the suggested retail ads. Most of what a smart phone does already is an AI component or is AI altogether.
Recently, AI has taken the steps to include generative AI with the popularity of products like ChatGPT. These products respond to a prompt to generate an answer of some sort. Using AI in academic work has its advantages and disadvantages.
The Library is not taking a pro or against stance on the various AI products, but this guide is to assist in how to use those products ethically.
AI tools can generate a lot of content in a short amount of time. This can be used to help you generate a lot of ideas so springboard into new ideas that you have not thought about.
Many generative AI tools can be told what specifically you are looking for and can be adapted.
There are many generative AI tools that assist in breaking down large bits of information and reforming it in a different way for easier consumption. This is sometimes helpful if a person is not used to a specific type of resource or information.
Generative AI tools need to be prompted in order to create a content. Sometimes the content does not make sense (also called hallucinations) and string together words that do not make sense together. Sometimes the produced content has falsified information. Research should always be used in order to verify what the generative AI has produced.
Some data has shown that there has been some underlying bias coming from some generative AI tools. This is the result of training tools that went into the AI model.
Some generative AI tools are not produced with real-time information. How do you know when the last time the AI model was updated? Research should always be done in order to verify the accuracy of the content.
This issue has been raised with high concern in the academic world. Not only does the idea of copyright come into play with most content, but how does one have intellectual property regarding generative AI?
Producing the technology that runs generative AI comes at a cost. The heat produced consumes a lot of energy, a lot of water to cool their processors, and leads to a lot of carbon emissions.
Artificial Intelligence - AI is a branch of computer science. It is comprised of hardware, algorithms, and data to create “intelligence” to do things like make decisions, discover patterns, and perform some sort of action. AI is a general term and there are more specific terms used in the field of AI. AI systems can be built in different ways, two of the primary ways are: (1) through the use of rules provided by a human (rule-based systems); or (2) with machine learning algorithms. Many newer AI systems use machine learning (see definition of machine learning below).
AI is an umbrella term over Generative AI, natural and large language models, and machine and deep learning. AI systems can be built in different ways. Two of the primary ways are: (1) through the use of rules provided by a human (rule-based systems); or (2) with machine learning algorithms. Many newer AI systems use machine learning
Generative AI - A form of Artificial Intelligence that can create new content such as text, visual images, code, audio, or video because its neural networks have been trained on a large amount of data. Outputs might include digital art, essays, short answers, blog posts, computer code, press releases, and other types of novel content.
Large Language Model A form of text-based generative Al (e.g., ChatGPT) that is trained on an enormous amount of text so that it can predict and create a given sequence of words. This capability allows the model to "understand" inquiries and replicate human language in a largely coherent (if not always accurate) way.
Machine Learning - The use and development of computer systems that can learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data (IBM, 2022). 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. 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.
Neural Networks - Computer networks that are built in such a way as to mimic the human brain with each node leading to other nodes, much as the brain is a complex collection of networked neurons. Neural networks may also be called Artificial Neural Networks (ANN) and are a subset of Machine Learning algorithms.
Deep Learning - A subset of machine learning that comprises a complex neural network with three or more layers of networks. It is a technique that teaches computers to do what comes naturally to humans: learn by example. Due to the multiple hidden layers, deep learning algorithms are potentially able to recognize more subtle and complex patterns. The decisions made 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).
This section was used with help from the The Academic Center of Excellence department on campus. They had gotten their glossary from the ICCOC which was from Educator CIRCLS, used under a Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/).”
Ruis, P., & Fusco, J. (2023). Glossary of artificial intelligence terms for educators. Educator CIRCLS Blog. https://circls.org/educatorcircls/ai-glossary
