Abell Library: Artificial Intelligence: Using Generative AI Tools

Basic Definitions

Artificial Intelligence: Artificial intelligence is the design, implementation, and use of programs, machines, and systems that exhibit human intelligence, with its most important activities being knowledge representation, reasoning, and learning. Artificial intelligence encompasses a number of important subareas, including voice recognition, image identification, natural language processing, expert systems, neural networks, planning, robotics, and intelligent agents.
Artificial Intelligence (AI) Literacy: AI literacy is the basic understanding of how AI works, the ability to make informed decisions about AI technologies, and the knowledge of how to use AI effectively and ethically.
Deep Learning: "A type of machine learning based on artificial neural networks in which multiple layers of processing are used to extract progressively higher level features from data" (Oxford Languages).
Generative AI: Technology that creates content by identifying patterns in large quantities of training data, and then creating original material that has similar characteristics.
Large Language Model: A type of neural network that learns skills by analyzing vast amounts of text from across the internet. The basic function is to predict the next word in a sequence, but these models have surprised experts by learning new abilities.

Advanced Data Analysis: AI has the ability to perform data analysis to uncover insights and aid in informed decision making.
Business Insights: Similar to data analysis, AI is able to provide market analysis, consumer behavior predictions, and trend forecasting.
Educational Tools: AI is able to provide many lesson plan ideas, along with personalized education and interactive tools.
Efficiency, Productivity, Innovation: AI is able to automate routine tasks and more complex calculations.
Personalization and User Experience: AI can customize each individual experience to the user's needs and increases engagement in marketing, entertainment, and education.

Access Inequality: Many AI toolsets require payment in order to access their services, which creates a divide between those who can afford it and those who can't.
Bias and Equity: Human biases are commonly reflected in AI systems since they are trained with human-generated data. Types of bias that can occur include selection bias, confirmation bias, measurement bias, stereotyping bias, and out-group homogeneity bias.
Ethical Concerns: Ethical concerns include AI's creation of misinformation and deepfakes.
Intellectual Property Concerns: By training AI toolsets with existing material created by external sources, there is increased concern over the unauthorized use of intellectual property.
Reliability: AI toolsets are human trained and prone to human error. AI pulls much of its information from non-scholarly sources and can generate untrue and unreliable answers, and often lacks the ability to credit where the information was pulled from.

The Library Artificial Intelligence page was adapted from and inspired by Chapman University's L eatherby Libraries and University of South Florida's T ampa Campus Research Library.