The video below explores how AI lacks the nuances of human reasoning and why it often makes mistakes.
AI Literacy is having the skills and competencies enabling individuals to use AI technologies and applications; communicate and collaborate effectively with AI; and question AI design and implementation while critically evaluating the information produced.
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.
AI is a branch of computer science. AI systems use 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).
A tool built with a type of AI model called natural language processing (see definition 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).
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.
Natural Language Processing is a field of Linguistics and Computer Science that also overlaps with AI. NLP uses an understanding of the structure, grammar, and meaning in words to help computers “understand and comprehend” language. NLP requires a large corpus of text (usually half a million words).
This is the data used to train the algorithm or machine learning model. It has been generated by humans in their work or other contexts in their past. While it sounds simple, training data is so important because the wrong data can perpetuate systemic biases. If you are training a system to help with hiring people, and you use data from existing companies, you will be training that system to hire the kind of people who are already there. Algorithms take on the biases that are already inside the data. People often think that machines are “fair and unbiased” but this can be a dangerous perspective. Machines are only as unbiased as the human who creates them and the data that trains them.
Used under a Creative Commons Attribution 4.0 International License. Pati Ruiz and Judi Fusco (2023) “Glossary of Artificial Intelligence Terms for Educators”. Educator CIRCLS Blog.
Hervieux, S., & Wheatley, A. (Eds.). (2022). The Rise of AI: Implications and Applications of Artificial Intelligence in Academic Libraries. Association of College & Research Libraries.
Long, D., & Magerko, B. (2020). What is AI Literacy? Competencies and Design Considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. ACM Digital Library. doi.org/10.1145/3313831.3376727
Ruiz, P., & Fusco, J. (2023). Glossary of Artificial Intelligence Terms for Educators. Educator CIRCLS Blog. Retrieved from https://circls.org/educatorcircls/ai-glossary
Voulgari, I., Stouraitis, E., Camilleri, V., & Karpouzis, K. (2022). Artificial Intelligence and Machine Learning Education and Literacy: Teacher Training for Primary and Secondary Education Teachers. In S. Xefteris (Ed.), Handbook of Research on Integrating ICTs in STEAM Education. Information Science Reference. doi.org/10.4018/978-1-6684-3861-9.ch001