Artificial Intelligence (AI): the simulation of human intelligence processes by machines or computer systems. AI can mimic human capabilities such as communication, learning, and decision-making. (Coursera Staff. (2024, March 19). Artificial Intelligence (AI) Terms: A to Z Glossary. Coursera. https://www.coursera.org/articles/ai-terms)
Algorithm: a sequence of rules given to an AI machine to perform a task or solve a problem. (Coursera Staff. (2024, March 19). Artificial Intelligence (AI) Terms: A to Z Glossary. Coursera. https://www.coursera.org/articles/ai-terms)
Bias: AI systems that produce biased results that reflect and perpetuate human biases within a society, including historical and current social inequality. Bias can be found in the initial training data, the algorithm, or the predictions the algorithm produces. (IBM Data and AI Team. (2023, October 16). Shedding light on AI bias with real world examples. IBM https://www.ibm.com/think/topics/shedding-light-on-ai-bias-with-real-world-examples)
Chatbot: A chatbot is a software application that is designed to imitate human conversation through text or voice commands. (Coursera Staff. (2024, March 19). Artificial Intelligence (AI) Terms: A to Z Glossary. Coursera. https://www.coursera.org/articles/ai-terms)
Data Mining: Data mining is the process of sorting through large data sets to identify patterns that can improve models or solve problems. (Coursera Staff. (2024, March 19). Artificial Intelligence (AI) Terms: A to Z Glossary. Coursera. https://www.coursera.org/articles/ai-terms)
Deep Learning: a function of AI that imitates the human brain by learning from how it structures and processes information to make decisions. Instead of relying on an algorithm that can only perform one specific task, this subset of machine learning can learn from unstructured data without supervision. (Coursera Staff. (2024, March 19). Artificial Intelligence (AI) Terms: A to Z Glossary. Coursera. https://www.coursera.org/articles/ai-terms)
Generative AI: refers to a class of artificial intelligence (AI) technologies that produce outputs such as text, images, datasets, or other media in response to user prompts (Carnegie Council for Ethics in International Affairs. (2024) Generative AI. Carnegie Council for Ethics in International Affairs. https://www.carnegiecouncil.org/explore-engage/key-terms/generative-ai).
Hallucination: refers to the generation of outputs that may sound plausible but are either factually incorrect or unrelated to the given context. These outputs often emerge from the AI model's inherent biases, lack of real-world understanding, or training data limitations. In other words, the AI system "hallucinates" information that it has not been explicitly trained on, leading to unreliable or misleading responses. (Marr, B. (2023, March 22). CHATGPT: What are hallucinations and why are they a problem for AI systems. Bernard Marr & Co.: Future, Business, Success. https://bernardmarr.com/chatgpt-what-are-hallucinations-and-why-are-they-a-problem-for-ai-systems/#:~:text=Hallucination%20in%20AI%20refers%20to,understanding%2C%20or%20training%20data%20limitations.)
Large Language Model (LLM): These models are built using deep learning techniques, which enable them to understand the nuances of language. Bohnert A. (2023, May 19). What Is a Large Language Model? HankerRank. https://www.hackerrank.com/blog/what-is-a-large-language-model/)
Machine Learning (ML): a subset of AI that incorporates aspects of computer science, mathematics, and coding. Machine learning focuses on developing algorithms and models that help machines learn from data and predict trends and behaviors, without human assistance. (Coursera Staff. (2024, March 19). Artificial Intelligence (AI) Terms: A to Z Glossary. Coursera. https://www.coursera.org/articles/ai-terms)
Natural Language Processing (NLP): the field of artificial intelligence where computer science meets linguistics to allow computers to understand and process human language. Harvard Online. (2023). The Benefits and Limitations of Generative AI: Harvard Experts Answer Your Questions.https://www.harvardonline.harvard.edu/blog/benefits-limitations-generative-ai
Neural Network: a deep learning technique designed to resemble the human brain’s structure. Neural networks require large data sets to perform calculations and create outputs, which enables features like speech and vision recognition. (Coursera Staff. (2024, March 19). Artificial Intelligence (AI) Terms: A to Z Glossary. Coursera. https://www.coursera.org/articles/ai-terms)
Prompt Engineering: the process of iterating a generative AI prompt to improve its accuracy and effectiveness. (Coursera Staff. (2024, October 2). What is Prompt Engineering? Definition and Examples. Coursera. https://www.coursera.org/articles/what-is-prompt-engineering)
Stochastic Parrots: an LM is a system for haphazardly stitching together sequences of linguistic forms it has observed in its vast training data, according to probabilistic information about how they combine, but without any reference to meaning. Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? Conference on Fairness, Accountability, and Transparency (FAccT ’21), https://doi.org/10.1145/3442188.3445922