Artificial Intelligence (AI)

Generative AI: Generative AI is a broad term that can be used for any AI system whose primary function is to generate content. This is in contrast to AI systems that perform other functions, such as classifying data (e.g., assigning labels to images), grouping data (e.g., identifying customer segments with similar purchasing behavior), or choosing actions (e.g., steering an autonomous vehicle). (Toner, 2023).

Hallucination: AI hallucination is a phenomenon wherein a large language model (LLM)—often a generative AI chatbot or computer vision tool—perceives patterns or objects that are nonexistent or imperceptible to human observers, creating outputs that are nonsensical or altogether inaccurate. (IBM, n.d.).

Large Language Model (LLM): Large Language Models (LLMs) are a type of AI system that works with language. In the same way that an aeronautical engineer might use software to model an airplane wing, a researcher creating an LLM aims to model language, i.e., to create a simplified—but useful—digital representation. The “large” part of the term describes the trend towards training language models with more parameters. (Toner, 2023).

Machine Learning: Machine learning (ML) is the subset of artificial intelligence (AI) that focuses on building systems that learn—or improve performance—based on the data they consume. (Oracle, n.d).

Prompt Engineering: Prompt engineering is the process of formulating a prompt for an AI system so that it produces an output that closely matches your expectations. (Bains, 2023).

Foundation Model: Foundation models are models trained on broad data (generally using self-supervision at scale) that can be adapted to a wide range of downstream tasks. These models, which are based on standard ideas in transfer learning and recent advances in deep learning and computer systems applied at a very large scale, demonstrate surprising emergent capabilities and substantially improve performance on a wide range of downstream tasks. (Bommasani & Liang, 2021).

Computer Vision: Computer vision is the field of artificial intelligence in which programs attempt to identify objects represented in digitized images provided by cameras, thus enabling computers to “see.” (Britannica, 2024).

References/Further Reading:

Bains, C. (2023, July 29). AI prompt engineering: Learn how not to ask a chatbot a silly question. The Guardian. https://www.theguardian.com/technology/2023/jul/29/ai-prompt-engineering-chatbot-questions-art-writing-dalle-midjourney-chatgpt-bard.

Bommasani, R., & Liang, P. (2021, October 18). Reflections on Foundation Models. Stanford University Human-Centered Artificial Intelligence. https://hai.stanford.edu/news/reflections-foundation-models.

Britannica. (2024, January 25). Computer vision. In Encyclopedia Britannica. https://www.britannica.com/technology/computer-vision.

IBM. (n.d.). What are AI hallucinations? https://www.ibm.com/topics/ai-hallucinations.

Oracle. (n.d.). What is machine learning? https://www.oracle.com/artificial-intelligence/machine-learning/what-is-machine-learning/.

Toner, H. (2023, May 12). What are generative AI, large language models, and foundation models? Center for Security and Emerging Technology. https://cset.georgetown.edu/article/what-are-generative-ai-large-language-models-and-foundation-models/.