This guide is designed to broaden one's basic understanding of AI, explore generative AI tools, and critically evaluate the available resources.
This toolkit is currently under construction. Please reach out to Chris Duffy with any additions or comments.
This file is licensed under the Creative Commons Attribution-Share Alike 4.0 International license.
Algorithm: In machine learning, algorithms refer to computational techniques that can find a way to connect a set of inputs to a desired set of outputs by learning relevant data.
Bias: In AI models refers to output errors caused by skewed training data. Such bias can cause models to produce inaccurate, offensive, or misleading predictions. Biased AI models arise when algorithms prioritize irrelevant or misleading data traits over meaningful patterns (Smith, 2019).
Chain-of-thought Prompting: Chain-of-thought prompting is when you use a series of intermediate reasoning steps to improve the accuracy and applicability of answers generated by LLMs (Bubeck et al., 2022).
Context window: The context window is the maximum number of tokens (words or parts of words) that an AI model can process and consider simultaneously when generating a response. It is essentially the “memory” capacity of the model during an interaction or task. Models with larger context windows can handle larger attachments/prompts/inputs and sustain “memory” of a conversation for longer (Fogarty, 2023).
Generative AI (GenAI): Generative AI refers to deep-learning models that can take raw data and “learn” to generate statistically probable outputs when prompted.
Hallucination: A phenomenon wherein a large language model perceives patterns or objects that are nonexistent or imperceptible to human observers, creating outputs that are nonsensical or altogether inaccurate.
Input data: Input data is data added to an artificial intelligence (AI) to explain a problem, situation, or request. Input data may be cleaned, labeled, and organized, or it may be raw data. Often an AI is asked to create or synthesize information based on input data and the AI’s algorithm (which itself might have been fine-tuned or trained using a separate data set) in order to create new data (“output data”).
Large Language Model: A category of foundation models trained on immense amounts of data.
References:
1. https://mitsloanedtech.mit.edu/ai/basics/glossary/
2. https://libguides.mssm.edu/ai/keyterminology
