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AI-enabled tools
AI systems that are implemented within a particular context (e.g., a natural language processing chatbot to assist customers, assistance with document editing and summaries, image enhancement, etc.). These tools may be stand-alone or may be add-ins to software currently in-use. (i.e. plugins to calendar systems, editors, web-conferencing systems, etc.)
AI system
a machine(1)-based system that is capable of influencing the environment by making recommendations, predictions, or decisions for a given set of objectives. It uses machine and/or human-based inputs/data to: i) perceive environments; ii) abstract these perceptions into models; and iii) interpret the models to formulate options for outcomes. AI systems are designed to operate with varying levels of autonomy. (2).
Artificial intelligence (AI)
technologies that aim to reproduce or exceed abilities using computational systems that would require human-like thinking to perform a wide range of tasks, from simple to sophisticated (3).
refers to how easy it is to understand the internal logic the model uses to make a prediction. Linear models (such as logistic regression) and small decision trees are on the more explainable end of the spectrum; neural nets and decision forests are on the less explainable end (often referred to as "black-box") (4).
Generative AI
neural networks capable of generating text, images, or other data using generative models, often in response to prompts. Generative AI models learn the patterns and structure of their input training data and then generate new data that has similar characteristics. A LLM is an example of a Generative AI system.
Large Language Model (LLM)
very large neural networks that are trained using massive amounts of text. With billions of parameters to learn from, LLMs are the backbone of natural language processing that can recognize, summarize, translate, predict and generate text. Perhaps the most well-known LLM is GPT, the engine that drives ChatGPT. Meta has developed LLaMA and Google has LaMDA (5).
Machine Learning (ML)
a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention (6).
Natural Language Processing (NLP)
combines computational linguistics—rule-based modeling of human language—with statistical, machine learning, and deep learning models. Together, these technologies enable computers to process human language in the form of text or voice data and to ‘understand’ its full meaning, complete with the speaker or writer’s intent and sentiment (7).
Neural Networks (NN)
Interconnected layers of software-based mathematical functions known as “neurons” form a neural network. In a neural network, neurons receive pieces of data, perform simple computations on them, and pass the results on to the next layer of units, eventually reaching the answer in the final layer. Large neural networks are a part of deep learning (8).


(1) Computer based system

(2) Perset, K. et al. (2020). A first look at the OECD’s Framework for the Classification of AI Systems, designed to give policymakers clarity. The AI Wonk (blog), OECD. AI Policy Observatory.

(3)  World Economic Forum. (2020). AI procurement in a box: AI government procurement guidelines

(4) University of California Presidential Working Group on AI. (2021) Responsible Artificial Intelligence\\ Recommendations to guide the University of California’s Artificial Intelligence strategy.

(5) Lanxon, N. et. al. (2023, March 10).  A Cheat Sheet to AI Buzzwords and Their Meanings. Accessed January 15, 2024.**

(6) SAS Insights. Machine Learning: What it is and Why It Matters.  Accessed January 15, 2024. 

(7) IBM. What is natural language processing? Accessed January 15, 2024.

(8) McKinsey & Company. An executive’s guide to AI. Accessed January 15th, 2024. 

Compiled by Dr. Carl Olimb (Mathematics) and Dr. Dan Steinwand (Computer Science)

**Requires Washington Post account