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4C Framework

An AI fluency framework built on four core competencies: Choice, Clarity, Critical Thinking, and Consistency.

4D Framework

A structured approach to AI fluency focusing on four key competencies.

AI Fluency

The ability to effectively work with and understand AI systems in practical contexts.

AI in Career Planning

Using AI tools to support career planning, job searching, and professional development.

AI in Learning

Using artificial intelligence to support and enhance learning new concepts.

Accuracy

A metric measuring the percentage of correct predictions made by a machine learning model.

Algorithm

A set of rules or instructions that a computer follows to solve a problem or complete a task.

Algorithm

A step-by-step procedure or formula for solving a problem or performing a task.

Anthropic

AI safety company focused on developing safe, beneficial AI systems using constitutional AI methods, creator of Claude.

Artificial Intelligence (AI)

The simulation of human intelligence in machines programmed to think, learn, and make decisions like humans.

Artificial Intelligence (AI)

The simulation of human intelligence in machines programmed to think, learn, and make decisions.

Bard

Google's original AI chatbot, now rebranded as Gemini.

Big Data

Extremely large datasets that require specialized tools and techniques to store, process, and analyze.

ChatGPT

A conversational AI model developed by OpenAI that can engage in human-like text conversations and assist with various tasks.

Chatbot

Computer programs designed to simulate human conversation through text or voice.

Claude

Anthropic's AI assistant family designed for safe, helpful, and honest AI interactions with advanced reasoning capabilities.

Content Generation

AI systems that create original text, images, audio, video, or code content.

Conversational SMS

Text messaging systems enabling natural two-way conversations between businesses and customers.

Description (AI Context)

The ability to communicate clearly and effectively with AI systems.

GPT

A family of large language models capable of generating human-like text, developed by OpenAI.

Generative AI

AI systems that can create new content such as text, images, audio, or code based on training data.

Machine Learning

A subset of AI that enables computers to learn and improve from experience without being explicitly programmed.

Machine Learning (ML)

A subset of AI where systems learn patterns from data without explicit programming.

Neural Network

A computational model inspired by the human brain, consisting of interconnected nodes (neurons) for processing information.

No-Code AI

Platforms that enable AI application development without traditional programming, using visual interfaces and pre-built components.

Training Data

The dataset used to teach a machine learning model to make predictions or decisions.

Training Data

The dataset used to teach an AI model how to make predictions or decisions.

AI Ethics

The study of moral principles and guidelines governing AI development and deployment.

AI Leverage

The ability to achieve disproportionate outputs through AI tools and automation, creating exponential productivity gains.

AI-First Workflows

Rebuilding processes from scratch around AI capabilities rather than bolting AI onto existing workflows.

API Rate Limiting

Controlling the frequency of API requests to manage costs and prevent service overload.

Amazon Lex

Amazon's cloud service for building conversational interfaces using voice and text.

Backpropagation

The process of adjusting weights in a neural network by propagating errors backward from the output layer to improve accuracy.

Batch Size

The number of training examples processed together in one forward/backward pass.

Bias

Systematic errors or prejudices in AI systems that can lead to unfair or discriminatory outcomes.

Black Box

AI systems whose internal decision-making processes are opaque and unexplainable.

Cloud AI

AI services and computations delivered through cloud platforms for scalable access.

Clustering

A machine learning technique that groups similar data points into distinct clusters.

Component-Based Architecture

Building applications using reusable, modular components that can incorporate AI features.

Computer Vision

The field of AI that enables machines to interpret and understand visual information from images and videos.

Confusion Matrix

A table used to evaluate classification model performance by showing true vs predicted classifications.

Context Window

The amount of text an AI model can consider at once when generating responses, measured in tokens.

Conversational User Interface (CUI)

Interfaces enabling natural language conversations between users and computers.

Data Mining

The process of discovering patterns, correlations, and insights from large datasets using statistical techniques.

Decision Tree

A tree-like model used for decision-making and conversation flow in AI systems.

Deep Learning

A subset of machine learning using neural networks with multiple layers to learn complex patterns in data.

Delegation (AI Context)

The competency of knowing when humans should do work versus when AI should.

Diligence (AI Context)

Ensuring responsible, transparent, and accountable interaction with AI systems.

Discernment (AI Context)

The ability to critically evaluate and assess AI outputs and recommendations.

Edge AI

Running AI computations locally on devices rather than in the cloud.

Epoch

One complete pass through the entire training dataset during model training.

F1 Score

The harmonic mean of precision and recall, balancing both measures in a single metric.

Feature Extraction

The process of identifying and selecting the most relevant characteristics from raw data for machine learning.

Hallucination

When AI models generate false or nonsensical information that appears plausible but is not based on training data.

Hyperparameter

A configuration setting for a machine learning model that is set before training begins.

LLMs

Advanced AI models trained on massive text datasets to understand and generate human-like language.

Large Language Model (LLM)

AI models trained on vast amounts of text data to understand and generate human-like language.

Machine Translation

AI systems that automatically translate text or speech from one language to another.

Natural Language Processing

The branch of AI that helps computers understand, interpret, and generate human language.

Neural Network

A computing system modeled after the human brain's network of neurons, used to recognize patterns and make decisions.

Overfitting

When a machine learning model learns training data too specifically, performing poorly on new data.

Overfitting

When a model performs well on training data but poorly on new, unseen data due to learning noise or details specific to the training set.

Precision

The fraction of positive predictions that were actually correct, measuring prediction quality.

Prompt Engineering

The practice of crafting effective instructions or queries to get desired outputs from AI models.

Recall (Sensitivity)

The fraction of actual positive cases that were correctly identified by the model.

Red Teaming

A cybersecurity practice where teams simulate attacks to identify vulnerabilities and weaknesses in AI systems.

Reinforcement Learning

A type of ML where an agent learns to make decisions by performing actions and receiving rewards or penalties.

Scalability

The capability of AI systems to handle increasing demands while maintaining performance.

Semantic Search

AI-powered search technology that understands the meaning and context of queries rather than just matching keywords.

Semi-Supervised Learning

A machine learning technique that uses both labeled and unlabeled data for training.

Speech and Audio Processing

AI techniques for analyzing, understanding, and generating spoken language and audio content.

Supervised Learning

Machine learning using labeled training data where the correct answers are provided during training.

Token

Basic units of text that AI models process, roughly equivalent to words or word parts.

Transfer Learning

A technique where models trained on one task are adapted for related tasks.

Transfer Learning

Using a pre-trained model on a new, related task to leverage existing knowledge and reduce training time.

Underfitting

When a machine learning model is too simple to capture underlying patterns in the data.

Unsupervised Learning

Machine learning using data without labeled examples, finding hidden patterns or structures.

Validation Data

A subset of data used to tune model hyperparameters and prevent overfitting during training.

AI Act

The European Union's comprehensive legislation regulating artificial intelligence systems based on risk levels.

AI Alignment

Ensuring AI systems behave according to human values and intentions, a critical area of AI safety research.

AI Governance

The frameworks, policies, and processes for overseeing AI development, deployment, and use.

AUC

A single number summarizing binary classifier performance across all threshold settings.

Active Learning

A machine learning approach where algorithms actively select the most informative data to learn from.

Adversarial Machine Learning

The study of attacks against machine learning systems and defenses to make them more robust.

Agent

AI systems that can take actions and make decisions autonomously to achieve specific goals.

Attention Mechanism

A technique that allows AI models to focus on specific parts of input data when making predictions.

Attention Mechanism

A technique in neural networks that focuses on specific parts of input data to improve performance on tasks like translation.

BERT

A pre-trained language model that understands context from both directions in a sentence.

Backpropagation

The algorithm used to train neural networks by propagating errors backward through layers.

Bayesian Optimization

A probabilistic approach to optimize hyperparameters by modeling the objective function as a probability distribution.

CNNs

A type of deep neural network particularly effective for analyzing visual imagery, using convolutional layers to detect features.

Cognitive Computing

AI systems designed to simulate human thought processes and reasoning.

Complex Systems Analysis

Using AI to understand and model complex systems with many interconnected components.

Constitutional AI

Training methodology that uses a set of principles to guide AI behavior and reduce harmful outputs.

Diffusion Models

Generative AI models that create content by learning to reverse a noise-adding process.

Dropout

A regularization technique that randomly sets neurons to zero during training to prevent overfitting.

Embedding

Converting data into numerical vector representations that capture semantic meaning and relationships.

Emergent Behavior

Unexpected capabilities that arise from AI systems beyond their explicit programming.

Explainable AI (XAI)

AI systems designed to provide clear, understandable explanations for their decisions and predictions.

Explainable AI (XAI)

Techniques and methods to make the decisions and predictions of AI models transparent and understandable to humans.

Federated Learning

A distributed learning approach that trains models across devices without centralizing data.

Federated Learning

A decentralized ML approach where models are trained across multiple devices without exchanging the data itself.

Few-Shot Learning

A machine learning technique that enables models to learn new tasks with only a few training examples.

Fine-tuning

The process of adapting a pre-trained model to perform well on a specific task or dataset.

Foundation Model

Large-scale AI models trained on broad datasets that can be adapted for various downstream tasks.

Fuzzy Logic

A computing approach handling degrees of truth rather than binary true/false logic.

GANs

A machine learning architecture with two competing neural networks that can generate realistic content.

Generative Adversarial Network (GAN)

A deep learning architecture where two neural networks compete to create realistic synthetic data.

Gradient Descent

An optimization algorithm that finds the minimum of a loss function through iterative descent.

Hyperparameter

Configuration settings that control the learning process of machine learning algorithms.

Loss Function

A mathematical function measuring the difference between predicted and actual values.

MLOps

Practices for deploying, monitoring, and maintaining machine learning models in production environments.

Multimodal AI

AI systems that can process and understand multiple types of data simultaneously.

RAG

Combining AI generation with real-time information retrieval for more accurate and up-to-date responses.

RNNs

Neural networks designed to process sequential data by maintaining memory of previous inputs.

ROC Curve

A graphical plot showing binary classifier performance across different threshold settings.

Reinforcement Learning

A type of machine learning where agents learn optimal actions through trial and error and reward feedback.

Robotics

The integration of AI with mechanical systems to create autonomous robots.

Robustness

The ability of AI systems to maintain performance when faced with unexpected inputs or changes.

SLMs

Compact language models designed for efficiency and deployment on resource-constrained devices.

Self-Supervised Learning

A machine learning approach where models learn from unlabeled data by creating their own supervision signals.

Transformer

A neural network architecture that uses attention mechanisms, fundamental to modern language models like GPT and Claude.

Variance

The amount by which a model's predictions change when trained on different datasets.

Vector Database

Specialized databases for storing and searching high-dimensional vector representations of data.

Zero-Shot Learning

A machine learning approach where models can perform tasks they have never seen during training.

Roxanne Persaud

Christopher Tague

William Magnarelli

Christopher Ryan

Christopher Friend

Crystal Peoples-Stokes

Angelo Santabarbara

Patricia Canzoneri-Fitzpatrick

Monica Martinez

Jacob Blumencranz

Samuel Pirozzolo

Brian Manktelow

David McDonough

Stacey Pheffer Amato

Michelle Hinchey

Zohran Kwame Mamdani

Patrick Gallivan

Jeffrey Dinowitz

Andrew Gounardes

Jonathan Rivera

William Barclay

Alec Brook-Krasny

Jonathan Jacobson

Anthony Palumbo

Nathalia Fernandez

Sarahana Shrestha

Jenifer Rajkumar

Kristen Gonzalez

Christopher Burdick

Michaelle Solages

Alphabetized