AI Glossary & Dictionary for “F”
Find the Flux+Form AI glossary & dictionary to help you make sense of common AI terms. Below you can find an AI Glossary & Dictionary for “F”:
Fairness Metrics: Quantitative measures used to evaluate whether an AI system produces equitable outcomes across different groups.
False Negative: A type of error where a model incorrectly predicts a positive case as negative, such as failing to flag a spam email.
False Positive: A type of error where a model incorrectly predicts a negative case as positive, such as flagging a legitimate message as spam.
Feature: An individual measurable property or characteristic of a phenomenon being observed.
Feature Engineering: The process of creating and selecting features that improve model performance.
Feature Importance: Metrics that quantify how much each feature contributes to a model’s predictions.
Federated Learning: A training approach where models are trained across multiple devices or servers holding local data sets, without centralising raw data
Feedback Loop: A cycle where a model’s predictions influence the data it receives, potentially reinforcing its own biases.
Feed‑Forward Network: A neural network architecture where information moves in one direction from input to output with no cycles.
Few‑Shot Learning: Techniques that enable models to learn new tasks from a very small number of labelled examples.
Filter: A convolutional kernel used in image processing to extract specific patterns such as edges or textures.
Fine‑Tuning: Taking a pre‑trained model and training it further on a specific task or data set to specialize its capabilities.
First‑Party Data: Information collected directly from your audience or customers, used to personalize marketing and avoid dependence on third‑party data.
Focal Loss: A loss function that down‑weights well‑classified examples to focus learning on difficult ones.
Forecasting: Using statistical methods or machine learning to predict future outcomes such as sales, demand or engagement.
Foundation Model: A large, pre‑trained model that can be adapted to a wide range of tasks, such as a general‑purpose language model.
Fourier Transform: A mathematical technique that decomposes signals into constituent frequencies; used in audio and image processing.
Framework: A set of libraries and tools that provide a structure for developing AI models and applications, like TensorFlow or PyTorch.
Frequent Pattern Mining: Discovering recurring patterns in data, such as common sequences of customer actions.
Frontier Model: A highly advanced AI model that pushes the limits of current capabilities, often requiring special governance.
Funnel Optimization: Analysing each stage of the marketing funnel and applying AI to improve conversion and retention rates.
Fuzzy Logic: Logic that allows reasoning with degrees of truth rather than strict binary decisions; used in recommendation systems.
This concludes the AI Glossary & Dictionary for “F”.