AI Reference 044 Neural Networks & Deep Learning Wikipedia guided Primary sources linked

Foundation Models

Definition

Foundation models are trained on broad data at scale and then adapted or prompted for many downstream tasks.

What is a foundation model?

A foundation model is a large model trained on broad data that can be adapted to many downstream tasks. The term was popularized by Stanford researchers in 2021 to describe the emerging role of large pretrained models as shared bases for many applications.

Why one model can support many tasks

Pretraining learns general statistical representations from large datasets. A single model can then be prompted, fine-tuned or connected to tools for tasks such as classification, summarization, coding or image generation.

Why the term matters

Foundation models change software economics because many applications can depend on one underlying model family. They also concentrate risk: limitations, biases, licensing terms or security issues in the base model can affect many downstream products.

Related terms, defined

Neural Networks & Deep Learning Multimodal AI Multimodal AI processes or generates multiple data types such as text, images, audio or video in one coordinated system. Neural Networks & Deep Learning Artificial Neural Networks Artificial neural networks are parameterized models built from layers of connected computational units. Training adjusts the parameters to reduce an objective function. Neural Networks & Deep Learning Backpropagation Backpropagation efficiently computes gradients through differentiable neural networks by applying the chain rule from outputs back toward earlier layers. Neural Networks & Deep Learning Convolutional Neural Networks Artificial neural networks are parameterized models built from layers of connected computational units. Training adjusts the parameters to reduce an objective function. Neural Networks & Deep Learning Recurrent Neural Networks Artificial neural networks are parameterized models built from layers of connected computational units. Training adjusts the parameters to reduce an objective function.

Reference guide and primary sources

Wikipedia is used here as a terminology and history reference guide. Current model versions, institutional statistics and product-specific claims are also linked to first-party or institutional sources because those details can change faster than encyclopedia articles.

← Self-Supervised Learning Multimodal AI →