Applications of FMs

- Overview

Foundation models (FMs) are a form of generative artificial intelligence (GenAI). They generate output from one or more inputs (prompts) in the form of human language instructions. Models are based on complex neural networks including generative adversarial networks (GANs), transformers, and variational encoders. 

AI FMs use neural networks to learn patterns and relationships to predict the next item in a sequence. They are trained on large amounts of data and can be used for a variety of tasks, including:

• Text generation: Predicting the next word or phrase in a string of text
• Image generation: Creating a sharper, more defined version of an image
• Question answering: Answering questions with or without context
• Summarization: Summarizing text
• Code generation: Generating code

FMs are versatile and can be used to create more human-like chatbots, analyze large data sets in medical research, and more. They can be fine-tuned to perform specific tasks for different industries.

Some key features of AI foundation models include:

• Scale and architecture: Foundation models (FMs) are large and can have billions or trillions of parameters. This allows them to capture complex information from data. They often use advanced deep learning (DL) architectures, such as transformers, which are good for handling sequential data.
• Pre-training: FMs use self-supervised learning techniques to learn from large amounts of unlabeled data. This pre-training is done on a variety of datasets, including books, websites, images, and articles.
• Adaptability: FMs can learn and improve their accuracy over time. They can detect patterns and correlations that might be difficult for humans to identify.
• Transferability and fine-tuning: Fine-tuning is a key part of making AI work for businesses. It allows companies to develop custom solutions that can give them an advantage in the market.