From [1]: "What is a Foundation Model" In recent years, a new successful paradigm for building AI systems has emerged: Train one model on a huge amount of data and adapt it to many applications. We call such a model a foundation model.

From their big paper [2] "A foundation model is any model that is trained on broad data (generally using self-supervision at scale) that can be adapted (e.g., fine-tuned) to a wide range of downstream tasks; current examples include BERT [Devlin et al.2019], GPT-3 [Brown et al. 2020], and CLIP [Radford et al. 2021]."

[1] https://crfm.stanford.edu/ [2] "On the Opportunities and Risks of Foundation Models", Bommasani/Hudson/et al

FM = Foundational Model. Titan is the model, Bedrock is the service.

According to GPT-4, and I think this is a good summary: A "Foundational Model" in AI programming refers to large-scale machine learning models that serve as a basis for a wide range of applications and tasks. These models are pre-trained on massive amounts of data and can be fine-tuned or adapted for specific tasks, domains, or applications.

One of the most well-known examples of a foundational model is the GPT (Generative Pre-trained Transformer) series developed by OpenAI. GPT models, like GPT-3 or GPT-4, are trained on large datasets containing diverse text from the internet, which enables them to generate human-like text, answer questions, translate languages, and perform various other tasks.

Foundational models are significant in the AI field because they allow researchers and developers to create a wide range of applications and solutions without having to train a new model from scratch for each specific task. This approach saves time, resources, and computational power while still providing a high level of performance across different tasks.