Pre-Publication V3. Foundational Model
Large scale AI models, typically utilizing transformer based architecture trained on extensive datasets across various domains, typically using unsupervised or self-supervised learning techniques that can be adapted to downstream tasks.
Deliberation Summary:
Revise to match the accepted term of art.
Pre-Publication V2. Large scale, advanced AI models, typically utilizing transformer based architecture often transformers, trained on extensive datasets across various domains, typically using unsupervised or self-supervised learning techniques that can be adapted to downstream tasks. This extensive training enables them to develop a comprehensive understanding of complex patterns in data, ranging from natural language to visual content and capable of performing a wide variety of tasks without additional training, serving as the “foundation” for many different applications.
Deliberation Summary:
(1) Describe the architecture as typically transformer-based rather than as often transformers for accuracy. (2) Remove language relating to understanding as the text should be framed in terms of pattern recognition, definition and would benefit from separating the concepts of model, dataset, and system. (3) Consider wording and caution against stating that these models perform many tasks without additional training, since specialization through prompting, retrieval, tool use, and fine-tuning is common.
Pre-Publication V1. Large scale, advanced AI models, often transformers, trained on extensive datasets across various domains, typically using unsupervised or self-supervised learning techniques. This extensive training enables them to develop a comprehensive understanding of complex patterns in data, ranging from natural language to visual content and capable of performing a wide variety of tasks without additional training, serving as the “foundation” for many different applications.