Artificial Intelligence
Foundation Models
Large-scale machine learning models trained on vast, diverse datasets that can be adapted or fine-tuned to perform a wide variety of downstream tasks.
Explanation
Foundation models represent a significant shift in artificial intelligence, moving away from task-specific models toward general-purpose systems. These models, such as GPT-4, BERT, or CLIP, are typically trained using self-supervised learning on massive amounts of data including text, images, or code. Once trained, they possess broad capabilities that can be specialized for specific applications through fine-tuning or prompting. Their name reflects their role as a base or foundation upon which other AI applications are built, characterized by their immense scale and emergent properties that allow them to perform tasks they were not explicitly programmed for.