menu icon

    Foundation Models

    Modern AI models that execute specific tasks in a single domain are giving way to ones that learn more generally, and work across domains and problems. Foundation models, which are trained on large, unlabeled datasets and fine-tuned for an array of applications, are driving this shift.


    Modern AI models can learn from millions of examples to help find new solutions to difficult problems. But building new systems tends to take time — and lots of data. The next wave in AI will replace task-specific models with ones that are trained on a broad set of unlabeled data that can be used for different tasks — with minimal fine-tuning. These are called foundation models. They can be the foundation for many applications of the AI model. Using self-supervised learning and fine-tuning, the model can apply information it has learned in general to a specific task. We believe that foundation models will dramatically accelerate AI adoption in business. Reducing time spent labeling data and programming models will make it much easier for businesses to dive in, allowing more companies to deploy AI in a wider range of mission-critical situations. Our goal is to bring the power of foundation models to every enterprise in a frictionless hybrid-cloud environment. Learn more about foundation models

    Our work


    Related topics