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.
Overview
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
Mitigating the environmental harm of PFAS ‘forever chemicals’
NewsKim Martineau- Accelerated Discovery
- AI
- Exploratory Science
- Foundation Models
- Generative AI
- Natural Language Processing
What is red teaming for generative AI?
ExplainerKim Martineau- Adversarial Robustness and Privacy
- AI
- AI Testing
- Fairness, Accountability, Transparency
- Foundation Models
- Natural Language Processing
- Security
- Trustworthy AI
Generative AI could offer a faster way to test theories of how the universe works
NewsKim Martineau- AI
- AI for Asset Management
- AI for Business Automation
- AI for IT
- Automated AI
- Foundation Models
A faster, systematic way to train large language models for enterprise
ResearchKim Martineau- AI
- Foundation Models
- Generative AI
In search of AI algorithms that mimic the brain
Q & AKim Martineau- AI
- Foundation Models
- Generative AI
- Machine Learning
- Science
A crystal ball made of AI transformers
ExplainerKim Martineau- AI
- AI for Business Automation
- Foundation Models
- Scaling AI
- See more of our work on Foundation Models
Publications
- Chen Chen
- Ruizhe Li
- et al.
- 2024
- ICLR 2024
- Yu-Lin Tsai
- Chia-yi Hsu
- et al.
- 2024
- ICLR 2024
- Yan Liu
- Yu Liu
- et al.
- 2024
- ICLR 2024
- Hongyi Wang
- Felipe Maia Polo
- et al.
- 2024
- ICLR 2024
- Subha Maity
- Mayank Agarwal
- et al.
- 2024
- ICLR 2024
- Marcus Min
- Robin (Yangruibo) Ding
- et al.
- 2024
- ICLR 2024