Foundation Models
Large adaptable models trained on broad data that can be reused across many downstream tasks and modalities.
Core metadata
- ID: foundation_models
- Era: Modern
- First known date: 2021 (exact)
- Region: Global AI research community
- Review status: source_checked
- Maturity: established
Prerequisites
- AI Training Clusters (ai_training_clusters)
- Model Pretraining & Fine-Tuning (model_pretraining_finetuning)
- Transformer Architectures (transformer_architectures)
Dependents
- Advanced AI Systems (advanced_ai)
- Humanoid General-Purpose Robots (humanoid_general_purpose_robots)
- On-Device Personal Foundation Models (on_device_personal_foundation_models)
Fields
Field lanes
- Artificial Intelligence & Machine Learning: Foundation Models
Node sources
- On the Opportunities and Risks of Foundation Models (arXiv / Stanford CRFM, 2021, primary_paper) • Supports: node, maturity
Prerequisite edge evidence
Edge/source evidence summary:
- Prerequisite edges: 3
- Average edge confidence: 68%
- Prerequisite sources: 3
- expert_inference: 3
| Prerequisite | Type | Confidence | Evidence level | Note | Sources |
|---|---|---|---|---|---|
| Transformer Architectures (transformer_architectures) | enabling | 68% | expert_inference | Transformer Architectures provides a capability that enables this technology without being the only possible path. |
|
| Model Pretraining & Fine-Tuning (model_pretraining_finetuning) | enabling | 68% | expert_inference | Model Pretraining & Fine-Tuning provides a capability that enables this technology without being the only possible path. |
|
| AI Training Clusters (ai_training_clusters) | enabling | 68% | expert_inference | AI Training Clusters provides a capability that enables this technology without being the only possible path. |
|
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