Grounded Inference: Principles for Deterministically Encapsulated Generative Models
Quick Answer
The paper outlines foundational principles for integrating generative models into traditional systems, emphasizing deterministic encapsulation to mitigate risks.
Quick Take
It identifies four AI architecture primitives and two anti-patterns to guide engineers in safely adopting AI technologies.
Key Points
- Defines four AI architecture primitives for deterministic encapsulation of probabilistic models.
- Establishes two anti-patterns to warn engineers about common pitfalls in AI integration.
- Aims to facilitate safe incorporation of AI into traditional computational systems.
- Provides a foundation for future generative model interfaces development.
Paper Resources
Source Excerpt
The incorporation of generative models into traditional computational systems presents both enormous opportunity and tremendous peril. Although many early adopters have realized these perils at great expense, the field still requires foundational frameworks to de-risk incorporation of AI into traditional systems. This manuscript establishes this foundation through the definition of four specific primitives of AI blended architecture, designed to enable deterministic encapsulation of probabilisti
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