"AI will be integrated into every surface area we interact with in our world. When that is the case, we should know which models we are interacting with."
Frontier-scale AI models stand as the most complex machine intelligences ever produced. They are already incredibly powerful and capable of doing things we've not yet imagined. However, to get the most we can from these systems, we need to understand how they interface with the world, with each other, how they work internally, and under what conditions. As we understand these frontier models at a deeper level, we can build the necessary tools to make sure we are using them under the best possible conditions.
VAIL is pioneering the discipline of Model Informatics to learn about models as complex information systems. Just as bioinformatics revolutionized how we handle genetic data, Model Informatics is transforming how we intelligently interact with artificial intelligence.
As we uncover important properties and ground truths about AI systems, we build and distribute the tools needed to verify these properties to ensure we are using AI as designed.
Model Informatics represents a new scientific discipline focused on understanding AI models as complex information systems. This involves developing scientific methodologies to study model behavior, relationships, and properties at scale.
By treating models as information systems, we can apply rigorous methods to understand their internal workings, external behaviors, and relationships with other models. This approach enables us to move beyond intuitive understanding to measurable, reproducible knowledge about AI systems.
Developing methods to understand how models process information, make decisions, and exhibit emergent behaviors across different contexts and inputs.
Creating frameworks to understand how models relate to each other through fine-tuning, knowledge distillation, and architectural similarities.
Establishing methodologies to identify and catalog the fundamental properties that define a model's capabilities and limitations.
Creating unique signatures that identify models through their runtime behavior, enabling verification of model identity across different deployment environments.
Developing tools that can confirm specific model properties and capabilities in real-time, ensuring deployed systems match expected specifications.
Building verification systems that can be deployed at any point where models are accessed, from APIs to edge devices to web applications.
Based on our Model Informatics research, we develop practical tools that can verify model properties and identities in real-world deployment scenarios, ensuring transparency and trust wherever AI systems are accessed.
These verification tools translate our scientific understanding of models into deployable systems that can operate in production environments. By embedding verification at every access point, we ensure that users always know exactly which model they're interacting with and can trust its properties.
Our work addresses a fundamental challenge in AI deployment: how do we ensure that the AI systems we interact with are what we expect them to be? As AI becomes embedded in critical infrastructure, from healthcare to finance to autonomous systems, the ability to verify model properties becomes essential for safety, reliability, and trust.
By pioneering Model Informatics and building verification tools, VAIL enables a future where AI deployment comes with the same level of transparency and verification that we expect from other critical systems. Organizations can deploy AI with confidence, users can trust the systems they interact with, and researchers can build upon a foundation of verifiable knowledge about model behavior.