Interpretable and Auditable AI Systems
We are buidling a new class of interpretable AI systems and foundation models that humans can reliably debug, trust, and understand.
Introducing Guide Labs: Engineering Interpretable and Auditable AI Systems
November 17, 2024
Understand which part of the prompt is responsible for the output.
THE PROBLEM
Current AI systems and foundation models:
- produce explanations and justifications that are unreliable and unrelated to their output.
- cannot be reliably debugged and fixed.
- are difficult to control and align with current approaches.
OUR SOLUTION
LLMs & Foundation models engineered to be interpretable
- Produces human-understandable factors for any output it generates.
- Produces reliable context citations.
- Specifies which training input data strongly influences the model’s generated output.
About Our Team
20+
YEARS OF EXPERIENCE
on interpretable machine learning with PhDs from MIT, UMD, & MILA.
24+
RESEARCH PAPERS
on interpretability published at top ML conferences
Developed the first
interpretable generative diffusion model & LLM
We are hiring
Proven best practices
