Guide Labs develops interpretable AI systems that provide clear explanations for their outputs, enabling users to understand the factors and training data influencing decisions. This approach addresses the unreliability and opacity of current AI models, allowing for effective debugging and alignment with user intent.
Funding
$500K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
LVFounders
Product
Problem
Current AI systems often lack transparency, making it difficult to understand the factors and training data that influence their decisions. This opacity hinders effective debugging, reduces user trust, and complicates alignment with intended outcomes.
Solution
Guide Labs is developing interpretable AI systems and foundation models that provide clear explanations for their outputs, enabling users to understand the reasoning behind AI decisions. Their approach focuses on identifying the specific parts of a prompt, relevant factors, and influential training data responsible for a model's output. By engineering AI to be inherently interpretable, Guide Labs aims to create systems that are more reliable, debuggable, and controllable.
Target Audience
The primary audience includes AI developers, machine learning engineers, and researchers who require interpretable and auditable AI systems for debugging, alignment, and building user trust.
Features
- Prompt Concept Training: Identifies the specific parts of a prompt that drive the output.
- Factor Identification: Pinpoints the key factors responsible for a given AI output.
- Training Data Attribution: Specifies which training inputs strongly influence the model's generated output.
- Human-understandable explanations for any output generated.
- Reliable context citations to support explanations.