Guide Labs
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About
Guide Labs is a product-focused research company dedicated to engineering a new class of interpretable and auditable AI systems and foundation models. The problem with current AI systems is their unreliable explanations, difficulty in debugging, and challenges in control and alignment. Guide Labs' solution involves building LLMs and foundation models designed to produce human-understandable factors for outputs, reliable context citations, and to specify influencing training data. Their team boasts over 20 years of experience in interpretable machine learning, with PhDs from MIT, UMD, and MILA, and has published over two dozen research papers. They've developed the first interpretable generative diffusion model and LLM. Guide Labs aims to enable domain experts to engage in meaningful and truthful dialogue with AI systems by integrating interpretability, safety, and reliability constraints into the model development pipeline without compromising performance.
Platform
Task
Features
• detects when models latch onto spurious signals
• identifies causes of erroneous outputs
• integrates interpretability, safety, and reliability constraints
• allows fine-grained control for domain experts
• enables reliable debugging and fixing of ai systems
• specifies which training input data strongly influences model output
• produces reliable context citations
• produces human-understandable factors for any generated output
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