Kyndi Briefing Note

Kyndi Briefing Note

Document ID: CGBN107 | Last Updated: Jan. 24, 2018

Right now, too much of what AI systems do are a “black box”. We have little visibility into how decisions are being made, conclusions drawn, objects identified, and more. An emerging area of AI called Explainable AI (XAI) aims to address the black-box decision making of AI systems and provide a way to inspect and understand the decision-making steps and models that the AI system is using to make the decisions. Kyndi is solving this problem of XAI by providing an approach for Machine Learning (ML) that inherently has explainability as a product of the process. Rather than use neural network approaches, Kyndi is leveraging patented advancements in knowledge graph technology and ontology generation.

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Right now, too much of what AI systems do are a "black box". We have little visibility into how decisions are being made, conclusions drawn, objects identified, and more. An emerging area of AI called Explainable AI (XAI) aims to address the black-box decision making of AI systems and provide a way to inspect and understand the decision-making steps and models that the AI system is using to make the decisions. Kyndi is solving this problem of XAI by providing an approach for Machine Learning (ML) that inherently has explainability as a product of the process. Rather than use neural network approaches, Kyndi is leveraging patented advancements in knowledge graph technology and ontology generation.
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