NEO induces compositional latent programs as world theories from observations and executes them to enable explanation-driven generalization.
arXiv preprint arXiv:1711.01134 , year=
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Explicit provenance across the full agentic AI lifecycle is the necessary condition for making responsibility computable and actionable.
A method is presented for calculating a transparency metric for ML model pipelines by analyzing the visibility of contributions from data sources and human developers.
Advanced AI systems are unexplainable in full and produce explanations that humans cannot comprehend.
citing papers explorer
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Learning to Theorize the World from Observation
NEO induces compositional latent programs as world theories from observations and executes them to enable explanation-driven generalization.
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Responsible Agentic AI Requires Explicit Provenance
Explicit provenance across the full agentic AI lifecycle is the necessary condition for making responsibility computable and actionable.
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Quantifying Transparency of Machine Learning Systems through Analysis of Contributions
A method is presented for calculating a transparency metric for ML model pipelines by analyzing the visibility of contributions from data sources and human developers.
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Unexplainability and Incomprehensibility of Artificial Intelligence
Advanced AI systems are unexplainable in full and produce explanations that humans cannot comprehend.