NEO is a probabilistic neural model that induces compositional programs as a learned Language of Thought from non-textual observations and executes them via a shared transition model to enable explanation-driven generalization.
arXiv preprint arXiv:1711.01134, 2017
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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.
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