AINeutralarXiv – CS AI · 18h ago6/10
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Unambiguous Representations in Neural Networks: An Information-Theoretic Approach to Intentionality
Researchers introduce an information-theoretic framework to measure representational ambiguity in neural networks, demonstrating that network connectivity structures can encode unambiguous content independent of behavioral performance. Using MNIST classification experiments, they achieve 100% accuracy in identifying output neuron class identity from relational structure alone in dropout-trained networks, suggesting neural systems can exhibit the low-ambiguity representations theorized as necessary for consciousness.