A vector generalization of fusion-fission group dynamics from physics forecasts when AI behavior shifts to undesirable states, validated at 90 percent across seven models and prior to real-world data.
Accuracy of a Generative Artificial Intelligence Model in a Complex Diagnostic Challenge
4 Pith papers cite this work, alongside 927 external citations. Polarity classification is still indexing.
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Tailored visualizations for OTC drug instructions improve response time and usability over text-only formats for both lay users and professionals, supported by a new taxonomy and generalizable design workflow.
Enhanced Baymex with parallelization and adaptive steering yields statistically similar or better classification performance than decision trees, logistic regression, naive Bayes and random forests on clinical data while returning multiple compact, inspectable Bayesian networks.
LLMs cannot solve the medical treatment problem through imitation alone because it requires evidence from experiments or observations, posing ethical challenges for training such systems.
citing papers explorer
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Fusion-fission forecasts when AI will shift to undesirable behavior
A vector generalization of fusion-fission group dynamics from physics forecasts when AI behavior shifts to undesirable states, validated at 90 percent across seven models and prior to real-world data.
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Enhance Comprehension of Over-the-Counter Drug Instructions for the General Public and Medical Professionals through Visualization Design
Tailored visualizations for OTC drug instructions improve response time and usability over text-only formats for both lay users and professionals, supported by a new taxonomy and generalizable design workflow.
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Parallel Adaptive Multi-Objective Evolutionary Learning of Discretized Bayesian Network Classifiers for Clinical Data
Enhanced Baymex with parallelization and adaptive steering yields statistically similar or better classification performance than decision trees, logistic regression, naive Bayes and random forests on clinical data while returning multiple compact, inspectable Bayesian networks.
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Treatment, evidence, imitation, and chat
LLMs cannot solve the medical treatment problem through imitation alone because it requires evidence from experiments or observations, posing ethical challenges for training such systems.