Artificial Intelligence Model
Artificial intelligence (AI) models are rapidly evolving, with current research focusing on improving their reliability, security, and fairness. Key areas of investigation include mitigating model errors (including adversarial attacks), ensuring robustness across diverse datasets and contexts, and addressing biases that may lead to unfair or culturally insensitive outputs. These advancements are crucial for building trust in AI systems and enabling their safe and effective deployment across various sectors, from healthcare and finance to manufacturing and autonomous systems.
Papers
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Prioritizing High-Consequence Biological Capabilities in Evaluations of Artificial Intelligence Models
Jaspreet Pannu, Doni Bloomfield, Alex Zhu, Robert MacKnight, Gabe Gomes, Anita Cicero, Thomas V. Inglesby
Online Resource Allocation for Edge Intelligence with Colocated Model Retraining and Inference
Huaiguang Cai, Zhi Zhou, Qianyi Huang
May 23, 2024
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