AI-1352 - USA (Washington, DC) - Field-Friendly Super Intelligence and Machine Learning Systems - Deadline January 6,2027

(1) Vendor needs to provide field–friendly super intelligence and machine learning systems. • Develop a Super Intelligence/Machine Learning (SI/ML) method that achieves the high–performance accuracy of modern Deep Neural Networks (DNNs) while mitigating their limitations.• Optimized Resource Utilization: Unlike DNNs that heuristically overparameterize to boost accuracy, the proposed model must quantitatively map parameter count to expected performance and enable users to identify the minimum configuration required for mission success, significantly reducing computational overhead, complexity, and deployment costs.• Transparency: The model must replace opaque, distributed neural layers with novel methods for interpretability and trust that go beyond the current post–hoc techniques. By making input–output logic similar to standard programming code, the model should ensure decisions are easily explainable, verifiable, and compliant with auditing standards