A semantic scheduler for LLM inference uses urgency labels and estimated remaining compute to cut waiting times for urgent requests, tested on emergency medical data.
Optimizing National Security Strategies through LLM-Driven Artificial Intelligence Integration
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abstract
As artificial intelligence and machine learning continue to advance, we must understand their strategic importance in national security. This paper focuses on unique AI applications in the military, emphasizes strategic imperatives for success, and aims to rekindle excitement about AI's role in national security. We will examine the United States progress in AI and ML from a military standpoint, discuss the importance of securing these technologies from adversaries, and explore the challenges and risks associated with their integration. Finally, we will highlight the strategic significance of AI to national security and a set of strategic imperatives for military leaders and policymakers
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cs.LG 1years
2025 1verdicts
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Semantic Scheduling for LLM Inference
A semantic scheduler for LLM inference uses urgency labels and estimated remaining compute to cut waiting times for urgent requests, tested on emergency medical data.