【专题】人工智能装备质量监督效能研究

近年来，人工智能技术迅速发展和渐趋成熟，在装备领域的应用大放异彩，极大地拓展了传统装备性能，特别是自主性和智能化水平。但人工智能算法本身固有的逻辑推理性差、具有不可解释性和需要学习训练等特性给人工智能装备的质量监督带来了新挑战，现有评价手段的欠缺也使人工智能装备发展在实用可靠方面充满不确定性。本文在总结归纳人工智能装备新特性拓展和质量监督新挑战的基础上，重点围绕有效提升人工智能装备的质量监督展开研究。提出应在夯实基础能力设施建设、注重日常数据采集整理、灵活技术状态管控、研究有效评价手段、强化人才队伍建设和闭环质量信息反馈与利用等六方面加强人工智能装备质量监督的措施建议，以期为人工智能装备的发展建设提供参考。

Improving the efficiency of quality supervision for Artificial Intelligence hardware

The rapid development and maturation of Artificial Intelligence (AI) technology in recent years have led to a surge in the adoption of AI in military hardware. This has considerably expanded the capabilities of conventional military hardware, especially in terms of autonomy and intelligence. However, new challenges have also been created in terms of quality supervision, as AI algorithms often exhibit intrinsic flaws in their logical reasoning, lack explainability, and require training. The weaknesses of current assessment methods have resulted in many practicality and reliability issues in the development of AI hardware. By reviewing the novel characteristics of AI hardware and the new challenges they create for quality supervision, this study discusses how the quality supervision of AI hardware may be conducted in an effective manner. Six suggestions are provided for improving quality supervision for AI hardware: strengthen basic capacity and infrastructure, increase attention to daily data collection and collation, implement flexible technical-status management methods, develop effective assessment methods, improve talent management, and establish information feedback loops. These suggestions may serve as a reference for the development of AI hardware.