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Matthijs M. Maas 《Contemporary Security Policy》2019,40(3):285-311
Many observers anticipate “arms races” between states seeking to deploy artificial intelligence (AI) in diverse military applications, some of which raise concerns on ethical and legal grounds, or from the perspective of strategic stability or accident risk. How viable are arms control regimes for military AI? This article draws a parallel with the experience in controlling nuclear weapons, to examine the opportunities and pitfalls of efforts to prevent, channel, or contain the militarization of AI. It applies three analytical lenses to argue that (1) norm institutionalization can counter or slow proliferation; (2) organized “epistemic communities” of experts can effectively catalyze arms control; (3) many military AI applications will remain susceptible to “normal accidents,” such that assurances of “meaningful human control” are largely inadequate. I conclude that while there are key differences, understanding these lessons remains essential to those seeking to pursue or study the next chapter in global arms control. 相似文献
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针对传统最小均方误差(Least Mean Square, LMS)自适应滤波算法由于步长固定,在解决稳态误差与收敛性之间的关系时,始终处于矛盾状态的问题,在对传统的固定步长LMS自适应滤波算法分析的基础上,根据变步长LMS自适应滤波算法的步长调整原则,通过构造步长因子与误差信号的非线性函数,提出了一种基于正态分布曲线的分段式变步长LMS自适应滤波算法,并分析了参数取值对算法性能的影响。针对实际信号处理过程中参考信号难以选取的问题,提出了一种基于分裂阵的参考信号选取方法。理论和海试数据分析结果表明:该算法的收敛速度和稳态误差明显优于固定步长的LMS自适应滤波算法和基于Sigmoid函数的变步长LMS自适应滤波算法。 相似文献
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