A NetAI Manifesto (Part II): Less Hubris, more Humility
A NetAI Manifesto (Part II): Less Hubris, more Humility
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NetAI 宣言（第二部分）：少一点傲慢，多一点谦虚
DOI：
10.1145/3626570.3626610
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Guo, Wenbo
中科院分区：
文献类型：
--
作者：
Willinger, Walter;Gupta, Arpit;Beltiukov, Roman;Guo, Wenbo
应用人工智能（AI）和机器学习（ML）的最新技术来改进和自动化解决现实世界网络安全和性能问题（简称NetAI）所需的决策，这在网络研究人员中引起了极大的兴奋。然而，网络运营商在其生产网络中部署基于NetAI的解决方案时仍然非常不情愿。在本宣言的第一部分，我们认为，为了获得运营商的信任，研究人员将不得不追求一种比过去更科学的方法来对待NetAI，即努力开发可解释和可推广的学习模型。在本文中，我们更进一步，并重申这种“开放的NetAI研究”将要求对NetAI的自信傲慢让位于健康的谦逊。与其继续颂扬黑箱模型的优点和“魔力”，这些模型在很大程度上混淆了所使用的数据在训练这些模型中的关键作用，还需要协调一致的研究工作来设计NetAI驱动的代理或系统，这些代理或系统在部署到生产环境中时可以预期表现良好，并且在面对模糊的情况和现实世界的不确定性时也需要表现出强大的鲁棒性。我们描述了一个这样的努力，旨在开发一个新的机器学习管道，用于生成努力满足这些期望和要求的训练模型。
The application of the latest techniques from artificial intelligence (AI) and machine learning (ML) to improve and automate the decision-making required for solving real-world network security and performance problems (NetAI, for short) has generated great excitement among networking researchers. However, network operators have remained very reluctant when it comes to deploying NetAIbased solutions in their production networks. In Part I of this manifesto, we argue that to gain the operators' trust, researchers will have to pursue a more scientific approach towards NetAI than in the past that endeavors the development of explainable and generalizable learning models. In this paper, we go one step further and posit that this "opening up of NetAI research" will require that the largely self-assured hubris about NetAI gives way to a healthy dose humility. Rather than continuing to extol the virtues and "magic" of black-box models that largely obfuscate the critical role of the utilized data play in training these models, concerted research efforts will be needed to design NetAI-driven agents or systems that can be expected to perform well when deployed in production settings and are also required to exhibit strong robustness properties when faced with ambiguous situations and real-world uncertainties. We describe one such effort that is aimed at developing a new ML pipeline for generating trained models that strive to meet these expectations and requirements.
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影响因子：
6.4
作者：
Bradshaw, Jeffrey M.;Hoffman, Robert R.;Woods, David D.
通讯作者：
Woods, David D.
DOI：
10.1145/3548606.3560609
发表时间：
2022-11
期刊：
Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security
影响因子：
--
作者：
A. Jacobs;Roman Beltiukov;W. Willinger;R. Ferreira;Arpit Gupta;L. Granville
通讯作者：
A. Jacobs;Roman Beltiukov;W. Willinger;R. Ferreira;Arpit Gupta;L. Granville
NetAI 宣言（第一部分）：更少的探索，更多的科学
DOI：
10.1145/3626570.3626609
发表时间：
2023
期刊：
ACM SIGMETRICS Performance Evaluation Review
影响因子：
--
作者：
Willinger, Walter;Gupta, Arpit;Jacobs, Arthur S.;Beltiukov, Roman;Ferreira, Ronaldo A.;Granville, Lisandro
通讯作者：
Granville, Lisandro
自治的风险
DOI：
--
发表时间：
2016
期刊：
影响因子：
--
作者：
D. Woods
通讯作者：
D. Woods