我们旨在通过考虑网络钓鱼，脆弱性发现以及修补和剥削之间的动态来证明数学模型对网络安全技术进步的政策辩论的价值。然后，我们将输入调整为那些数学模型，以匹配其基础技术的一些可能进步。我们发现AI对网络钓鱼的影响可能被高估，但可能导致更多攻击未被发现。脆弱性发现的进步有可能帮助攻击者比防守者更多。与编写补丁程序的自动化相比，编写利用的自动化对攻击者更有用，尽管有助于更快地部署补丁的进步具有比任何一个更具影响力的潜力。

人工智能（AI）有可能极大地改善社会，但是与任何强大的技术一样，它的风险和责任也增加。当前的AI研究缺乏有关如何管理AI系统（包括投机性长期风险）的长尾风险的系统讨论。请记住，AI可能是提高人类的长期潜力不可或缺的一部分，人们担心建立更聪明，更强大的AI系统最终可能会导致比我们更强大的系统。有人说这就像玩火，并推测这可能会造成生存风险（X风险）。为了增加这些讨论，我们回顾了来自危害分析和系统安全的时间测试概念的集合，这些概念旨在将大型流程引导到更安全的方向上。然后，我们讨论AI研究人员如何对AI系统的安全产生长期影响。最后，我们讨论如何稳健地塑造将影响安全和一般能力之间平衡的过程。

随着数字时代的出现，由于技术进步，每天的任务都是自动化的。但是，技术尚未为人们提供足够的工具和保障措施。随着互联网连接全球越来越多的设备，确保连接设备的问题以均匀的螺旋速率增长。数据盗窃，身份盗窃，欺诈交易，密码妥协和系统漏洞正在成为常规的日常新闻。最近的人工智能进步引起了网络攻击的激烈威胁。 AI几乎应用于不同科学和工程的每个领域。 AI的干预不仅可以使特定任务自动化，而且可以提高效率。因此，很明显，如此美味的传播对网络犯罪分子来说是非常开胃的。因此，传统的网络威胁和攻击现在是``智能威胁''。本文讨论了网络安全和网络威胁，以及传统和智能的防御方式，以防止网络攻击。最终，结束讨论，以潜在的潜在前景结束讨论AI网络安全。

机器学习（ML）系统的大小迅速增加，正在获取新功能，并且越来越多地部署在高赌注设置中。与其他强大的技术一样，ML的安全应成为主要的研究优先权。为了应对ML的新兴安全挑战，例如由最近的大型模型引入的策略，我们为ML安全提供了新的路线图，并完善了现场需要解决的技术问题。我们为研究提供了四项问题，即危害危险（“鲁棒性”），识别危险（“监测”），转向ML系统（“对齐”），减少部署危险（“外部安全性”）。在整个过程中，我们澄清了每个问题的动机并提供了具体的研究方向。

讨论了与科学，工程，建筑和人为因素相关的月球表面上的运输设施问题。未来十年制造的后勤决策可能对财务成功至关重要。除了概述一些问题及其与数学和计算的关系外，本文还为决策者，科学家和工程师提供了有用的资源。

Artificial Intelligence (AI) is one of the most transformative technologies of the 21st century. The extent and scope of future AI capabilities remain a key uncertainty, with widespread disagreement on timelines and potential impacts. As nations and technology companies race toward greater complexity and autonomy in AI systems, there are concerns over the extent of integration and oversight of opaque AI decision processes. This is especially true in the subfield of machine learning (ML), where systems learn to optimize objectives without human assistance. Objectives can be imperfectly specified or executed in an unexpected or potentially harmful way. This becomes more concerning as systems increase in power and autonomy, where an abrupt capability jump could result in unexpected shifts in power dynamics or even catastrophic failures. This study presents a hierarchical complex systems framework to model AI risk and provide a template for alternative futures analysis. Survey data were collected from domain experts in the public and private sectors to classify AI impact and likelihood. The results show increased uncertainty over the powerful AI agent scenario, confidence in multiagent environments, and increased concern over AI alignment failures and influence-seeking behavior.

防御网络攻击的计算机网络需要及时应对警报和威胁情报。关于如何响应的决定涉及基于妥协指标的多个节点跨多个节点协调动作，同时最大限度地减少对网络操作的中断。目前，PlayBooks用于自动化响应过程的部分，但通常将复杂的决策留给人类分析师。在这项工作中，我们在大型工业控制网络中提出了一种深度增强学习方法，以便在大型工业控制网络中进行自主反应和恢复。我们提出了一种基于关注的神经结构，其在保护下灵活地灵活。要培训和评估自治防御者代理，我们提出了一个适合加强学习的工业控制网络仿真环境。实验表明，学习代理可以有效减轻在执行前几个月几个月的可观察信号的进步。所提出的深度加强学习方法优于模拟中完全自动化的Playbook方法，采取更少的破坏性动作，同时在网络上保留更多节点。学习的政策对攻击者行为的变化也比PlayBook方法更加强大。

\ EMPH {人工智能}（AI）系统越来越多地参与影响我们生活的决策，确保自动决策是公平的，道德已经成为最优先事项。直观地，我们觉得类似人的决定，人工代理人的判断应该必然地以一些道德原则为基础。然而，如果有关决定所基础的所有有关因素的全部信息，可以真正伦理（人类或人为）和公平（根据任何道德理论）和公平（根据公平的任何概念）的规定在决策时。这提出了两个问题：（1）在设置中，我们依赖使用通过监督学习获得的分类器的AI系统，存在一些感应/泛化，即使在学习期间也可能不存在一些相关属性。 （2）根据游戏揭示任何 - 无论是道德的纯策略都不可避免地易于剥削，建模这些决定。此外，在许多游戏中，只能通过使用混合策略来获得纳什均衡，即实现数学上最佳结果，决定必须随机化。在本文中，我们认为，在监督学习设置中，存在至少以及确定性分类器的随机分类器，因此在许多情况下可能是最佳选择。我们支持我们的理论效果，具有一个实证研究，表明对随机人工决策者的积极社会态度，并讨论了与使用与当前的AI政策和标准化举措相关的随机分类器相关的一些政策和实施问题。

Recent years have seen a proliferation of research on adversarial machine learning. Numerous papers demonstrate powerful algorithmic attacks against a wide variety of machine learning (ML) models, and numerous other papers propose defenses that can withstand most attacks. However, abundant real-world evidence suggests that actual attackers use simple tactics to subvert ML-driven systems, and as a result security practitioners have not prioritized adversarial ML defenses. Motivated by the apparent gap between researchers and practitioners, this position paper aims to bridge the two domains. We first present three real-world case studies from which we can glean practical insights unknown or neglected in research. Next we analyze all adversarial ML papers recently published in top security conferences, highlighting positive trends and blind spots. Finally, we state positions on precise and cost-driven threat modeling, collaboration between industry and academia, and reproducible research. We believe that our positions, if adopted, will increase the real-world impact of future endeavours in adversarial ML, bringing both researchers and practitioners closer to their shared goal of improving the security of ML systems.

众多学者对超级AIS接管世界的可能性进行了深入的研究。本文重点介绍了超级掌权的前提下的多ai竞争方案。首先，本文指出了支持单ai统治的现有参数的缺陷，并提出了有利于多AI竞争的论点。然后，文章得出结论，多AI竞争情况是不可忽略的可能性。然后，注意将多AI竞争比单个AI掌权的情况更好地对人类的整体利益更好。在分析了最佳，最坏和中间场景之后，该文章得出结论，多AI竞争对人类更有利。最后，考虑到与多个AI的最佳情况相关的因素，该文章对AI开发中当前的计划提出了一些建议。

Alphazero，Leela Chess Zero和Stockfish Nnue革新了计算机国际象棋。本书对此类引擎的技术内部工作进行了完整的介绍。该书分为四个主要章节 - 不包括第1章（简介）和第6章（结论）：第2章引入神经网络，涵盖了所有用于构建深层网络的基本构建块，例如Alphazero使用的网络。内容包括感知器，后传播和梯度下降，分类，回归，多层感知器，矢量化技术，卷积网络，挤压网络，挤压和激发网络，完全连接的网络，批处理归一化和横向归一化和跨性线性单位，残留层，剩余层，过度效果和底漆。第3章介绍了用于国际象棋发动机以及Alphazero使用的经典搜索技术。内容包括minimax，alpha-beta搜索和蒙特卡洛树搜索。第4章展示了现代国际象棋发动机的设计。除了开创性的Alphago，Alphago Zero和Alphazero我们涵盖Leela Chess Zero，Fat Fritz，Fat Fritz 2以及有效更新的神经网络（NNUE）以及MAIA。第5章是关于实施微型α。 Shexapawn是国际象棋的简约版本，被用作为此的示例。 Minimax搜索可以解决六ap峰，并产生了监督学习的培训位置。然后，作为比较，实施了类似Alphazero的训练回路，其中通过自我游戏进行训练与强化学习结合在一起。最后，比较了类似α的培训和监督培训。

数字化和远程连接扩大了攻击面，使网络系统更脆弱。由于攻击者变得越来越复杂和资源丰富，仅仅依赖传统网络保护，如入侵检测，防火墙和加密，不足以保护网络系统。网络弹性提供了一种新的安全范式，可以使用弹性机制来补充保护不足。一种网络弹性机制（CRM）适应了已知的或零日威胁和实际威胁和不确定性，并对他们进行战略性地响应，以便在成功攻击时保持网络系统的关键功能。反馈架构在启用CRM的在线感应，推理和致动过程中发挥关键作用。强化学习（RL）是一个重要的工具，对网络弹性的反馈架构构成。它允许CRM提供有限或没有事先知识和攻击者的有限攻击的顺序响应。在这项工作中，我们审查了Cyber​​恢复力的RL的文献，并讨论了对三种主要类型的漏洞，即姿势有关，与信息相关的脆弱性的网络恢复力。我们介绍了三个CRM的应用领域：移动目标防御，防守网络欺骗和辅助人类安全技术。 RL算法也有漏洞。我们解释了RL的三个漏洞和目前的攻击模型，其中攻击者针对环境与代理商之间交换的信息：奖励，国家观察和行动命令。我们展示攻击者可以通过最低攻击努力来欺骗RL代理商学习邪恶的政策。最后，我们讨论了RL为基于RL的CRM的网络安全和恢复力和新兴应用的未来挑战。

该体验表明，配合和通信计算系统包括隔离的单处理器，具有严重的性能限制。在他经典的“初稿”von neumann警告说，使用“太快的处理器”验证他简单的“程序”（但不是他的计算模式！）;此外，使用用于模仿神经元操作的经典计算范例，是不健全的。 Amdahl添加了包含许多处理器的大型机器具有固有的劣势。鉴于Ann的组件彼此严重沟通，它们是由大量设计/制造的用于传统计算的组件构建，此外，他们试图使用不正当的技术解决方案模仿生物运行，其可实现的有效载荷计算性能在概念上适度。基于AI的系统生成的工作量的类型导致了极低的有效载荷计算性能，以及其设计/技术将其尺寸限制在“玩具”级系统之上：基于处理器的ANN系统的缩放是强烈的非线性的。鉴于ANN系统的扩散和不断增长的规模，我们建议提前估计设备或应用的效率的想法。通过分析发布的测量，我们提供了证据表明数据转移时间的作用大大影响了ANNS性能和可行性。讨论了一些主要的理论限制因素，ANN的层结构及其技术实施方法的沟通方式影响了它们的效率。纸张从von Neumann的原始模型开始，而不忽视分开的转移时间与加工时间分开;衍生适当的解释和处理Amdahl的法律。它表明，在这种解释中，Amdahl的法律正确地描述了Anns。

我们考虑单个强化学习与基于事件驱动的代理商金融市场模型相互作用时学习最佳执行代理的学习动力。交易在事件时间内通过匹配引擎进行异步进行。最佳执行代理在不同级别的初始订单尺寸和不同尺寸的状态空间上进行考虑。使用校准方法考虑了对基于代理的模型和市场的影响，该方法探讨了经验性风格化事实和价格影响曲线的变化。收敛，音量轨迹和动作痕迹图用于可视化学习动力学。这表明了最佳执行代理如何在模拟的反应性市场框架内学习最佳交易决策，以及如何通过引入战略订单分类来改变模拟市场的反反应。

This chapter sheds light on the synaptic organization of the brain from the perspective of computational neuroscience. It provides an introductory overview on how to account for empirical data in mathematical models, implement them in software, and perform simulations reflecting experiments. This path is demonstrated with respect to four key aspects of synaptic signaling: the connectivity of brain networks, synaptic transmission, synaptic plasticity, and the heterogeneity across synapses. Each step and aspect of the modeling and simulation workflow comes with its own challenges and pitfalls, which are highlighted and addressed in detail.

The introductory programming sequence has been the focus of much research in computing education. The recent advent of several viable and freely-available AI-driven code generation tools present several immediate opportunities and challenges in this domain. In this position paper we argue that the community needs to act quickly in deciding what possible opportunities can and should be leveraged and how, while also working on how to overcome or otherwise mitigate the possible challenges. Assuming that the effectiveness and proliferation of these tools will continue to progress rapidly, without quick, deliberate, and concerted efforts, educators will lose advantage in helping shape what opportunities come to be, and what challenges will endure. With this paper we aim to seed this discussion within the computing education community.

We are currently unable to specify human goals and societal values in a way that reliably directs AI behavior. Law-making and legal interpretation form a computational engine that converts opaque human values into legible directives. "Law Informs Code" is the research agenda capturing complex computational legal processes, and embedding them in AI. Similar to how parties to a legal contract cannot foresee every potential contingency of their future relationship, and legislators cannot predict all the circumstances under which their proposed bills will be applied, we cannot ex ante specify rules that provably direct good AI behavior. Legal theory and practice have developed arrays of tools to address these specification problems. For instance, legal standards allow humans to develop shared understandings and adapt them to novel situations. In contrast to more prosaic uses of the law (e.g., as a deterrent of bad behavior through the threat of sanction), leveraged as an expression of how humans communicate their goals, and what society values, Law Informs Code. We describe how data generated by legal processes (methods of law-making, statutory interpretation, contract drafting, applications of legal standards, legal reasoning, etc.) can facilitate the robust specification of inherently vague human goals. This increases human-AI alignment and the local usefulness of AI. Toward society-AI alignment, we present a framework for understanding law as the applied philosophy of multi-agent alignment. Although law is partly a reflection of historically contingent political power - and thus not a perfect aggregation of citizen preferences - if properly parsed, its distillation offers the most legitimate computational comprehension of societal values available. If law eventually informs powerful AI, engaging in the deliberative political process to improve law takes on even more meaning.

Several policy options exist, or have been proposed, to further responsible artificial intelligence (AI) development and deployment. Institutions, including U.S. government agencies, states, professional societies, and private and public sector businesses, are well positioned to implement these policies. However, given limited resources, not all policies can or should be equally prioritized. We define and review nine suggested policies for furthering responsible AI, rank each policy on potential use and impact, and recommend prioritization relative to each institution type. We find that pre-deployment audits and assessments and post-deployment accountability are likely to have the highest impact but also the highest barriers to adoption. We recommend that U.S. government agencies and companies highly prioritize development of pre-deployment audits and assessments, while the U.S. national legislature should highly prioritize post-deployment accountability. We suggest that U.S. government agencies and professional societies should highly prioritize policies that support responsible AI research and that states should highly prioritize support of responsible AI education. We propose that companies can highly prioritize involving community stakeholders in development efforts and supporting diversity in AI development. We advise lower levels of prioritization across institutions for AI ethics statements and databases of AI technologies or incidents. We recognize that no one policy will lead to responsible AI and instead advocate for strategic policy implementation across institutions.

人类开发人员可以使用网络安全缺陷生产代码。可以新兴'智能'代码完成工具有助于修复这些缺点吗？在这项工作中，我们研究了对零拍摄漏洞修复的代码（如Openai的Codex和AI21的侏罗纪J-1）使用大型语言模型（如Openai的Codex和AI21的J-1）。我们调查设计方面的挑战，提示将Coax LLMS进入生成不安全代码的修复版本。由于许多方法来短语和句法 - 具有自然语言，这很困难。通过对四个商业，黑盒子，“现成的”典型的模型进行大规模研究，以及局部训练的模型，在合成，手工制作和现实世界的安全错误场景的混合中，我们的实验表明，LLMS可以共同修复100％的综合生成和手工制作的情景，以及58％的脆弱性，在真实的开源项目中的历史错误中选择。

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.