机器学习是粒子物理学中的一个重要研究领域,从20世纪90年代和21世纪初的高级物理分析应用开始,随后在2010年的粒子和事件识别和重建应用爆炸式增长。在本文档中,我们将讨论粒子物理学中机器学习的未来未来研究和开发领域,其中包括实施,软件和硬件资源需求,与数据科学界,学术界和工业界的协作计划,以及数据科学中粒子物理学社区的培训。该文件的主要目的是通过High-Luminosity Large HadronCollider的物理驱动程序和未来的中微子实验来连接和激发这些研究领域的发展,并确定实施的资源需求。此外,我们还确定了与六大社区合作将带来巨大利益的领域。
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机器学习研究的最新进展,即深度学习,引入了优于传统算法以及人类不同复杂任务的方法,范围从检测图像和语音识别中的对象到玩困难的战略游戏。然而,机器学习研究的当前方法以及因此这种算法的实际应用的实现似乎具有重复的HARKing(结果已知的假设)问题。在这项工作中,我们会详细说明这种现象的算法,经济和社会原因以及后果。我们提供了当前传导机器学习研究的常见实践(例如,避免报告负面结果)和所提算法的泛化能力失败以及实际实际使用中的数据集的例子。此外,还讨论了从可讨论,无偏见,道德和隐私意识算法决策的角度来看机器学习研究和开发的潜在未来轨迹。我们想强调的是,通过这次讨论,我们既不会提出详尽的论证,也不会责怪任何具体的制度或个人。这只是机器学习领域的内部人员提出的讨论,反映在我们身上。
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在过去的几十年中,深度学习(DL)系统已经取得了巨大的成功,并且在诸如智能机器,图像处理,语音处理和医学诊断等各种应用中获得了极大的普及。深度神经网络是其重新成功背后的关键驱动力,但似乎仍然是一个缺乏可解释性和理解的魔术黑盒子。这带来了许多开放的安全和安全问题,对严格的方法和工程实践提出了质量提升的迫切要求。大量研究表明,最先进的DL系统存在缺陷和漏洞,可能导致严重的损失和悲剧,特别是在应用于真实世界的安全关键应用时。在本文中,我们进行了大规模的研究,并建立了223个相关工作的论文库,以保证深度学习的质量保证,安全性和解释。从软件质量保证的角度来看,我们针对普遍安全的深度学习工程找出了挑战和未来机遇。我们希望这项工作和随附的纸质存储库能够为软件工程社区解决安全智能应用的迫切工业需求铺平道路。
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Articles WINTER 2015 105 A rtificial intelligence (AI) research has explored a variety of problems and approaches since its inception, but for the last 20 years or so has been focused on the problems surrounding the construction of intelligent agents-systems that perceive and act in some environment. In this context, the criterion for intelligence is related to statistical and economic notions of rationality-colloquially, the ability to make good decisions, plans, or inferences. The adoption of probabilistic representations and statistical learning methods has led to a large degree of integration and cross-fertilization between AI, machine learning, statistics, control theory, neuroscience, and other fields. The establishment of shared theoretical frameworks, combined with the availability of data and processing power, has yielded remarkable successes in various component tasks such as speech recognition , image classification, autonomous vehicles, machine translation, legged locomotion, and question-answering systems .
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深度学习(DL)在最近的过去取得了巨大的成功,在图像识别和自然语言处理等各个领域引领了最先进的成果。这种成功的原因之一是DL模型的尺寸越来越大,并且可以获得大量的训练数据。为了不断提高DL的性能,增加DL系统的可扩展性是必要的。在本次调查中,我们对可扩展DLon分布式基础架构的挑战,技术和工具进行了广泛而彻底的调查。这包括用于DL的基础设施,用于并行DL训练的方法,多租户资源调度以及训练和模型数据的管理。此外,我们分析和比较了11个当前的开源DL框架和工具,并研究了哪些技术在实践中通常得到实施。最后,我们重点介绍DL系统的未来研究趋势,值得进一步研究。
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最近,许多人工智能研究人员和从业人员开始研究涉及为“好”做人工智能的研究。这是将人工智能研究和实践与道德思维融合在一起的一般驱动力的一部分。当前道德准则中的一个常见主题是要求AI对所有人都有益,或者:为共同利益做出贡献。但什么是共同利益,是否想要变得更好?通过四个引导问题,我将从AI的角度确定共同利益是什么以及如何通过AI增强它来说明挑战和陷阱。问题是:问题是什么/什么是问题?谁定义了问题?知识的作用是什么?,什么是重要的副作用和动态?该插图将使用“AI for Social Good”领域的一个例子,更具体地说是“社会善的数据科学”。即使这些问题的重要性可能在抽象层面上已知,但在实践中并没有得到充分的要求,正如对该领域近期会议的99项贡献的探索性研究所示。将这些挑战和陷阱转化为积极的建议,作为结论,我将借鉴计算机科学思想和实践的另一个特征,使这些障碍可见并减弱它们:“攻击”作为改进设计的方法。这导致了道德笔测试的提议,作为帮助AI设计更好地贡献共同利益的方法。
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Deep learning has recently seen rapid development and received significant attention due to its state-of-the-art performance on previously-thought hard problems. However, because of the internal complexity and nonlinear structure of deep neural networks, the underlying decision making processes for why these models are achieving such performance are challenging and sometimes mystifying to interpret. As deep learning spreads across domains, it is of paramount importance that we equip users of deep learning with tools for understanding when a model works correctly, when it fails, and ultimately how to improve its performance. Standardized toolkits for building neural networks have helped democratize deep learning; visual analytics systems have now been developed to support model explanation, interpretation, debugging, and improvement. We present a survey of the role of visual analytics in deep learning research, which highlights its short yet impactful history and thoroughly summarizes the state-of-the-art using a human-centered interrogative framework, focusing on the Five W's and How (Why, Who, What, How, When, and Where). We conclude by highlighting research directions and open research problems. This survey helps researchers and practitioners in both visual analytics and deep learning to quickly learn key aspects of this young and rapidly growing body of research, whose impact spans a diverse range of domains.
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Machine learning sits at the core of many essential products and services at Facebook. This paper describes the hardware and software infrastructure that supports machine learning at global scale. Facebook's machine learning workloads are extremely diverse: services require many different types of models in practice. This diversity has implications at all layers in the system stack. In addition, a sizable fraction of all data stored at Facebook flows through machine learning pipelines, presenting significant challenges in delivering data to high-performance distributed training flows. Computational requirements are also intense, leveraging both GPU and CPU platforms for training and abundant CPU capacity for real-time inference. Addressing these and other emerging challenges continues to require diverse efforts that span machine learning algorithms, software, and hardware design.
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机器学习已发展成为各种高成功应用的支持技术。这种成功的继续和加速的潜力使机器学习(ML)成为研究,经济和政治议程的首要问题。这种前所未有的兴趣得益于ML适用性的愿景,这种愿景延伸到医疗保健,交通,国防和其他具有重要社会意义的领域。实现这一愿景需要在安全关键应用中使用ML,这些应用需要超出当前ML应用所需的保证水平。我们的论文提供了对ML保证的最新技术的全面调查,即产生ML对其预期用途足够安全的证据。该调查涵盖了能够在机器学习生命周期的不同阶段提供此类证据的方法,即从用于训练系统的ML组件的数据的收集开始的复杂的迭代过程,以及在该系统内部署该组件的过程。系统。本文首先系统地介绍了ML生命周期及其阶段。然后,我们定义每个阶段的保证需求,审查有助于实现这些需求的现有方法,并确定需要进一步研究的开放式挑战。
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人工智能(AI)有机会彻底改变美国国防部(DoD)和情报界(IC)应对不断演变的威胁,数据泛滥和快速行动过程的挑战。开发端到端的人工智能系统需要并行开发不同的部分,这些部分必须协同工作才能提供决策者,作战人员和分析师可以使用的能力。这些部分包括数据收集,数据调节,算法,计算,强大的人工智能和人机组合。虽然今天流行的媒体围绕着算法和计算的进步,但大多数现代人工智能系统都利用了许多不同领域的进步。而且,虽然某些组件可能不像其他组件那样对最终用户可见,但我们的经验表明,这些组件中的每一个都是相互关联的。组件在AI系统的成功或失败中起着重要作用。本文旨在重点介绍端到端AI系统中涉及的许多这些技术。本文的目的是为读者提供学术界,行业界和政府的学术,技术细节和最新亮点的概述。在可能的情况下,我们会指出可用于进一步阅读和理解的相关资源。
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机器学习(ML),人工智能(AI)和其他现代统计方法为操作以前未开发和快速增长的数据来源提供了新的机会,以便患者受益。虽然目前正在进行许多有前景的研究,但整个文学缺乏:透明度;明确报告以促进复制;探索潜在的道德问题;并且,明确有效的演示。造成这些问题的原因有很多,但我们提供初步解决方案的最重要原因之一是目前缺乏针对ML / AI的最佳实践指南。尽管最佳实践在这一领域的看法尚未达成共识,但我们认为,在开展健康领域的ML / AI研究和影响项目的跨学科团体将从回答基于开展此类工作时存在的重要问题的一系列问题中受益。在这里,我们提出了涵盖整个项目生命周期的20个问题,从理论,数据分析和模型评估到实施,作为促进项目规划和事后(结构化)独立评估的平均值。通过开始在不同的环境中回答这些问题,我们可以开始理解什么是一个好的答案,并且我们希望所得到的讨论对于制定一个国际共识框架来进行透明,可复制,道德和有效的人工智能研究(AI-TREE)是至关重要的。为了健康。
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正式方法已经为调查软件工程基础提供了方法,并且还具有改进可靠系统保证中的当前实践的高潜力。在本文中,我们总结了形式方法的已知长度和弱点。从机器人和自治系统(RAS)的保证的角度来看,我们强调了关于形式化方法的整合,对综合形式方法的基础研究以及成功地将两种研究成果转移到RAS保证方面的独特机会。基于这些机会,我们使用集成的正式方法表达我们对可认证RAS保证的立场。从这个位置,我们争论未来研究和研究转移的方向以及对此类研究的有用结果的期望。
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Deep learning has been popularized by its recent successes on challenging artificial intelligence problems. One of the reasons for its dominance is also an ongoing challenge: the need for immense amounts of computational power. Hardware architects have responded by proposing a wide array of promising ideas, but to date, the majority of the work has focused on specific algorithms in somewhat narrow application domains. While their specificity does not diminish these approaches, there is a clear need for more flexible solutions. We believe the first step is to examine the characteristics of cutting edge models from across the deep learning community. Consequently, we have assembled Fathom: a collection of eight archetypal deep learning workloads for study. Each of these models comes from a seminal work in the deep learning community, ranging from the familiar deep convolutional neural network of Krizhevsky et al., to the more exotic memory networks from Facebook's AI research group. Fathom has been released online, and this paper focuses on understanding the fundamental performance characteristics of each model. We use a set of application-level modeling tools built around the TensorFlow deep learning framework in order to analyze the behavior of the Fathom workloads. We present a breakdown of where time is spent, the similarities between the performance profiles of our models, an analysis of behavior in inference and training, and the effects of parallelism on scaling.
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本报告描述了18个项目,这些项目探讨了如何在国家实验室中将商业云计算服务用于科学计算。这些演示包括在云环境中部署专有软件,以利用已建立的基于云的分析工作流来处理科学数据集。总的来说,这些项目非常成功,并且他们共同认为云计算可以成为国家实验室科学计算的宝贵计算资源。
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Recent rapid advances in Artificial Intelligence (AI) and Machine Learning have raised many questions about the regulatory and governance mechanisms for autonomous machines. Many commentators , scholars, and policy-makers now call for ensuring that algorithms governing our lives are transparent , fair, and accountable. Here, I propose a conceptual framework for the regulation of AI and algorith-mic systems. I argue that we need tools to program, debug and maintain an algorithmic social contract, a pact between various human stakeholders, mediated by machines. To achieve this, we can adapt the concept of human-in-the-loop (HITL) from the fields of mod-eling and simulation, and interactive machine learning. In particular, I propose an agenda I call society-in-the-loop (SITL), which combines the HITL control paradigm with mechanisms for negotiating the values of various stakeholders affected by AI systems, and monitoring compliance with the agreement. In short, 'SITL = HITL + Social Contract.'
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机器学习(ML)系统扩大社会不公平和不公平的可能性越来越受到人们的欢迎和学术界的关注。近期工作的激增主要集中在开发算法工具以评估和减轻这种不公平性。但是,如果这些工具要对工业实践产生积极影响,那么通过了解现实世界的需求来了解其设计至关重要。通过35次半结构式访谈和267名ML从业者的匿名调查,我们首次系统地调查了商业产品团队面临的挑战和需求,以支持不公平的ML系统。我们确定了行业从业者所面临的挑战与公平的ML研究文献中提出的解决方案之间的一致性和脱节。基于这些发现,我们强调了未来ML和HCI研究的指导,这些研究将更好地解决行业实践者的需求。
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Visual analytics systems combine machine learning or other analytic techniques with interactive data visualization to promote sensemaking and analytical reasoning. It is through such techniques that people can make sense of large, complex data. While progress has been made, the tactful combination of machine learning and data visualization is still under-explored. This state-of-the-art report presents a summary of the progress that has been made by highlighting and synthesizing select research advances. Further, it presents opportunities and challenges to enhance the synergy between machine learning and visual analytics for impactful future research directions.
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In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms.
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机器学习算法的准确性和可靠性对于人工智能(AI)服务的供应商来说是一个重要的问题,但是超出准确性的考虑,例如安全性,安全性和出处,也是促使消费者对服务的信任的关键因素。在本文中,我们提出供应商的AI服务符合性声明(SDoC),以帮助提高对AI服务的信任。 SDoC是一个透明的,标准化的,但通常不是法律要求的文件,用于许多行业和部门,以描述产品的沿袭以及经历的安全性和性能测试。我们设想用于人工智能服务的SDoC包含目的,性能,安全性,安全性和出处信息,由AI服务提供商完成并自愿发布,供消费者检查。重要的是,它传达产品级而不是组件级功能测试。我们建议为AI量身定制一套声明项目,并为两个虚构的AI服务提供示例。
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物联网(IoT)集成了数十亿个智能设备,这些设备可以通过最少的人为干预相互通信。它是计算史上发展最快的领域之一,到2020年底估计有500亿台设备。一方面,物联网在增强几种可提高生活质量的真实智能应用方面起着至关重要的作用。另一方面此外,物联网系统的横切性质以及涉及此类系统部署的多学科组件引入了新的安全挑战。对物联网设备及其固有漏洞实施安全措施,如加密,认证,访问控制,网络安全和应用安全是无效的。因此,应加强现有的安全方法,以有效保护物联网系统。机器学习和深度学习(ML / DL)在过去几年中取得了令人瞩目的进步,机器智能已经从实验室好奇心转变为实用机械的几个重要应用。因此,ML / DL方法对于将物联网系统的这些安全性转变为仅仅促进设备与基于安全的智能系统之间的安全通信非常重要。这项工作的目标是提供对ML / DL方法的全面调查,可用于开发物联网系统的增强安全方法。提出了与固有或新引入的威胁相关的物联网安全威胁,并讨论了各种潜在的物联网系统攻击面以及与每个表面相关的可能威胁。然后,我们彻底审查了物联网安全的ML / DL方法,并提出了每种方法的机会,优点和缺点。讨论将ML / DL应用于IoTsecurity所涉及的机遇和挑战。这些机遇和挑战可以作为潜在的未来研究方向。
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