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Machines: Problem Solving II01:30

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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公平意识的机器学习工程:我们有多远?

Carmine Ferrara1, Giulia Sellitto1, Filomena Ferrucci1

  • 1Software Engineering (SeSa) Lab, University of Salerno, Salerno, Italy.

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|November 29, 2023
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概括
此摘要是机器生成的。

机器学习中的公平性在软件工程中经常被忽视. 一项调查显示,公平被视为次要问题,强调需要更好的工具和方法来确保公平的AI发展.

关键词:
经验软件工程是经验软件工程.机器学习 机器学习从从业者的角度来看.软件的公平性 软件的公平性调查研究调查研究研究.

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科学领域:

  • 软件工程 软件工程 软件工程
  • 人工智能的人工智能
  • 机器学习伦理学 机器学习伦理学

背景情况:

  • 机器学习 (ML) 算法越来越多地被整合到全球的日常生活和业务运营中.
  • 机器学习算法的偏差可能导致不公平的决策,并使歧视延续.
  • 软件工程界对软件公平性越来越感兴趣,但对公平的机器学习工程的理解仍然有限.

研究的目的:

  • 调查ML系统中公平性的实际感知和管理.
  • 确定有关从业者的意识,技能和最佳发展阶段的知识差距,以解决公平问题.
  • 提供对实际工具和方法的见解,以有效地处理ML开发中的公平性.

主要方法:

  • 对参与ML开发的117名专业人士进行了一项调查.
  • 该调查收集了有关从业者的经验,意识和与ML公平性相关的感知挑战的数据.
  • 分析的重点是了解软件工程生命周期中目前如何管理公平性.

主要成果:

  • 公平主要被视为人工智能系统开发中的次要质量属性.
  • 实践者强调需要特定的方法,开发环境和自动化验证工具来解决公平性问题.
  • 关于公平的机器学习系统的工程,在技能和成熟度方面存在着公认的差距.

结论:

  • 在AI系统的实际开发中,公平性还不是主要考虑因素.
  • 开发专门的工具和环境对于在整个软件生命周期中整合公平考虑至关重要.
  • 解决公平性需要转向将其视为机器学习工程中的一流质量方面.