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相关概念视频

Hindsight Biases01:12

Hindsight Biases

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Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
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A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
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Variability: Analysis01:11

Variability: Analysis

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Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Instinctive drift refers to the tendency of animals to revert to their innate behaviors despite repeated reinforcement. Breland and Breland demonstrated this concept in an experiment with a raccoon. The raccoon was trained to pick up two coins and place them in a container in exchange for food. Initially, the raccoon learned to associate the coins with food, making them a conditioned stimulus or a substitute for food. However, over time, the raccoon became less willing to put the coins into the...
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In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
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新闻是可以预测的吗?

Clara Fannjiang1, Jennifer Listgarten1

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概括
此摘要是机器生成的。

机器学习 (ML) 有助于科学设计,但平衡新发现与失败风险是关键. 本研究探讨了基于ML的蛋白质设计策略,以实现新特性,同时管理不确定性.

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科学领域:

  • 计算化学和结构生物学
  • 人工智能在科学发现中的作用
  • 分子设计和工程学

背景情况:

  • 机器学习 (ML) 加快了科学设计,特别是分子,材料和蛋白质的科学设计.
  • ML的应用包括药物开发,环境修复和碳捕获.
  • 一个核心挑战是平衡ML模型中的新性探索与风险管理.

研究的目的:

  • 为了应对在基于机器学习的设计中实现新型属性价值的挑战.
  • 探索在ML模型中平衡外推和风险控制的策略.
  • 专注于蛋白质设计,同时为基于ML的设计提供更广泛的适用性.

主要方法:

  • 对ML模型推断和风险的概念分析.
  • 在生成模型中进行受控勘探的框架开发.
  • 专注于蛋白质特性优化的案例研究.

主要成果:

  • 确定了模型推断和设计失败之间的关键权衡.
  • 提出了一个平衡的方法来ML驱动的设计新性和可靠性.
  • 对蛋白质设计挑战的证明适用性.

结论:

  • 在信任ML模型和控制外推之间取得平衡对于成功的科学设计至关重要.
  • 这种方法可以发现具有所需性质的新型蛋白质.
  • 讨论的原则广泛适用于各种基于机器学习的设计领域.