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相关概念视频

Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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Steps in the Modeling Process01:14

Steps in the Modeling Process

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Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
Attention is the first necessary component for observational learning. It involves focusing on what the model is doing and saying. For example, if you decide to take a drawing class to enhance your skills, you need to pay close attention to the instructor's words and hand movements. The characteristics of the model significantly...
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Observational Learning01:12

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Stereotype Content Model02:16

Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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相关实验视频

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通过LLMs进行文化注释的逐步方法.

Edgar Dubourg1, Valentin Thouzeau1, Nicolas Baumard1

  • 1Département d'études cognitives, Institut Jean Nicod, École normale supérieure, Université PSL, Paris, France.

Frontiers in artificial intelligence
|May 17, 2024
PubMed
概括

本研究介绍了使用大型语言模型 (LLM) 进行注释的文化大数据的管道. 这种方法使研究人员能够对文化现象进行高效,可扩展的分析.

科学领域:

  • 计算社会科学 计算社会科学
  • 数字人文学科 数字人文学科
  • 文化分析 文化分析

背景情况:

  • 大型语言模型 (LLM) 在处理复杂数据方面展示了越来越大的能力.
  • 现有研究强调了LLMs在各种分析任务中的潜力.
  • 文化大数据对传统分析方法提出了独特的挑战.

研究的目的:

  • 通过使用LLM,特别是生成预训练型变换器 (GPT) 来说明文化大数据的结构化管道.
  • 为研究人员提供详细的方法来利用GPT的计算能力进行文化数据分析.
  • 展示LLMs在人类文化的实证研究中的潜力.

主要方法:

  • 基于LLM的数据注释结构化管道的开发.
  • 用生成预训练变压器 (GPT) 来处理和解释文化数据的应用.
  • 为分析大型文化数据集的效率和可扩展性而设计的方法.

主要成果:

  • 证明法学士在注释非工业社会描述方面的熟练程度.
  • 在不同文化背景的叙事中成功测量主题的重要性.
  • 在跨越不同社会和历史时期的文本中评估心理构造.
关键词:
这是一个注释循环.自动注释自动注释人类文化 人类文化大型语言模型.这是一个自学教程.

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结论:

  • 法学士课程为人类文化的实证研究提供了一个多功能工具.
  • 拟议的管道有助于对文化现象进行有效和可扩展的分析.
  • 这种方法在文化人类学,心理学,历史和相关领域有广泛的应用.