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相关概念视频

Uncertainty: Overview00:59

Uncertainty: Overview

988
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
988
Reason and Intuition01:37

Reason and Intuition

6.9K
The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
6.9K
Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

4.9K
An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
4.9K
Natural and Artificial Concepts01:24

Natural and Artificial Concepts

267
In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
267
Information Processing Approach01:30

Information Processing Approach

162
The information-processing theory of cognitive development centers on fundamental mental processes, including attention, memory, and problem-solving skills. Researchers in this field examine how cognitive abilities, such as working memory, evolve and influence children's overall development. Studies indicate that children with stronger working memory tend to excel in reading comprehension, math, and problem-solving compared to peers with less efficient memory skills. Low working memory is...
162
Ethical Issues01:27

Ethical Issues

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Nurses are essential in patient care, upholding the ethical principles of their profession and effectively navigating ethical dilemmas. Neglecting ethical issues can lead to inadequate patient care, compromised therapeutic relationships, and moral distress among healthcare workers.
Ethical Concerns in Healthcare:
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相关实验视频

Updated: Sep 15, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

587

一个统一的本体学和可解释的框架来解码来自新闻数据的AI风险.

Chuan Chen1, Peng Luo2,3, Huilin Zhao4

  • 1Chair of Cartography and Visual Analytics, Technical University of Munich, Munich, Germany.

Scientific reports
|July 15, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一个统一的本体模型,以全面地绘制人工智能 (AI) 风险,从广泛的类别到特定事件. 它使用视觉分析和机器学习来分析AI风险数据,提供对驱动因素的新见解.

关键词:
人工智能伦理学人工智能风险AI风险可解释的机器学习新闻数据新闻数据存在论模型是存在论模型.

相关实验视频

Last Updated: Sep 15, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

587

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 数据科学数据科学数据科学
  • 风险管理 风险管理

背景情况:

  • 人工智能 (AI) 越来越多地融入日常生活,导致人们对其潜在风险感到担忧.
  • 目前关于人工智能风险的研究是分散的,缺乏统一的框架来将高层次风险类型与特定的现实事件联系起来.
  • 需要一个全面的方法来系统地理解和分析AI相关风险的多方面的性质.

研究的目的:

  • 开发一种新的本体风险模型,以便在多个尺度上统一地表示AI风险.
  • 从原始新闻数据构建一个丰富的AI风险事件数据库.
  • 使用先进的分析技术,识别AI风险事件的关键特征和驱动因素.

主要方法:

  • 开发一个本体风险模型来结构AI风险信息.
  • 将原始新闻数据系统地提取和结构化为丰富的AI风险事件数据库.
  • 视觉分析的应用,以总结AI风险事件特征.
  • 整合可解释的机器学习 (ML) 来识别风险驱动因素.

主要成果:

  • 一个全面的框架来表示AI风险,弥合宏观层面的类型学和微观层面的实例.
  • 从新闻数据中获得的AI风险事件的丰富数据库.
  • 通过视觉和ML分析识别与不同AI风险属性相关的关键模式和潜在因果因素.

结论:

  • 拟议的本体模型为理解AI风险提供了一种新的定量方法.
  • 该研究通过本体学建模提供结构性见解,并通过可解释的ML提供机械解释.
  • 这一框架为研究人员和政策制定者提供了对人工智能风险的更全面和可操作的理解.