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相关概念视频

Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

213
Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
213
Language and Cognition01:27

Language and Cognition

704
Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
704
Higher Mental Functions of the Brain: Language01:10

Higher Mental Functions of the Brain: Language

3.4K
Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
Language formation and comprehension take place in the dominant hemisphere. The dominant hemisphere is responsible for understanding the meaning of spoken, written, or sign language, as well as the ability to communicate. For most people, the left hemisphere is the dominant one. The right hemisphere, then, gives tone and emotional context to the...
3.4K

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相关实验视频

Updated: Jan 14, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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一个以人为中心的自动化机器学习代理,具有用于多式联络数据管理和分析的大型语言模型.

Rong Huang1, Su Tao2

  • 1Tawa Supermarket, Inc., Buena Park, CA, United States.

Frontiers in artificial intelligence
|October 24, 2025
PubMed
概括

本研究介绍了一种使用大型语言模型 (LLM) 的人工智能代理,以使自动机器学习 (AutoML) 更容易获得. 该LLM驱动的代理通过自然语言简化复杂的ML工作流程,增强用户体验和性能.

关键词:
在AutoML中使用AutoML.在法学士 (LLM) 课程中.经纪人 代理人 代理人 代理人深度学习是一种深度学习.多模式数据分析数据分析.

相关实验视频

Last Updated: Jan 14, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.0K

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 自然语言处理自然语言处理.

背景情况:

  • 自动机器学习 (AutoML) 旨在简化机器学习模型的开发.
  • 当前的AutoML系统通常需要技术专业知识和结构化数据,限制了可访问性.
  • 大型语言模型 (LLM) 在理解和生成代码方面表现有前途,但它们与AutoML的整合尚未开始.

研究的目的:

  • 为可访问,端到端的AutoML开发一个LLM驱动的AI代理.
  • 在整个机器学习工作流程中实现自然语言交互.
  • 减少对AutoML预定义规则和技术专业知识的依赖.

主要方法:

  • 实施了一个端到端的ML管道,包括自动化数据加载,预处理,任务识别和模型培训.
  • 提出了一种基于LLM的新型数据处理方法来解释各种数据格式.
  • 开发了一种结合LLM知识和绩效反的自适应性超参数优化策略.

主要成果:

  • 通过自然语言交互,LLM驱动的代理成功地自动化了整个ML管道.
  • 拟议的数据处理方法可以在没有人工干预的情况下处理各种数据格式.
  • 适应性超参数优化提高了搜索效率和模型性能.
  • 对10个不同数据集的评估显示,与传统的AutoML框架相比,性能优越.

结论:

  • 通过启用自然语言交互,LLM驱动的AI代理显著提高了AutoML的可访问性.
  • 这种方法弥合了用户意图和ML实施之间的差距,降低了技术障碍.
  • 开发的方法有助于一个更直观,更强大的AutoML框架.