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相关概念视频

Language Development01:22

Language Development

368
Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
368
Higher Mental Functions of the Brain: Language01:10

Higher Mental Functions of the Brain: Language

875
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...
875
Observational Learning01:12

Observational Learning

181
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...
181
Language and Cognition01:27

Language and Cognition

348
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.
348
Neuroplasticity01:01

Neuroplasticity

367
Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
367
Language01:16

Language

230
Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
Corballis and Suddendorf (2007) and Tomasello and Rakoczy (2003) highlight the role of language in...
230

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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一种人工神经网络方法用于语言学习模型.

Zulqurnain Sabir1, Salem Ben Said2, Qasem Al-Mdallal3

  • 1Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon.

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|December 20, 2023
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概括

这项研究介绍了一种人工智能 (AI) 方法,使用尺度对联梯度神经网络 (SCJGNN) 来解决基于语言的差异模型. 人工智能方法准确地模拟语言学习阶段,错误最小.

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科学领域:

  • 计算语言学 计算语言学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 基于语言的差异模型对于理解学习过程至关重要.
  • 为这些模型开发精确的数值解决方案在计算上具有挑战性.
  • 现有的方法在捕捉学习动态时可能缺乏效率或精度.

研究的目的:

  • 用人工智能呈现基于语言的差分模型的数值解决方案.
  • 实施和验证一个尺度联梯度神经网络 (SCJGNN) 程序.
  • 将语言学习分为未知的,熟悉的和掌握的阶段.

主要方法:

  • 采用了一种基于尺度结合梯度神经网络 (SCJGNN) 的人工智能 (AI) 程序.
  • 采用亚当方案来最大限度地减少数据集概括的平均平方误差.
  • 配置了SCJGNN具有日志-西格激活功能,12个神经元和特定层结构,在训练 (75%),验证 (13%) 和测试 (12%) 中处理数据的比率.

主要成果:

  • 在数值解中达到高精度,绝对误差范围为所有学习类的10-06到10-08.
  • 通过每个学习阶段的回归分析证明了完美的模型性能.
  • 使用直方图和功能适应性分析验证了SCJGNN方法的可靠性.

结论:

  • 基于人工智能的SCJGNN为解决基于语言的差分模型提供了强大而准确的方法.
  • 该模型有效地区分了未知的,熟悉的和掌握的语言学习状态.
  • 该SCJGNN方法显示了计算机语言学和教育技术应用的巨大潜力.