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相关概念视频

Types of Surveys01:27

Types of Surveys

35
Surveys are essential for marking property boundaries near water bodies. Different types of surveys are defined, each with its own function. Land surveys mark the property boundaries, while route surveys determine the position of properties on nearby highways. Topographic surveys create maps by capturing the three-dimensional features of the land. Hydrographic surveys focus on the shapes of underwater areas and the movement of streams through the properties. Mine surveys determine the relative...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Higher Mental Functions of the Brain: Language01:10

Higher Mental Functions of the Brain: Language

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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...
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Typical Model Studies01:30

Typical Model Studies

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Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
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Multicompartment Models: Overview01:14

Multicompartment Models: Overview

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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
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Data Collection by Survey01:07

Data Collection by Survey

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The systematic method of obtaining and analyzing accurate information of a population is called data collection. A survey is a standard method of data collection that involves collecting information from a target human population about their experience, opinion, or knowledge of a product, service, or process. The responses are recorded and interpreted. The most common survey examples are written questionnaires, face-to-face or telephonic conversations, focus groups, and electronic (e-mail or...
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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对多式联运大型语言模型的调查.

Shukang Yin1, Chaoyou Fu2,3, Sirui Zhao1

  • 1School of Artificial Intelligence and Data Science, University of Science and Technology of China, Hefei 230026, China.

National science review
|December 16, 2024
PubMed
概括
此摘要是机器生成的。

多模式大语言模型 (MLLMs) 显示出新兴的能力,向人工通用智能迈进. 这篇论文调查了最近的MLLM进展,涵盖了架构,培训和未来的研究方向.

关键词:
大型语言模型多模式大型语言模型视觉语言模型 视觉语言模型

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科学领域:

  • 人工智能的人工智能
  • 计算机视觉 计算机视觉
  • 自然语言处理自然语言处理.

背景情况:

  • 多模式大语言模型 (MLLMs),以GPT-4V为例,是一个快速发展的研究领域.
  • 在复杂的多式联络任务中,MLLM利用大型语言模型 (LLM),展现出超越传统方法的新兴能力.
  • 律师学位的发展正在加速,来自学术界和工业界的重大贡献.

研究的目的:

  • 提供多模大型语言模型 (MLLMs) 近期进展的全面概述和摘要.
  • 概述MLLM的基本表述,架构,培训策略,数据和评估指标.
  • 探索MLLM领域内的扩展和挑战,包括多模式,多语言和高级推理技术.

主要方法:

  • 系统审查和总结当前的MLLM研究.
  • 对MLLM架构,培训方法和数据集的分析.
  • 探索新兴研究主题,如增强细分化,模式支持,语言功能和场景应用.
  • 调查多模式幻觉和先进的技术,如上下文学习,思维链和LLM辅助的视觉推理.

主要成果:

  • 最近的MLLM展示了令人惊的新兴能力,包括基于图像的故事生成和无光学字符识别的数学推理.
  • 在开发与GPT-4V.等现有基准竞争或超越的MLLM方面取得了显著进展.
  • 该领域正在迅速发展,目前正在努力在各个维度中增强MLLM能力.

结论:

  • 由于其新兴的能力,MLLM代表了向人工通用智能迈出的重要一步.
  • 本调查提供了对MLLM景观的结构化理解,突出了关键概念和研究轨迹.
  • 未来的研究应该解决当前的挑战,并探索有前途的方向,以进一步推进MLLM技术.