人工智能(AI)与机械智能(Machine Intelligence, MI)的核心差异

一、人工智能(AI)与机械智能(Machine Intelligence, MI)的核心差异

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1. 定义与范畴
  • 人工智能(AI)
    旨在通过算法模拟人类的感知、推理、学习和决策能力,核心是“大脑”层面的信息处理。其技术基础包括深度学习、自然语言处理等,强调通过数据驱动模型实现智能行为。
  • 机械智能(MI)
    更侧重物理环境中的自主执行能力,结合机器人硬件与实时决策算法,强调“感知-分析-执行”闭环控制。MI是AI在工业场景的具象化延伸,更注重任务导向的可靠性和效率。
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2. 技术路径对比
维度 人工智能(AI) 机械智能(MI)
核心目标 模拟人类认知能力 实现物理任务的
TensorFlow For Machine Intelligence: A hands-on introduction to learning algorithms by Sam Abrahams English | 23 July 2016 | ASIN: B01IZ43JV4 | 322 Pages | AZW3/MOBI/EPUB/PDF (conv) | 26.87 MB This book is a hands-on introduction to learning algorithms. It is for people who may know a little machine learning (or not) and who may have heard about TensorFlow, but found the documentation too daunting to approach. The learning curve is gentle and you always have some code to illustrate the math step-by-step. TensorFlow, a popular library for machine learning, embraces the innovation and community-engagement of open source, but has the support, guidance, and stability of a large corporation. Because of its multitude of strengths, TensorFlow is appropriate for individuals and businesses ranging from startups to companies as large as, well, Google. TensorFlow is currently being used for natural language processing, artificial intelligence, computer vision, and predictive analytics. TensorFlow, open sourced to the public by Google in November 2015, was made to be flexible, efficient, extensible, and portable. Computers of any shape and size can run it, from smartphones all the way up to huge computing clusters. This book starts with the absolute basics of TensorFlow. We found that most tutorials on TensorFlow start by attempting to teach both machine learning concepts and TensorFlow terminology at the same time. Here we first make sure you've had the opportunity to become comfortable with TensorFlow's mechanics and core API before covering machine learning concepts.
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