用数学程序表达物理概念，让机器理解真实世界。
Digital Gene: Learning about the Physical World through Analytic Concepts
- 用数学程序表示物理概念，构建可计算的抽象
- 为神经网络引入结构化先验，使其遵守物理规律
- 适合想让AI理解真实世界的开发者与研究者
回顾过去十年人工智能的进展，对象检测、图像生成、大语言模型等技术使AI系统能产生更具语义意义的输出，并广泛应用于互联网场景。然而，在理解和交互物理世界方面，AI仍面临挑战。这揭示了一个关键问题：仅依赖互联网数据（如文本、图像）学习的语义概念，不足以让机器真正理解物理世界——当前机器智能缺乏有效学习物理世界的方式。本研究提出‘分析性概念’（analytic concept）这一新思想：通过数学程序的代码形式来表征与物理世界相关的概念，为机器智能提供感知、推理和交互物理世界的新途径。本文不仅阐述了设计哲学并给出应用指南，还介绍了围绕分析性概念构建的基础设施。研究旨在回答两个核心问题：机器智能应如何抽象物理世界的一般性概念？如何系统性地将结构化先验融入神经网络，以约束AI系统遵循物理定律？
原文摘要 · Abstract (English)
Reviewing the progress in artificial intelligence over the past decade, various significant advances (e.g. object detection, image generation, large language models) have enabled AI systems to produce more semantically meaningful outputs and achieve widespread adoption in internet scenarios. Nevertheless, AI systems still struggle when it comes to understanding and interacting with the physical world. This reveals an important issue: relying solely on semantic-level concepts learned from internet data (e.g. texts, images) to understand the physical world is far from sufficient -- machine intelligence currently lacks an effective way to learn about the physical world. This research introduces the idea of analytic concept -- representing the concepts related to the physical world through programs of mathematical procedures, providing machine intelligence a portal to perceive, reason about, and interact with the physical world. Except for detailing the design philosophy and providing guidelines for the application of analytic concepts, this research also introduce about the infrastructure that has been built around analytic concepts. I aim for my research to contribute to addressing these questions: What is a proper abstraction of general concepts in the physical world for machine intelligence? How to systematically integrate structured priors with neural networks to constrain AI systems to comply with physical laws?
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