摘要
相当多证据表明,因果学习和因果理解能力极大地增强了人类操纵物理世界的能力,是将人类与其他灵长类动物区分开来的重要因素。如何让蠢笨的机器人因果地思考、回答“为什么”的问题,甚至理解此类问题的意义,是实现人工智能的关键之一。朱迪亚·珀尔认为要实现类人智能,先从模仿孩子的智能开始,于是提出“因果推断引擎”帮助未来的人工智能进行因果推理,以通过迷你图灵测试,甚至成为明辨善恶的道德主体。本文的目标是,既根据人工智能的理论设想和建构目标为儿童教育的发展提供思路,又根据儿童教育的实践来反观人工智能的因果模型径路。
There is considerable evidence to prove that causal learning and causal understanding ability have greatly enhanced our ability to manipulate the physical world and they are important factors that distinguish humans from other primates.How to get stupid robots to think causally,answer the questions raised with “why” and even understand the meaning of such questions is one of the keys to realizing artificial intelligence.Judea Pearl believes that to achieve human-like intelligence,we must start by imitating the intelligence of children,so he proposed a “causal inference engine” to help future artificial intelligence conduct causal inference,pass the mini-Turing test,and even become a moral subject who can discern good from evil.This paper attempts to provide some different thinking dimensions for the development of children’s education from the basic assumptions and construction goals of artificial intelligence,and at the same time reflects on the current practice of artificial intelligence through children’s education.
作者
张端
吴小安
和继军
ZHANG Duan;WU Xiaoan;HE Jijun(College of Elementary Education,Capital Normal University,Bejing,100048;School of Marxism,Northwestern Polytechnical University,Xi'an,Shaanxi,710072)
出处
《自然辩证法通讯》
CSSCI
北大核心
2022年第9期27-37,共11页
Journal of Dialectics of Nature
基金
国家社科基金后期资助项目“实际因果的结构方程径路研究”(项目编号:21FZXB040)
教育部人文社会科学项目“实际因果前沿问题研究”(项目编号:21XJC720002)
中央高校基本科研业务费专项资金资助项目“因果模型前沿问题研究”(项目编号:G2021KY05109)。
关键词
因果推断引擎
反事实
自由意志
科学教育
Causal inference engine
Counterfactuals
Free will
Science education
作者简介
张端(1983-)男,陕西蒲城人,首都师范大学初等教育学院副教授,研究方向为科学教育及小学教师教育。Email:zhangduan@cnu.edu.cn;吴小安(1984-)男,江苏南通人,西北工业大学马克思主义学院副教授,研究方向为因果模型、条件句逻辑和死亡哲学。Email:wuxiaoan1984@126.com;和继军(1979-)男,河北邢台人,首都师范大学初等教育学院教授,研究方向为自然环境与生态保护、科学教育及小学教师教育。Email:hejiun_200018@163.com。
