提出转化信念框架，用以模拟科学创造力，助力强人工智能发展
Towards Strong AI: Transformational Beliefs and Scientific Creativity
- 基于天文史与科学革命，构建弱信念转化框架
- 通过统计科学案例验证其对创造力的建模潜力
- 为强人工智能中的创造性思维提供理论基础
强人工智能被设想为具备类人的一般认知能力与科学创造力，涵盖知识获取与问题解决。尽管弱人工智能已取得显著进展，但强人工智能的实现仍存在激烈争议与深入探讨。本文考察天文学与物理学史上的关键创新，聚焦海王星发现及科学革命概念，结合科学哲学家观点，提出一个简单的理论与统计框架——转化信念（Transformational Belief, TB）框架，旨在作为科学创造力建模的基础。通过统计科学中的若干典型案例，展示该框架在理解、分析乃至促进创造力方面的潜力，为强人工智能的发展提供新路径。最后，论文反思未来研究方向与潜在突破。
原文摘要 · Abstract (English)
Strong artificial intelligence (AI) is envisioned to possess general cognitive abilities and scientific creativity comparable to human intelligence, encompassing both knowledge acquisition and problem-solving. While remarkable progress has been made in weak AI, the realization of strong AI remains a topic of intense debate and critical examination. In this paper, we explore pivotal innovations in the history of astronomy and physics, focusing on the discovery of Neptune and the concept of scientific revolutions as perceived by philosophers of science. Building on these insights, we introduce a simple theoretical and statistical framework of weak beliefs, termed the Transformational Belief (TB) framework, designed as a foundation for modeling scientific creativity. Through selected illustrative examples in statistical science, we demonstrate the TB framework's potential as a promising foundation for understanding, analyzing, and even fostering creativity -- paving the way toward the development of strong AI. We conclude with reflections on future research directions and potential advancements.
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