# Towards Strong AI: Transformational Beliefs and Scientific Creativity

arXiv: https://arxiv.org/abs/2412.19938 · HTML: https://mlsanity.com/paper/2412.19938/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 68 references that are in the map.
- First posted on arXiv 27 Dec 2024.

## Builds on (most cited first, 5 of 5)

- [Chain-of-Verification Reduces Hallucination in Large Language Models](https://mlsanity.com/paper/2309.11495/) (2023)
- [A Survey on Large Language Model-based Agents for Statistics and Data Science](https://mlsanity.com/paper/2412.14222/) (2024)
- [Repro Samples Method for Finite- and Large-Sample Inferences](https://mlsanity.com/paper/2206.06421/) (2022)
- [Demystifying Inferential Models: A Fiducial Perspective](https://mlsanity.com/paper/2205.05612/) (2022)
- [Estimation of Over-parameterized Models from an Auto-Modeling Perspective](https://mlsanity.com/paper/2206.01824/) (2022)

## Abstract (arXiv)

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.