A typicality principle that penalizes parameter values under which observed data look atypical is shown to fix maximum likelihood failures in three examples and to yield calibrated plausibility regions.
Towards Strong AI: Transformational Beliefs and Scientific Creativity
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abstract
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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The typicality principle and its implications for statistics and data science
A typicality principle that penalizes parameter values under which observed data look atypical is shown to fix maximum likelihood failures in three examples and to yield calibrated plausibility regions.