Building a human-like observer using deep learning in an extended Wigner's friend experiment

Abstract

There has been a longstanding demand for artificial intelligence with human-level cognitive sophistication to address loopholes in Bell-type experiments. In this study, we propose a novel experimental framework that integrates advanced deep learning techniques, employing neural network-based artificial intelligence in an extended Wigner's friend experiment. We demonstrate the framework through simulations and introduce three new analytical metrics-morphing polygons, averaged Shannon entropy, and probability density maps-to evaluate the results. These results can be used to determine whether our artificial intelligence qualifies as a bona fide observer and whether superposition applies to macroscopic systems, including observers.