Knowledge Evolution and Scientific Breakthroughs triggered by AI Hallucinations - A Paradigm Shift?

Abstract

The interdisciplinary impact of artificial intelligence (AI) in science has been especially emphasized by the fact that both, the Nobel Prize in Physics and in Chemistry in 2024 have been awarded for pioneering research with results, decisively based on artificial neural networks. The core of the excelling achievement in chemistry is described as: capturing of the full computational understanding of living matter at atomic level (Abriata, 2024). An interesting detail behind this highly acclaimed success, is that one of the laureates had praised AI hallucinations to be the designers of de novo proteins (Anishchenko, 2021). AI hallucinations are defined as incorrect or misleading results, usually produced by models implementing generative AI. Hallucinating AI systems are particularly associated with large language models, chat bots and computer vision tools and their occasionally nonsensical or altogether inaccurate outputs can be welcome in domains such as imaginary and visionary art but they can have significant negative consequences in practical applications. AI systems lack human wisdom. They do not solve problems via understanding context or using ideas of their own. They work with predefined inputs and in the case of generative AI, they generate new patterns some of which may deviate from the knowledge implemented in the algorithm or even defy the wisdom of the algorithm designer. Still, they can prove to be compatible with reality as is the case with de novo proteins. AI hallucinations could then be viewed as glimpses into a future, one yet to be created, for instance when introducing man made proteins and organisms into the existing biosphere. Epistemological questions arising from the perspective that creative mistakes of AI can promote science more effective than human ideas will be discussed. Possible risks in connection to a rapid application of in silico results in structural biology, created mostly with machine learning, will also be considered.

Keywords: Generative Artificial Intelligence, Computational Biology, De Novo Protein Engineering, Biophysics, AI Hallucinations, Molecular Design, AI Reproducibility, Cognitive Disruption

DOI: 10.54941/ahfe1005956