The Use of AI-Robotic Systems for Scientific Discovery

The process of developing theories and models and testing them with experiments is fundamental to the scientific method. Automating the entire scientific method then requires not only automation of the induction of theories from data, but also experimentation from design to implementation. This is t...

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Bibliographic Details
Published inarXiv.org
Main Authors Gower, Alexander H, Korovin, Konstantin, Brunnsåker, Daniel, Kronström, Filip, Reder, Gabriel K, Tiukova, Ievgeniia A, Reiserer, Ronald S, Wikswo, John P, King, Ross D
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 25.06.2024
Subjects
Automation
Bioreactors
Machine learning
Robot learning
Robotics
Robots
Scientific method
Scientists
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Summary:The process of developing theories and models and testing them with experiments is fundamental to the scientific method. Automating the entire scientific method then requires not only automation of the induction of theories from data, but also experimentation from design to implementation. This is the idea behind a robot scientist -- a coupled system of AI and laboratory robotics that has agency to test hypotheses with real-world experiments. In this chapter we explore some of the fundamentals of robot scientists in the philosophy of science. We also map the activities of a robot scientist to machine learning paradigms, and argue that the scientific method shares an analogy with active learning. We demonstrate these concepts using examples from previous robot scientists, and also from Genesis: a next generation robot scientist designed for research in systems biology, comprising a micro-fluidic system with 1000 computer-controlled micro-bioreactors and interpretable models based in controlled vocabularies and logic.
ISSN:2331-8422