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| Hauptverfasser: | , , , , , , , , |
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| Format: | Preprint |
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2024
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| Online-Zugang: | https://arxiv.org/abs/2406.17835 |
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| _version_ | 1866915776264404992 |
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| author | Gower, Alexander H. Korovin, Konstantin Brunnsåker, Daniel Kronström, Filip Reder, Gabriel K. Tiukova, Ievgeniia A. Reiserer, Ronald S. Wikswo, John P. King, Ross D. |
| author_facet | Gower, Alexander H. Korovin, Konstantin Brunnsåker, Daniel Kronström, Filip Reder, Gabriel K. Tiukova, Ievgeniia A. Reiserer, Ronald S. Wikswo, John P. King, Ross D. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_17835 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Use of AI-Robotic Systems for Scientific Discovery Gower, Alexander H. Korovin, Konstantin Brunnsåker, Daniel Kronström, Filip Reder, Gabriel K. Tiukova, Ievgeniia A. Reiserer, Ronald S. Wikswo, John P. King, Ross D. Machine Learning Artificial Intelligence 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. |
| title | The Use of AI-Robotic Systems for Scientific Discovery |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2406.17835 |