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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Tamura SciTechAdvMatMeth2023 3-1.jpeg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Niszczota EconBusRev23 9-2.png|240px]]</div>
'''"[[Journal:NIMS-OS: An automation software to implement a closed loop between artificial intelligence and robotic experiments in materials science|NIMS-OS: An automation software to implement a closed loop between artificial intelligence and robotic experiments in materials science]]"'''
'''"[[Journal:Judgements of research co-created by generative AI: Experimental evidence|Judgements of research co-created by generative AI: Experimental evidence]]"'''


NIMS-OS (NIMS Orchestration System) is a [[Python (programming language)|Python]] library created to realize a closed loop of [[Laboratory automation|robotic]] experiments and [[artificial intelligence]] (AI) without human intervention for automated [[Materials science|materials exploration]]. It uses various combinations of modules to operate autonomously. Each module acts as an AI for materials exploration or a controller for a robotic experiments. As AI techniques, Optimization Tools for PHYSics Based on Bayesian Optimization (PHYSBO), BoundLess Objective-free eXploration (BLOX), phase diagram construction (PDC), and random exploration (RE) methods can be used. Moreover, a system called NIMS Automated Robotic Electrochemical Experiments (NAREE) is available as a set of robotic experimental equipment ... ('''[[Journal:NIMS-OS: An automation software to implement a closed loop between artificial intelligence and robotic experiments in materials science|Full article...]]''')<br />
The introduction of [[ChatGPT]] has fuelled a public debate on the appropriateness of using generative [[artificial intelligence]] (AI) ([[large language model]]s or LLMs) in work, including a debate on how they might be used (and abused) by researchers. In the current work, we test whether delegating parts of the research process to LLMs leads people to distrust researchers and devalues their scientific work. Participants (''N'' = 402) considered a researcher who delegates elements of the research process to a PhD student or LLM and rated three aspects of such delegation. Firstly, they rated whether it is morally appropriate to do so. Secondly, they judged whether—after deciding to delegate the research process—they would trust the scientist (who decided to delegate) to oversee future projects ... ('''[[Journal:Judgements of research co-created by generative AI: Experimental evidence|Full article...]]''')<br />
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Latest revision as of 15:26, 20 May 2024

Fig1 Niszczota EconBusRev23 9-2.png

"Judgements of research co-created by generative AI: Experimental evidence"

The introduction of ChatGPT has fuelled a public debate on the appropriateness of using generative artificial intelligence (AI) (large language models or LLMs) in work, including a debate on how they might be used (and abused) by researchers. In the current work, we test whether delegating parts of the research process to LLMs leads people to distrust researchers and devalues their scientific work. Participants (N = 402) considered a researcher who delegates elements of the research process to a PhD student or LLM and rated three aspects of such delegation. Firstly, they rated whether it is morally appropriate to do so. Secondly, they judged whether—after deciding to delegate the research process—they would trust the scientist (who decided to delegate) to oversee future projects ... (Full article...)
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