人間とAIの協力:人間と人工知能を組み合わせた新システムで実験を改善(Human-AI coworking:New system combines human, artificial intelligence to improve experimentation)

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2024-2-21 オークリッジ国立研究所(ORNL)

人工知能は実験における人間のエラーを減らしますが、因果関係の特定や小規模なデータセットの取り扱いでは人間の専門家がAIを上回ります。この課題に対処するため、ORNLの科学者は人間とAIの協力による実験性能向上のための推奨システムを開発しました。実験中、システムの機械学習アルゴリズムは人間がレビューするための初期観察結果を表示し、研究者はデータに投票してAIに似た情報を表示させたり方向を変えさせたりします。このシステムにより、初期の指示の後、アルゴリズムは少ない人間の入力で関連するデータを明らかにすることができます。

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好奇心主導の部分的Human-in-the-Loop自動実験のための動的ベイズ最適化アクティブ推薦システム A dynamic Bayesian optimized active recommender system for curiosity-driven partially Human-in-the-loop automated experiments

Arpan Biswas,Yongtao Liu,Nicole Creange,Yu-Chen Liu,Stephen Jesse,Jan-Chi Yang,Sergei V. Kalinin,Maxim A. Ziatdinov & Rama K. Vasudevan
npj Computational Materials  Published:03 February 2024
DOI:https://doi.org/10.1038/s41524-023-01191-5

人間とAIの協力:人間と人工知能を組み合わせた新システムで実験を改善(Human-AI coworking:New system combines human, artificial intelligence to improve experimentation)

Abstract

Optimization of experimental materials synthesis and characterization through active learning methods has been growing over the last decade, with examples ranging from measurements of diffraction on combinatorial alloys at synchrotrons, to searches through chemical space with automated synthesis robots for perovskites. In virtually all cases, the target property of interest for optimization is defined a priori with the ability to shift the trajectory of the optimization based on human-identified findings during the experiment is lacking. Thus, to highlight the best of both human operators and AI-driven experiments, here we present the development of a human–AI collaborated experimental workflow, via a Bayesian optimized active recommender system (BOARS), to shape targets on the fly with human real-time feedback. Here, the human guidance overpowers AI at early iteration when prior knowledge (uncertainty) is minimal (higher), while the AI overpowers the human during later iterations to accelerate the process with the human-assessed goal. We showcase examples of this framework applied to pre-acquired piezoresponse force spectroscopy of a ferroelectric thin film, and in real-time on an atomic force microscope, with human assessment to find symmetric hysteresis loops. It is found that such features appear more affected by subsurface defects than the local domain structure. This work shows the utility of human–AI approaches for curiosity driven exploration of systems across experimental domains.

1600情報工学一般
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