Artificial Intelligence–Assisted Scientific Discovery and the Changing Role of the Researcher: Scientific, Social, Economic and Geopolitical Implications for the Twenty-First Century

Engineering notes

Key topics: self-driving, prediction. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

This conceptual review examines how artificial intelligence is transforming scientific discovery and redefining the role of researchers in the twenty-first century. Rather than viewing AI merely as a computational tool, the paper argues that it is becoming part of a broader scientific discovery ecosystem that integrates researchers, autonomous laboratories, advanced scientific instruments, high-performance computing, large-scale data infrastructures, metrology, and institutional governance. The study critically evaluates the scientific, social, economic, and geopolitical implications of AI-assisted scientific discovery. It discusses the transition from traditional hypothesis-driven research to AI-supported exploration of large hypothesis spaces, the emergence of self-driving laboratories, the growing importance of measurement science and trustworthy AI, and the changing relationship between prediction, explanation, and scientific understanding. The paper also analyzes research productivity, scientific integrity, platform dependency, environmental sustainability, open science, research security, and global technological competition. A central argument of the paper is that artificial intelligence will not replace researchers but will fundamentally redefine their responsibilities. Future researchers will increasingly act as architects of scientific questions, guardians of evidence and measurement, managers of human–AI research ecosystems, epistemic auditors, and custodians of ethical and societal responsibility. The paper concludes that scientific leadership in the AI era will depend not only on advanced AI models but also on trustworthy data, metrology, robust research infrastructures, interdisciplinary expertise, ethical governance, and international collaboration. AI-assisted scientific discovery is therefore presented not simply as a technological innovation, but as a foundational transformation in the future architecture of science and civilization.

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