人工智慧在化學研究中的風險與效益
The risks and benefits of AI in chemical research
將人工智慧（AI）整合進化學研究是一項變革性的發展，在提升效率的同時，也帶來了複雜的安全與倫理挑戰。
The integration of Artificial Intelligence (AI) into chemical research is a transformative development, offering significant efficiency gains while introducing complex safety and ethical challenges.
AI，特別是機器學習，正在劇烈地縮短研發週期。
AI, particularly machine learning, is drastically shortening the research and development lifecycle.
透過分析龐大的數據集，AI 工具加速了新藥物與材料的發現。
By analyzing vast datasets, AI tools accelerate the discovery of new pharmaceuticals and materials.
這些系統同時優化了合成規劃，減少廢棄物與能源消耗，進而推動永續化學。
These systems also optimize synthesis planning, reducing waste and energy consumption, which promotes sustainable chemistry.
此外，預測建模使研究人員能以高準確度評估分子交互作用與毒性，通常能降低對人工、費時實驗的需求。
Furthermore, predictive modeling allows researchers to assess molecular interactions and toxicity with high accuracy, often minimizing the need for manual, time-consuming experiments.
AI 工具可能被濫用來設計危險物質，且系統容易產生幻覺——即生成看似確信但科學上無效的指令。
AI tools can be misused to design hazardous substances, and systems are prone to hallucinations—generating confident but scientifically invalid instructions.
為了應對這種局勢，科學界強調「人在迴路（human-in-the-loop）」的方法，將 AI 視為輔助工具而非自主權威。
To navigate this landscape, the scientific community emphasizes a "human-in-the-loop" approach, viewing AI as an assistive tool rather than an autonomous authority.
嚴謹的基準測試與全球安全框架目前正持續發展，以確保由 AI 驅動的化學創新保持安全、合乎倫理且負責任。
Rigorous benchmarking and global security frameworks are now being developed to ensure that AI-driven chemical innovation remains safe, ethical, and responsible.
