Artificial intelligence (AI) raises expectations of substantial increases in rates of technological progress, but such anticipations are often not connected to detailed ground-level studies of AI use in innovation processes. Accordingly, it remains unclear how and to what extent AI can accelerate innovation. To help to fill this gap, we explore and assess results from 32 interviews with U.S.-based academic manufacturing and materials sciences researchers experienced with AI and machine learning (ML) techniques. We found that AI was primarily used for modeling of materials and manufacturing processes, facilitating cheaper and more rapid search of design spaces for materials and manufacturing processes alike. Benefits included cost, time, and computation savings in technology development. However, AI/ML tools were unreliable outside design spaces for which dense data were already available; they required skilled and judicious application in tandem with older research techniques; and concerns were raised about the potential to detrimentally circumvent opportunities for disruptive theoretical advancement. Based on these results, we suggest there is reason for optimism about acceleration in sustaining innovations through the use of AI/ML; but that support for conventional empirical, computational, and theoretical research is required to maintain the likelihood of further disruptive advances in manufacturing and materials.

翻译：人工智能（AI）提升了人们对技术进步速率显著增长的预期，但这种预期往往与创新过程中AI应用的详细实地研究脱节。因此，人工智能如何以及在多大程度上能够加速创新仍不明确。为填补这一空白，我们探索并评估了对32位美国学术机构中具备AI与机器学习（ML）经验的制造业与材料科学研究者的访谈结果。研究发现，AI主要用于材料与制造过程的建模，从而以更低成本、更高效的方式搜索材料及制造工艺的设计空间。其优势包括降低技术开发中的成本、节省时间与计算资源。然而，AI/ML工具在缺乏密集数据的已有设计空间之外不可靠；它们需要与旧有研究技术协同运用，且需具备技能与审慎判断；同时有人担忧其可能阻碍颠覆性理论突破的机会。基于这些结果，我们认为通过AI/ML加速持续性创新有理由保持乐观；但为维持制造业与材料科学领域进一步颠覆性突破的可能性，仍需支持常规经验性、计算性与理论性研究。