Kagan Brett J, Mahlis Michael, Bhat Anjali, Bongard Josh, Cole Victor M, Corlett Phillip, Gyngell Christopher, Hartung Thomas, Jupp Bianca, Levin Michael, Lysaght Tamra, Opie Nicholas, Razi Adeel, Smirnova Lena, Tennant Ian, Wade Peter Thestrup, Wang Ge
Cortical Labs, Brunswick, VIC 3056, Australia.
Department of Biochemistry and Pharmacology, University of Melbourne, Parkville, VIC 3010, Australia.
Innovation (Camb). 2024 Jun 17;5(5):100658. doi: 10.1016/j.xinn.2024.100658. eCollection 2024 Sep 9.
Disagreements about language use are common both between and within fields. Where interests require multidisciplinary collaboration or the field of research has the potential to impact society at large, it becomes critical to minimize these disagreements where possible. The development of diverse intelligent systems, regardless of the substrate (e.g., silicon vs. biology), is a case where both conditions are met. Significant advancements have occurred in the development of technology progressing toward these diverse intelligence systems. Whether progress is silicon based, such as the use of large language models, or through synthetic biology methods, such as the development of organoids, a clear need for a community-based approach to seeking consensus on nomenclature is now vital. Here, we welcome collaboration from the wider scientific community, proposing a pathway forward to achieving this intention, highlighting key terms and fields of relevance, and suggesting potential consensus-making methods to be applied.
关于语言使用的分歧在不同领域之间以及领域内部都很常见。当利益需要多学科合作，或者研究领域有可能对整个社会产生影响时，尽可能减少这些分歧就变得至关重要。无论其基础是什么（例如硅基与生物基），多种智能系统的开发都满足这两个条件。在朝着这些多样化智能系统发展的技术开发方面已经取得了重大进展。无论是基于硅基的进展，如使用大语言模型，还是通过合成生物学方法，如类器官的开发，现在迫切需要一种基于社区的方法来就命名达成共识。在此，我们欢迎更广泛科学界的合作，提出实现这一目标的前进道路，突出关键术语和相关领域，并建议应用潜在的达成共识的方法。
