Martin Charles H, Mani Ganesh
Calculation Consulting, San Francisco, CA, USA.
Carnegie Mellon University, Pittsburgh, PA, USA.
Patterns (N Y). 2024 Nov 25;5(12):101099. doi: 10.1016/j.patter.2024.101099. eCollection 2024 Dec 13.
This article examines the convergence of physics, chemistry, and artificial intelligence (AI), highlighted by recent Nobel Prizes. It traces the historical development of neural networks, emphasizing interdisciplinary research's role in advancing AI. The authors advocate for nurturing AI-enabled polymaths to bridge the gap between theoretical advancements and practical applications, driving progress toward artificial general intelligence (AGI).
本文探讨了物理学、化学和人工智能(AI)的融合,近期的诺贝尔奖凸显了这一点。它追溯了神经网络的历史发展,强调跨学科研究在推动人工智能发展中的作用。作者主张培养具备人工智能能力的博学者,以弥合理论进步与实际应用之间的差距,推动通向通用人工智能(AGI)的进展。