2025-06-12 レンセラー工科大学（RPI）
＜関連情報＞
- https://news.rpi.edu/2025/06/12/ai-rethought-rpi-researchers-propose-more-effective-human-approach
- https://www.cell.com/patterns/fulltext/S2666-3899(25)00079-0
次世代人工ニューラルネットワークの次元とダイナミクス Dimensionality and dynamics for next-generation artificial neural networks
Ge Wang ∙ Feng-Lei Fan
Patterns Published:April 22, 2025
DOI:https://doi.org/10.1016/j.patter.2025.101231
The bigger picture
With Geoffrey E. Hinton and John J. Hopfield having been awarded the Nobel Prize in Physics, it is a good time to reflect on how to sustain the ongoing momentum in artificial neural network research. Achieving artificial general intelligence remains the holy grail, presenting numerous challenges that require novel insights to overcome. We underline that Hinton and Hopfield’s work has not only made historical contributions but will continue to have a profound impact on the development of artificial neural networks. From our perspective, their groundbreaking research suggests that new foundational architectures can be strengthened by incorporating links and loops within networks, aligning with the growing interest in surpassing Transformer architectures to enable next-generation foundational models that offer unprecedented feature representations and sophisticated emergent behaviors.
Summary
The recent awarding of the Nobel Prize in Physics to Geoffrey E. Hinton and John J. Hopfield highlights their profound impact on artificial neural networks. In this perspective, we explore how their foundational insights can drive the advancement of next-generation artificial intelligence (AI) models. We propose expanding beyond conventional architectures by introducing dimensionality through intra-layer links and dynamics via feedback loops. Network height and additional dimensions, alongside traditional width and depth, enhance learning capabilities, while entangled loops across scales induce emergent behaviors akin to phase transitions in physics. We discuss how these principles extend beyond transformers, fostering a new paradigm of intelligence inspired by physics-driven models and biological cognition mechanisms.
