Moving boundaries: An appreciation of John Hopfield
The 2024 Nobel Prize in Physics was awarded to John Hopfield and Geoffrey Hinton, "for foundational discoveries and inventions that enable machine learning with artificial neural networks." As noted by the Nobel committee, their work moved the boundaries of physics. This is a brief reflection on Hopfield's work, its implications for the emergence of biological physics as a part of physics, the path from his early papers to the modern revolution in artificial intelligence, and prospects for the future.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Evidence of Scaling Regimes in the Hopfield Dynamics of Whole Brain Model
It is shown that a Hopfield recurrent neural network exhibits a scaling regime, whose specific exponents depend on the number of parcels used and the decay length of the coupling strength. This scaling regime recovers th…
Modern Hopfield Networks meet Encoded Neural Representations -- Addressing Practical Considerations
Content-addressable memories such as Modern Hopfield Networks (MHN) have been studied as mathematical models of auto-association and storage/retrieval in the human declarative memory, yet their practical use for large-sc…
Natural Language QueriesRetrievalLLaVA-Docent: Instruction Tuning with Multimodal Large Language Model to Support Art Appreciation Education
Despite the development of various AI systems to support learning in various domains, AI assistance for art appreciation education has not been extensively explored. Art appreciation, often perceived as an unfamiliar and…
BenchmarkingChatbotEmotional IntelligenceLanguage Modeling+3Brain memory working. Optimal control behavior for improved Hopfield-like models
Recent works have highlighted the need for a new dynamical paradigm in the modeling of brain function and evolution. Specifically, these models should incorporate non-constant and asymmetric synaptic weights \(T_{ij}\) i…
Geometric and dynamical analysis of attractor boundaries and storage limits in kernel Hopfield networks
High-capacity associative memories based on Kernel Logistic Regression (KLR) exhibit strong storage capabilities, but the dynamical and geometric mechanisms underlying their stability remain poorly understood. This paper…