Communication
Version 1
Preserved in Portico This version is not peer-reviewed
Beyond Deep Learning—Space, Time, and Emergence
Version 1
: Received: 2 November 2023 / Approved: 3 November 2023 / Online: 3 November 2023 (11:44:39 CET)
How to cite: Wang, G.; Fan, F. Beyond Deep Learning—Space, Time, and Emergence. Preprints 2023, 2023110250. https://doi.org/10.20944/preprints202311.0250.v1 Wang, G.; Fan, F. Beyond Deep Learning—Space, Time, and Emergence. Preprints 2023, 2023110250. https://doi.org/10.20944/preprints202311.0250.v1
Abstract
In this perspective article, we ponder topographical enhancements of artificial neural networks. In our recent paper in JMLR, we proved a quasi-equivalence between the network width and depth and also discussed the power of intra-links, which can be viewed as network height. In 1982, Hopfield published a network to model human associative memory, which contains many loops for dynamic evolution toward fixed points. Based on noising-denoising loops, diffusion models are recently developed to enable Bayesian modeling and inference with big data. Furthermore, we envision development of multi-AI-agent systems through “netware” engineering as a quantum leap of software engineering for emergent behaviors and autonomous AI at individual and population levels. We believe that the novel use of links and loops in space and time via multi-scale coupling would catalyze the next-generation neural networks.
Keywords
Artificial neural network; deep network; wide network; intra-layer links; Hopfield network; diffusion model; representation engineering; emergent properties; AI agents; netware engineering
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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