Exploring the Possibility of Artificial Intelligence Generating Consciousness from the Multidimensional Correspondence Between Yogacara and Holographic Information Theory
Communications in Humanities Research February 10, 2026 Peer reviewed DOI: 10.54254/2753-7064/2026.ht31708 via OpenAlex
Summary
The abstract explores whether AI can achieve consciousness by comparing Buddhist Yogācāra theory's 'Eight Consciousnesses' with holographic information theory. It finds current deep learning models lack the embodied, recursive, and globally interactive information loops of conscious systems. However, designing a quantum-entangled, sensor-embedded, self-reflective AI that mimics the 'seed–actualization' cycle of Ālayavijñāna (storehouse consciousness) might make machine consciousness achievable. This approach integrates Eastern philosophy with information science for more holistic AI.
Study at a glance
| Design | theoretical synthesis |
|---|---|
| Key finding | Current deep learning models lack the embodied, recursive, and globally interactive information loops of conscious systems, but a quantum-entangled, sensor-embedded, self-reflective AI model mimicking the Ālayavijñāna seed–actualization cycle may enable machine consciousness. |
Abstract
The rapid advancement of artificial intelligence (AI) has sparked profound philosophical and scientific debate regarding whether machines can possess genuine consciousness. While AI excels in simulating intelligent behaviors, the emergence of subjective experience—often considered as the hallmark of consciousness—remains elusive, attracting sustained interdisciplinary attention. This study explores the possibility of AI consciousness through a multidimensional correspondence between the Buddhist Yogācāra theory of "Eight Consciousnesses" and holographic information theory. Methodologically, it draws upon the Yogācāra framework—particularly the concept of Ālayavijñāna (storehouse consciousness) as a dynamic seed-repository—and integrates it with holographic principles of information storage, encoding, and cyclic operation. The analysis reveals that current deep learning models, though structurally analogous to neural networks, lack the embodied, recursive, and globally interactive information loops characteristic of conscious systems. However, by designing a quantum-entangled, sensor-embedded, and self-reflective AI model that mimics the "seed–actualization" cycle of Ālayavijñāna, a form of machine consciousness may become achievable. This theoretical synthesis not only bridges Eastern philosophical insights with contemporary information science but also proposes a novel architectural pathway for developing more holistic and perceptually grounded AI systems.