Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Deep Learning Models vs. Hamzah Equation Superiority |
| Συγγραφείς: |
JALALI, SEYED RASOUL |
| Στοιχεία εκδότη: |
Zenodo, 2025. |
| Έτος έκδοσης: |
2025 |
| Θεματικοί όροι: |
ψ–Hamzah, deep learning, neural networks, CNN, RNN, Transformer, memory, meaning, consciousness, fractional derivatives, non-local memory, fractal structures, semantic fields, time-space integration, machine learning, artificial intelligence, AGI, cognitive science, cognitive memory, fractal dynamics, non-recursive, semantic representation, artificial consciousness, neural representation, brain-inspired models, semantic models, human cognition, multi-scale perception, memory fields, consciousness theories, AGI architecture, field theories, brain models, neural connections, deep learning paradigms, field-based learning, semantic modeling, knowledge representation, linguistic models, neural architectures, non-linear dynamics, non-Euclidean space, machine perception, fractal geometry, temporal learning, human brain, computational neuroscience, learning algorithms, data representation, symbolic representation, deep neural networks, dynamic systems, cognitive architectures, memory storage, symbolic cognition, knowledge integration, quantum cognition, cognitive models, AI consciousness, fractal fields, complex systems, systems theory, evolutionary learning, time-domain models, entropy minimization, energy minimization, AGI theory, cognitive evolution, information processing, dynamic fields, self-organizing systems, recursive systems, data-driven learning, evolutionary algorithms, AI modeling, artificial memory, advanced learning models, human-AI interaction, neural evolution, semantic networks, predictive models, dimensionality reduction, cognitive models, model comparison, nonlinear learning, field dynamics, artificial brain, cognitive networks, deep cognitive learning, pattern recognition, spatiotemporal models, neural field theory, fractal perception, machine memory, temporal evolution, semantic perception, symbolic learning, learning theories, dynamic memory fields, deep learning evolution, cognitive science models, field-based computation, fractal cognition, non-linear learning, adaptive memory, memory-based learning, multidimensional AI, machine meaning, cognitive processes, conceptual learning, perception modeling, deep AI, memory in AI, multi-dimensional learning, fractal memory, cognitive computational models, integrated memory, neural field dynamics, AI interpretation, language models, memory systems, brain field, quantum AI, dynamic system modeling, artificial knowledge, deep perception, temporal dynamics, machine learning evolution, cognitive structure, knowledge evolution, space-time models, cognitive patterns, deep semantic networks, memory evolution, temporal memory, computational memory, memory structures, neural networks evolution, learning through memory, integrated neural systems, field-based perception, multi-scale learning, memory-based cognition, data-field integration, AI perception, machine learning systems, human intelligence models, fractal AI, conceptual evolution, artificial meaning, neural semantics, knowledge integration systems, AI memory systems, dynamic learning fields, multi-domain learning, semantic systems, neural structures, memory fields in AI, AI models comparison, symbolic learning systems, computational cognition, fractal data modeling, AI and cognition, neural field learning, learning by evolution, cognitive perception, memory theory, field memory dynamics, multi-dimensional cognition, complex memory systems, machine memory fields, AI cognition. Ask ChatGPT |
| Περιγραφή: |
The ψ–Hamzah model represents a revolutionary shift in understanding deep learning, memory, meaning, and consciousness, moving beyond traditional statistical paradigms. Unlike classic deep learning models such as CNN, RNN, and Transformers, which rely on discrete layers, numerical weights, and gradient-based algorithms, ψ–Hamzah introduces a fractal and semantically rich framework based on continuous, non-recursive memory fields. By utilizing fractional derivatives and time-space integration, it reconstructs perception and meaning in a dynamic, field-based structure. Through a multi-stage analysis, this article explores the model’s superiority in capturing the non-linear, multi-scale nature of human cognition and consciousness. The comparison with classical deep learning models highlights key differences in dynamics, memory, and adaptability, positioning ψ–Hamzah as a more sophisticated and interpretable approach. This model demonstrates how learning, rather than merely optimizing a loss function, involves the evolution of a living cognitive field that interacts with time, memory, and meaning. Furthermore, the ψ–Hamzah model has profound interdisciplinary applications, including in cognitive science, linguistics, consciousness theories, AGI (Artificial General Intelligence), and social sciences. By redefining language, memory, and consciousness, it offers a new vision for human-computer interaction, educational systems, and societal dynamics. The article concludes with a philosophical and civilizational outlook on how ψ–Hamzah could reshape not only the future of AI but the broader landscape of knowledge and human understanding. In essence, ψ–Hamzah is not just an algorithm but a new worldview based on fields, continuity, and memory, offering the potential for a deep, meaningful transformation in both artificial and human intelligence. |
| Τύπος εγγράφου: |
Other literature type |
| DOI: |
10.5281/zenodo.15885291 |
| Rights: |
CC BY |
| Αριθμός Καταχώρησης: |
edsair.doi...........d097f0a8c67ed42d5b8b02e9017b134b |
| Βάση Δεδομένων: |
OpenAIRE |