Cancermorphic Computing Toward Multilevel Machine Intelligence

2025-03-17

AI Summary

Existing computing systems exhibit weak fault tolerance and low security under dynamic, adversarial, and resource-constrained conditions. Method: This work introduces “carcinomorphic computing,” a novel paradigm that formally integrates somatic mutation, metastasis, and immune evasion mechanisms from pathological biology into computational theory. It establishes a multi-level, intelligence- and context-driven mutation model, synergizing distributed propagation, resource-aware heuristic optimization, and immunological analogy-based adaptive architecture—balancing neuromorphic plasticity with controllable chaos. Contribution/Results: The paradigm provides a scalable theoretical framework for fault-tolerant computing, cybersecurity, and autonomous systems. By grounding bio-inspired computation in mechanistic principles rather than superficial analogies, it advances the field toward mechanism-driven biomimetic intelligence.

Technology Category

Search and Optimization: Evolutionary ComputationMachine Learning: Bio-inspired LearningCognitive Modeling & Cognitive Systems: (Computational) Cognitive Architectures

Application Category

Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystemsUser Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systemsSystems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT

Abstract

Despite their potential to address crucial bottlenecks in computing architectures and contribute to the pool of biological inspiration for engineering, pathological biological mechanisms remain absent from computational theory. We hereby introduce the concept of cancer-inspired computing as a paradigm drawing from the adaptive, resilient, and evolutionary strategies of cancer, for designing computational systems capable of thriving in dynamic, adversarial or resource-constrained environments. Unlike known bioinspired approaches (e.g., evolutionary and neuromorphic architectures), cancer-inspired computing looks at emulating the uniqueness of cancer cells survival tactics, such as somatic mutation, metastasis, angiogenesis and immune evasion, as parallels to desirable features in computing architectures, for example decentralized propagation and resource optimization, to impact areas like fault tolerance and cybersecurity. While the chaotic growth of cancer is currently viewed as uncontrollable in biology, randomness-based algorithms are already being successfully demonstrated in enhancing the capabilities of other computing architectures, for example chaos computing integration. This vision focuses on the concepts of multilevel intelligence and context-driven mutation, and their potential to simultaneously overcome plasticity-limited neuromorphic approaches and the randomness of chaotic approaches. The introduction of this concept aims to generate interdisciplinary discussion to explore the potential of cancer-inspired mechanisms toward powerful and resilient artificial systems.

Problem

• Introduces cancer-inspired computing for resilient systems
• Emulates cancer survival tactics for fault tolerance
• Explores multilevel intelligence to overcome computing limitations

Innovation

• Cancer-inspired computing for resilient systems
• Emulates cancer survival tactics in architectures
• Focuses on multilevel intelligence and context-driven mutation