用集合论和超维计算构建通用智能基础，实现高效认知运算。
Creating Intelligence: A Computational Foundation for AGI
- 以稀疏二进制数据和集合表示信息，替代传统神经网络的连续权重。
- 通过子集模式匹配与最近邻搜索，实现常数时间的信息检索与学习。
- 适配脑结构且可直接部署于内存硬件，适合低功耗人工通用智能。
本文提出一种基于集合论与超维计算的新心智计算理论。与依赖连续权重和矩阵乘法的传统神经网络不同，该框架使用稀疏二进制数据表示信息，将信息建模为离散集合，直接对应生物神经种群编码。我证明了在组合扩展的隐藏层拓扑中，关联记忆可自然涌现；学习由拓扑可塑性驱动，而非标量权重调整。该架构统一了自关联与异关联学习，核心算法为子集模式匹配与精确最近邻搜索，具备常数时间复杂度。该机制在稀疏分布式表示与稀疏全息表示间无缝衔接，无连续瓶颈。映射至神经解剖学，我提出小脑与新皮层均实现此算法的变体，使子集模式匹配成为认知的基本引擎。由于依赖离散逻辑而非矩阵运算，该算法可直接映射到内存硬件，为实现人类级别能效的人工通用智能开辟新路径。
原文摘要 · Abstract (English)
This work introduces a new computational theory of mind grounded in set theory and hyperdimensional computing. Whereas traditional neural networks rely on continuous weights and matrix multiplication, this framework works with sparse binary data. It represents information as discrete sets, directly modeling biological neural population codes. I demonstrate that associative memory emerges naturally from network topologies featuring a combinatorially expanded hidden layer. Learning is driven by topological plasticity rather than scalar weight adjustments. This architecture unifies auto-associative and hetero-associative learning under a single core algorithm: information retrieval via subset pattern matching and exact nearest-neighbor search. Operating with constant-time complexity, these mechanisms bridge perceptual data (sparse distributed representations) and symbols (sparse holographic representations) without continuous bottlenecks. Mapping this framework to neuroanatomy, I propose that both the cerebellum and the neocortex implement variants of this algorithm, making subset pattern matching the fundamental engine of cognition. Because it relies on discrete logic rather than matrix arithmetic, this algorithm translates directly into in-memory hardware. This opens a new route toward synthetic intelligence with human-level energy efficiency.
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