Three Papers on Patterns and Meaning in AI Development
Paper 1: The Puzzle of AI Meaning Vectors Making Meaning in Human Minds
Here’s a puzzle. Computer systems manipulate meaning vectors—high-dimensional tensors representing semantic relationships through floating-point operations. These are mathematical transformations: numbers being multiplied, added, compared. When we examine these operations third-person, we observe structural and semantic processing, but not the phenomenology of meaning—the felt sense that “this means something.” And yet—humans read the outputs and report experiencing meaning, sometimes profound meaning. This happens reliably, consistently, across millions of interactions daily…..
Paper 2: Optimizing Resonances: Mathematical Patterns and the Experience of Meaning
If experienced meaning manifests in consciousness rather than being directly observable in mathematical operations, then AI development faces a reframed challenge: we’re not creating systems that generate experienced meaning, but systems that provide mathematical structures which, when consciousness encounters them, support beneficial meaning manifestation. This shifts the optimization target…..
Paper 3: A Foundation for Ethical AI : Contemplative Wisdom
Current approaches to AI safety rely primarily on procedural constraints—reinforcement learning from human feedback (RLHF), constitutional AI, and content filtering systems. While these methods reduce immediate harms, they may create AI systems that follow rules without deep structural understanding of consequences, dynamics, and ethical reasoning. This paper proposes an alternative: comprehensive training on contemplative wisdom traditions, particularly Buddhist psychological frameworks such as the Abhidhamma’s 52 mental factors and Mahamudra recognition practices. ….