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- Rename 'three-pronged' folder to 'knowledge-layers' — prong metaphor
was misleading (implied parallel tines), replaced with epistemic layers
(deductive base, empirical middle, probabilistic oracle — vertical stack)
- Collapse 11 overlapping files into 3 coherent documents:
- knowledge-layers/_index.org: core framework (two engines + one store,
World Model formula, 0-14 layer table, provenance store design,
conflict resolution, cold-start, stage mapping)
- knowledge-layers/practical-implications.org: design-world-aware-of-
physics, 10 powers, Schafmeister existence proof, epistemic transparency
- knowledge-layers/neurological-empirical.org: neural networks in
provenance framework (kept intact)
- Relocate wolfram/mathematica and Schafmeister docs to ideas/viability/
- Integrate into main architecture _index.org:
- Gate: expanded from two vectors (ACL2+LLM) to three (deductive,
provenance/empirical, LLM oracle)
- Autodidactic loop: split into Track 1 (deductive hardening, fast)
and Track 2 (empirical validation, slow, experimental-feedback-driven)
- See also: added Knowledge Layers cross-reference
- Add all-lisp geometry engine note (ideas/lisp-geometry-engine.org) as
concrete illustration of the empirical layer's effect on design work
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ANN vs Neuromorphic vs Symbolic
ANN vs Neuromorphic vs Symbolic
Core insight
The gap between ANNs and biology is not substrate (binary vs analog). Both are continuous mathematics running on discrete hardware. The real differences are architectural:
- Memory-binding: ANNs store weights separately from compute (von Neumann bottleneck). Biology co-locates weight and signal at the synapse.
- Local vs global learning: ANNs need a global error signal backpropagated through every layer. Biology uses purely local plasticity (STDP) — each synapse adjusts based on its own pre/post partners.
- Time: Biology is asynchronous, continuous, with rich temporal dynamics. ANNs are synchronous — everything computed in lockstep. Recurrence is an awkward addition.
- One substrate, many functions: A biological synapse does memory, signal propagation, temporal integration, and plasticity in one structure. ANNs separate these across different passes and optimizers.
When each mathematics is appropriate
| Mathematics | Naturally good at | Awkward at |
——————————-————
| ANN / gradient descent | Smooth function approximation, interpolation, pattern completion from dense data | Symbolic reasoning, exact constraints, sparse data, multi-step verification |
| Neuromorphic / spiking dynamics | Temporal pattern recognition, event-driven control, low-power always-on sensing | Complex multi-step planning, precise arithmetic, storing large lookup tables |
| Symbolic / deduction | Exact reasoning, proof, constraint satisfaction, verifiable behavior | Learning from raw data, generalization, handling noisy inputs |
How Passepartout uses all three
- LLM (ANN on GPU) handles the noisy real world — language, vision, imperfect input
- Screamer (symbolic constraint search on CPU) handles combinatorial reasoning — "find valid configuration"
- ACL2 (deductive proof) handles the verifiable kernel — "prove this decision follows from rules"
- P150 (RISC-V parallel accelerator, in-between arch) handles ambient awareness, parallel dispatch, anomaly detection
Each mathematics where it belongs. The failure mode of both pure ANN and pure symbolic approaches is forcing one mathematics to do what the other is better at.
The neuromorphic opportunity
A neuromorphic chip (Loihi-level) would add unsupervised temporal learning — learning daily rhythms, behavioral patterns, and detecting deviations without training, labels, or LLM involvement. This is the difference between responding to commands and anticipating needs.
But the P150 gets 80% there with programmable cores controlled directly, without waiting for neuromorphic hardware to mature.