One Neuron.
Five Layers. Strictly.
| # | Layer | Neuron element | Symbol | Role |
|---|---|---|---|---|
| I · World | World AI | X₁…Xₙ · Raw inputs | θᵗ | All published knowledge before any weighting — every paper, trial, grey document. The raw signal the network is handed. Passive. Pre-epistemic. |
| II · Perception | Perception AI | b · Bias & wᵢ · Weights | b + Σwᵢ·Xᵢ | The bias b is the institutional prior — what the director already knows before screening a single paper. The weights wᵢ encode relevance: how much each input should matter. Together, bias and weights constitute perception — the weighted, prejudiced summation of the world. |
| III · Agentic | Agentic AI | f(·) · Activation function | f(σ²,λ,ε) | Does this fire? Given uncertainty σ², irreversibility penalty λ, and noise ε — the activation function decides what clears the threshold. This is agency: not passive summation but a decision gate. Most weighted evidence does not make it through. |
| IV · Generative | Generative AI | ŷ · Output token | ε_FGT | The single token — the policy brief, the recommendation, the predicted loss. Not a truth claim. ε_FGT is the neuron's estimate of how wrong it is. Generative AI is the moment of emission: one token that minimises predicted error given everything upstream. |
| V · Embodied | Embodied AI | Δw · Weight update | L(θᵗ⁺¹) | y_true arrives — the real-world outcome. In silicon: backpropagation. In biology: feedback. Same mathematics, different substrate. The difference (y_true − ŷ) updates the weights; the next review begins with a better neuron. The human auditor is this mechanism. |
Backpropagation is just feedback — biology knew first
The formal name is backpropagation. The biological name is feedback. Same thing. When the real-world outcome arrives — did the PM-JAY expansion actually reduce catastrophic expenditure in the target quintile? — that signal travels back through the layers and updates the weights. In a neural network we have a named algorithm and a gradient. In a living system we have consequence. The mathematics are not different; the substrate is.
This is what the Embodied layer is: the ground truth loop. ε_FGT is the predicted error; the feedback is the measured error. The gap between them is what learning is made of.
Six rows disguise the structure. The neuron has five functional moments — input, weighting, activation, output, update — and so does the TMVES. Forcing the bias into Perception (where it belongs, as prior) and forcing backprop into Embodied (where it belongs, as consequence) makes the isomorphism visible. Any other assignment is decoration.
The output is a loss estimate, not an answer
The Generative layer emits ε_FGT — the predicted federated ground-truth error. The neuron does not output a recommendation; it outputs its best estimate of how wrong it is. A policy brief with "moderate certainty" is not hedging. It is a precise statement about ε_FGT. Strip the uncertainty and you have removed the most important part of the output.
Perception holds both the bias and the weights
The old framing split bias into its own row. That was wrong. The bias term b and the weights wᵢ are both perceptual operations — both happen before activation, both constitute the weighted view of the world. Perception is b + Σwᵢ·Xᵢ, the whole summation. The bias is not noise; it is the institutional prior that shifts the summation before a single paper is screened. But it lives in the same layer as the weights.
AGI is when Embodied closes the loop without a human
Currently the Embodied layer is a human auditor. She reads the outcome, compares it to what the brief predicted, and updates the weights. AGI, in this frame, is the point at which that update mechanism no longer requires her. Not because she is replaced — because enough y_true signals have accumulated that the weight updates can be trusted to happen autonomously. The direction is visible in the diagram. We are not there.