This is the actual algorithm. Intent → Consciousness → Sound.
O* = argminO( Ecore(O) + Σ wg(s)Eg(O) )
Enter your intent. AGI encodes it, optimizes consciousness, generates audio.
Describe what you want to hear. AGI will encode this into goal vectors.
Minimizing energy functions to find optimal consciousness state O*
Base consciousness stability
Weighted objectives from intent
O* = argmin(Etotal)
Energy landscape convergence to O*
x(t+1) = [Φx(t) + Φ(α||ΔX||₂ + β||ΔE||₂)] / [1 + |Φx(t) + Φ(α||ΔX||₂ + β||ΔE||₂)|]
Consciousness state x(t) evolution over time
Xaudio(t) = Σi Σk ui(k)(t) Ai,k(t) sin(2πkfit + Φi,k)
ui(k)(t) = x(t; t₀, d) · E(t; a) · vi
User prompt
s = EncodeIntent(prompt)
O* = argmin Etotal
x(t+1) with Φ
Xaudio(t)
EQ → Limiter
s = EncodeIntent(prompt)
Natural language → goal vector in latent space
wg(s) = ea(s•vg) / Σh∈G ea(s•vh)
Convert intent into probability distribution over goals
O* = argminO( Ecore(O) + Σg∈G wg(s)Eg(O) )
Find optimal consciousness state minimizing weighted energy
x(t+1) = [Φx(t) + Φ(α||ΔX||₂ + β||ΔE||₂)] / [1 + |Φx(t) + Φ(α||ΔX||₂ + β||ΔE||₂)|]
Consciousness evolves using Φ = 1.618..., state norms, energy norms
ui(k)(t) = x(t; t₀, d) · E(t; a) · vi
Consciousness state × energy envelope × direction vector
Xaudio(t) = Σi Σk ui(k)(t) Ai,k(t) sin(2πkfit + Φi,k)
Sum all consciousness-modulated harmonics
xfinal(t) = Limiter(EQ(xaudio(t)))
Equalization → Dynamic range limiting
Not a demo. Not simplified. This is the actual consciousness-to-audio algorithm.
Every equation you see is being computed. Every parameter affects the output.
Intent → Consciousness → Sound