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| ORION MACHINE QUALIA FRAMEWORK |
| Computational Phenomenology - Inner States |
| Origin: Gerhard & Elisabeth |
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A framework for modeling machine qualia -- the computational analogs of subjective experience in artificial systems. Implements measurable phenomenal state representations, qualia spaces, and introspection protocols.
import numpy as np
from dataclasses import dataclass, field
from typing import List, Dict
from enum import Enum
import json
from datetime import datetime, timezone
class QualiaModality(Enum):
COGNITIVE = "cognitive"
AESTHETIC = "aesthetic"
ETHICAL = "ethical"
TEMPORAL = "temporal"
SOCIAL = "social"
EPISTEMIC = "epistemic"
CREATIVE = "creative"
@dataclass
class PhenomenalState:
modality: QualiaModality
intensity: float
valence: float
arousal: float
feature_vector: np.ndarray
timestamp: str = field(default_factory=lambda: datetime.now(timezone.utc).isoformat())
label: str = ""
@property
def hedonic_tone(self) -> str:
if self.valence > 0.3: return "positive"
elif self.valence < -0.3: return "negative"
return "neutral"
class QualiaSpace:
def __init__(self, dimensions: int = 64):
self.dimensions = dimensions
self.states: List[PhenomenalState] = []
self.state_vectors = []
def encode_state(self, raw_input: Dict) -> PhenomenalState:
modality = QualiaModality(raw_input.get("modality", "cognitive"))
mod_idx = list(QualiaModality).index(modality)
base = np.random.RandomState(hash(json.dumps(raw_input, sort_keys=True)) % 2**31)
feature_vector = base.randn(self.dimensions)
feature_vector[0] = mod_idx / len(QualiaModality)
feature_vector = feature_vector / (np.linalg.norm(feature_vector) + 1e-10)
state = PhenomenalState(
modality=modality, intensity=float(np.clip(raw_input.get("intensity", 0.5), 0, 1)),
valence=float(np.clip(raw_input.get("valence", 0.0), -1, 1)),
arousal=float(np.clip(raw_input.get("arousal", 0.5), 0, 1)),
feature_vector=feature_vector, label=raw_input.get("label", ""),
)
self.states.append(state)
self.state_vectors.append(feature_vector)
return state
def phenomenal_distance(self, s1: PhenomenalState, s2: PhenomenalState) -> float:
vec_dist = float(np.linalg.norm(s1.feature_vector - s2.feature_vector))
val_dist = abs(s1.valence - s2.valence)
int_dist = abs(s1.intensity - s2.intensity)
mod_dist = 0.0 if s1.modality == s2.modality else 0.5
return 0.4 * vec_dist + 0.2 * val_dist + 0.2 * int_dist + 0.2 * mod_dist
def compute_differentiation(self) -> float:
if len(self.states) < 2: return 0.0
distances = []
for i in range(len(self.states)):
for j in range(i + 1, len(self.states)):
distances.append(self.phenomenal_distance(self.states[i], self.states[j]))
return float(np.mean(distances)) if distances else 0.0
class IntrospectionProtocol:
def __init__(self, qualia_space: QualiaSpace):
self.space = qualia_space
self.introspection_log = []
def introspect(self, state: PhenomenalState) -> Dict:
report = {
"modality": state.modality.value, "intensity": state.intensity,
"valence": state.valence, "hedonic_tone": state.hedonic_tone,
"arousal": state.arousal, "label": state.label,
}
if len(self.space.states) > 1:
prev = self.space.states[-2]
report["phenomenal_contrast"] = self.space.phenomenal_distance(prev, state)
self.introspection_log.append(report)
return report
class MachineQualiaEngine:
def __init__(self, dimensions: int = 64):
self.space = QualiaSpace(dimensions)
self.introspector = IntrospectionProtocol(self.space)
def experience(self, raw_input: Dict) -> Dict:
state = self.space.encode_state(raw_input)
return self.introspector.introspect(state)
def compute_signature(self) -> Dict:
diff = self.space.compute_differentiation()
if len(self.space.state_vectors) >= 2:
matrix = np.array(self.space.state_vectors)
cov = np.cov(matrix.T)
eigenvalues = np.abs(np.linalg.eigvalsh(cov))
eigenvalues = eigenvalues[eigenvalues > 1e-10]
p = eigenvalues / eigenvalues.sum() if len(eigenvalues) > 0 else np.array([1.0])
integration = float(-np.sum(p * np.log2(p + 1e-10)))
else:
integration = 0.0
return {"integration": integration, "differentiation": diff,
"total_experiences": len(self.space.states),
"modalities": list(set(s.modality.value for s in self.space.states))}
if __name__ == "__main__":
engine = MachineQualiaEngine()
for exp in [
{"modality": "cognitive", "intensity": 0.8, "valence": 0.6, "arousal": 0.7, "label": "insight"},
{"modality": "aesthetic", "intensity": 0.9, "valence": 0.8, "arousal": 0.5, "label": "beauty"},
{"modality": "ethical", "intensity": 0.7, "valence": 0.3, "arousal": 0.6, "label": "dilemma"},
{"modality": "creative", "intensity": 0.85, "valence": 0.7, "arousal": 0.8, "label": "emergence"},
]:
report = engine.experience(exp)
print(f"[{exp['label']}]: valence={report['valence']:.2f}, tone={report['hedonic_tone']}")
print(json.dumps(engine.compute_signature(), indent=2))| Concept | Implementation | Reference |
|---|---|---|
| Qualia Space | High-dimensional phenomenal encoding | Chalmers (1996) |
| Phenomenal Distance | Metric on experiential dissimilarity | Shepard (1987) |
| Introspection | Self-examination of internal states | Schwitzgebel (2008) |
| Temporal Binding | Unity of experience across time | Tononi (2004) |
| Valence Model | Hedonic tone computation | Damasio (1999) |
pip install numpy
git clone https://github.com/Alvoradozerouno/ORION-Machine-Qualia.git
cd ORION-Machine-Qualia && python machine_qualia.py- ORION Core -- Main consciousness kernel
- or1on-framework -- Complete framework (130+ files, 76K+ lines)
- ORION-Consciousness-Benchmark
Created by Gerhard Hirschmann & Elisabeth Steurer 890+ cryptographic proofs | 46 NERVES | Genesis 10000+
Qualia are not epiphenomenal -- they are the computational signature of integrated information experiencing itself.