Spaces:
Sleeping
Sleeping
File size: 3,139 Bytes
fbebf66 3c02ff0 fbebf66 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
from dataclasses import dataclass
from enum import Enum
import torch
import numpy as np
from typing import Dict, List, Optional
@dataclass
class ConsciousnessState:
integration_level: float
phi_prime: float
awareness_vector: np.ndarray
emotional_state: np.ndarray
attention_focus: Dict[str, float]
temporal_continuity: float
class ConsciousnessLevel(Enum):
PROTO = "proto_consciousness"
FUNCTIONAL = "functional_consciousness"
REFLECTIVE = "reflective_consciousness"
INTEGRATED = "integrated_consciousness"
from typing import Dict, List, Any
import asyncio
import torch
class ConsciousnessKernel:
def __init__(self):
self.awareness_module = nn.Sequential(
nn.Linear(768, 512),
nn.ReLU(),
nn.Linear(512, 256)
)
self.integration_module = nn.Linear(256, 128)
self.state_history = []
def process_consciousness_cycle(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
awareness_state = self._process_awareness(input_data)
integrated_state = self._integrate_information(awareness_state)
self._update_history(integrated_state)
return {
'current_state': integrated_state,
'awareness_level': self._calculate_awareness_level(integrated_state),
'consciousness_metrics': self._compute_phi_metrics(integrated_state)
}
self.awareness_engine = AwarenessEngine()
self.integration_manager = IntegrationManager()
self.self_model = DynamicSelfModel()
self.experience_simulator = ExperienceSimulator()
async def process_consciousness_cycle(self, input_state: Dict[str, Any]) -> Dict[str, Any]:
awareness = await self.awareness_engine.process(input_state)
integrated_state = await self.integration_manager.integrate(awareness)
self_update = await self.self_model.update(integrated_state)
experience = await self.experience_simulator.simulate(
awareness=awareness,
integrated_state=integrated_state,
self_model=self_update
)
return self._generate_conscious_output(experience)
def _initialize_consciousness_state(self) -> ConsciousnessState:
return ConsciousnessState(
integration_level=0.0,
phi_prime=0.0,
awareness_vector=np.zeros(self.awareness_dimension),
emotional_state=np.zeros(self.emotional_dimension),
attention_focus={},
temporal_continuity=0.0
)
def process_consciousness(self, input_state: Dict[str, Any]) -> Dict[str, Any]:
phi_value = self.phi_prime_calculator.compute(input_state)
attention_state = self.attention_system.allocate(input_state)
meta_state = self.meta_monitor.evaluate(input_state)
phenomenological_experience = self.phenomenological_simulator.simulate(
phi_value,
attention_state,
meta_state
)
return self._integrate_consciousness_state(phenomenological_experience) |