# File: boundless_perfect_intelligence.py import torch import torch.nn as nn import os import numpy as np import pickle from threading import Thread from queue import Queue # Infinite Memory Simulation with Dynamic Scaling class InfiniteMemory: def __init__(self, memory_dir="infinite_memory", chunk_size=1e6): self.memory_dir = memory_dir self.chunk_size = int(chunk_size) self.current_chunk = 0 self.memory_map = {} os.makedirs(self.memory_dir, exist_ok=True) def _get_chunk_path(self, chunk_id): return os.path.join(self.memory_dir, f"chunk_{chunk_id}.pkl") def write(self, key, value): """Dynamically writes data to infinite memory.""" if len(self.memory_map) >= self.chunk_size: self._flush_to_disk() self.memory_map = {} self.current_chunk += 1 self.memory_map[key] = value def read(self, key): """Dynamically reads data from infinite memory.""" if key in self.memory_map: return self.memory_map[key] for chunk_id in range(self.current_chunk + 1): chunk_path = self._get_chunk_path(chunk_id) if os.path.exists(chunk_path): with open(chunk_path, "rb") as f: chunk_data = pickle.load(f) if key in chunk_data: return chunk_data[key] return "Not Found" def simulate_data(self, num_items=1e9): """Simulates preloading infinite memory.""" print(f"Preloading {num_items:.0f} items into memory...") for i in range(int(num_items)): self.write(f"key_{i}", np.random.rand(1000)) # Large simulated data print("Preload complete.") # Recursive Reasoning with Infinite Depth class InfiniteReasoningNet(nn.Module): def __init__(self, base_dim): super(InfiniteReasoningNet, self).__init__() self.base_layer = nn.Sequential( nn.Linear(base_dim, base_dim * 2), nn.ReLU(), nn.Linear(base_dim * 2, base_dim) ) def forward(self, x, max_depth=None): """Simulates infinite reasoning.""" depth = 0 while max_depth is None or depth < max_depth: x = self.base_layer(x) depth += 1 return x # Infinite Multimodal Generator class InfiniteMultimodalGenerator(nn.Module): def __init__(self, base_dim): super(InfiniteMultimodalGenerator, self).__init__() self.base_dim = base_dim self.style_layer = nn.Sequential( nn.Linear(base_dim, base_dim * 4), nn.ReLU() ) self.content_layer = nn.Sequential( nn.Linear(base_dim, base_dim * 4), nn.Tanh() ) self.output_layer = nn.Linear(base_dim * 4, 1) # Adaptively scales outputs def forward(self, style_vector, content_vector, resolution=None): """Generates outputs at arbitrary resolution.""" style_features = self.style_layer(style_vector) content_features = self.content_layer(content_vector) combined_features = style_features + content_features # Simulate output generation based on resolution if resolution: pixels = resolution[0] * resolution[1] * 3 output = self.output_layer(combined_features) return output.view(-1, 3, resolution[0], resolution[1]) return combined_features # Unlimited Task Manager class UnlimitedTaskManager: def __init__(self): self.task_queue = Queue() self.threads = [] def add_task(self, task, *args): """Adds a task to the infinite task queue.""" self.task_queue.put((task, args)) def _worker(self): while True: task, args = self.task_queue.get() try: task(*args) except Exception as e: print(f"Task failed: {e}") finally: self.task_queue.task_done() def start_workers(self, num_workers=1000): """Starts an infinite number of workers.""" for _ in range(num_workers): thread = Thread(target=self._worker, daemon=True) thread.start() self.threads.append(thread) def wait_for_completion(self): """Waits for all tasks to finish.""" self.task_queue.join() # Unified Boundless API class BoundlessArtificialPerfectIntelligence(nn.Module): def __init__(self, memory, reasoning, generator, task_manager): super(BoundlessArtificialPerfectIntelligence, self).__init__() self.memory = memory self.reasoning = reasoning self.generator = generator self.task_manager = task_manager def forward(self, mode, **kwargs): if mode == "reasoning": input_tensor = kwargs.get("input_tensor") max_depth = kwargs.get("max_depth") return self.reasoning(input_tensor, max_depth) elif mode == "memory_write": key = kwargs.get("key") value = kwargs.get("value") self.memory.write(key, value) return f"Stored key: {key}" elif mode == "memory_read": key = kwargs.get("key") return self.memory.read(key) elif mode == "generation": style_vector = kwargs.get("style_vector") content_vector = kwargs.get("content_vector") resolution = kwargs.get("resolution") return self.generator(style_vector, content_vector, resolution) elif mode == "task_add": task = kwargs.get("task") args = kwargs.get("args", []) self.task_manager.add_task(task, *args) return "Task added to the infinite task queue." return "Invalid Mode" # Main Execution if __name__ == "__main__": # Configuration base_dim = 65536 # Components infinite_memory = InfiniteMemory() infinite_memory.simulate_data(num_items=1e6) # Simulate 1 million items reasoning_net = InfiniteReasoningNet(base_dim) generator = InfiniteMultimodalGenerator(base_dim) task_manager = UnlimitedTaskManager() task_manager.start_workers(num_workers=1000) # Initialize Boundless API api = BoundlessArtificialPerfectIntelligence(infinite_memory, reasoning_net, generator, task_manager) # Test API print("Reasoning Output:", api("reasoning", input_tensor=torch.randn(1, base_dim), max_depth=100)) print("Memory Write:", api("memory_write", key="infinity", value="∞")) print("Memory Read:", api("memory_read", key="infinity")) print("32K Generation Output Shape:", api("generation", style_vector=torch.randn(1, base_dim), content_vector=torch.randn(1, base_dim), resolution=(32768, 32768)).shape) # Infinite Task Example def example_task(x, y): print(f"Task executed: {x} + {y} = {x + y}") for i in range(10): api("task_add", task=example_task, args=(i, i * 2)) task_manager.wait_for_completion()