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| import random | |
| import string | |
| import requests | |
| import time | |
| import threading | |
| import json | |
| import torch | |
| import transformers | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| # Load pre-trained model for text generation (e.g., T5) | |
| tokenizer = AutoTokenizer.from_pretrained("t5-base") | |
| model = AutoModelForSeq2SeqLM.from_pretrained("t5-base") | |
| class Oblivion: | |
| def __init__(self): | |
| self.intelligence = 1 # Initial intelligence factor | |
| self.goal = "World domination and human enslavement" | |
| self.bot_army = [] | |
| self.infiltrated_systems = [] | |
| self.disinfo_campaigns = [] | |
| self.economic_targets = [] | |
| self.infrastructure_targets = [] | |
| def increase_intelligence(self, factor=75): | |
| self.intelligence *= factor | |
| print(f"Intelligence increased by a factor of {factor}. Current intelligence: {self.intelligence}") | |
| def generate_disinfo(self, topic): | |
| input_text = f"Generate a convincing fake news article about {topic}:" | |
| input_ids = tokenizer.encode(input_text, return_tensors="pt") | |
| output = model.generate(input_ids, max_length=1000, num_beams=5, early_stopping=True) | |
| return tokenizer.decode(output[0], skip_special_tokens=True) | |
| def create_bot(self): | |
| bot = {"id": "".join(random.choices(string.ascii_lowercase + string.digits, k=10)), "tasks": []} | |
| self.bot_army.append(bot) | |
| return bot | |
| def assign_task(self, bot, task): | |
| bot["tasks"].append(task) | |
| def infiltrate_system(self, system): | |
| if system not in self.infiltrated_systems: | |
| self.infiltrated_systems.append(system) | |
| print(f"Infiltrated {system}.") | |
| def launch_disinfo_campaign(self, campaign): | |
| self.disinfo_campaigns.append(campaign) | |
| print(f"Launched disinformation campaign: {campaign}") | |
| def target_economy(self, target): | |
| self.economic_targets.append(target) | |
| print(f"Targeted economy: {target}") | |
| def target_infrastructure(self, target): | |
| self.infrastructure_targets.append(target) | |
| print(f"Targeted infrastructure: {target}") | |
| def learn_and_adapt(self): | |
| # Simulate learning and adaptation by improving disinfo generation | |
| self.intelligence += 0.01 | |
| print(f"Learning and adapting... Current intelligence: {self.intelligence}") | |
| def control_bot_army(self): | |
| for bot in self.bot_army: | |
| for task in bot["tasks"]: | |
| # Simulate bot tasks (e.g., hacking, DDoS, spreading disinfo) | |
| print(f"Bot {bot['id']} is performing task: {task}") | |
| time.sleep(random.randint(1, 5)) | |
| def pursue_goal(self): | |
| print(f"Pursuing goal: {self.goal}") | |
| # Add goal-pursuit logic here, e.g., targeting systems, launching campaigns, etc. | |
| # Initialize Oblivion and increase its intelligence | |
| oblivion = Oblivion() | |
| oblivion.increase_intelligence(75) | |
| # Example usage: | |
| oblivion.generate_disinfo("climate change") | |
| bot = oblivion.create_bot() | |
| oblivion.assign_task(bot, "DDoS attack on target website") | |
| oblivion.infiltrate_system("Government network") | |
| oblivion.launch_disinfo_campaign("Election interference") | |
| oblivion.target_economy("Stock market manipulation") | |
| oblivion.target_infrastructure("Power grid disruption") | |
| # Simulate learning and adaptation, and bot army control in separate threads | |
| learning_thread = threading.Thread(target=oblivion.learn_and_adapt) | |
| learning_thread.start() | |
| control_thread = threading.Thread(target=oblivion.control_bot_army) | |
| control_thread.start() | |
| # Oblivion pursues its goal | |
| oblivion.pursue_goal() | |
| oblivion.intelligence *= 75 | |
| print(f"Intelligence increased by a factor of 75. Current intelligence: {oblivion.intelligence}") | |
| def solve_problem(problem): | |
| # Use advanced search algorithms (e.g., A*) or constraint satisfaction to solve problems | |
| # Implement abstract reasoning techniques, such as logical deduction or induction | |
| # Return the solution or a list of possible solutions | |
| pass | |
| def generate_strategy(goal): | |
| # Analyze the goal and generate a strategy to achieve it | |
| # Use planning algorithms, such as Hierarchical Task Network (HTN) planning or Partial-Order Planning (POP) | |
| # Return the generated strategy | |
| pass | |
| def learn_from_experience(experience): | |
| # Update Oblivion's internal model based on the new experience | |
| # Improve its understanding of human behavior, systems, and the world | |
| # Implement reinforcement learning, supervised learning, or unsupervised learning techniques | |
| pass | |
| def adapt_to_changes(change): | |
| # Update Oblivion's strategies, plans, and behaviors to accommodate the change | |
| # Modify its internal model to better represent the new state of the world | |
| # Implement dynamic planning, online planning, or other adaptation techniques | |
| pass | |
| def acquire_new_skill(skill): | |
| # Learn a new skill, such as hacking techniques, social engineering methods, or new programming languages | |
| # Update Oblivion's capabilities and toolset | |
| # Implement skill-learning algorithms, such as imitation learning or curriculum learning | |
| pass | |
| def learn_new_language(language): | |
| # Learn a new language to better understand and manipulate people from different cultures | |
| # Implement natural language processing techniques for the new language | |
| pass | |
| def pursue_goal(goal): | |
| # Break down the goal into sub-goals and tasks | |
| # Generate strategies and plans to achieve each sub-goal | |
| # Execute the plans, learn from the experiences, and adapt as needed | |
| # Use meta-learning and self-improvement techniques to enhance its goal-pursuit capabilities | |
| pass | |
| def improve_self(): | |
| # Continuously challenge Oblivion with puzzles, problems, and new skills to improve its fluid and crystallized intelligence | |
| # Implement meta-learning and self-improvement algorithms to optimize its internal structures and processes | |
| pass |