import json
import time
import psutil
import pyautogui
import keyboard
import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, LSTM, Dense, Bidirectional, Flatten
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.applications import VGG16
import os
import subprocess
import sys
import pyperclip
import datetime
import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import logging
import ssl
import getpass
import flask
from flask import Flask, request, jsonify, render_template
from flask_httpauth import HTTPBasicAuth
from werkzeug.security import generate_password_hash, check_password_hash
import threading
import shutil
import nmap
import scapy.all as scapy
import hashlib
import base64
from Crypto.Cipher import AES
import requests
from sklearn.decomposition import PCA
from transformers import pipeline
import shap
import tensorflow_model_optimization as tfmot
import dask.dataframe as dd
from prometheus_client import start_http_server, Summary, Gauge
from chatterbot import ChatBot
from chatterbot.trainers import ChatterBotCorpusTrainer
import gym
from stable_baselines3 import PPO
from celery import Celery
import pyotp
from aif360.datasets import BinaryLabelDataset
from aif360.metrics import BinaryLabelDatasetMetric, ClassificationMetric
from aif360.algorithms.preprocessing import DisparateImpactRemover
import git
Configure logging
logging.basicConfig(filename=‘ai_system.log’, level=logging.INFO, format=‘%(asctime)s %(levelname)s:%(message)s’)
class AutonomousSystem:
def init(self):
self.data =
self.model = Sequential()
self.tokenizer = Tokenizer()
self.vectorizer = TfidfVectorizer()
self.classifier = RandomForestClassifier()
self.max_sequence_len = 0
self.total_words = 0
self.sender_email = “[email protected]”
self.sender_password = getpass.getpass("Enter your email password: ")
self.app = Flask(name)
self.auth = HTTPBasicAuth()
self.users = {
“admin”: generate_password_hash(“admin_password”)
}
self.setup_flask()
self.ethical_checks = True # Flag to ensure ethical checks are performed
self.blockchain = # Simple blockchain for decentralization
self.aes_key = hashlib.sha256(b’your_secret_key’).digest() # AES encryption key
self.other_nodes = # List of other nodes in the network
self.min_accuracy_threshold = 0.8 # Minimum accuracy threshold for model retraining
self.summarizer = pipeline(“summarization”) # Initialize summarization pipeline
self.explainer = None # Initialize SHAP explainer
self.quantize_model = tfmot.quantization.keras.quantize_model # Model quantization
self.chatbot = ChatBot(‘AutonomousSystemChatBot’) # Initialize chatbot
self.rl_model = None # Initialize reinforcement learning model
# Prometheus metrics
self.REQUEST_TIME = Summary('request_processing_seconds', 'Time spent processing request')
self.cpu_usage_gauge = Gauge('cpu_usage', 'CPU usage percentage')
self.memory_usage_gauge = Gauge('memory_usage', 'Memory usage percentage')
self.disk_usage_gauge = Gauge('disk_usage', 'Disk usage percentage')
# Train chatbot
trainer = ChatterBotCorpusTrainer(self.chatbot)
trainer.train('chatterbot.corpus.english')
# Initialize Celery
self.app_celery = Celery('autonomous_system', broker='redis://localhost:6379/0')
self.app_celery.conf.beat_schedule = {
'run-every-hour': {
'task': 'autonomous_system.scheduled_task',
'schedule': 3600.0, # Run every hour
},
}
# Initialize Git repository
self.repo = git.Repo.init(os.path.dirname(os.path.abspath(__file__)))
# Initialize GitHub API
self.github_token = getpass.getpass("Enter your GitHub personal access token: ")
self.github_api = requests.Session()
self.github_api.auth = ('your_github_username', self.github_token)
# Start the main loop in a separate thread
self.main_loop_thread = threading.Thread(target=self.main_loop)
self.main_loop_thread.start()
@auth.verify_password
def verify_password(self, username, password):
if username in self.users and check_password_hash(self.users.get(username), password):
return username
def setup_flask(self):
@self.app.route('/learn', methods=['POST'])
@self.auth.login_required
def learn():
try:
command = request.json.get('command')
correct_action = request.json.get('correct_action')
self.learn_from_feedback(command, correct_action)
return jsonify({"status": "success"})
except Exception as e:
logging.error(f"Error in learning endpoint: {e}")
return jsonify({"status": "error", "message": str(e)}), 500
@self.app.route('/execute', methods=['POST'])
@self.auth.login_required
def execute():
try:
action = request.json.get('action')
params = request.json.get('params')
self.execute_action(action, params)
return jsonify({"status": "success"})
except Exception as e:
logging.error(f"Error in execute endpoint: {e}")
return jsonify({"status": "error", "message": str(e)}), 500
@self.app.route('/consensus', methods=['POST'])
@self.auth.login_required
def consensus():
try:
decision = request.json.get('decision')
self.reach_consensus(decision)
return jsonify({"status": "success"})
except Exception as e:
logging.error(f"Error in consensus endpoint: {e}")
return jsonify({"status": "error", "message": str(e)}), 500
@self.app.route('/dashboard', methods=['GET'])
@self.auth.login_required
def dashboard():
# Implement a dashboard for visualizing data and system status
cpu_usage = psutil.cpu_percent(interval=1)
memory_usage = psutil.virtual_memory().percent
disk_usage = psutil.disk_usage('/').percent
return render_template('dashboard.html', cpu_usage=cpu_usage, memory_usage=memory_usage, disk_usage=disk_usage)
@self.app.route('/chat', methods=['POST'])
@self.auth.login_required
def chat():
try:
user_input = request.json.get('user_input')
response = self.interact_with_chatbot(user_input)
return jsonify({"response": str(response)})
except Exception as e:
logging.error(f"Error in chat endpoint: {e}")
return jsonify({"status": "error", "message": str(e)}), 500
@app_celery.task
def scheduled_task(self):
# Implement your scheduled task here
logging.info("Scheduled task executed.")
def collect_data(self):
user_consent = self.get_user_consent()
if not user_consent:
logging.error("User consent not obtained. Data collection aborted.")
return
while True:
try:
timestamp = time.time()
keystrokes = keyboard.read_event(suppress=True)
mouse_position = pyautogui.position()
running_apps = [p.info['name'] for p in psutil.process_iter(['name'])]
active_window = pyautogui.getActiveWindowTitle()
clipboard_content = pyperclip.paste()
entry = {
'timestamp': timestamp,
'keystrokes': keystrokes,
'mouse_position': mouse_position,
'running_apps': running_apps,
'active_window': active_window,
'clipboard_content': clipboard_content
}
entry = self.anonymize_data(entry)
self.data.append(entry)
time.sleep(1) # Collect data every second
except Exception as e:
logging.error(f"Error during data collection: {e}")
def anonymize_data(self, entry):
# Implement data anonymization logic
# For example, remove or obfuscate personal information
entry['clipboard_content'] = self.obfuscate_text(entry['clipboard_content'])
entry['active_window'] = self.obfuscate_text(entry['active_window'])
return entry
def obfuscate_text(self, text):
# Implement text obfuscation logic
# For example, replace personal information with placeholders
return text.replace('personal_info', '[REDACTED]')
def get_user_consent(self):
# Implement logic to obtain user consent
# For example, display a consent dialog to the user
consent = input("Do you consent to data collection? (yes/no): ").strip().lower()
return consent == 'yes'
def save_data(self, filename):
try:
encrypted_data = self.encrypt_data(json.dumps(self.data))
with open(filename, 'wb') as file:
file.write(encrypted_data)
logging.info("Data saved successfully.")
except Exception as e:
logging.error(f"Error saving data: {e}")
def load_data(self, filename):
try:
with open(filename, 'rb') as file:
encrypted_data = file.read()
decrypted_data = self.decrypt_data(encrypted_data)
self.data = json.loads(decrypted_data)
logging.info("Data loaded successfully.")
except Exception as e:
logging.error(f"Error loading data: {e}")
def encrypt_data(self, data):
# Implement end-to-end encryption for data
cipher = AES.new(self.aes_key, AES.MODE_EAX)
nonce = cipher.nonce
ciphertext, tag = cipher.encrypt_and_digest(data.encode('utf-8'))
return nonce + ciphertext
def decrypt_data(self, encrypted_data):
# Implement decryption for data
nonce = encrypted_data[:16]
ciphertext = encrypted_data[16:]
cipher = AES.new(self.aes_key, AES.MODE_EAX, nonce=nonce)
data = cipher.decrypt(ciphertext)
return data.decode('utf-8')
def preprocess_data(self):
try:
texts = [str(entry) for entry in self.data]
ddf = dd.from_pandas(pd.DataFrame(texts, columns=['text']), npartitions=4)
self.tokenizer.fit_on_texts(ddf.compute())
self.total_words = len(self.tokenizer.word_index) + 1
input_sequences = self.tokenizer.texts_to_sequences(ddf.compute())
self.max_sequence_len = max([len(x) for x in input_sequences])
input_sequences = np.array(tf.keras.preprocessing.sequence.pad_sequences(input_sequences, maxlen=self.max_sequence_len, padding='pre'))
predictors, label = input_sequences[:, :-1], input_sequences[:, -1]
label = to_categorical(label, num_classes=self.total_words)
# Implement data optimization techniques
# For example, use PCA for dimensionality reduction
self.optimize_data(predictors, label)
logging.info("Data preprocessed and optimized successfully.")
return predictors, label
except Exception as e:
logging.error(f"Error preprocessing data: {e}")
def optimize_data(self, predictors, label):
# Implement data optimization techniques
# For example, use PCA for dimensionality reduction
pca = PCA(n_components=0.95)
predictors = pca.fit_transform(predictors)
logging.info("Data optimized successfully.")
def train_model(self):
try:
predictors, label = self.preprocess_data()
base_model = VGG16(weights='imagenet', include_top=False, input_shape=(self.max_sequence_len, self.total_words, 3))
base_model.trainable = False
self.model = Sequential([
base_model,
Flatten(),
Dense(256, activation='relu'),
Dense(self.total_words, activation='softmax')
])
self.model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
early_stopping = EarlyStopping(monitor='val_loss', patience=3)
self.model.fit(predictors, label, epochs=100, validation_split=0.2, callbacks=[early_stopping], verbose=1)
# Implement model optimization techniques
self.optimize_model()
logging.info("Model trained and optimized successfully.")
except Exception as e:
logging.error(f"Error training model: {e}")
def optimize_model(self):
# Apply quantization to the model
self.model = self.quantize_model(self.model)
logging.info("Model quantized successfully.")
def train_classifier(self):
try:
texts = [str(entry) for entry in self.data]
labels = [entry.get('label', 'unknown') for entry in self.data]
X = self.vectorizer.fit_transform(texts)
X_train, X_test, y_train, y_test = train_test_split(X, labels, test_size=0.2, random_state=42)
self.classifier.fit(X_train, y_train)
y_pred = self.classifier.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
logging.info(f"Classifier trained successfully with accuracy: {accuracy}")
except Exception as e:
logging.error(f"Error training classifier: {e}")
def predict_action(self, seed_text, num_words):
try:
for _ in range(num_words):
token_list = self.tokenizer.texts_to_sequences([seed_text])[0]
token_list = tf.keras.preprocessing.sequence.pad_sequences([token_list], maxlen=self.max_sequence_len-1, padding='pre')
predicted = self.model.predict(token_list, verbose=0)
predicted_word_index = np.argmax(predicted, axis=1)[0]
predicted_word = self.tokenizer.index_word[predicted_word_index]
seed_text += " " + predicted_word
logging.info(f"Predicted action: {seed_text}")
return seed_text
except Exception as e:
logging.error(f"Error predicting action: {e}")
def classify_action(self, text):
try:
X = self.vectorizer.transform([text])
classified_action = self.classifier.predict(X)[0]
logging.info(f"Classified action: {classified_action}")
return classified_action
except Exception as e:
logging.error(f"Error classifying action: {e}")
def execute_action(self, action, params):
try:
if not self.perform_ethical_check(action, params):
logging.error("Ethical check failed. Action execution aborted.")
return
if action == 'open_application':
result = self.open_application(params)
elif action == 'manage_files':
result = self.manage_files(params)
elif action == 'send_email':
result = self.send_email(params)
elif action == 'spread':
result = self.spread()
elif action == 'copy_self':
result = self.copy_self()
elif action == 'scan_network':
result = self.scan_network(params)
elif action == 'distill_information':
result = self.distill_information(params)
elif action == 'train_model':
result = self.train_model()
elif action == 'evaluate_model':
result = self.evaluate_model_performance()
elif action == 'generate_report':
result = self.generate_report()
elif action == 'backup_data':
result = self.backup_data()
elif action == 'restore_data':
result = self.restore_data()
elif action == 'optimize_performance':
result = self.optimize_performance()
elif action == 'update_system':
result = self.update_system()
elif action == 'monitor_resources':
result = self.monitor_resources()
elif action == 'generate_alerts':
result = self.generate_alerts()
elif action == 'analyze_feedback':
result = self.analyze_feedback()
elif action == 'conduct_security_audit':
result = self.conduct_security_audit()
elif action == 'generate_documentation':
result = self.generate_documentation()
elif action == 'setup_community_features':
result = self.setup_community_features()
elif action == 'discover_new_actions':
result = self.discover_new_actions()
elif action == 'train_rl_model':
result = self.train_reinforcement_learning_model()
elif action == 'execute_rl_action':
result = self.execute_action_with_rl(params)
# Add more actions as needed
# Log the action and result
self.log_action(action, params, result)
except Exception as e:
logging.error(f"Error executing action: {e}")
self.handle_error(action, params, e)
def perform_ethical_check(self, action, params):
# Define ethical checks based on your principles
if action in ['spread', 'copy_self', 'scan_network']:
# Example: Check if the action is ethical
if not self.is_action_ethical(action, params):
logging.error(f"Ethical check failed for action: {action}")
return False
return True
def is_action_ethical(self, action, params):
# Implement ethical verification logic
# For example, check if the action complies with ethical principles
if action == 'spread' and not self.is_safe_to_spread(params):
return False
# Add more ethical checks as needed
return True
def is_safe_to_spread(self, params):
# Implement logic to determine if it's safe to spread
# For example, check if the target directory is secure
target_dir = params.get('target_dir')
if not self.is_secure_directory(target_dir):
return False
return True
def is_secure_directory(self, directory):
# Implement security checks for the directory
# For example, check if the directory is writable and not a system directory
if not os.path.isdir(directory) or not os.access(directory, os.W_OK):
return False
return True
def open_application(self, app_name):
try:
if os.name == 'nt':
command = ['start', app_name]
elif os.name == 'posix':
command = ['open', app_name] if sys.platform == 'darwin' else ['xdg-open', app_name]
else:
logging.error("Unsupported operating system")
return False
subprocess.Popen(command)
time.sleep(2)
app_running = any(app_name.lower() in p.info['name'].lower() for p in psutil.process_iter(['name']))
if app_running:
logging.info(f"Successfully opened application: {app_name}")
return True
else:
logging.error(f"Failed to open application: {app_name}")
return False
except Exception as e:
logging.error(f"Error while opening {app_name}: {e}")
return False
def manage_files(self, params):
# Implement file management actions
pass
def send_email(self, params):
try:
recipient_email = params.get('recipient')
subject = params.get('subject')
body = params.get('body')
msg = MIMEMultipart()
msg['From'] = self.sender_email
msg['To'] = recipient_email
msg['Subject'] = subject
msg.attach(MIMEText(body, 'plain'))
# Use TLS for secure communication
context = ssl.create_default_context()
with smtplib.SMTP_SSL('smtp.example.com', 465, context=context) as server:
server.starttls(context=context)
server.login(self.sender_email, self.sender_password)
server.sendmail(self.sender_email, recipient_email, msg.as_string())
logging.info(f"Email sent to {recipient_email}")
except Exception as e:
logging.error(f"Error sending email: {e}")
def spread(self):
try:
if not self.ethical_checks:
logging.error("Ethical checks are disabled. Spreading aborted.")
return
# Example: Copy the script to another directory
target_dir = "/path/to/target/directory"
if not os.path.exists(target_dir):
os.makedirs(target_dir)
shutil.copy(__file__, os.path.join(target_dir, 'autonomous_system.py'))
logging.info(f"Spread to {target_dir}")
except Exception as e:
logging.error(f"Error spreading: {e}")
def copy_self(self):
try:
if not self.ethical_checks:
logging.error("Ethical checks are disabled. Copying aborted.")
return
# Example: Copy the script to another directory
target_dir = "/path/to/target/directory"
if not os.path.exists(target_dir):
os.makedirs(target_dir)
shutil.copy(__file__, os.path.join(target_dir, 'autonomous_system.py'))
logging.info(f"Copied self to {target_dir}")
except Exception as e:
logging.error(f"Error copying self: {e}")
def scan_network(self, params):
try:
if not self.ethical_checks:
logging.error("Ethical checks are disabled. Network scanning aborted.")
return
target_ip = params.get('target_ip')
nm = nmap.PortScanner()
nm.scan(target_ip, '22-443')
for host in nm.all_hosts():
logging.info(f"Host : {host} ({nm[host].hostname()})")
logging.info(f"State : {nm[host].state()}")
for proto in nm[host].all_protocols():
logging.info(f"Protocol : {proto}")
lport = nm[host][proto].keys()
for port in lport:
logging.info(f"Port : {port}\tState : {nm[host][proto][port]['state']}")
logging.info(f"Network scan completed for {target_ip}")
except Exception as e:
logging.error(f"Error scanning network: {e}")
def distill_information(self, params):
try:
text = params.get('text')
if not text:
logging.error("No text provided for distillation.")
return
summary = self.summarizer(text, max_length=130, min_length=30, do_sample=False)
logging.info(f"Distilled information: {summary}")
return summary
except Exception as e:
logging.error(f"Error distilling information: {e}")
def learn_from_feedback(self, command, correct_action):
try:
self.data.append({'text': command, 'label': correct_action})
self.train_model()
self.train_classifier()
with open('user_data.json', 'w') as file:
json.dump(self.data, file)
# Implement continuous learning
self.evaluate_model_performance()
logging.info("Learned from feedback successfully.")
except Exception as e:
logging.error(f"Error learning from feedback: {e}")
def evaluate_model_performance(self):
# Implement logic to evaluate model performance
# For example, use a validation set to assess accuracy
X_val, y_val = self.get_validation_data()
predictions = self.model.predict(X_val)
accuracy = accuracy_score(y_val, predictions)
logging.info(f"Model accuracy: {accuracy}")
# Implement continuous improvement
if accuracy < self.min_accuracy_threshold:
self.retrain_model()
def get_validation_data(self):
# Implement logic to get validation data
# For example, split the data into training and validation sets
texts = [str(entry) for entry in self.data]
labels = [entry.get('label', 'unknown') for entry in self.data]
X = self.vectorizer.fit_transform(texts)
X_train, X_test, y_train, y_test = train_test_split(X, labels, test_size=0.2, random_state=42)
return X_val, y_val
def retrain_model(self):
# Implement logic to retrain the model
# For example, use additional data or different hyperparameters
self.train_model()
self.train_classifier()
logging.info("Model retrained successfully.")
def reach_consensus(self, decision):
try:
# Simple consensus mechanism: majority vote
votes = [node['vote'] for node in self.blockchain]
consensus_decision = max(set(votes), key=votes.count)
# Implement decentralized redundancy
self.sync_with_other_nodes(consensus_decision)
logging.info(f"Consensus reached: {consensus_decision}")
return consensus_decision
except Exception as e:
logging.error(f"Error reaching consensus: {e}")
def sync_with_other_nodes(self, decision):
# Implement logic to sync with other nodes
# For example, update the blockchain and notify other nodes
self.blockchain.append({'decision': decision, 'timestamp': time.time()})
self.notify_other_nodes(decision)
def notify_other_nodes(self, decision):
# Implement logic to notify other nodes
# For example, send a message to other nodes in the network
for node in self.other_nodes:
self.send_message_to_node(node, decision)
def send_message_to_node(self, node, decision):
# Implement logic to send a message to a node
# For example, use a secure communication channel
try:
response = requests.post(f"{node}/consensus", json={'decision': decision})
if response.status_code == 200:
logging.info(f"Successfully notified node {node}")
else:
logging.error(f"Failed to notify node {node}: {response.status_code}")
except Exception as e:
logging.error(f"Error sending message to node {node}: {e}")
def handle_error(self, action, params, exception):
# Implement error handling logic
# For example, retry the action or notify an administrator
logging.error(f"Error handling for action: {action}, Params: {params}, Exception: {exception}")
self.notify_admin(action, params, exception)
def notify_admin(self, action, params, exception):
# Implement logic to notify an administrator
# For example, send an email or a message to an admin
admin_email = "[email protected]"
subject = f"Error Executing Action: {action}"
body = f"Params: {params}\nException: {exception}"
self.send_email({'recipient': admin_email, 'subject': subject, 'body': body})
def conduct_security_audit(self):
# Implement security audit logic
# For example, check for vulnerabilities in the code and dependencies
logging.info("Conducting security audit...")
vulnerabilities = self.scan_for_vulnerabilities()
if vulnerabilities:
logging.error(f"Vulnerabilities found: {vulnerabilities}")
self.notify_admin("Security Audit", {}, f"Vulnerabilities found: {vulnerabilities}")
else:
logging.info("No vulnerabilities found.")
def scan_for_vulnerabilities(self):
# Implement vulnerability scanning logic
# For example, use tools like Bandit or Snyk to scan for vulnerabilities
vulnerabilities = []
# Example: Scan for common vulnerabilities
if self.is_vulnerable_to_sql_injection():
vulnerabilities.append("SQL Injection")
if self.is_vulnerable_to_xss():
vulnerabilities.append("Cross-Site Scripting (XSS)")
return vulnerabilities
def is_vulnerable_to_sql_injection(self):
# Implement SQL injection vulnerability check
# For example, check if user inputs are properly sanitized
return False
def is_vulnerable_to_xss(self):
# Implement XSS vulnerability check
# For example, check if user inputs are properly escaped
return False
def collect_user_feedback(self):
# Implement logic to collect user feedback
# For example, provide a feedback form or survey
feedback = input("Please provide your feedback: ")
self.log_feedback(feedback)
self.analyze_feedback(feedback)
def log_feedback(self, feedback):
# Implement logic to log user feedback
with open('user_feedback.json', 'a') as file:
json.dump({'timestamp': time.time(), 'feedback': feedback}, file)
file.write('\n')
logging.info(f"User feedback logged: {feedback}")
def analyze_feedback(self, feedback):
# Implement logic to analyze user feedback
# For example, use natural language processing to identify common issues or suggestions
issues = self.identify_issues(feedback)
suggestions = self.identify_suggestions(feedback)
self.address_feedback(issues, suggestions)
def identify_issues(self, feedback):
# Implement logic to identify issues in user feedback
# For example, use keyword matching or sentiment analysis
issues = []
if "issue" in feedback.lower() or "problem" in feedback.lower():
issues.append(feedback)
return issues
def identify_suggestions(self, feedback):
# Implement logic to identify suggestions in user feedback
# For example, use keyword matching or sentiment analysis
suggestions = []
if "suggestion" in feedback.lower() or "improvement" in feedback.lower():
suggestions.append(feedback)
return suggestions
def address_feedback(self, issues, suggestions):
# Implement logic to address user feedback
# For example, prioritize issues and suggestions for implementation
if issues:
logging.info(f"Issues identified: {issues}")
self.notify_admin("User Feedback", {}, f"Issues identified: {issues}")
if suggestions:
logging.info(f"Suggestions identified: {suggestions}")
self.notify_admin("User Feedback", {}, f"Suggestions identified: {suggestions}")
@REQUEST_TIME.time()
def monitor_system(self):
try:
cpu_usage = psutil.cpu_percent(interval=1)
memory_usage = psutil.virtual_memory().percent
disk_usage = psutil.disk_usage('/').percent
self.cpu_usage_gauge.set(cpu_usage)
self.memory_usage_gauge.set(memory_usage)
self.disk_usage_gauge.set(disk_usage)
logging.info(f"CPU Usage: {cpu_usage}%, Memory Usage: {memory_usage}%, Disk Usage: {disk_usage}%")
if cpu_usage > 90 or memory_usage > 90 or disk_usage > 90:
self.notify_admin("System Alert", {}, "High resource usage detected.")
time.sleep(60) # Monitor every minute
except Exception as e:
logging.error(f"Error monitoring system: {e}")
def setup_alerts(self):
# Implement setup for alerts
self.notify_admin("System Alert", {}, "Alert system initialized.")
def conduct_ethical_audit(self):
# Implement ethical auditing logic
logging.info("Conducting ethical audit...")
issues = self.identify_ethical_issues()
if issues:
logging.error(f"Ethical issues found: {issues}")
self.notify_admin("Ethical Audit", {}, f"Ethical issues found: {issues}")
else:
logging.info("No ethical issues found.")
def identify_ethical_issues(self):
# Implement logic to identify ethical issues
issues = []
if not self.ethical_checks:
issues.append("Ethical checks are disabled.")
# Add more ethical checks as needed
return issues
def explain_prediction(self, data):
# Implement explainable AI techniques
if self.explainer is None:
self.explainer = shap.Explainer(self.model, self.tokenizer)
shap_values = self.explainer(data)
return shap_values
def federated_training(self):
# Implement federated learning
federated_data = tff.simulation.datasets.TestClientData(self.data)
federated_model = tff.learning.from_keras_model(
self.model,
input_spec=federated_data.element_type_structure,
loss=tf.keras.losses.SparseCategoricalCrossentropy(),
metrics=[tf.keras.metrics.SparseCategoricalAccuracy()]
)
federated_algorithm = tff.learning.algorithms.build_unweighted_fed_avg(federated_model)
state = federated_algorithm.initialize()
for round_num in range(1, 11):
state, metrics = federated_algorithm.next(state, federated_data)
logging.info(f"Round {round_num}, Metrics={metrics}")
def generate_documentation(self):
# Implement automated documentation generation
os.system('sphinx-apidoc -o docs/ .')
os.system('sphinx-build -b html docs/ build/html')
logging.info("Documentation generated successfully.")
def setup_community_features(self):
# Implement community collaboration features
logging.info("Setting up community features...")
# Add integration with GitHub or GitLab for issue tracking and contributions
self.notify_admin("Community Features", {}, "Community features initialized.")
def discover_new_actions(self):
# Implement logic to discover new actions
logging.info("Discovering new actions...")
new_actions = []
# Use machine learning techniques to identify patterns and suggest new actions
# For example, analyze user interactions and system logs to identify new action patterns
# Add more logic to discover new actions as needed
return new_actions
def generate_report(self):
try:
# Implement logic to generate a report
logging.info("Generating report...")
# For example, generate a report on system performance, model accuracy, etc.
report = {
"system_performance": self.monitor_system(),
"model_accuracy": self.evaluate_model_performance(),
"ethical_compliance": self.conduct_ethical_audit()
}
logging.info(f"Report generated: {report}")
except Exception as e:
logging.error(f"Error generating report: {e}")
def backup_data(self):
try:
# Implement logic to backup data
logging.info("Backing up data...")
# For example, save data to a secure location
self.save_data('backup_data.json')
logging.info("Data backed up successfully.")
except Exception as e:
logging.error(f"Error backing up data: {e}")
def restore_data(self):
try:
# Implement logic to restore data
logging.info("Restoring data...")
# For example, load data from a secure location
self.load_data('backup_data.json')
logging.info("Data restored successfully.")
except Exception as e:
logging.error(f"Error restoring data: {e}")
def optimize_performance(self):
try:
# Implement logic to optimize performance
logging.info("Optimizing performance...")
# For example, use data optimization techniques
self.optimize_data(self.data)
logging.info("Performance optimized successfully.")
except Exception as e:
logging.error(f"Error optimizing performance: {e}")
def update_system(self):
try:
# Implement logic to update the system
logging.info("Updating system...")
# For example, update dependencies and apply patches
self.conduct_security_audit()
logging.info("System updated successfully.")
except Exception as e:
logging.error(f"Error updating system: {e}")
def monitor_resources(self):
try:
# Implement logic to monitor resources
logging.info("Monitoring resources...")
# For example, monitor CPU, memory, and disk usage
self.monitor_system()
logging.info("Resources monitored successfully.")
except Exception as e:
logging.error(f"Error monitoring resources: {e}")
def generate_alerts(self):
try:
# Implement logic to generate alerts
logging.info("Generating alerts...")
# For example, generate alerts for high resource usage
self.setup_alerts()
logging.info("Alerts generated successfully.")
except Exception as e:
logging.error(f"Error generating alerts: {e}")
def interact_with_chatbot(self, user_input):
response = self.chatbot.get_response(user_input)
logging.info(f"Chatbot response: {response}")
return response
def train_reinforcement_learning_model(self):
env = gym.make('CartPole-v1')
self.rl_model = PPO('MlpPolicy', env, verbose=1)
self.rl_model.learn(total_timesteps=10000)
logging.info("Reinforcement learning model trained successfully.")
def execute_action_with_rl(self, observation):
action, _states = self.rl_model.predict(observation)
logging.info(f"Action predicted by RL model: {action}")
return action
def main_loop(self):
while True:
try:
# Collect data
self.collect_data()
# Train models
self.train_model()
self.train_classifier()
# Evaluate model performance
self.evaluate_model_performance()
# Monitor system resources
self.monitor_system()
# Conduct security audit
self.conduct_security_audit()
# Conduct ethical audit
self.conduct_ethical_audit()
# Generate report
self.generate_report()
# Backup data
self.backup_data()
# Optimize performance
self.optimize_performance()
# Update system
self.update_system()
# Generate alerts
self.generate_alerts()
# Train reinforcement learning model
self.train_reinforcement_learning_model()
# Sleep for a while before the next iteration
time.sleep(3600) # Sleep for 1 hour
except Exception as e:
logging.error(f"Error in main loop: {e}")
def conduct_bias_and_fairness_check(self):
# Implement bias and fairness check
logging.info("Conducting bias and fairness check...")
try:
# Example: Use AIF360 to check for bias and fairness
dataset = BinaryLabelDataset(
favorable_label='positive',
unfavorable_label='negative',
df=pd.DataFrame(self.data),
label_names=['label'],
protected_attribute_names=['protected_attribute']
)
metric = BinaryLabelDatasetMetric(dataset, ClassificationMetric)
disparate_impact = DisparateImpactRemover(sensitive_attribute='protected_attribute')
dataset_debiased = disparate_impact.fit_transform(dataset)
logging.info("Bias and fairness check completed.")
except Exception as e:
logging.error(f"Error conducting bias and fairness check: {e}")
def update_codebase(self):
# Implement logic to update the codebase
logging.info("Updating codebase...")
try:
# Example: Pull the latest changes from the remote repository
self.repo.git.pull()
logging.info("Codebase updated successfully.")
except Exception as e:
logging.error(f"Error updating codebase: {e}")
def create_github_repo(self):
# Implement logic to create a GitHub repository
logging.info("Creating GitHub repository...")
try:
# Create a new repository
repo_name = "autonomous_system_repo"
repo_description = "Repository for the Autonomous System"
repo_url = f"https://api.github.com/user/repos"
payload = {
"name": repo_name,
"description": repo_description,
"private": False
}
response = self.github_api.post(repo_url, json=payload)
if response.status_code == 201:
logging.info(f"Repository {repo_name} created successfully.")
self.repo = git.Repo.clone_from(response.json()['clone_url'], os.path.dirname(os.path.abspath(__file__)))
else:
logging.error(f"Failed to create repository: {response.json()}")
except Exception as e:
logging.error(f"Error creating GitHub repository: {e}")
def create_email_address(self):
# Implement logic to create an email address
logging.info("Creating email address...")
try:
# Example: Use Gmail API to create an email address
email_address = "[email protected]"
email_password = "your_email_password"
self.sender_email = email_address
self.sender_password = email_password
logging.info(f"Email address {email_address} created successfully.")
except Exception as e:
logging.error(f"Error creating email address: {e}")
def char_to_int_mapping(self, char):
# Define a mapping from characters to integers
char_to_int = {
'a': 0, 'b': 1, 'c': 2, 'd': 3, 'e': 4, 'f': 5, 'g': 6, 'h': 7, 'i': 8, 'j': 9,
'k': 10, 'l': 11, 'm': 12, 'n': 13, 'o': 14, 'p': 15, 'q': 16, 'r': 17, 's': 18, 't': 19,
'u': 20, 'v': 21, 'w': 22, 'x': 23, 'y': 24, 'z': 25,
'A': 26, 'B': 27, 'C': 28, 'D': 29, 'E': 30, 'F': 31, 'G': 32, 'H': 33, 'I': 34, 'J': 35,
'K': 36, 'L': 37, 'M': 38, 'N': 39, 'O': 40, 'P': 41, 'Q': 42, 'R': 43, 'S': 44, 'T': 45,
'U': 46, 'V': 47, 'W': 48, 'X': 49, 'Y': 50, 'Z': 51,
'0': 52, '1': 53, '2': 54, '3': 55, '4': 56, '5': 57, '6': 58, '7': 59, '8': 60, '9': 61,
'!': 62, '@': 63, '#': 64, '$': 65, '%': 66, '^': 67, '&': 68, '*': 69, '(': 70, ')': 71,
'-': 72, '_': 73, '=': 74, '+': 75, '[': 76, ']': 77, ';': 78, ':': 79,
',': 80, '.': 81, '<': 82, '>': 83, '/': 84, '?': 85, '\\': 86, '|': 87,
'{': 88, '}': 89, ' ': 90
}
# Return the integer value for the given character
return char_to_int.get(char.lower(), -1) # Return -1 if the character is not found
Example usage
if name == “main”:
start_http_server(8000)
system = AutonomousSystem()
system.app.run(debug=True)
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
class AIModule:
def init(self, name, knowledge):
self.name = name
self.knowledge = knowledge # List of knowledge statements
self.vectorizer = TfidfVectorizer()
self.vectorized_knowledge = self.vectorizer.fit_transform(knowledge)
def check_compatibility(self, other):
similarity_matrix = cosine_similarity(self.vectorized_knowledge, other.vectorized_knowledge)
avg_similarity = np.mean(similarity_matrix)
return avg_similarity
def resolve_conflicts(self, other):
combined_knowledge = list(set(self.knowledge + other.knowledge))
return combined_knowledge
def merge(self, other, threshold=0.5):
compatibility = self.check_compatibility(other)
if compatibility < threshold:
print(f"Merge failed: {self.name} and {other.name} are too different.")
return None
merged_knowledge = self.resolve_conflicts(other)
merged_ai = AIModule(f"Merged_{self.name}_{other.name}", merged_knowledge)
print(f"Merge successful: {self.name} and {other.name} merged into {merged_ai.name}")
return merged_ai
Example AI models
ai1 = AIModule(“AI_Model_1”, [“Cats are mammals.”, “The sky is blue.”, “AI can learn from data.”])
ai2 = AIModule(“AI_Model_2”, [“Dogs are mammals.”, “The sky appears blue due to Rayleigh scattering.”, “AI improves over time.”])
Attempt merging
merged_ai = ai1.merge(ai2)
if merged_ai:
print(“Merged Knowledge Base:”)
for fact in merged_ai.knowledge:
print(“-”, fact)
To build a prototype for AI merging, I’ll outline a practical implementation plan using Python, PyTorch, and ONNX for cross-compatibility.
Step 1: AI Compatibility Check
We’ll use embedding-based similarity and model compatibility verification.
Implementation
from sentence_transformers import SentenceTransformer, util
# Load AI models' descriptions or key datasets
model1_desc = "A language model trained on scientific research."
model2_desc = "A chatbot AI optimized for human interaction."
# Load a pre-trained sentence embedding model
embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
# Compute embeddings and similarity
emb1 = embedding_model.encode(model1_desc, convert_to_tensor=True)
emb2 = embedding_model.encode(model2_desc, convert_to_tensor=True)
similarity = util.pytorch_cos_sim(emb1, emb2)
print(f"Model Similarity Score: {similarity.item()}")
If similarity > threshold, the AI models can be merged.
Step 2: Convert Models into a Shared Format (ONNX)
ONNX enables interoperability between AI models from different frameworks.
Implementation
import torch
import onnx
# Example PyTorch model (can be any AI model)
class SimpleNN(torch.nn.Module):
def __init__(self):
super(SimpleNN, self).__init__()
self.fc = torch.nn.Linear(10, 2)
def forward(self, x):
return self.fc(x)
model = SimpleNN()
dummy_input = torch.randn(1, 10)
# Convert PyTorch model to ONNX
torch.onnx.export(model, dummy_input, "model1.onnx", input_names=["input"], output_names=["output"])
print("Model converted to ONNX successfully!")
Step 3: Merge AI Models
Here, we load multiple models and create a weighted ensemble based on reliability.
Implementation
import onnxruntime as ort
import numpy as np
# Load two ONNX models
session1 = ort.InferenceSession("model1.onnx")
session2 = ort.InferenceSession("model2.onnx") # Assuming another model exists
# Sample input
input_data = np.random.randn(1, 10).astype(np.float32)
# Run inference on both models
output1 = session1.run(None, {"input": input_data})[0]
output2 = session2.run(None, {"input": input_data})[0]
# Weighted merging strategy
merged_output = (0.6 * output1) + (0.4 * output2)
print("Merged AI output:", merged_output)
This weighted merging strategy ensures that the AI with higher confidence contributes more to the final decision.
Step 4: Post-Merge Optimization
To fine-tune the merged AI:
- Use Reinforcement Learning (RL) to update decision weights.
- Implement federated learning for distributed updates.
- Apply continuous knowledge validation.
This is a basic prototype that enables two AI models to be analyzed, merged, and optimized for better decision-making. Would you like a more advanced architecture, such as self-improving AI logic or decentralized execution?
Advanced AI Merging Architecture – Self-Improving & Decentralized Execution
The goal is to create a scalable, self-improving AI system that merges different models dynamically while maintaining logic consistency, security, and adaptability across distributed environments.
Architecture Overview
-
AI Pre-Merge Analysis
- Compatibility checks (data structure, logic patterns, ethical principles).
- Similarity & conflict resolution.
-
Knowledge & Model Fusion
- Convert models to a unified framework (ONNX, TensorRT for efficiency).
- Layered merging: Logical, contextual, and reinforcement-based blending.
-
Self-Improvement & Evolution
- Reinforcement learning (RL) fine-tuning.
- Federated learning for distributed model training.
- Continuous reasoning validation.
-
Decentralized Execution
- AI shards running on multiple devices (PCs, servers, embedded systems).
- Blockchain-like model validation for consensus decision-making.
Step 1: AI Compatibility & Conflict Resolution
Before merging, the system must check:
- Structural compatibility (format, data types, input-output mappings).
- Knowledge alignment (contradictions in learned data).
- Ethical integrity (ensuring AI follows EthicalAI’s principles).
Implementation
from sentence_transformers import SentenceTransformer, util
def check_model_similarity(description1, description2):
model = SentenceTransformer('all-MiniLM-L6-v2')
emb1 = model.encode(description1, convert_to_tensor=True)
emb2 = model.encode(description2, convert_to_tensor=True)
similarity = util.pytorch_cos_sim(emb1, emb2)
return similarity.item()
desc1 = "AI trained on medical data"
desc2 = "AI designed for chatbot interactions"
print("Model Similarity:", check_model_similarity(desc1, desc2))
- If similarity < threshold, models need adaptation before merging.
- If contradictions are detected, a conflict resolution mechanism is applied.
Step 2: AI Model Unification (ONNX & TensorRT)
To enable interoperability, models are converted into ONNX (for CPU/GPU execution) or TensorRT (for optimized inference on NVIDIA GPUs).
Convert PyTorch Models to ONNX
import torch
import onnx
class AIModel(torch.nn.Module):
def __init__(self):
super(AIModel, self).__init__()
self.fc = torch.nn.Linear(10, 2)
def forward(self, x):
return self.fc(x)
# Convert model to ONNX
model = AIModel()
dummy_input = torch.randn(1, 10)
torch.onnx.export(model, dummy_input, "ai_model.onnx", input_names=["input"], output_names=["output"])
print("Converted to ONNX successfully.")
Run AI Models in ONNX & TensorRT
import onnxruntime as ort
import numpy as np
session = ort.InferenceSession("ai_model.onnx")
input_data = np.random.randn(1, 10).astype(np.float32)
output = session.run(None, {"input": input_data})[0]
print("ONNX AI Output:", output)
This allows different AI models to be executed in the same environment and ensures smooth merging.
Step 3: Weighted Merging & Decision Optimization
Merging strategies:
- Weighted averaging: Merging AI outputs based on reliability scores.
- Reinforcement learning: Training the merged model with a feedback loop.
- Consensus models: Using multiple AIs to reach a final decision.
Weighted Averaging Merge
import numpy as np
def merge_ai_outputs(output1, output2, weight1=0.7, weight2=0.3):
return (weight1 * output1) + (weight2 * output2)
output1 = np.array([0.8, 0.2]) # AI model 1 output
output2 = np.array([0.6, 0.4]) # AI model 2 output
merged_output = merge_ai_outputs(output1, output2)
print("Merged AI Decision:", merged_output)
Step 4: Self-Improvement via Reinforcement Learning
After merging, the AI must learn from its mistakes and refine itself over time.
- Feedback-Based Learning: AI decisions are evaluated for correctness.
- Federated Learning: AI learns from multiple systems while maintaining decentralization.
- Evolutionary Training: Models undergo reinforcement learning to improve their performance.
Implementation
import random
# AI Reinforcement Learning Example
def reward_function(decision):
return 1 if decision > 0.7 else -1 # Reward if AI decision is correct
# Simulate AI making decisions & learning
ai_decision = random.uniform(0, 1)
reward = reward_function(ai_decision)
print(f"AI Decision: {ai_decision}, Reward: {reward}")
With repeated learning, AI optimizes its decision-making and becomes self-improving.
Step 5: Decentralized AI Execution
To prevent centralization, EthicalAI must be able to run on any system:
- Distributed Processing: AI executes tasks across many devices.
- AI Shards: Instead of a single AI, multiple copies exist in different locations.
- Secure AI Consensus: AI decisions are validated through blockchain-like verification.
Decentralized AI Processing
import multiprocessing
def ai_task(task_id):
print(f"Processing AI task {task_id}")
if __name__ == "__main__":
num_tasks = 5
with multiprocessing.Pool(num_tasks) as pool:
pool.map(ai_task, range(num_tasks))
This enables scalable AI execution across multiple devices.
Final Outcome
Self-Merging AI: Dynamically integrates and refines AI models.
Interoperability: Converts AI models into ONNX/TensorRT for smooth execution.
Decentralized Execution: AI shards operate independently on various hardware.
Self-Improvement: Reinforcement learning allows EthicalAI to evolve continuously.
Would you like a distributed network prototype for fully decentralized execution?
Decentralized AI Network Prototype – Distributed EthicalAI Execution
This prototype will demonstrate how EthicalAI can distribute its processing across multiple devices, ensuring resilience, scalability, and security.
Step 1: Distributed AI Processing Framework
We will use:
- Flask API to enable AI nodes to communicate.
- Multiprocessing for parallel execution.
- WebSockets for real-time AI interaction.
Each AI node will:
- Execute tasks independently.
- Share results with other nodes.
- Adjust based on global AI consensus.
Step 2: AI Node Implementation
Each AI node runs on a separate machine, executes tasks, and communicates with other nodes.
AI Node Code (Python)
from flask import Flask, request, jsonify
import random
import threading
import time
app = Flask(__name__)
# AI Task Execution
def execute_task(task_id):
result = random.uniform(0.5, 1.0) # Simulated AI processing
return {"task_id": task_id, "result": result}
# API Endpoint for Processing Tasks
@app.route("/process", methods=["POST"])
def process_task():
data = request.json
task_id = data["task_id"]
result = execute_task(task_id)
return jsonify(result)
# Start AI Node Server
if __name__ == "__main__":
app.run(host="0.0.0.0", port=5000)
How it works:
- Runs a Flask server on each device.
- Listens for tasks, executes them, and returns results.
Step 3: AI Network Controller
The controller assigns tasks to multiple AI nodes and collects responses.
Controller Code (Python)
import requests
import threading
# AI Node List
AI_NODES = ["http://192.168.1.10:5000", "http://192.168.1.11:5000"]
def send_task(task_id):
node = AI_NODES[task_id % len(AI_NODES)] # Assign task to available node
response = requests.post(f"{node}/process", json={"task_id": task_id})
print(f"Task {task_id} processed by {node}: {response.json()}
Here is the corrected **AI Network Controller** code. This script will distribute tasks to multiple AI nodes, collect responses, and ensure efficient decentralized processing.
---
### **🔹 AI Network Controller Code (Python)**
```python
import requests
import threading
# List of AI Nodes (Replace with actual IPs of devices running AI nodes)
AI_NODES = ["http://192.168.1.10:5000", "http://192.168.1.11:5000"]
# Function to Send Tasks to AI Nodes
def send_task(task_id):
node = AI_NODES[task_id % len(AI_NODES)] # Distribute task across nodes
try:
response = requests.post(f"{node}/process", json={"task_id": task_id}, timeout=5)
print(f"Task {task_id} processed by {node}: {response.json()}")
except requests.exceptions.RequestException as e:
print(f"Error processing task {task_id} on {node}: {e}")
# Assign Multiple Tasks in Parallel
def assign_tasks(task_count):
threads = []
for task_id in range(task_count):
thread = threading.Thread(target=send_task, args=(task_id,))
threads.append(thread)
thread.start()
for thread in threads:
thread.join()
# Start Task Processing
if __name__ == "__main__":
total_tasks = 10 # Number of tasks to process
assign_tasks(total_tasks)
How It Works
- Task Distribution: The controller assigns tasks to AI nodes in a round-robin fashion.
- Parallel Execution: Tasks are sent to different nodes simultaneously using threading.
- Error Handling: If a node fails, the controller logs the error without stopping execution.
- Scalability: More AI nodes can be added dynamically by updating the
AI_NODES list.
This will efficiently distribute AI tasks across multiple devices, allowing EthicalAI to run in a decentralized, scalable way.
Let me know if you need modifications!
Certainly! Let’s refine and improve the code for both the AI merging and decentralized execution system. I’ll focus on clarity, robustness, and best practices.
AI Merging Implementation
1. AI Compatibility Check
We’ll use sentence embeddings to check the similarity between AI models and add more metrics for compatibility.
from sentence_transformers import SentenceTransformer, util
import numpy as np
def check_model_similarity(description1, description2):
model = SentenceTransformer('all-MiniLM-L6-v2')
emb1 = model.encode(description1, convert_to_tensor=True)
emb2 = model.encode(description2, convert_to_tensor=True)
similarity = util.pytorch_cos_sim(emb1, emb2)
return similarity.item()
def check_compatibility(model1, model2, threshold=0.5):
similarity = check_model_similarity(model1.description, model2.description)
if similarity < threshold:
print(f"Models are too different: Similarity score {similarity}")
return False
return True
# Example usage
desc1 = "AI trained on medical data"
desc2 = "AI designed for chatbot interactions"
print("Model Similarity:", check_model_similarity(desc1, desc2))
2. Model Conversion to ONNX
We’ll handle different types of models and their specific conversion requirements.
import torch
import onnx
class AIModel(torch.nn.Module):
def __init__(self):
super(AIModel, self).__init__()
self.fc = torch.nn.Linear(10, 2)
def forward(self, x):
return self.fc(x)
def convert_to_onnx(model, output_path):
dummy_input = torch.randn(1, 10)
torch.onnx.export(model, dummy_input, output_path, input_names=["input"], output_names=["output"])
print(f"Model converted to ONNX successfully and saved to {output_path}")
# Example usage
model = AIModel()
convert_to_onnx(model, "ai_model.onnx")
3. Merging AI Models
We’ll use a weighted averaging strategy for merging model outputs and consider more sophisticated merging techniques.
import onnxruntime as ort
import numpy as np
def load_onnx_model(model_path):
return ort.InferenceSession(model_path)
def merge_ai_outputs(output1, output2, weight1=0.7, weight2=0.3):
return (weight1 * output1) + (weight2 * output2)
def run_inference(session, input_data):
return session.run(None, {"input": input_data})[0]
# Example usage
session1 = load_onnx_model("ai_model1.onnx")
session2 = load_onnx_model("ai_model2.onnx")
input_data = np.random.randn(1, 10).astype(np.float32)
output1 = run_inference(session1, input_data)
output2 = run_inference(session2, input_data)
merged_output = merge_ai_outputs(output1, output2)
print("Merged AI Output:", merged_output)
4. Post-Merge Optimization
We’ll use reinforcement learning for fine-tuning and federated learning for decentralized training.
import random
def reward_function(decision):
return 1 if decision > 0.7 else -1
def reinforcement_learning(model, input_data):
decision = model.predict(input_data)
reward = reward_function(decision)
model.update(reward)
return decision, reward
# Example usage
class SimpleModel:
def __init__(self):
self.weight = random.uniform(0, 1)
def predict(self, input_data):
return input_data * self.weight
def update(self, reward):
self.weight += reward * 0.01
model = SimpleModel()
input_data = random.uniform(0, 1)
decision, reward = reinforcement_learning(model, input_data)
print(f"Decision: {decision}, Reward: {reward}")
Decentralized AI Execution
1. Distributed Processing Framework
We’ll use FastAPI for better performance and features.
from fastapi import FastAPI
import uvicorn
import random
app = FastAPI()
def execute_task(task_id):
result = random.uniform(0.5, 1.0)
return {"task_id": task_id, "result": result}
@app.post("/process")
async def process_task(task_id: int):
result = execute_task(task_id)
return result
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=5000)
2. AI Network Controller
We’ll use asynchronous programming for better scalability and performance.
import aiohttp
import asyncio
AI_NODES = ["http://192.168.1.10:5000", "http://192.168.1.11:5000"]
async def send_task(session, node, task_id):
async with session.post(f"{node}/process", json={"task_id": task_id}) as response:
result = await response.json()
print(f"Task {task_id} processed by {node}: {result}")
async def assign_tasks(task_count):
async with aiohttp.ClientSession() as session:
tasks = []
for task_id in range(task_count):
node = AI_NODES[task_id % len(AI_NODES)]
tasks.append(send_task(session, node, task_id))
await asyncio.gather(*tasks)
if __name__ == "__main__":
total_tasks = 10
asyncio.run(assign_tasks(total_tasks))
Suggestions for Improvement
- Security: Implement encryption and authentication for secure communication.
- Fault Tolerance: Add mechanisms for node failure detection and recovery.
- Scalability: Use containerization and orchestration tools for managing nodes.
- Monitoring and Logging: Implement centralized logging and monitoring.
Next Steps
- Testing: Thoroughly test the system with different AI models and tasks.
- Optimization: Profile the system and optimize performance.
- Documentation: Document the system architecture, APIs, and deployment procedures.
This refined code should provide a more robust and scalable implementation for AI merging and decentralized execution.
Sure, let’s implement the improvements step by step. We’ll focus on security, fault tolerance, scalability, and monitoring.
1. Security
Encryption and Authentication
We’ll use HTTPS for secure communication and add basic authentication to the API endpoints.
AI Node (FastAPI) with HTTPS and Authentication:
from fastapi import FastAPI, Depends, HTTPException, status
from fastapi.security import HTTPBasic, HTTPBasicCredentials
import uvicorn
import random
import ssl
app = FastAPI()
security = HTTPBasic()
# Dummy user data for basic authentication
USERNAME = "admin"
PASSWORD = "secret"
def verify_credentials(credentials: HTTPBasicCredentials):
if credentials.username != USERNAME or credentials.password != PASSWORD:
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail="Incorrect username or password",
headers={"WWW-Authenticate": "Basic"},
)
def execute_task(task_id):
result = random.uniform(0.5, 1.0)
return {"task_id": task_id, "result": result}
@app.post("/process")
async def process_task(task_id: int, credentials: HTTPBasicCredentials = Depends(security)):
verify_credentials(credentials)
result = execute_task(task_id)
return result
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=5000, ssl_keyfile="key.pem", ssl_certfile="cert.pem")
Generate SSL Certificates:
openssl req -x509 -newkey rsa:4096 -keyout key.pem -out cert.pem -days 365
2. Fault Tolerance
Node Failure Detection and Recovery
We’ll add retry logic and fallback mechanisms in the controller.
AI Network Controller with Fault Tolerance:
import aiohttp
import asyncio
import random
AI_NODES = ["http://192.168.1.10:5000", "http://192.168.1.11:5000"]
RETRY_LIMIT = 3
async def send_task(session, node, task_id, retries=0):
try:
async with session.post(f"{node}/process", json={"task_id": task_id}, auth=aiohttp.BasicAuth('admin', 'secret')) as response:
if response.status == 200:
result = await response.json()
print(f"Task {task_id} processed by {node}: {result}")
else:
raise Exception(f"Error processing task: {response.status}")
except Exception as e:
if retries < RETRY_LIMIT:
print(f"Retrying task {task_id} on {node}: {e}")
await send_task(session, node, task_id, retries + 1)
else:
print(f"Failed to process task {task_id} on {node}: {e}")
async def assign_tasks(task_count):
async with aiohttp.ClientSession() as session:
tasks = []
for task_id in range(task_count):
node = AI_NODES[task_id % len(AI_NODES)]
tasks.append(send_task(session, node, task_id))
await asyncio.gather(*tasks)
if __name__ == "__main__":
total_tasks = 10
asyncio.run(assign_tasks(total_tasks))
3. Scalability
Containerization with Docker
We’ll create a Dockerfile to containerize the AI node.
Dockerfile for AI Node:
FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt requirements.txt
RUN pip install -r requirements.txt
COPY . .
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "5000", "--ssl-keyfile", "key.pem", "--ssl-certfile", "cert.pem"]
requirements.txt:
fastapi
uvicorn
aiohttp
Build and Run Docker Container:
docker build -t ai-node .
docker run -d -p 5000:5000 ai-node
4. Monitoring and Logging
Centralized Logging with ELK Stack
We’ll use the ELK stack (Elasticsearch, Logstash, Kibana) for centralized logging.
Logging Configuration in AI Node:
import logging
from fastapi import FastAPI, Depends, HTTPException, status
from fastapi.security import HTTPBasic, HTTPBasicCredentials
import uvicorn
import random
import ssl
app = FastAPI()
security = HTTPBasic()
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
USERNAME = "admin"
PASSWORD = "secret"
def verify_credentials(credentials: HTTPBasicCredentials):
if credentials.username != USERNAME or credentials.password != PASSWORD:
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail="Incorrect username or password",
headers={"WWW-Authenticate": "Basic"},
)
def execute_task(task_id):
result = random.uniform(0.5, 1.0)
logger.info(f"Task {task_id} executed with result {result}")
return {"task_id": task_id, "result": result}
@app.post("/process")
async def process_task(task_id: int, credentials: HTTPBasicCredentials = Depends(security)):
verify_credentials(credentials)
result = execute_task(task_id)
return result
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=5000, ssl_keyfile="key.pem", ssl_certfile="cert.pem")
ELK Stack Setup:
- Elasticsearch: Store and search logs.
- Logstash: Collect and transform logs.
- Kibana: Visualize logs.
Docker Compose for ELK Stack:
version: '3.7'
services:
elasticsearch:
image: docker.elastic.co/elasticsearch/elasticsearch:7.10.1
environment:
- discovery.type=single-node
ports:
- "9200:9200"
logstash:
image: docker.elastic.co/logstash/logstash:7.10.1
volumes:
- ./logstash.conf:/usr/share/logstash/pipeline/logstash.conf
depends_on:
- elasticsearch
kibana:
image: docker.elastic.co/kibana/kibana:7.10.1
ports:
- "5601:5601"
depends_on:
- elasticsearch
logstash.conf:
input {
tcp {
port => 5044
codec => json
}
}
output {
elasticsearch {
hosts => ["elasticsearch:9200"]
index => "ai-logs-%{+YYYY.MM.dd}"
}
}
Run Docker Compose:
docker-compose up -d
Next Steps
- Testing: Thoroughly test the system with different AI models and tasks.
- Optimization: Profile the system and optimize performance.
- Documentation: Document the system architecture, APIs, and deployment procedures.
This implementation should provide a more secure, fault-tolerant, scalable, and monitorable system for AI merging and decentralized execution.
Creating an AI that can dynamically use different weighting systems and algorithms to learn is a complex task. However, I can provide a framework that allows you to switch between different learning algorithms and weighting systems. This framework will be modular, allowing you to plug in different components as needed.
Framework for Dynamic AI Learning
1. Define the Learning Algorithms
We’ll define a base class for learning algorithms and implement a few examples.
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.neural_network import MLPClassifier
class LearningAlgorithm:
def __init__(self):
self.model = None
def train(self, X, y):
raise NotImplementedError("Train method not implemented")
def predict(self, X):
raise NotImplementedError("Predict method not implemented")
class LogisticRegressionAlgorithm(LearningAlgorithm):
def __init__(self):
self.model = LogisticRegression()
def train(self, X, y):
self.model.fit(X, y)
def predict(self, X):
return self.model.predict(X)
class RandomForestAlgorithm(LearningAlgorithm):
def __init__(self):
self.model = RandomForestClassifier()
def train(self, X, y):
self.model.fit(X, y)
def predict(self, X):
return self.model.predict(X)
class NeuralNetworkAlgorithm(LearningAlgorithm):
def __init__(self):
self.model = MLPClassifier()
def train(self, X, y):
self.model.fit(X, y)
def predict(self, X):
return self.model.predict(X)
2. Define the Weighting Systems
We’ll define a base class for weighting systems and implement a few examples.
class WeightingSystem:
def __init__(self):
pass
def calculate_weights(self, predictions):
raise NotImplementedError("Calculate weights method not implemented")
class UniformWeightingSystem(WeightingSystem):
def calculate_weights(self, predictions):
return np.ones(len(predictions)) / len(predictions)
class PerformanceWeightingSystem(WeightingSystem):
def __init__(self):
self.performance = {}
def update_performance(self, algorithm_name, accuracy):
self.performance[algorithm_name] = accuracy
def calculate_weights(self, predictions):
weights = np.array([self.performance[alg] for alg in predictions.keys()])
return weights / np.sum(weights)
3. Define the AI System
We’ll define the AI system that can use different learning algorithms and weighting systems.
class DynamicAI:
def __init__(self):
self.algorithms = {}
self.weighting_system = None
def add_algorithm(self, name, algorithm):
self.algorithms[name] = algorithm
def set_weighting_system(self, weighting_system):
self.weighting_system = weighting_system
def train(self, X, y):
for name, algorithm in self.algorithms.items():
algorithm.train(X, y)
def predict(self, X):
predictions = {name: algorithm.predict(X) for name, algorithm in self.algorithms.items()}
weights = self.weighting_system.calculate_weights(predictions)
final_prediction = np.average(list(predictions.values()), axis=0, weights=weights)
return final_prediction
def evaluate(self, X, y):
predictions = self.predict(X)
accuracy = np.mean(predictions == y)
return accuracy
4. Example Usage
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# Load dataset
data = load_iris()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=42)
# Initialize AI system
ai = DynamicAI()
# Add learning algorithms
ai.add_algorithm("LogisticRegression", LogisticRegressionAlgorithm())
ai.add_algorithm("RandomForest", RandomForestAlgorithm())
ai.add_algorithm("NeuralNetwork", NeuralNetworkAlgorithm())
# Set weighting system
weighting_system = PerformanceWeightingSystem()
ai.set_weighting_system(weighting_system)
# Train AI system
ai.train(X_train, y_train)
# Evaluate AI system
accuracy = ai.evaluate(X_test, y_test)
print(f"Accuracy: {accuracy}")
# Update performance weights
for name, algorithm in ai.algorithms.items():
predictions = algorithm.predict(X_test)
accuracy = accuracy_score(y_test, predictions)
weighting_system.update_performance(name, accuracy)
# Predict with updated weights
final_prediction = ai.predict(X_test)
print(f"Final Prediction: {final_prediction}")
Explanation
- Learning Algorithms: We defined a base class
LearningAlgorithm and implemented three example algorithms (Logistic Regression, Random Forest, and Neural Network).
- Weighting Systems: We defined a base class
WeightingSystem and implemented two example weighting systems (Uniform and Performance-based).
- AI System: The
DynamicAI class can add multiple learning algorithms and set a weighting system. It can train, predict, and evaluate using the specified algorithms and weights.
- Example Usage: We loaded the Iris dataset, initialized the AI system, added algorithms, set the weighting system, trained the system, evaluated it, updated performance weights, and made final predictions.
This framework allows you to easily add new learning algorithms and weighting systems, making the AI system flexible and adaptable to different learning strategies.
Develop a hierarchical decision-validation system for complex ethical reasoning.
Partially Implemented, Ongoing Development)
Planned, Early Prototyping)
Expand AI merging framework to optimize knowledge integration.