My take on Artificial Intelligence Learning Systems (AILS)—AI that truly learns on its own. No constant retraining, no human babysitting. Just continuous adaptation from real-world data. Built with Python, TensorFlow, and a strong commitment to ethical AI development.
🔮 The Future of AILS "The journey into the world of Artificial Intelligence Learning Systems isn't just a technical pursuit; it's a moral imperative. The tools you've acquired are not merely instruments of innovation; they are instruments of responsibility. Use them wisely, ethically, and with unwavering commitment to a future where AI serves humanity, not the other way around."
AILS represents not just a technological advancement but a new way of thinking about intelligence—artificial and human. As these systems learn and evolve, so must our understanding of their implications and our commitment to their responsible development.
The future of AILS is not predetermined. It's a story we write together, line by line, code by code, decision by decision. Your contribution matters. The world needs your skills, your insight, and your commitment to ethical AI development.
Welcome to the journey.
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A comprehensive, open-source framework for autonomous AI learning — from data acquisition to model deployment.
"Harnessing the power of autonomous AI to learn, adapt, and evolve — responsibly."
Created & Maintained by Cherry Computer Ltd.
- 🌟 About AILS
- 🏢 About Cherry Computer Ltd.
- ✨ Key Features
- 🏗️ Architecture Overview
- 📁 Repository Structure
- 🚀 Quick Start
- 💻 Installation
- 📦 Core Modules
- 🐍 Python Code Examples
- 🏭 Industry Applications
- 📊 Model Evaluation & Metrics
- 🔒 Privacy-Preserving AI
- ☁️ Deployment & Scaling
- 🌐 AILS in the Metaverse
- 🔬 Research & Emerging Trends
- 🧪 Testing
- 🤝 Contributing
- 📄 License
- 📞 Contact
Artificial Intelligence Learning System (AILS) are advanced AI programs capable of independent learning and continuous self-improvement by processing open-source data from the internet. Unlike traditional machine learning models that require constant human supervision and manually curated datasets, AILS autonomously acquires, processes, and integrates new knowledge — evolving continuously without human intervention.
AILS represents a paradigm shift in AI development:
| Traditional Machine Learning | AILS Autonomous Learning |
|---|---|
| Requires curated datasets | Learns from open-source internet data |
| Static model after training | Continuously evolving |
| Human-supervised updates | Autonomous self-improvement |
| Domain-specific knowledge | Cross-domain knowledge acquisition |
| Fixed performance ceiling | Theoretically unbounded growth |
Data → Acquisition → Preprocessing → Learning → Evaluation → Deployment → Monitor → Repeat ♾️
AILS systems embody three foundational principles:
- Autonomous Adaptation — Continuous learning from new data without retraining pipelines
- Ethical Responsibility — Bias-aware, privacy-preserving, and transparent by design
- Scalable Architecture — From edge devices to enterprise cloud deployments
Cherry Computer Ltd. is the creator and primary maintainer of the AILS repository. As a forward-thinking technology company, Cherry Computer Ltd. is dedicated to building responsible, scalable, and ethical AI solutions that empower developers, researchers, and organizations worldwide.
"Innovation with Integrity — Cherry Computer Ltd."
- 🌐 Website: cherrycomputer.ltd
- 📧 Contact: contact@cherrycomputer.ltd
- 🐙 GitHub: @CherryComputerLtd
- 💼 Mission: To democratize access to responsible AI technologies
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┌─────────────────────────────────────────────────────────────────────────┐
│ AILS — Core Architecture │
│ Created by Cherry Computer Ltd. │
├─────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────┐ ┌──────────────┐ ┌────────────────────────────┐ │
│ │ DATA LAYER │ │ PROCESSING │ │ LEARNING ENGINE │ │
│ ├─────────────┤ │ LAYER │ ├────────────────────────────┤ │
│ │ Web Scraper │───▶│ NLP Module │───▶│ Deep Learning (TensorFlow)│ │
│ │ API Clients │ │ CV Module │ │ Reinforcement Learning │ │
│ │ MySQL / DB │ │ Preprocess │ │ Transfer Learning │ │
│ │ NoSQL Store │ │ Tokenizer │ │ Ensemble Methods │ │
│ └─────────────┘ └──────────────┘ └────────────────────────────┘ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ ┌─────────────────────────────────────────────────────────────────┐ │
│ │ ETHICS & SAFETY LAYER │ │
│ │ Bias Detection │ Fairness Metrics │ XAI │ Privacy Preservation │ │
│ └─────────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────┐ │
│ │ DEPLOYMENT & MONITORING LAYER │ │
│ │ Model Registry │ REST API │ Monitoring │ Auto-Scaling │ │
│ └─────────────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────┘
AILS/
├── 📄 README.md # This file
├── 📄 CONTRIBUTING.md # Contribution guidelines
├── 📄 CODE_OF_CONDUCT.md # Community standards
├── 📄 LICENSE # MIT License
├── 📄 SECURITY.md # Security policy
├── 📄 CHANGELOG.md # Version history
├── 📄 requirements.txt # Python dependencies
├── 📄 setup.py # Package setup
├── 📄 pyproject.toml # Build configuration
├── 📄 Makefile # Common dev commands
├── 📄 .gitignore # Git ignore rules
│
├── 🐙 .github/
│ ├── workflows/
│ │ ├── ci.yml # Continuous Integration
│ │ ├── cd.yml # Continuous Deployment
│ │ └── codeql.yml # Security scanning
│ ├── ISSUE_TEMPLATE/
│ │ ├── bug_report.md
│ │ └── feature_request.md
│ └── PULL_REQUEST_TEMPLATE.md
│
├── 🖼️ assets/
│ ├── ails_logo.png # AILS Logo
│ └── cherry_computer_banner.png # Cherry Computer Ltd. banner
│
├── 🐍 src/
│ ├── __init__.py
│ ├── data/
│ │ ├── __init__.py
│ │ ├── scraper.py # Web scraping (BeautifulSoup + Selenium)
│ │ ├── database.py # MySQL & NoSQL database manager
│ │ ├── preprocessor.py # Data cleaning & preprocessing
│ │ └── pipeline.py # Full data pipeline orchestration
│ ├── nlp/
│ │ ├── __init__.py
│ │ ├── tokenizer.py # Tokenization & stemming
│ │ ├── sentiment.py # Sentiment analysis
│ │ └── ner.py # Named entity recognition
│ ├── vision/
│ │ ├── __init__.py
│ │ ├── cnn_model.py # CNN architecture
│ │ └── image_processor.py # Image preprocessing
│ ├── models/
│ │ ├── __init__.py
│ │ ├── neural_network.py # TensorFlow neural networks
│ │ ├── rnn_lstm.py # RNN, LSTM, GRU models
│ │ ├── reinforcement.py # RL agents & environments
│ │ ├── ensemble.py # Ensemble methods
│ │ └── trainer.py # Model training & evaluation
│ ├── ethics/
│ │ ├── __init__.py
│ │ ├── bias_detector.py # Bias detection & fairness metrics
│ │ ├── explainability.py # XAI (LIME, SHAP)
│ │ └── privacy.py # Privacy-preserving techniques
│ └── utils/
│ ├── __init__.py
│ ├── config.py # Configuration management
│ ├── logger.py # Logging utilities
│ └── metrics.py # Performance metrics
│
├── 🧪 tests/
│ ├── test_scraper.py
│ ├── test_database.py
│ ├── test_nlp.py
│ ├── test_models.py
│ └── test_ethics.py
│
├── 📚 docs/
│ ├── installation.md
│ ├── quickstart.md
│ ├── api_reference.md
│ ├── ethical_guidelines.md
│ └── deployment_guide.md
│
├── 💡 examples/
│ ├── sentiment_analysis_pipeline.py # Full sentiment pipeline
│ ├── stock_data_scraper.py # Financial data scraping
│ ├── image_classifier.py # CNN image classifier
│ └── rl_trading_agent.py # RL trading example
│
└── 📓 notebooks/
├── 01_data_acquisition.ipynb
├── 02_nlp_tutorial.ipynb
├── 03_computer_vision.ipynb
├── 04_deep_learning.ipynb
└── 05_ethics_fairness.ipynb
# Clone the repository
git clone https://github.com/CherryComputerLtd/AILS.git
cd AILS
# Create virtual environment
python -m venv ails-env
source ails-env/bin/activate # Windows: ails-env\Scripts\activate
# Install dependencies
pip install -r requirements.txt
# Run a quick demo
python examples/sentiment_analysis_pipeline.pyfrom src.data.scraper import AILSScraper
from src.nlp.sentiment import SentimentAnalyzer
from src.models.neural_network import AILSNeuralNetwork
# 1. Scrape data
scraper = AILSScraper()
data = scraper.scrape(url="https://example.com/reviews", tag="p", class_="review-text")
# 2. Analyze sentiment
analyzer = SentimentAnalyzer()
results = analyzer.analyze(data)
# 3. Train model
nn = AILSNeuralNetwork(input_dim=10, hidden_units=[128, 64], output_dim=1)
nn.compile_model()
nn.train(X_train, y_train, epochs=20)
nn.evaluate(X_test, y_test)- Python 3.9+
- TensorFlow 2.x
- MySQL 8.0+ (optional, for persistent storage)
- 4GB+ RAM (8GB+ recommended for large models)
# Install from PyPI (coming soon)
pip install ails-framework
# Or install from source
git clone https://github.com/CherryComputerLtd/AILS.git
cd AILS
pip install -e .# Pull the Docker image
docker pull cherrycomputerltd/ails:latest
# Run with Docker Compose
docker-compose up -dtensorflow>=2.10.0
torch>=2.0.0
beautifulsoup4>=4.12.0
selenium>=4.10.0
mysql-connector-python>=8.0.33
scikit-learn>=1.3.0
numpy>=1.24.0
pandas>=2.0.0
nltk>=3.8.0
transformers>=4.30.0
lime>=0.2.0.1
shap>=0.42.0
requests>=2.31.0
pymongo>=4.4.0
fastapi>=0.100.0
uvicorn>=0.23.0
pytest>=7.4.0
AILS provides a comprehensive data pipeline covering the full lifecycle from collection to storage.
# src/data/scraper.py
import requests
from bs4 import BeautifulSoup
from typing import List, Dict, Optional
import logging
class AILSScraper:
"""
AILS Web Scraper Module
Handles static and dynamic web scraping for autonomous data acquisition.
Created by Cherry Computer Ltd.
"""
def __init__(self, headers: Optional[Dict] = None, timeout: int = 30):
self.headers = headers or {
"User-Agent": (
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
"AppleWebKit/537.36 (KHTML, like Gecko) "
"Chrome/91.0.4472.124 Safari/537.36"
)
}
self.timeout = timeout
self.logger = logging.getLogger("AILS.Scraper")
def scrape(self, url: str, tag: str = "p",
class_: Optional[str] = None) -> List[str]:
"""Scrape text data from a URL."""
try:
response = requests.get(url, headers=self.headers, timeout=self.timeout)
response.raise_for_status()
soup = BeautifulSoup(response.content, "html.parser")
elements = soup.find_all(tag, class_=class_) if class_ else soup.find_all(tag)
return [el.get_text(strip=True) for el in elements if el.get_text(strip=True)]
except requests.RequestException as e:
self.logger.error(f"Scraping error for {url}: {e}")
return []
def scrape_table(self, url: str, table_id: Optional[str] = None) -> List[Dict]:
"""Scrape tabular data (e.g., stock tables) from a URL."""
try:
response = requests.get(url, headers=self.headers, timeout=self.timeout)
soup = BeautifulSoup(response.content, "html.parser")
table = soup.find("table", id=table_id) if table_id else soup.find("table")
if not table:
return []
headers = [th.get_text(strip=True) for th in table.find_all("th")]
rows = []
for tr in table.find_all("tr")[1:]:
cells = [td.get_text(strip=True) for td in tr.find_all("td")]
if cells:
rows.append(dict(zip(headers, cells)))
return rows
except Exception as e:
self.logger.error(f"Table scraping error: {e}")
return []# Dynamic scraping for JavaScript-heavy pages
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.chrome.options import Options
from bs4 import BeautifulSoup
class DynamicScraper:
"""
AILS Dynamic Scraper — for JavaScript-rendered content.
Created by Cherry Computer Ltd.
"""
def __init__(self, headless: bool = True):
options = Options()
if headless:
options.add_argument("--headless")
options.add_argument("--no-sandbox")
options.add_argument("--disable-dev-shm-usage")
self.driver = webdriver.Chrome(options=options)
self.wait = WebDriverWait(self.driver, 15)
def scrape_dynamic(self, url: str, wait_selector: str) -> List[Dict]:
"""Scrape data after waiting for a CSS selector to appear."""
try:
self.driver.get(url)
self.wait.until(EC.presence_of_element_located(
(By.CSS_SELECTOR, wait_selector)
))
soup = BeautifulSoup(self.driver.page_source, "html.parser")
table = soup.find("table")
if not table:
return []
headers = [th.get_text(strip=True) for th in table.find_all("th")]
rows = []
for tr in table.find_all("tr")[1:]:
cells = [td.get_text(strip=True) for td in tr.find_all("td")]
if cells:
rows.append(dict(zip(headers, cells)))
return rows
finally:
self.driver.quit()# src/data/database.py
import mysql.connector
from mysql.connector import Error
from typing import List, Dict, Any, Optional
import logging
class AILSDatabaseManager:
"""
AILS Database Manager — MySQL integration for persistent data storage.
Created by Cherry Computer Ltd.
"""
def __init__(self, host: str = "localhost", user: str = "root",
password: str = "", database: str = "AILS_data"):
self.config = {
"host": host, "user": user,
"password": password, "database": database
}
self.connection = None
self.logger = logging.getLogger("AILS.Database")
def connect(self) -> bool:
"""Establish database connection."""
try:
self.connection = mysql.connector.connect(**self.config)
self.logger.info("✅ Database connection established.")
return True
except Error as e:
self.logger.error(f"❌ Connection failed: {e}")
return False
def create_table(self, table_name: str, schema: str) -> None:
"""Create a database table with the given schema."""
cursor = self.connection.cursor()
cursor.execute(f"CREATE TABLE IF NOT EXISTS {table_name} ({schema})")
self.connection.commit()
self.logger.info(f"Table '{table_name}' ready.")
def insert_many(self, table_name: str, columns: List[str],
records: List[tuple]) -> None:
"""Batch insert records using executemany for performance."""
cursor = self.connection.cursor()
placeholders = ", ".join(["%s"] * len(columns))
col_str = ", ".join(columns)
sql = f"INSERT INTO {table_name} ({col_str}) VALUES ({placeholders})"
cursor.executemany(sql, records)
self.connection.commit()
self.logger.info(f"✅ Inserted {len(records)} records into '{table_name}'.")
def fetch_all(self, table_name: str,
condition: Optional[str] = None) -> List[tuple]:
"""Retrieve all records from a table."""
cursor = self.connection.cursor()
query = f"SELECT * FROM {table_name}"
if condition:
query += f" WHERE {condition}"
cursor.execute(query)
return cursor.fetchall()
def close(self) -> None:
"""Close database connection."""
if self.connection and self.connection.is_connected():
self.connection.close()
self.logger.info("Database connection closed.")# src/nlp/sentiment.py
import nltk
from nltk.tokenize import word_tokenize
from nltk.stem import PorterStemmer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import LabelEncoder
import numpy as np
from typing import List, Tuple
nltk.download("punkt", quiet=True)
nltk.download("stopwords", quiet=True)
class SentimentAnalyzer:
"""
AILS NLP Sentiment Analysis Module.
Supports tokenization, stemming, TF-IDF vectorization, and classification.
Created by Cherry Computer Ltd.
"""
def __init__(self):
self.stemmer = PorterStemmer()
self.vectorizer = TfidfVectorizer(max_features=5000, ngram_range=(1, 2))
self.label_encoder = LabelEncoder()
def preprocess(self, text: str) -> str:
"""Tokenize and stem a text string."""
tokens = word_tokenize(text.lower())
stemmed = [self.stemmer.stem(t) for t in tokens if t.isalpha()]
return " ".join(stemmed)
def fit_transform(self, texts: List[str]) -> np.ndarray:
"""Fit TF-IDF vectorizer and transform texts."""
preprocessed = [self.preprocess(t) for t in texts]
return self.vectorizer.fit_transform(preprocessed).toarray()
def transform(self, texts: List[str]) -> np.ndarray:
"""Transform new texts using fitted vectorizer."""
preprocessed = [self.preprocess(t) for t in texts]
return self.vectorizer.transform(preprocessed).toarray()
def analyze(self, texts: List[str]) -> List[str]:
"""
Simple rule-based sentiment analysis.
Returns 'positive', 'negative', or 'neutral' for each text.
"""
positive_words = {"good", "great", "excellent", "amazing", "love",
"best", "wonderful", "fantastic", "superb", "happy"}
negative_words = {"bad", "terrible", "awful", "poor", "hate",
"worst", "horrible", "disappointing", "broken", "sad"}
results = []
for text in texts:
tokens = set(word_tokenize(text.lower()))
pos = len(tokens & positive_words)
neg = len(tokens & negative_words)
if pos > neg:
results.append("positive")
elif neg > pos:
results.append("negative")
else:
results.append("neutral")
return results# src/models/neural_network.py
import tensorflow as tf
import numpy as np
from typing import List, Tuple, Optional
import logging
class AILSNeuralNetwork:
"""
AILS Core Neural Network — TensorFlow Sequential Model.
Supports binary and multi-class classification.
Created by Cherry Computer Ltd.
"""
def __init__(self, input_dim: int, hidden_units: List[int],
output_dim: int = 1, dropout_rate: float = 0.3):
self.input_dim = input_dim
self.hidden_units = hidden_units
self.output_dim = output_dim
self.dropout_rate = dropout_rate
self.model = None
self.history = None
self.logger = logging.getLogger("AILS.NeuralNetwork")
def build(self) -> tf.keras.Model:
"""Build the Sequential neural network model."""
layers = [
tf.keras.layers.Dense(
self.hidden_units[0], activation="relu",
input_shape=(self.input_dim,),
kernel_regularizer=tf.keras.regularizers.l2(0.001)
),
tf.keras.layers.Dropout(self.dropout_rate),
]
for units in self.hidden_units[1:]:
layers.append(tf.keras.layers.Dense(
units, activation="relu",
kernel_regularizer=tf.keras.regularizers.l2(0.001)
))
layers.append(tf.keras.layers.Dropout(self.dropout_rate))
# Output layer
activation = "sigmoid" if self.output_dim == 1 else "softmax"
layers.append(tf.keras.layers.Dense(self.output_dim, activation=activation))
self.model = tf.keras.Sequential(layers)
return self.model
def compile_model(self, optimizer: str = "adam",
learning_rate: float = 0.001) -> None:
"""Compile the model with optimizer and loss function."""
if self.model is None:
self.build()
opt = tf.keras.optimizers.Adam(learning_rate=learning_rate)
loss = "binary_crossentropy" if self.output_dim == 1 else "sparse_categorical_crossentropy"
self.model.compile(
optimizer=opt, loss=loss,
metrics=["accuracy", tf.keras.metrics.Precision(),
tf.keras.metrics.Recall()]
)
self.logger.info("✅ Model compiled successfully.")
self.model.summary()
def train(self, X_train: np.ndarray, y_train: np.ndarray,
epochs: int = 50, batch_size: int = 32,
validation_split: float = 0.2) -> tf.keras.callbacks.History:
"""Train the model with early stopping and learning rate scheduling."""
callbacks = [
tf.keras.callbacks.EarlyStopping(
monitor="val_loss", patience=5, restore_best_weights=True
),
tf.keras.callbacks.ReduceLROnPlateau(
monitor="val_loss", factor=0.5, patience=3, min_lr=1e-6
),
]
self.history = self.model.fit(
X_train, y_train,
epochs=epochs,
batch_size=batch_size,
validation_split=validation_split,
callbacks=callbacks,
verbose=1
)
return self.history
def evaluate(self, X_test: np.ndarray, y_test: np.ndarray) -> Dict:
"""Evaluate model and return metrics."""
results = self.model.evaluate(X_test, y_test, verbose=0)
metrics = dict(zip(self.model.metrics_names, results))
self.logger.info(f"📊 Evaluation Results: {metrics}")
return metrics
def predict(self, X: np.ndarray) -> np.ndarray:
"""Generate predictions."""
return self.model.predict(X)
def save(self, path: str) -> None:
"""Save model to disk."""
self.model.save(path)
self.logger.info(f"💾 Model saved to {path}")
@classmethod
def load(cls, path: str) -> "AILSNeuralNetwork":
"""Load a saved model."""
instance = cls.__new__(cls)
instance.model = tf.keras.models.load_model(path)
return instance# src/models/rnn_lstm.py
import tensorflow as tf
from typing import Optional
class AILSLSTMModel:
"""
AILS LSTM Model for sequential and time-series data.
Supports RNN, LSTM, and GRU architectures.
Created by Cherry Computer Ltd.
"""
def __init__(self, vocab_size: int, embedding_dim: int = 128,
lstm_units: int = 64, output_dim: int = 1,
model_type: str = "lstm"):
self.vocab_size = vocab_size
self.embedding_dim = embedding_dim
self.lstm_units = lstm_units
self.output_dim = output_dim
self.model_type = model_type.lower()
self.model = self._build()
def _build(self) -> tf.keras.Model:
"""Build LSTM/GRU/RNN model."""
cell_map = {
"lstm": tf.keras.layers.LSTM,
"gru": tf.keras.layers.GRU,
"rnn": tf.keras.layers.SimpleRNN
}
RecurrentLayer = cell_map.get(self.model_type, tf.keras.layers.LSTM)
model = tf.keras.Sequential([
tf.keras.layers.Embedding(self.vocab_size, self.embedding_dim,
mask_zero=True),
tf.keras.layers.Bidirectional(
RecurrentLayer(self.lstm_units, return_sequences=True)
),
tf.keras.layers.Bidirectional(RecurrentLayer(self.lstm_units // 2)),
tf.keras.layers.Dense(64, activation="relu"),
tf.keras.layers.Dropout(0.3),
tf.keras.layers.Dense(
self.output_dim,
activation="sigmoid" if self.output_dim == 1 else "softmax"
)
])
model.compile(
optimizer="adam",
loss="binary_crossentropy" if self.output_dim == 1
else "sparse_categorical_crossentropy",
metrics=["accuracy"]
)
return model# src/models/reinforcement.py
import numpy as np
import random
from collections import deque
from typing import Tuple, List
import tensorflow as tf
class AILSRLAgent:
"""
AILS Reinforcement Learning Agent — Deep Q-Network (DQN).
Supports custom environments (trading, game AI, robotics).
Created by Cherry Computer Ltd.
"""
def __init__(self, state_size: int, action_size: int,
learning_rate: float = 0.001, gamma: float = 0.95,
epsilon: float = 1.0, epsilon_min: float = 0.01,
epsilon_decay: float = 0.995):
self.state_size = state_size
self.action_size = action_size
self.memory = deque(maxlen=2000)
self.gamma = gamma # Discount factor
self.epsilon = epsilon # Exploration rate
self.epsilon_min = epsilon_min
self.epsilon_decay = epsilon_decay
self.learning_rate = learning_rate
self.model = self._build_model()
def _build_model(self) -> tf.keras.Model:
"""Build DQN neural network."""
model = tf.keras.Sequential([
tf.keras.layers.Dense(64, input_dim=self.state_size, activation="relu"),
tf.keras.layers.Dense(64, activation="relu"),
tf.keras.layers.Dense(self.action_size, activation="linear")
])
model.compile(loss="mse",
optimizer=tf.keras.optimizers.Adam(lr=self.learning_rate))
return model
def remember(self, state, action: int, reward: float,
next_state, done: bool) -> None:
"""Store experience in replay memory."""
self.memory.append((state, action, reward, next_state, done))
def act(self, state) -> int:
"""Choose action using epsilon-greedy policy."""
if np.random.rand() <= self.epsilon:
return random.randrange(self.action_size)
act_values = self.model.predict(state, verbose=0)
return np.argmax(act_values[0])
def replay(self, batch_size: int = 32) -> None:
"""Train on a random batch from replay memory."""
if len(self.memory) < batch_size:
return
minibatch = random.sample(self.memory, batch_size)
for state, action, reward, next_state, done in minibatch:
target = reward if done else (
reward + self.gamma * np.amax(
self.model.predict(next_state, verbose=0)[0]
)
)
target_f = self.model.predict(state, verbose=0)
target_f[0][action] = target
self.model.fit(state, target_f, epochs=1, verbose=0)
if self.epsilon > self.epsilon_min:
self.epsilon *= self.epsilon_decay# src/ethics/bias_detector.py
import numpy as np
import pandas as pd
from typing import Dict, List, Tuple
from sklearn.metrics import confusion_matrix
import logging
class AILSBiasDetector:
"""
AILS Ethics Module — Bias Detection and Fairness Analysis.
Implements demographic parity, equalized odds, and disparate impact metrics.
Created by Cherry Computer Ltd.
"""
def __init__(self):
self.logger = logging.getLogger("AILS.Ethics.BiasDetector")
def demographic_parity(self, y_pred: np.ndarray,
sensitive_attr: np.ndarray) -> Dict:
"""
Compute demographic parity: P(Ŷ=1 | A=0) ≈ P(Ŷ=1 | A=1)
A difference > 0.1 indicates potential bias.
"""
groups = np.unique(sensitive_attr)
parity = {}
for g in groups:
mask = sensitive_attr == g
parity[str(g)] = float(np.mean(y_pred[mask]))
diff = max(parity.values()) - min(parity.values())
parity["disparity"] = diff
parity["biased"] = diff > 0.1
self.logger.info(f"Demographic Parity: {parity}")
return parity
def equalized_odds(self, y_true: np.ndarray, y_pred: np.ndarray,
sensitive_attr: np.ndarray) -> Dict:
"""
Compute equalized odds: Equal TPR and FPR across groups.
"""
groups = np.unique(sensitive_attr)
odds = {}
for g in groups:
mask = sensitive_attr == g
yt, yp = y_true[mask], y_pred[mask]
tn, fp, fn, tp = confusion_matrix(yt, yp, labels=[0, 1]).ravel()
tpr = tp / (tp + fn) if (tp + fn) > 0 else 0
fpr = fp / (fp + tn) if (fp + tn) > 0 else 0
odds[str(g)] = {"TPR": round(tpr, 4), "FPR": round(fpr, 4)}
self.logger.info(f"Equalized Odds: {odds}")
return odds
def disparate_impact(self, y_pred: np.ndarray,
sensitive_attr: np.ndarray,
privileged_group: int = 1) -> float:
"""
Compute Disparate Impact Ratio.
A ratio < 0.8 (80% rule) is considered discriminatory.
"""
priv_mask = sensitive_attr == privileged_group
unpriv_mask = ~priv_mask
priv_rate = np.mean(y_pred[priv_mask])
unpriv_rate = np.mean(y_pred[unpriv_mask])
ratio = unpriv_rate / priv_rate if priv_rate > 0 else 0
self.logger.info(
f"Disparate Impact Ratio: {ratio:.4f} "
f"({'⚠️ Discriminatory' if ratio < 0.8 else '✅ Fair'})"
)
return ratio
def generate_fairness_report(self, y_true: np.ndarray,
y_pred: np.ndarray,
sensitive_attr: np.ndarray,
privileged_group: int = 1) -> Dict:
"""Generate a comprehensive fairness report."""
return {
"demographic_parity": self.demographic_parity(y_pred, sensitive_attr),
"equalized_odds": self.equalized_odds(y_true, y_pred, sensitive_attr),
"disparate_impact_ratio": self.disparate_impact(
y_pred, sensitive_attr, privileged_group
),
}# examples/sentiment_analysis_pipeline.py
"""
AILS Full Sentiment Analysis Pipeline
Scrape → Store → Preprocess → Train → Evaluate
Created by Cherry Computer Ltd.
"""
import numpy as np
import mysql.connector
import requests
from bs4 import BeautifulSoup
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
import tensorflow as tf
# ── Step 1: Scrape Product Reviews ──────────────────────────────────────────
def scrape_reviews(url: str) -> list:
response = requests.get(url, headers={"User-Agent": "Mozilla/5.0"})
soup = BeautifulSoup(response.content, "html.parser")
return [r.get_text(strip=True) for r in soup.find_all("p", class_="review")]
# ── Step 2: Store in MySQL ───────────────────────────────────────────────────
def store_reviews(reviews: list, labels: list) -> None:
db = mysql.connector.connect(
host="localhost", user="root", password="", database="AILS_data"
)
cursor = db.cursor()
cursor.execute("""
CREATE TABLE IF NOT EXISTS reviews (
id INT AUTO_INCREMENT PRIMARY KEY,
text TEXT NOT NULL,
sentiment VARCHAR(10)
)
""")
records = [(r, l) for r, l in zip(reviews, labels)]
cursor.executemany("INSERT INTO reviews (text, sentiment) VALUES (%s, %s)", records)
db.commit()
db.close()
print(f"✅ Stored {len(records)} reviews.")
# ── Step 3: Preprocess & Vectorize ──────────────────────────────────────────
def preprocess(reviews: list) -> tuple:
vectorizer = TfidfVectorizer(max_features=5000, ngram_range=(1, 2))
X = vectorizer.fit_transform(reviews).toarray()
return X, vectorizer
# ── Step 4: Build & Train AILS Neural Network ───────────────────────────────
def build_and_train(X: np.ndarray, y: np.ndarray) -> tf.keras.Model:
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42
)
model = tf.keras.Sequential([
tf.keras.layers.Dense(128, activation="relu", input_shape=(X.shape[1],)),
tf.keras.layers.Dropout(0.3),
tf.keras.layers.Dense(64, activation="relu"),
tf.keras.layers.Dropout(0.3),
tf.keras.layers.Dense(1, activation="sigmoid")
])
model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
model.fit(X_train, y_train, epochs=20, batch_size=32,
validation_split=0.2, verbose=1)
loss, acc = model.evaluate(X_test, y_test, verbose=0)
print(f"\n📊 Test Accuracy: {acc:.4f} | Loss: {loss:.4f}")
return model
# ── Step 5: Main Execution ───────────────────────────────────────────────────
if __name__ == "__main__":
# Demo with synthetic data
sample_reviews = [
"This product is amazing! Highly recommend.",
"Terrible quality, broke after one use.",
"Good value for money, works as expected.",
"Worst purchase ever, complete waste of money.",
"Excellent build quality and fast delivery!",
] * 100
labels = np.array([1, 0, 1, 0, 1] * 100)
X, vectorizer = preprocess(sample_reviews)
model = build_and_train(X, labels)
model.save("ails_sentiment_model.h5")
print("🚀 Pipeline complete! Model saved.")AILS powers intelligent systems across multiple sectors:
- Personalized learning paths based on student performance
- Automated content generation and curriculum adaptation
- Real-time knowledge gap identification
- Intelligent tutoring systems
- Medical image diagnosis using Computer Vision CNNs
- Drug discovery through molecular pattern recognition
- Patient outcome prediction and risk stratification
- Clinical trial data analysis
- Real-time stock market analysis and trend prediction
- Fraud detection using anomaly detection models
- Algorithmic trading via Reinforcement Learning agents
- Risk assessment and credit scoring
- Automated news article generation and summarization
- Personalized content recommendations
- Creative writing assistance
- Multi-language content localization
AILS provides comprehensive evaluation tools:
# src/utils/metrics.py
from sklearn.metrics import (
classification_report, confusion_matrix,
precision_score, recall_score, f1_score, roc_auc_score
)
import numpy as np
from typing import Dict
def evaluate_model(y_true: np.ndarray, y_pred: np.ndarray,
y_prob: np.ndarray = None) -> Dict:
"""
Comprehensive AILS model evaluation.
Returns precision, recall, F1-score, AUC-ROC.
Created by Cherry Computer Ltd.
"""
metrics = {
"precision": precision_score(y_true, y_pred, average="weighted"),
"recall": recall_score(y_true, y_pred, average="weighted"),
"f1_score": f1_score(y_true, y_pred, average="weighted"),
"confusion_matrix": confusion_matrix(y_true, y_pred).tolist(),
"classification_report": classification_report(y_true, y_pred)
}
if y_prob is not None:
metrics["auc_roc"] = roc_auc_score(y_true, y_prob)
return metrics| Metric | Description | Target |
|---|---|---|
| Precision | True positives / (True + False positives) | > 0.85 |
| Recall | True positives / (True positives + False negatives) | > 0.80 |
| F1-Score | Harmonic mean of Precision & Recall | > 0.82 |
| AUC-ROC | Area under the ROC curve | > 0.90 |
| Demographic Parity | Fairness across demographic groups | < 0.10 disparity |
| Disparate Impact | 80% rule compliance | ≥ 0.80 ratio |
AILS implements state-of-the-art privacy techniques:
# src/ethics/privacy.py
import numpy as np
from typing import Union
class PrivacyPreserver:
"""
AILS Privacy-Preserving Module.
Implements Differential Privacy, Data Minimization, and Anonymization.
Created by Cherry Computer Ltd.
"""
@staticmethod
def add_differential_privacy(data: np.ndarray,
epsilon: float = 1.0,
sensitivity: float = 1.0) -> np.ndarray:
"""
Add Laplace noise for differential privacy.
Lower epsilon = stronger privacy guarantee.
"""
scale = sensitivity / epsilon
noise = np.random.laplace(0, scale, data.shape)
return data + noise
@staticmethod
def anonymize(data: np.ndarray,
k: int = 5) -> np.ndarray:
"""
k-Anonymity: Generalize data so each record
matches at least k-1 others.
"""
# Simplified k-anonymity via rounding
return np.round(data / k) * k
@staticmethod
def data_minimization(features: list,
essential_features: list) -> list:
"""
Return only essential features (data minimization principle).
Removes all non-required data fields.
"""
return [f for f in features if f in essential_features]| Technique | Description | Use Case |
|---|---|---|
| Differential Privacy | Adds calibrated Laplace noise to data | Dataset release, model training |
| Federated Learning | Train locally, share only weights | Mobile/edge AI |
| Homomorphic Encryption | Compute on encrypted data | Cloud AI services |
| k-Anonymity | Generalize data to match k records | Health & financial data |
| Data Minimization | Collect only necessary data | GDPR compliance |
# src/api.py
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import numpy as np
import tensorflow as tf
import logging
app = FastAPI(
title="AILS API",
description="Artificial Intelligence Learning System REST API — Cherry Computer Ltd.",
version="1.0.0"
)
class PredictionRequest(BaseModel):
text: str
class PredictionResponse(BaseModel):
sentiment: str
confidence: float
# Load model at startup
model = None
@app.on_event("startup")
async def load_model():
global model
try:
model = tf.keras.models.load_model("ails_sentiment_model.h5")
logging.info("✅ AILS model loaded.")
except Exception as e:
logging.warning(f"Model not found: {e}")
@app.get("/")
async def root():
return {
"name": "AILS API",
"version": "1.0.0",
"creator": "Cherry Computer Ltd.",
"status": "operational"
}
@app.post("/predict", response_model=PredictionResponse)
async def predict(request: PredictionRequest):
if model is None:
raise HTTPException(status_code=503, detail="Model not loaded")
# Placeholder vectorization
features = np.random.rand(1, 5000) # Replace with actual vectorizer
probability = float(model.predict(features)[0][0])
sentiment = "positive" if probability >= 0.5 else "negative"
return PredictionResponse(sentiment=sentiment, confidence=round(probability, 4))
@app.get("/health")
async def health():
return {"status": "healthy", "model_loaded": model is not None}# Dockerfile
FROM python:3.11-slim
LABEL maintainer="Cherry Computer Ltd. <contact@cherrycomputer.ltd>"
LABEL description="AILS - Artificial Intelligence Learning System"
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
EXPOSE 8000
CMD ["uvicorn", "src.api:app", "--host", "0.0.0.0", "--port", "8000"]AILS extends into immersive virtual environments:
- 🎭 Intelligent NPCs — AI-driven characters that learn from player interactions
- 🌍 Adaptive World Generation — Dynamic environments shaped by user behavior
- 💬 Real-time NLP Conversation — Natural dialogue in virtual spaces
- 👥 Social Pattern Analysis — Understanding community dynamics in virtual worlds
- 🛒 Personalized Commerce — AI-curated virtual goods and experiences
AILS aligns with cutting-edge AI research:
| Research Area | Description | AILS Implementation |
|---|---|---|
| Foundation Models | Large pre-trained models (GPT, BERT) | Transfer learning integration |
| Multimodal AI | Text + image + audio fusion | Cross-modal learning pipelines |
| Neuromorphic Computing | Brain-inspired AI hardware | Optimized inference layers |
| Quantum ML | Quantum-enhanced algorithms | Experimental quantum module |
| Causal AI | Understanding cause-and-effect | Causal graph integration |
| AutoML | Automated model selection | AILS AutoML scheduler |
| Edge AI | On-device inference | TensorFlow Lite export |
# Run all tests
pytest tests/ -v --cov=src --cov-report=html
# Run specific module tests
pytest tests/test_nlp.py -v
pytest tests/test_models.py -v
pytest tests/test_ethics.py -v
# Run with coverage report
pytest tests/ --cov=src --cov-report=term-missing# tests/test_models.py
import pytest
import numpy as np
from src.models.neural_network import AILSNeuralNetwork
class TestAILSNeuralNetwork:
def setup_method(self):
self.nn = AILSNeuralNetwork(
input_dim=10, hidden_units=[64, 32], output_dim=1
)
self.nn.compile_model()
def test_build(self):
assert self.nn.model is not None
def test_predict_shape(self):
X = np.random.rand(5, 10)
preds = self.nn.predict(X)
assert preds.shape == (5, 1)
def test_train(self):
X = np.random.rand(100, 10)
y = np.random.randint(0, 2, 100)
history = self.nn.train(X, y, epochs=2)
assert "accuracy" in history.historyWe welcome contributions from the community! Cherry Computer Ltd. encourages developers, researchers, and AI enthusiasts to help improve AILS.
- Fork the repository
- Clone your fork:
git clone https://github.com/YOUR_USERNAME/AILS.git - Create a branch:
git checkout -b feature/your-feature-name - Make your changes following our coding standards
- Run tests:
pytest tests/ -v - Commit:
git commit -m "feat: add your feature" - Push:
git push origin feature/your-feature-name - Open a Pull Request to the
mainbranch
- Follow PEP 8 for Python code
- Add docstrings to all functions and classes
- Write unit tests for all new features
- Update documentation as needed
- Ensure ethical compliance — all contributions must respect AILS ethical guidelines
| Area | Status | Help Needed |
|---|---|---|
| NLP Models | 🟢 Active | New language models |
| Computer Vision | 🟡 In Progress | Video analysis support |
| Reinforcement Learning | 🟡 In Progress | More RL environments |
| Ethics Module | 🟢 Active | Additional fairness metrics |
| Documentation | 🟢 Active | Tutorials & guides |
| Testing | 🔴 Needs Help | More test coverage |
| Deployment | 🟡 In Progress | K8s Helm charts |
See CONTRIBUTING.md for detailed guidelines.
MIT License
Copyright (c) 2024 Cherry Computer Ltd.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
Cherry Computer Ltd.
Creators & Maintainers of AILS
| Channel | Link |
|---|---|
| 🐙 GitHub | @CherryComputerLtd |
| contact@cherrycomputer.ltd | |
| 🐦 Twitter/X | @CherryComputerLtd |
| Cherry Computer Ltd. |
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