Artificial Intelligence (AI)

Try it Yourself — AI vs Human: Simple Pattern Detection

Python (simulating AI logic with rules)

# Simulating a simple AI that detects sentiment from keywords
def detect_sentiment(text):
    positive_words = ["good", "great", "happy", "excellent", "amazing"]
    negative_words = ["bad", "terrible", "sad", "poor", "awful"]

    text_lower = text.lower()
    score = 0

    for word in positive_words:
        if word in text_lower:
            score += 1
    for word in negative_words:
        if word in text_lower:
            score -= 1

    if score > 0:
        return "Positive sentiment"
    elif score < 0:
        return "Negative sentiment"
    else:
        return "Neutral sentiment"

# Test the AI
print(detect_sentiment("This course is great and amazing!"))  # Positive
print(detect_sentiment("The service was terrible and poor."))  # Negative
print(detect_sentiment("The meeting is at 3pm."))             # Neutral

Try it Yourself — Simple Machine Learning Classifier (Python)

# A simple ML classifier using scikit-learn
# Run this in Google Colab or locally with: pip install scikit-learn

from sklearn import tree

# Features: [weight_grams, texture_smooth(0=rough,1=smooth)]
# 0 = orange, 1 = apple
features = [
    [150, 0],  # orange
    [170, 0],  # orange
    [180, 1],  # apple
    [200, 1],  # apple
    [140, 0],  # orange
    [190, 1],  # apple
]

# Labels: 0 = orange, 1 = apple
labels = [0, 0, 1, 1, 0, 1]

# Train the decision tree classifier
classifier = tree.DecisionTreeClassifier()
classifier = classifier.fit(features, labels)

# Predict a new fruit: 160g, smooth texture (should be apple)
result = classifier.predict([[160, 1]])
fruit = "Apple" if result[0] == 1 else "Orange"
print(f"The AI predicts: {fruit}")
# Output: The AI predicts: Apple

Try it Yourself — Neural Network in Python (Keras/TensorFlow)

# A minimal neural network using TensorFlow (Keras)
# Install: pip install tensorflow

import tensorflow as tf
import numpy as np

# Simple dataset: learn the XOR logic gate
# Input: [0,0], [0,1], [1,0], [1,1]
# Output: 0, 1, 1, 0
inputs = np.array([[0,0], [0,1], [1,0], [1,1]], dtype=float)
outputs = np.array([[0], [1], [1], [0]], dtype=float)

# Build the neural network
model = tf.keras.Sequential([
    tf.keras.layers.Dense(4, activation='relu', input_shape=(2,)),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

# Compile the model
model.compile(optimizer='adam', loss='mse')

# Train the model
print("Training the neural network...")
model.fit(inputs, outputs, epochs=500, verbose=0)
print("Training complete!")

# Test predictions
predictions = model.predict(inputs, verbose=0)
for i, inp in enumerate(inputs):
    print(f"Input: {inp} => Predicted: {predictions[i][0]:.4f} (expected: {outputs[i][0]})")

Try it Yourself — Calling an AI API (Python with OpenAI)

# Using OpenAI's API to generate text (requires API key)
# Install: pip install openai

import openai

# Set your API key (get one from platform.openai.com)
# openai.api_key = "your-api-key-here"

# Simulated response (works without a real API key)
def chat_with_ai(prompt):
    # This simulates what the AI would return
    # In reality, you would call: openai.chat.completions.create()
    responses = {
        "What is AI?": "AI stands for Artificial Intelligence — the ability of machines to perform tasks that normally require human intelligence.",
        "Explain machine learning": "Machine Learning is a subset of AI where computers learn patterns from data without being explicitly programmed.",
        "default": "I'm an AI assistant trained to help with questions about technology and learning."
    }
    return responses.get(prompt, responses["default"])

# Test the simulated AI
questions = [
    "What is AI?",
    "Explain machine learning",
    "What is the capital of South Sudan?"
]

for q in questions:
    answer = chat_with_ai(q)
    print(f"Q: {q}")
    print(f"A: {answer}")
    print()

# Output:
# Q: What is AI?
# A: AI stands for Artificial Intelligence...
# Q: Explain machine learning
# A: Machine Learning is a subset of AI...
# Q: What is the capital of South Sudan?
# A: I'm an AI assistant trained to help...

Try it Yourself — Detecting Bias in a Dataset

Python

# Simulating a bias check on a hiring dataset
# Check if the dataset is balanced across genders

def check_dataset_bias(data):
    total = len(data)
    counts = {}
    
    for item in data:
        category = item.get('gender', 'unknown')
        counts[category] = counts.get(category, 0) + 1
    
    print("Dataset Distribution:")
    for category, count in counts.items():
        percentage = (count / total) * 100
        print(f"  {category}: {count} ({percentage:.1f}%)")
    
    # Check if any group is underrepresented (less than 30%)
    for category, count in counts.items():
        percentage = (count / total) * 100
        if percentage < 30 and percentage > 0:
            print(f"\n⚠️ Warning: {category} is only {percentage:.1f}% of the dataset.")
            print("  The AI model may perform poorly for this group!")
    
    print("\n✅ Bias check complete.")

# Example hiring dataset
hiring_data = [
    {"name": "Applicant A", "gender": "male", "hired": True},
    {"name": "Applicant B", "gender": "male", "hired": True},
    {"name": "Applicant C", "gender": "male", "hired": False},
    {"name": "Applicant D", "gender": "female", "hired": True},
    {"name": "Applicant E", "gender": "female", "hired": False},
    {"name": "Applicant F", "gender": "male", "hired": True},
    {"name": "Applicant G", "gender": "male", "hired": True},
    {"name": "Applicant H", "gender": "male", "hired": False},
    {"name": "Applicant I", "gender": "male", "hired": True},
    {"name": "Applicant J", "gender": "female", "hired": False},
]

check_dataset_bias(hiring_data)
# Output will show gender distribution and flag any imbalance