Artificial Intelligence (AI) Outline

I. Introduction to Artificial Intelligence

A. What is Artificial Intelligence?

• Definition of AI
• Narrow AI vs. General AI
• Examples students already use:
  • Siri / Alexa
  • Streaming recommendations (Netflix/YouTube)
  • Social media feeds (TikTok/Instagram)
  • Search engines (Google)
  • Self-driving or driver-assist features

B. Brief History of AI

• 1950s: Alan Turing & the Turing Test
• 1997: IBM Deep Blue beats a chess champion
• 2010s: Rapid growth of machine learning
• Modern AI: Chatbots, image generation, recommendation systems

C. Why AI Matters

• Career opportunities
• Business impact
• Ethical concerns
• Cybersecurity and national security implications

II. How AI Works (Foundations)

A. Algorithms

• What is an algorithm?
• Decision trees
• Rule-based systems

B. Data

• Why data is important
• Structured vs. unstructured data
• The idea of “Big Data”

C. Machine Learning Basics

• What is machine learning?
• Training vs. testing data
• Supervised learning
• Unsupervised learning

III. Types of AI

A. Narrow AI (Weak AI)

• Designed for one task
• Examples:
  • Spam filters
  • Face recognition
  • Recommendation systems

B. General AI (Theoretical)

• Human-level intelligence across many tasks
• Why it doesn’t exist yet (still a research goal)

C. Reactive Machines vs. Learning Systems

• Reactive: responds without “learning”
• Learning systems: improve over time using data

IV. Machine Learning Deep Dive

A. Supervised Learning

• Classification (choose a label/category)
• Regression (predict a number)
• Example: Email spam detection

B. Unsupervised Learning

• Clustering
• Pattern discovery

C. Reinforcement Learning

• Learning through rewards and penalties
• Used in game-playing AI and robotics

V. Neural Networks (Intro Level)

A. What is a Neural Network?

• Inspired by the human brain
• Inputs → Hidden Layers → Outputs

B. Basic Vocabulary

• Neurons
• Weights
• Bias
• Activation function

C. Real-World Applications

• Image recognition
• Voice recognition
• Autonomous vehicles

VI. AI in Everyday Life

VII. AI and Ethics

A. Bias in AI

• Data bias
• Algorithmic bias

B. Privacy Concerns

• Data collection
• Facial recognition issues

C. Job Automation

• Jobs replaced
• Jobs created

D. Deepfakes & Misinformation

• AI-generated videos/images
• Trust and verification

VIII. AI and Cybersecurity

A. AI as a Defensive Tool

• Threat detection
• Malware analysis
• Network monitoring

B. AI as an Offensive Tool

• Automated phishing
• Deepfake scams
• Social engineering at scale

IX. Careers in AI

A. Common Roles

• AI Engineer
• Data Scientist
• Machine Learning Engineer
• AI Ethics Researcher
• Cybersecurity Analyst (AI tools)

B. Education Pathways

• Computer Science
• Mathematics
• Statistics
• Engineering

X. Hands-On Projects (High School Friendly)

Beginner

• Create a simple rule-based chatbot
• AI decision tree using if/else
• Train a basic model using Teachable Machine

Intermediate

• Python + scikit-learn: simple classifier
• AI image classifier
• Build a spam detector

Advanced

• Neural network with TensorFlow
• Recommendation engine (simple)
• AI cybersecurity simulation

XI. AI Tools Students Can Explore

• Teachable Machine
• Scratch AI extensions
• Python + scikit-learn
• ChatGPT (responsible use)
• Google Colab
• Kaggle datasets

XII. Future of AI

• Artificial General Intelligence (AGI)
• AI in medicine
• AI in education
• AI regulation and policy
• Human + AI collaboration

XIII. Assessment Ideas

• AI ethics debate
• Build-a-bot challenge
• Predict-the-output ML activity
• Research project on AI careers
• AI in cybersecurity case study

Key Learning Objectives

By the end of this unit/course, students should be able to:

1. Define artificial intelligence.
2. Explain basic machine learning concepts.
3. Identify real-world AI applications.
4. Analyze ethical concerns.
5. Build a simple AI-based project.