Lesson 1.1: What is AI?

1. Lesson Overview

In this opening lesson, learners will:

Define Artificial Intelligence (AI) and trace its brief history
Understand core AI paradigms (e.g., rule-based, machine learning, deep learning)
Explore how AI mimics facets of human intelligence
Reflect on real-world touchpoints of AI in daily life
Consider opportunities for AI to transform their own business context

By the end of this lesson, participants will be able to:

LO1: Articulate a clear definition of AI and distinguish between its main subfields.
LO2: Explain key techniques—such as pattern recognition, decision-making, and natural language processing—that mirror human cognitive abilities.
LO3: Identify at least three AI applications they encounter in everyday life.
LO4: Hypothesize one or two ways AI could impact or innovate within their business sector.

Term	Definition
Artificial Intelligence	The field of computer science focused on creating systems capable of tasks that require human-like intelligence.
Machine Learning (ML)	A subset of AI where algorithms learn patterns from data to make predictions or decisions.
Deep Learning (DL)	A branch of ML using multi-layered neural networks to model complex patterns.
Natural Language Processing (NLP)	Techniques enabling computers to understand, interpret, and generate human language.
Computer Vision	Methods that allow machines to ‘see’ and interpret visual information from the world.

1950s – 1970s: Foundational ideas (Turing Test, symbolic AI, expert systems)
1980s – 1990s: Rise and fall of “AI winters,” renewed interest with statistical approaches
2000s – Today: Explosion of data and compute power fueling deep learning breakthroughs

Perception: Computer vision systems identify objects much like humans recognize shapes.
Reasoning: Rule-based and probabilistic models emulate logical decision pathways.
Learning: Neural networks adjust internal weights similarly to how human brains form connections.
Language: NLP models comprehend and generate text, paralleling our understanding of language.

Voice Assistants (e.g., Siri, Alexa): Speech recognition + NLP to understand commands
Recommendation Engines (e.g., Netflix, Amazon): ML models predict user preferences
Fraud Detection (FinTech): Anomaly detection algorithms flag unusual transactions
Customer Service Chatbots: Automated responders handling FAQs and routing complex queries

Task: Over the next 24 hours, note down every interaction you have with an AI system (apps, websites, devices).
Deliverable: A short list (3–5) with brief descriptions of each touchpoint.

Prompt: “Which AI interaction surprised you the most, and why?”
Format: Break into small groups (3–4 learners), share findings, then report one key insight back to the whole class.

Individual Reflection (Written):
- “In what ways does AI already ease tasks in my personal life?”
- “What repetitive or data-driven process in my business could AI streamline?”
Pair & Share:
- Exchange reflections with a partner and brainstorm one pilot AI project idea.
Homework Assignment:
- Research a case study of AI implementation in your industry. Prepare a one-page summary covering:
  - The business challenge
  - AI solution deployed
  - Measurable outcomes (e.g., cost savings, efficiency gains)

Quiz (10 mins):
- Five multiple-choice questions on definitions and paradigms.
- Two short-answer questions on AI examples and their business relevance.
Submission:
- Upload your AI Scavenger Hunt list and industry case study summary to the course portal by next session.

Readings:
- Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Chapters 1 & 2.
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. Introduction.
Videos:
- “What is AI?” (3 min explainer) – [YouTube]
- “From ML to DL: The Evolution of AI” (8 min) – [Coursera snippet]
Tools to Explore:
- Google’s Teachable Machine (rapid prototyping of ML models)
- IBM Watson’s demo playground for NLP and computer vision