This opening lesson traces the arc of artificial intelligence from its origins in the 1950s through to today's generative AI revolution. No technical background in machine learning is assumed. The goal is to build a shared conceptual vocabulary and historical understanding that will ground every subsequent week of this course.
Learning Objectives
By the end of this session, you will be able to:
Trace AI's Historical Development
Follow the evolution from Turing's foundational questions through expert systems, neural networks, deep learning, and ultimately to modern generative AI. Understand the key breakthroughs, setbacks (including the "AI winters"), and turning points that brought us to today.
Explain How Generative AI Works
Describe in plain language—without technical jargon—how systems like ChatGPT, DALL-E, Midjourney, and similar tools generate their outputs. Understand the fundamental concepts of training, patterns, and prediction that make these systems possible.
Distinguish Different Types of AI
Differentiate between rule-based systems, discriminative AI (classification/regression), and generative AI using everyday analogies. Understand when each approach is appropriate and what their respective strengths and limitations are.
Connect AI to Your Research
Identify which AI paradigms are most relevant to different tasks in your own research field. Recognize opportunities where generative AI might assist your work, while understanding its current limitations and appropriate use cases.