Core Exam Guide
 Prof. Jack Reilly
 S2026

Overall Guidelines
•	The exam will be in class, paper and pen (no pencils, please - they smudge!). No computers will be used or needed. Write legibly.
•	The exam will have two sections - IDs and short answer.
–	An “ID”, or identification, question, simply gives you a term and asks you to define it and identify its relevance for the study of AI and AI Policy.
–	A short answer is like a short essay - nothing terribly long, but should be structured, potentially with short paragraphs. You could also be given a news article or data graphic about AI and be asked to interpret and situate it, saying why it is important to the broader discussion of AI.
•	I do not imagine the exam will take the entire class period, but you will have the entire class period to complete it.
Definitions and Key Concepts
Philosophy of AI
1.	Foundations
–	Intelligence
–	Consciousness
–	Sentience
–	“Common Sense”
–	Autonomous Systems
–	Simulation
–	Emergence
–	Game of Life
2.	AI Design & Assessment
–	Strong AI
–	Weak AI
–	Turing Test
–	Chinese Room Argument
–	Embodiment
–	Agency
–	Responsibility
–	Role of Language in Thought & Intelligence
Technology of AI
1.	Hardware
–	Turing Machine
–	Basic parts of a computer (processor, memory, etc)
–	Serial vs. Parallel Processing
–	CPUs vs GPUs (and other, more specialized processing)
–	Moore’s Law
–	Silicon - design vs. fabrication
–	“Die shrinks”
–	Chip fabrication requirements
2.	Software
–	Low vs. High Level programming languages
–	Modeling
–	Machine Learning
–	Large Language Models
–	Tokens
–	Transformers
–	Omitted Data/Algorithmic Bias
–	Out of Sample Predictions/Long-Tail Problems
–	Overfitting
Business of AI
1.	Building AI Systems
–	Infrastructure
–	Bubbles
–	Products
–	B2B vs Consumer
–	Disruption
–	Moats
–	Startup tradeoffs: Profit vs Market Share
–	Revenue streams
–	AI product possibilities
–	Market positions of current major tech & AI companies
–	“Hyperscalers”
2.	Implications
–	Data Access
–	Energy

–	Labor Replacement
–	Materials & supply chains
Example Short Answer
•	Given what we know about AI, how would you advise a firm looking to adopt an AI system to support its employees? What kinds of concerns should the firm have, and how might it ameliorate them?
•	What is the difference between “Weak AI” and “Strong AI”?
•	What is the Turing Test, and is it a sufficient “test” of AI?
•	What would be an example of algorithmic bias in the world, why might it have arisen, and what is a proposal for managing it?