Core Exam Guide

Artificial Intelligence Policy

Author

Prof. Jack Reilly

Published

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.1
  • 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?

Footnotes

  1. I’ll try to decipher everything, but ultimately, if I can’t read it, it can’t earn points.↩︎