Here, you can find the syllabus we’ll use throughout the policy fellowship. It contains all the papers we’ll read together, as well as several others, marked “further readings,” which we’ve provided for participants to (optionally) read independently if they’re especially interested in a particular topic.

Week 1: What is AI? How does it work? Where are we now?

1. VIDEO: Large Language Models explained briefly (3Blue1Brown)
2. Large Language Models Explained (Part 2)
3. VIDEO: But what is a neural network? (3Blue1Brown, 2017)
4. Benchmarking, where things are now
5. (SKIM) A guide to the AI tribes - by Michel Justen

Further readings

How AI works

• Large Language Models Explained (Part 1)
• How Does AI Learn? A Beginner’s Guide with Examples (Adam Jones)

Week 2: Will we ever reach superintelligence? How long will it take?

1. Why do people disagree about when powerful AI will arrive?
2. AI As Profoundly Abnormal Technology - by Scott Alexander (Parts 1 and 2)
3. METR time horizon
4. Situational Awareness: From AGI to superintelligence

Further readings

AI timelines

• Why we might expect weird things to happen sometime soon: This Can’t Go On
• Open questions: Unresolved debates about the future of AI by Helen Toner (until “#3: Will future AIs still basically be tools, or something else?”)