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In the fall of 2023, I'm teaching a course called "Philosophy and The Challenge of the Future"[1] which is focused on AI risk and safety. I designed the syllabus keeping in mind that my students:

  • will have no prior exposure to what AI is or how it works
  • will not necessarily have a strong philosophy background (the course is offered by the Philosophy department, but is open to everyone)
  • will not necessarily be familiar with Effective Altruism at all


My approach combines three perspectives: 1) philosophy, 2) AI safety, and 3) Science, Technology, Society (STS); this combination reflects my training in these fields and attempts to create an alternative introduction to AI safety (that doesn't just copy the AISF curriculum). That said, I plan to recommend the AISF course towards the end of the semester; since my students are majoring in all sorts of different things, from CS to psychology, it'd be great if some of them considered AI safety research as their career path. 

Course Overview 


Week 1 (8/28-9/1): The foundations of Artificial Intelligence (AI)

Required Readings: 

  • Artificial Intelligence, A Modern Approach, pp. 1-27, Russell & Norvig. 
  • Superintelligence, pp. 1-16, Bostrom. 

Week 2 (9/5-8): AI, Machine Learning (ML), and Deep Learning (DL)

Required Readings: 

Week 3 (9/11-16): What can current AI models do? 

Required Readings: 


Week 4 (9/18-22): What are the stakes? 

Required Readings: 

  • The Precipice, pp. 15-21, Ord. 
  • Existential risk and human extinction: An intellectual history, Moynihan.  
  • Everything might change forever this century (video) 

Week 5 (9/25-29): What are the risks? 

Required Readings: 

  • Taxonomy of Risks posed by Language Models, Weidinger et al. 
  • Human Compatible, pp. 140-152, Russell. 
  • Loss of Control: “Normal Accidents and AI Systems”, Chan. 

Week 6 (10/2-6): From Intelligence to Superintelligence 

Required Readings: 

  • A Collection of Definitions of Intelligence, Legg & Hutter. 
  • Artificial Intelligence as a positive and negative factor in global risk, Yudkowsky. 
  • Paths to Superintelligence, Bostrom.

Week 7 (10/10-13): Human-Machine interaction and cooperation 

Required Readings: 


Week 8 (10/16-20): Value learning and goal-directed behavior  

Required Readings: 

  • Machines Learning Values, Petersen.
  • The Basic AI Drives, Omuhundro.  
  • The Value Learning Problem, Soares. 

Week 9 (10/23-27): Instrumental rationality and the orthogonality thesis  

Required Readings: 

  • The Superintelligent Will: Motivation and Instrumental Rationality in Advanced Artificial Agents, Bostrom.  
  • General Purpose Intelligence: Arguing The Orthogonality Thesis, Armstrong. 


Week 10 (10/30-11/4): Thinking about the Singularity

Required Readings: 

  • The Singularity: A Philosophical Analysis, Chalmers.
  • Can Intelligence Explode?, Hutter. 

Week 11 (11/6-11): AI and Consciousness 

Required Readings: 

  • Could a Large Language Model be Conscious?, Chalmers. 
  • Will AI Achieve Consciousness? Wrong Question, Dennett. 


Week 12 (11/13-17): What are the moral challenges of high-risk technologies?   

Required Readings: 

  • Human Compatible, “Misuses of AI”, Russell.
  • The Ethics of Invention, “Risk and Responsibility”, Jasanoff. 

Week 13 (11/20-22): Do we owe anything to the future? 

Required Readings: 


Week 14 (11/27-12/1): Technical AI Alignment 

Required Readings: 

Week 15 (12/4-8): AI governance and regulation 

Required Readings: 


Feedback is welcome! Especially if you have readings in mind that you can imagine your 19-year-old self being excited about. 

  1. ^

    It's Phil 122, at Queens College, CUNY. 

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I think Thorstad's "Against the singularity hypothesis" might complement the week 10 readings.

I'd also potentially include the latest version of Carlsmiths chapter on Power-seeking AI.

This seems great!

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