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. 

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15 comments, sorted by Click to highlight new comments since: Today at 3:04 PM

To what extent, if any, will this course acknowledge that some people disagree very vigorously with what I take to be the positions you're generally advocating for?

(I ask not because I think those people are right and you're wrong -- I think those people are often wrong and sometimes very silly indeed and expect I would favour your position over theirs at least 80% of the time -- but because I think it's important that your students be able to distinguish "this is uncontroversial fact about which basically no one disagrees" from "this is something I am very confident of, but if you talked to some of the other faculty they might think I'm as crazy as I think they are" from "this is my best guess and I am not terribly sure it's right", and the fact that pretty much all the required reading is from an LW-ish EA-ish perspective makes me wonder whether you're making those distinctions clearly. My apologies in advance if I turn out to be being too uncharitable, which I may well be.)

In addition to acknowledging uncertainty, I think the proper way to address this is to 'teach the controversy.' Have some articles and tweets by Yann LeCun peppered throughout, for example. Also that Nature article: "Stop Worrying About AI Doomsday." Etc.

I'm not sure how much space to give the more unreasonable criticisms like the ones you point out. My call would be that the most high quality considerations in all directions should be prioritized over critics being influential or figures of authority - although of course that these voices exist deserves mention, although it might illustrate less the factual dimension than the social one. 

I agree those criticisms are pretty unreasonable. However I think they are representative of the discourse -- e.g. Yann LeCun is a very important and influential person, and also an AI expert, so he's not cherry-picked. 

Also see this recent review from someone who seems thoughtful and respected: Notes on Existential Risk from Artificial Superintelligence ( who says 

I will say this: those pieces all make a case for extraordinary risks from AI (albeit in different ways); I am somewhat surprised that I have not been able to find a work of similar intellectual depth arguing that the risks posed by ASI are mostly of "ordinary" types which humanity knows how to deal with. This is often asserted as "obviously" true, and given a brief treatment; unfortunately-often the rebuttal is mere proof by ridicule, or by lack-of-imagination (often people whose main motivation appears to be that people they don't like are worried about ASI xrisk). It's perhaps not so surprising: "the sky is not falling" is not an obvious target for a serious book-length treatment. Still, I hope someone insightful and imaginative will fill the gap. Three brief-but-stimulating shorter treatments are: Anthony Zador and Yann LeCun, Don't Fear the Terminator (2019); Katja Grace, Counterarguments to the basic AI x-risk case (2022); and: David Krueger, A list of good heuristics that the case for AI x-risk fails (2019).↩︎

i.e. he thinks there just isn't much actually good criticism out there, to the point where he thinks LeCun is one of the top three!!!! (And note that the other two aren't exactly harsh critics, they are kinda AI safety people playing devil's advocate...)

Completely agreed on the state of the discourse. I think the more interesting discussions start once you acknowledge at least the vague general possibility of serious risk (see e.g. the recent debate posts on the EA forum). I still think these are wrong, but at least worth engaging with.

If I was giving a course, I just wouldn't really know what to do with actively bad opinions beyond "this person says XYZ" and maybe having the students reason about it as an exercise. But if you do this too much it feels like gloating.

Honestly I think the strongest criticism will come from someone arguing that there's not enough leverage in our world for superintelligence to be much more powerful than us, for good or bad. People who argue that ASI is absolutely necessary because it will make us immortal and colonise the stars but doesn't warrant any worry about the possibility it may direct its vast power to less desirable goals are just unserious though. Also obviously the possibility that AGI may actually be still far off, but that doesn't say much about whether it's dangerous, just whether the danger is imminent.

oh, great, I'm glad someone is doing this! Will you collect some data about how your students respond, and write up what you feel worked well or badly? Are you aware of any existing syllabi that you took inspiration from? It'd be great if people doing this sort of thing could learn from one another!

This is very much not what I (or I think anyone) would expect to be in a course with the very general-sounding title "Philosophy and the challenge of the future". Is it the case that anyone choosing whether to study this will first look at the syllabus (or maybe some other document that gives a shorter summary of what's going to be in the course) and therefore not be at risk of being misled? If not, you might consider a more informative title, or maybe a subtitle. "Philosophy and the challenge of artificial intelligence". "Philosophy and the challenge of the future: hard thinking about AI". "Opportunities and threats of artificial intelligence: a philosophical perspective". Or something.

Course titles are fixed so I didn't choose that, but because it's a non-intro course it's up to the instructor to decide the course's focus. And yes, the students had seen the description before selecting it.

Huh. So is there a course every year titled "Philosophy and the challenge of the future", with radically different content each time depending on the particular interests of whoever's lecturing that year?

This doesn't appear to be too unusual, almost every department I've been in has such "topics" courses in certain areas. One point is that the lecturer can present their specific knowledge or current developments.

Yep, I think my university called these "special topics" or "selected topics" papers sometimes. As in, a paper called "Special Topics In X" would just be "we got three really good researchers who happen to study different areas of X, we asked them each to spend one-third of the year teaching you about their favourite research, and then we test you on those three areas at the end of the year". Downside is that you don't necessarily get the optimal three topics that you wanted to learn about, upside is you get to learn from great researchers.

Yep that's how it was in my program at UNC

Really cool stuff, I'll be interested to hear how it goes! I know some people who taught a similar course at UNC in previous years on the same topics, if you like I can put you in touch and you can compare notes!

IMO you should lean more in the direction of having less EA content and more technical AI alignment and forecasting and governance content.

E.g. Ajeya's training game report, Ajeya's timelines model + the Davidson-Epoch extension.