I designed an AI safety course (for a philosophy department)
In the fall of 2023, I’m teaching a course called “Philosophy and The Challenge of the Future” 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.
INTRO TO AI
Week 1 (8/28-9/1): The foundations of Artificial Intelligence (AI)
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)
You Look Like a Thing and I Love You, Chapters 1, 2, and 3, Shane.
But what is a neural network? (video)
ML Glossary (optional but helpful for terminological references)
Week 3 (9/11-16): What can current AI models do?
Artificial Intelligence, A Modern Approach, pp. 27-34, Russell & Norvig.
ChatGPT Explained (video)
What is Stable Diffusion? (video)
AI AND THE FUTURE OF HUMANITY
Week 4 (9/18-22): What are the stakes?
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?
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
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
Cooperative AI: machines must learn to find common ground, Dafoe et. al.
THE BASICS OF AI SAFETY
Week 8 (10/16-20): Value learning and goal-directed behavior
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
The Superintelligent Will: Motivation and Instrumental Rationality in Advanced Artificial Agents, Bostrom.
General Purpose Intelligence: Arguing The Orthogonality Thesis, Armstrong.
METAPHYSICAL & EPISTEMOLOGICAL CONSIDERATIONS
Week 10 (10/30-11/4): Thinking about the Singularity
The Singularity: A Philosophical Analysis, Chalmers.
Can Intelligence Explode?, Hutter.
Week 11 (11/6-11): AI and Consciousness
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?
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?
What We Owe The Future, Chapter 1, MacAskill.
The Future of Humanity, Bostrom.
WHAT CAN WE DO NOW
Week 14 (11/27-12/1): Technical AI Alignment
Concrete Problems in AI Safety, Amodei et al.
Week 15 (12/4-8): AI governance and regulation
AI Governance, A research agenda, Dafoe.
AI Strategy, Policy, and Governance (optional but helpful video).
Feedback is welcome! Especially if you have readings in mind that you can imagine your 19-year-old self being excited about.
It’s Phil 122, at Queens College, CUNY.