This curriculum, a follow-up to the Alignment Fundamentals curriculum (the ‘101’ to this 201 curriculum), aims to give participants enough knowledge about alignment to understand the frontier of current research discussions. It assumes that participants have read through the Alignment Fundamentals curriculum, taken a course on deep learning, and taken a course on reinforcement learning (or have an equivalent level of knowledge).
Although these are the basic prerequisites, we expect that most people who intend to work on alignment should only read through the full curriculum after they have significantly more ML experience than listed above, since upskilling via their own ML engineering or research projects should generally be a higher priority for early-career alignment researchers.
When reading this curriculum, it’s worth remembering that the field of alignment aims to shape the goals of systems that don’t yet exist; and so alignment research is often more speculative than research in other fields. You shouldn’t assume that there’s a consensus about the usefulness of any given research direction; instead, it’s often worth developing your own views about whether techniques discussed in this curriculum might plausibly scale up to help align AGI.
The curriculum was compiled, and is maintained, by Richard Ngo. For now, it’s primarily intended to be read independently; once we’ve run a small pilot program, we’ll likely extend it to a discussion-based course.
Curriculum overview
Week 1: Further understanding the problem
Week 2: Decomposing tasks for better supervision
Week 3: Preventing misgeneralization
Week 4: Interpretability
Week 5: Reasoning about Reasoning
Weeks 6 & 7 (Track 1): Eliciting Latent Knowledge
Weeks 6 & 7 (Track 2): Agent Foundations
Weeks 6 & 7 (Track 3): Science of Deep Learning
Weeks 8 & 9: Literature Review or Project Proposal
See the full curriculum here. Note that the curriculum is still under revision, and feedback is very welcome!
I don't think anyone is aiming for provable alignment properties (except maybe for Stuart Russell); this just seems too hard.
But if AGIs could develop a very sophisticated understanding of other domains that humans don't understand very well, by virtue of being more intelligent than humans, I don't see why they wouldn't be able to understand this domain very well too.
This is how classic ML would do it. But in the modern paradigm, ML systems can infer all sorts of information from being trained on a very wide range of data (e.g. all the books, all the internet, etc), and so we should expect that they can infer human values from that too. There's some preliminary evidence that language models can perform well on common-sense moral reasoning, and alignment researchers generally expect that future language models will be capable of answering questions about ethics to a superhuman level "by default".
More generally, it sounds like you're gesturing towards the difference between "narrow alignment" and "ambitious alignment", as discussed in this blog post. Broadly speaking, the goal of the former is basically to have AI that can be controlled; the goal of the latter is to have AI that could be trusted steer the world. One reason that most researchers focus on the former is because if we could narrowly align AI, we could then use it to help us with the more complex task of ambitious alignment. And the properties required for an AI to be narrowly aligned (like "helpful", "honest", etc) are sufficiently common-sense that I don't think we gain much from a very in-depth study of them.