6-week salary to contribute to foundational resources in the nascent field of "Singular Learning Theory x AI Alignment"
Produce a literature review on Singular Learning Theory, as a foundational resource to help orient newcomers to the field.
Salary for Matthew Farrugia-Roberts during the 6 week period (annualized $91,260/year).
A detailed survey of the literature has already been completed by Matthew as part of his MS thesis, but has yet to be written up. This substantial preparation will enable the work to be completed relatively quickly.
Matthew has published a joint first-author theoretical ML paper in ICML, a top-teir venue, and completed an MS thesis at the University of Melbourne with a mark of 95+, reserved for students 'typically encountered only a handful of times throughout an academic career'.
Singular Learning Theory provides a potential path to better understanding ML systems. Although better understanding of systems can be helpful for safety, it could also lead to insights improving the efficiency of ML training procedures potentially enabling more powerful systems to be trained sooner without a corresponding improvement in alignment. This risk holds for science of deep learning and interpretability methods in general; on balance, the benefits seem to outweigh the risks, but it is important to at least remain aware of the downside.
Singular Learning Theory is a speculative research direction. Foundational resources will enable more people to on-board to it. However, there's a possibility it's a dead-end and these people would have been better spending their time elsewhere. On balance, it seems worth exploring Singular Learning Theory and enabling newcomers to more rapidly on-board should decrease the overall cost to exploring this direction if resources are allocated efficiently.
No other funding during this period. Matthew was previously receiving an RA salary for a previous project, and will receive an RA salary for a new project after completion of this six week project.
Thanks for the writeup, Adam! I like that the grant rationale is understandable even for myself (with little background in the field of alignment), and that you've pulled out comparison points for this salary ask.
I generally would advocate for independently conducted research to receive lower compensation than at alignment organizations, as I usually expect people to be significantly more productive in an organization where they can receive mentorship (and many of these organizations are at least partially funding constrained).
I share the instinct that "working as an independent researcher is worse than in an org/team", but hadn't connected that to "and thus funders should set higher salaries for at orgs", so thanks for mentioning.
Tangent: I hope one side effect of our public grant process is that "how much salary should I ask for in my application" becomes easier for grantees. (I would love to establish something like Levels.fyi for alignment work.)
There's been an explosion of interest in Singular Learning Theory lately in the alignment community, and good introductory resources could save people a lot of time. A scholarly literature review also has the benefit of making this area more accessible to the ML research community more broadly. Matthew seems well placed to conduct this, having already familiarized himself with the field during his MS thesis and collected a database of papers. He also has extensive teaching experience and experience writing publications aimed at the ML research community.
I'm unsure how useful Singular Learning Theory is going to be for alignment. I'm most unsure whether it'll actually deliver on the promise of better understanding deep networks. The positive case is that traditional statistical learning theory has some serious limitations, making predictions that contradict empirical results on deep networks, so we need some replacement. But grandiose theories pop up now and again (the neural tangent kernel was hot last year, for example) yet rarely pan out. Singular learning theory has been around for several decades, so that it only recently gained popularity in ML should also give some pause for thought. It seems plausible enough and enough people are excited by it what I'm willing to give it a shot for a relatively small grant like this, but this grant is definitely not me endorsing singular learning theory -- I'd need to understand it a lot better to really give an inside-view evaluation.
Conditional on singular learning theory actually enabling deeper understanding of neural networks, there's still a question of it that's actually useful for alignment. I feel reasonably confident that it would be a positive development: generally having theoretical frameworks to engage with (even if approximate) seems a key component of engineering systems with strong guarantees. Whereas just making something that works well most of the time is much more tractable via a trial-and-error approach. So, understanding seems to differentially help building reliable systems than just systems that mostly work. But, understanding does accelerate both -- so there is a non-trivial backfire risk.
Fully funded Matthew's ask, which amounts to $92,260/year annualized. The salary seems reasonable given his experience level. It's higher than US PhD stipends (~50k/year), but below that of most alignment research non-profits in the SF Bay Area (LCA filings from Redwood show at least $140k/year for an ML Researcher; FAR AI's pay scale is $80k-$175k/year for Research Engineers) and significantly below for-profit tech jobs. Matthew will be working from Australia where tech salaries are lower; Levels.fyi gives a median of $54k/year USD total comp, but short-term contractor positions are often up to 2x that of salaried employees, so I still consider the ask to be in a reasonable range.
Not directly relevant in this grant, but I generally would advocate for independently conducted research to receive lower compensation than at alignment organizations, as I usually expect people to be significantly more productive in an organization where they can receive mentorship (and many of these organizations are at least partially funding constrained).
Please disclose e.g. any romantic, professional, financial, housemate, or familial relationships you have with the grant recipient(s).
I supervised Matthew for an internship in 2021 at CHAI; I have continued collaborating with him (although relatively light-touch) to see that project through to publication.