Co-Executive Director at ML Alignment & Theory Scholars Program (2022-present)
Co-Founder & Board Member at London Initiative for Safe AI (2023-present)
Manifund Regrantor (2023-present) | RFPs here
Advisor, Catalyze Impact (2023-present) | ToC here
Advisor, AI Safety ANZ (2024-present)
Advisor, Pivotal Research (2024-present)
Ph.D. in Physics at the University of Queensland (2017-2023)
Group organizer at Effective Altruism UQ (2018-2021)
Give me feedback! :)
It seems plausible to me that if AGI progress becomes strongly bottlenecked on architecture design or hyperparameter search, a more “genetic algorithm”-like approach will follow. Automated AI researchers could run and evaluate many small experiments in parallel, covering a vast hyperparameter space. If small experiments are generally predictive of larger experiments (and they seem to be, a la scaling laws) and model inference costs are cheap enough, this parallelized approach might be be 1) computationally affordable and 2) successful at overcoming the architecture bottleneck.
Apr 18, 11:59 pm PT :)
Hi! Yes, MATS is always open to newbies, though our bar has raised significantly over the last few years. AISF is great, but I would also recommend completing ARENA or ML4Good courses if you are pursuing a technical project, or completing an AI gov research project.
It seems plausible to me that if AGI progress becomes strongly bottlenecked on architecture design or hyperparameter search, a more “genetic algorithm”-like approach will follow. Automated AI researchers could run and evaluate many small experiments in parallel, covering a vast hyperparameter space.
LISA’s current leadership team consists of an Operations Director (Mike Brozowski) and a Research Director (James Fox). LISA is hiring for a new CEO role; there has never been a LISA CEO.
How fast should the field of AI safety grow? An attempt at grounding this question in some predictions.
Ryan Greenblatt seems to think we can get a 30x speed-up in AI R&D using near-term, plausibly safe AI systems; assume every AIS researcher can be 30x’d by Alignment MVPs
Tom Davidson thinks we have <3 years from 20%-AI to 100%-AI; assume we have ~3 years to align AGI with the aid of Alignment MVPs
Assume the hardness of aligning TAI is equivalent to the Apollo Program (90k engineer/scientist FTEs x 9 years = 810k FTE-years); therefore, we need ~9k more AIS technical researchers
The technical AIS field is currently ~500 people; at the current growth rate of 28% per year, it will take 12 years to grow to 9k people (Oct 2036)
Alternatively, if we bound by the Manhattan Project (25k FTEs x 5 years = 125 FTE-years), this will take 6.5 years (Jul 2031)
Metaculus predicts weak AGI in 2026 and strong AGI in 2030; clearly, more talent development is needed if we want to make the Nov 2030 AGI deadline!
If we want to make the 9k researchers goal by Nov 2030 AGI deadline, we need an annual growth rate of 65%, 2.3x the current growth rate of 28%
Ah, that’s a mistake. Our bad.
Crucial questions for AI safety field-builders:
What is the most important problem in your field? If you aren’t working on it, why?
Where is everyone else dropping the ball and why?
Are you addressing a gap in the talent pipeline?
What resources are abundant? What resources are scarce? How can you turn abundant resources into scarce resources?
How will you know you are succeeding? How will you know you are failing?
What is the “user experience” of my program?
Who would you copy if you could copy anyone? How could you do this?
Am I better than the counterfactual?
Who are your clients? What do they want?
Additional resources, thanks to Avery:
COT Scaling implies slower takeoff speeds (Zoellner − 10 min)
o1: A Technical Primer (Hoogland − 20 min)
Unpacking o1 and the Path to AGI (Brown—up to 8:38)
Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters (Deepmind, Snell − 20 min to skim)
Speculations on Test-Time Scaling (Rush—from 4:25 to 21:25, 17 min total)
And 115 prospective mentors applied for Summer 2025!
When onboarding advisors, we made it clear that we would not reveal their identities without their consent. I certainly don’t want to require that our advisors make their identities public, as I believe this might compromise the intent of anonymous peer review: to obtain genuine assessment, without fear of bias or reprisals. As with most academic journals, the integrity of the process is dependent on the editors; in this case, the MATS team and our primary funders.
It’s possible that a mere list of advisor names (without associated ratings) would be sufficient to ensure public trust in our process without compromising the peer review process. We plan to explore this option with our advisors in future.
Not currently. We thought that we would elicit more honest ratings of prospective mentors from advisors, without fear of public pressure or backlash, if we kept the list of advisors internal to our team, similar to anonymous peer review.
I’m tempted to set this up with Manifund money. Could be a weekend project.
I expect mech interp to be particularly easy to automate at scale. If mech interp has capabilities externalities (e.g., uncovering useful learned algorithms or “retargeting the search”), this could facilitate rapid performance improvements.