Max Nadeau
Posts 2
Comments14
I'd love to hear his thoughts on defensive measures for "fuzzier" threats from advanced AI, e.g. manipulation, persuasion, "distortion of epistemics", etc. Since it seems difficult to delineate when these sorts of harms are occuring (as opposed to benign forms of advertising/rhetoric/expression), it seems hard to construct defenses.
This is a related concept mechanisms for collective epistemics like prediction markets or community notes, which Vitalik praises here. But the harms from manipulation are broader, and could route through "superstimuli", addictive platforms, etc. beyond just the spread of falsehoods. See manipulation section here for related thoughts.
Disclaimer: I joined OP two weeks ago in the Program Associate role on the Technical AI Safety team. I'm leaving some comments describing questions I wanted to know to assess whether I should take the job (which, obviously, I ended up doing).
What sorts of personal/career development does the PA role provide? What are the pros and cons of this path over e.g. technical research (which has relatively clear professional development in the form of published papers, academic degrees, high-status job titles that bring public credibility)?
Disclaimer: I joined OP two weeks ago in the Program Associate role on the Technical AI Safety team. I'm leaving some comments describing questions I wanted to know to assess whether I should take the job (which, obviously, I ended up doing).
How inclined are you/would the OP grantmaking strategy be towards technical research with theories of impact that aren’t “researcher discovers technique that makes the AI internally pursue human values” -> “labs adopt this technique”. Some examples of other theories of change that technical research might have:
- Providing evidence for the dangerous capabilities of current/future models (should such capabilities emerge) that can more accurately inform countermeasures/policy/scaling decisions.
- Detecting/demonstrating emergent misalignment from normal training procedures. This evidence would also serve to more accurately inform countermeasures/policy/scaling decisions.
- Reducing the ease of malicious misuse of AIs by humans.
- Limiting the reach/capability of models instead of ensuring their alignment.
Disclaimer: I joined OP two weeks ago in the Program Associate role on the Technical AI Safety team. I'm leaving some comments describing questions I wanted to know to assess whether I should take the job (which, obviously, I ended up doing).
How much do the roles on the TAIS team involve engagement with technical topics? How do the depth and breadth of “keeping up with” AI safety research compare to being an AI safety researcher?
Disclaimer: I joined OP two weeks ago in the Program Associate role on the Technical AI Safety team. I'm leaving some comments describing questions I wanted to know to assess whether I should take the job (which, obviously, I ended up doing).
What does OP’s TAIS funding go to? Don’t professors’ salaries already get paid by their universities? Can (or can't) PhD students in AI get no-strings-attached funding (at least, can PhD students at prestigious universities)?
Disclaimer: I joined OP two weeks ago in the Program Associate role on the Technical AI Safety team. I'm leaving some comments describing questions I wanted to know to assess whether I should take the job (which, obviously, I ended up doing).
Is it way easier for researchers to do AI safety research within AI scaling labs (due to: more capable/diverse AI models, easier access to them (i.e. no rate limits/usage caps), better infra for running experiments, maybe some network effects from the other researchers at those labs, not having to deal with all the logistical hassle that comes from being a professor/independent researcher)?
Does this imply that the research ecosystem OP is funding (which is ~all external to these labs) isn't that important/cutting-edge for AI safety?
Sampled from my areas of personal interest, and not intended to be at all thorough or comprehensive:
AI researchers (in no particular order):
- Prof. Jacob Steinhardt: author of multiple fascinating pieces on forecasting AI progress and contributor/research lead on numerous AI safety-relevant papers.
- Dan Hendrycks: director of the multi-faceted and hard-to-summarize research and field-building non-profit CAIS.
- Prof. Sam Bowman: has worked on many varieties of AI safety research at Anthropic and NYU
- Ethan Perez: researcher doing fascinating work to display and address misalignments in today’s AIs.
- Toby Shevlane: Model Evaluations for Extreme Risks
- Jess Whittlestone: head of AI policy at Center for Long-Term Resilience, much research here
- Plenty of others: Jade Leung (AI governance and evaluations at OpenAI), Prof. David Krueger (varied AI safety research), Prof. Percy Liang (evaluating models), Prof. Roger Grosse (influence functions for interpretability), many others listed here.
Economists who have written (esp. but not only deflationary arguments contra Davidson) on AI’s economic impact:
- Chad Jones (see here)
- Ben Jones (see e.g. this, but also all his research)
- Matt Clancy (see this debate, though an episode with him should also address his non-AI work as well!)
- Daron Acemoglu (see Power and Progress)
- Maybe other reviewers here?
Ethicists:
- Iason Gabriel: has worked both on critiques of effective altruism, AI evaluations (extreme risks, representation), and normative questions related to AI alignment. This excellent FLI interview had so many ideas that would be great to explore in more depth.
- David Thorstad: has written critiques of existential risk reduction and longtermism.
- Emma Curran: author of contractualist reply to longtermism
The three I would personally be most excited to listen to: Toby Shevlane, Matt Clancy, Iason Gabriel.
I love seeing posts from people making tangible progress towards preventing catastrophes—it's very encouraging!
I know nothing about this area, so excuse me if my question doesn't make sense or was addressed in your post. I'm curious what the returns are on spending more money on sequencing, e.g. running the machine more than one a week or running it on more samples. If we were spending $10M a year instead of $1.5M on sequencing, how much less than 0.2% of people would have to be infected before an alert was raised?
Some other questions:
Thanks for the update; it was interesting even as a layperson.