Biomedical Engineering > Philosophy of AI student trying to figure out how I can robustly set myself up for meaningfully contributing to making transformative AI go well, likely through AI governance and/or field building.
Jelle Donders(JD)
Karma: 3
ACX 2023 Meetup Eindhoven
If this event is full at some point, there’s a meetup in Eindhoven on the same day :)
43. This situation you see when you look around you is not what a surviving world looks like.
A similar argument could have been made during the cold war to argue that nuclear war is inevitable, yet here we are.
Is it conclusive the meetup will be tomorrow? There seemed to be some uncertainty at the Eindhoven meetup...
Thanks for this post! I’ve been thinking a lot about AI governance strategies and their robustness/tractability lately, much of which feels like a close match to what you’ve written here.
For many AI governance strategies, I think we are more clueless than many seem to assume about whether a strategy ends up positively shaping the development of AI or backfiring in some foreseen or unforeseen way. There are many crucial considerations for AI governance strategies, miss or get one wrong and the whole strategy can fall apart, or become actively counterproductive. What I’ve been trying to do is:
Draft a list of trajectories for how the development and governance of AI up until we get to TAI, estimating the likelihood and associated xrisk from AI for each trajectory.
e.g. “There ends up being no meaningful international agreements or harsh regulation and labs race each other until TAI. Probability of trajectory: 10%, Xrisk from AI for scenario: 20%.”
Draft a list of AI governance strategies that can be pursued.
e.g. “push for slowing down frontier AI development by licensing the development of large models above a compute threshold and putting significant regulatory burden on them”.
For each combination of trajectory and strategy, assess whether we are clueless about the what the sign before the impact of said strategy would be, or if the strategy would be robustly good (~predictably lowers xrisk from AI in expectation), at least for this trajectory. A third option would of course be robustly bad.
e.g. “Clueless, it’s not clear which consideration should have more weight, and backfiring could be as bad as success is good.
+ This strategy would make this trajectory less likely and possibly shift it to a trajectory with lower xrisk from AI.
- Getting proper international agreement seems unlikely for this pessimistic trajectory. Partial regulation could disproportionally slow down good actors, or lead to open source proliferation and increases misuse risk.”
Try to identify strategies that are robust across a wide array of trajectories.
I’m just winging it without much background in how such foresight-related work is normally done, so any thoughts or feedback on how to approach this kind of investigation, or what existing foresight frameworks you think would be particularly helpful here are very much appreciated!