Thanks for sharing that analysis, it is indeed reassuring!
Nice project and writeup. I particularly liked the walkthrough of thought processes throughout the project
Decision square’s Euclidean distance to the top-right 5×5 corner, positive (+1.326).We are confused and don’t fully understand which logical interactions produce this positive regression coefficient.
Decision square’s Euclidean distance to the top-right 5×5 corner, positive (+1.326).
We are confused and don’t fully understand which logical interactions produce this positive regression coefficient.
I’d be weary about interpreting the regression coefficients of features that are correlated (see Multicollinearity). Even the sign may be misleading.
It might be worth making a cross-correlation plot of the features. This won’t give you a new coefficients to put faith in, but it might help you decide how much to trust the ones you have. It can also be useful looking at how unstable the coefficients are during training (or e.g. when trained on a different dataset).
Hi Nora. We used rapidsai’s cuml which has GPU compatibility. Beware, the only “metric” available is “euclidean”, despite what the docs say (issue).
I think the risk level becomes clearer when stepping back from stories of how pursuing specific utility functions lead to humanity’s demise. An AGI will have many powerful levers on the world at its disposal. Very few combinations of lever pulls result in a good outcome for humans.From the perspective of ants in an anthill, the actual utility function(s) of the humans is of minor relevance; the ants will be destroyed by a nuclear bomb in much the same way as they will be destroyed by a new construction site or a group of mischievous kids playing around.
(I think your Fermi AGI paradox is a good point, I don’t quite know how to factor that into my AGI risk assessment.)
I have a different intuition here; I would much prefer the alignment team at e.g. DeepMind to be working at DeepMind as opposed to doing their work for some “alignment-only” outfit. My guess is that there is a non-negligible influence that an alignment team can have on a capabilities org in the form of:
The alignment team interacting with other staff either casually in the office or by e.g. running internal workshops open to all staff (like DeepMind apparently do)
The org consulting with the alignment team (e.g. before releasing models or starting dangerous projects)
Staff working on raw capabilities having somewhere easy to go if they want to shift to alignment work
I think the above benefits likely outweigh the impact of the influence in the other direction (such as the value drift from having economic or social incentives linked to capabilities work)
Nice list!Conditioned on the future containing AIs that are capable of suffering in a morally relevant way, interpretability work may also help identify and even reduce this suffering (and/or increase pleasure and happiness). While this may not directly reduce x-risk, it is a motivator for people taken in by arguments on s-risks from sentient AIs to work on/advocate for interpretability research.