Hey, wanted to chip into the comments here because they are disappointingly negative.
I think your paper and this post are extremely good work. They won’t push forward the all-things-considered viewpoint, but they surely push forward the lower bound (or adversarial) viewpoint. Also because Open Phil and Future Fund use some fraction of lower-end risk in their estimate, this should hopefully wipe that put. Together they much more rigorously lay out classic x-risk arguments.
I think that getting the prior work peer reviewed is also a massive win at least in a social sense. While it isn’t much of a signal here on LW, it is in the wider world. I have very high confidence that I will be referring to that paper in arguments I have in the future, any time the other participant doesn’t give me the benefit of the doubt.
I think we can get additional information from the topological representation. We can look at the relationship between the different level sets under different cumulative probabilities. Although this requires evaluating the model over the whole dataset.
Let’s say we’ve trained a continuous normalizing flow model (which are equivalent to ordinary differential equations). These kinds of model require that the input and output dimensionality are the same, but we can narrow the model as the depth increases by directing many of those dimensions to isotropic gaussian noise. I haven’t trained any of these models before, so I don’t know if this works in practice.
Here is an example of the topology of an input space. The data may be knotted or tangled, and includes noise. The contours show level sets Si={x∣p(x)>pi}.
The model projects the data into a high dimensionality, then projects it back down into an arbitrary basis, but in the process untangling knots. (We can regularize the model to use the minimum number of dimensions by using an L1 activation loss
Lastly, we can view this topology as the Cartesian product of noise distributions and a hierarchical model. (I have some ideas for GAN losses that might be able to discover these directly)
We can use topological structures like these as anchors. If a model is strong enough, they will correspond to real relationships between natural classes. This means that very similar structures will be present in different models. If these structures are large enough or heterogeneous enough, they may be unique, in which case we can use them to find transformations between (subspaces of) the latent spaces of two different models trained on similar data.