Thanks, that's the kind of answer I was looking for
Interesting discussion; thanks for posting!I'm curious about what elementary units in NNs could be.
the elementary units are not the neurons, but some other thing.
I tend to model NNs as computational graphs where activation spaces/layers are the nodes and weights/tensors are the edges of the graph. Under this framing, my initial intuition is that elementary units are either going to be contained in the activation spaces or the weights.There does seem to be empirical evidence that features of the dataset are represented as linear directions in activation space.I'd be interested in any thoughts regarding what other forms elementary units in NNs could take. In particular, I'd be surprised if they aren't represented in subspaces of activation spaces.
Thanks for pointing this out. I'll look into it and modify the post accordingly.
With ideal objective detection methods, the inner alignment problem is solved (or partially solved in the case of non-ideal objective detection methods), and governance would be needed to regulate which objectives are allowed to be instilled in an AI (i.e., government does something like outer alignment regulation).
Ideal objective oversight essentially allows an overseer instill whatever objectives it wants the AI to have. Therefore, if the overseer includes the government, the government can influence whatever target outcomes the AI pursues.
So practically, this means that the governance policies would require the government to have access to the objective detection method results, directly or indirectly through the AI labs.
Thanks for the reponse, it's useful to hear that we can to the same conclusions. I quoted your post in the first paragraph. Thanks for bringing Fabien's post to my attention! I'll reference it. Looking forward to your upcoming post.
Interesting! Quick thought: I feel as though it over-compressed the post, compared to the summary I used. Perhaps you can tweak things to generate multiple summaries in varying degrees of length.
Thanks for the feedback! I guess the intention of this post was to lay down the broad framing/motivation for upcoming work that will involve looking at the more concrete details.
I do resonate with the feeling that the post as a whole feels a bit empty as it stands and the effort could have been better spent elsewhere.
It’s been about a year since I became involved in AI Alignment. Here is a super high-level overview of the research direction I intend to pursue over the next six or so months.