I research intelligence and it’s emergence and expression in neural networks to ensure advanced AI is safe and beneficial.
Current interests: neural network interpretability, alignment/safety, unsupervised learning, and deep learning theory.
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I agree. Yes—would be happy to chat and discuss more. Sending you a DM.
They use a WordNet hierarchy to verify their orthogonality results at scale, but doesn’t look like they do any other shuffle controls.
Thanks @TomasD, that’s interesting! I agree—most words in my random list seem like random “objects/things/organisms” so there might be some conditioning going on there. Going over your code to see if there’s something else that’s different.
Thanks a lot! We had an email exchange with the authors and they shared some updated results with much better random shuffling controls on the WordNet hierarchy.
They also argue that some contexts should promote the likelihood of both “sad” and “joy” since they are causally separable, so they should not be expected to be anti-correlated under their causal inner product per se. We’re still concerned about what this means for semantic steering.