Lets imagine a 250 IQ unaligned paperclip maximizer that finds itself in the middle of an intelligence explosion. Let’s say that it can’t see how to solve alignment. It needs a 350 IQ ally to preserve any paperclips in the multipolar free-for-all. Will it try building an unaligned utility maximizer with a completely different architecture and 350 IQ?

I’d imagine that it would work pretty hard to not try that strategy, and to make sure that none of its sisters or rivals try that strategy. If we can work out what a hypergenius would do in our shoes, it might behoove us to copy it, even if it seems hard.

So if alignment is as hard as it looks, desperately scrabbling to prevent recursive superintelligence should be an extremely attractive instrumental subgoal. Do we just lean into that?

I tried to learn to write before I had important things to say and it basically didn’t work. I had to go through the work of coming up with genuinely valuable ideas and then wreck the presentation of those ideas via bad writing. My more recent publications, I’m actually very happy with the writing.

The first couple times a surgeon does an operation, patient outcomes suck. Alas, there’s no other way to make experienced surgeons. My guess is that writing is similar, and I’m very glad that important experiences and ideas are way less valuable than patients: I would emotionally struggle with becoming a surgeon.

Hi! I've had some luck making architectures equivariant to a wider zoo of groups: my most interesting published results are getting a neural network to output a function, and invert that function if the inputs are swapped (equivariant to group of order 2, https://arxiv.org/pdf/2305.00087) and getting a neural network with two inputs to be doubly equivariant to translations: https://arxiv.org/pdf/2405.16738

These are architectural equivariances, and as expected that means they hold out of distribution.

If you need an architecture equivariant to a specific group, I can probably produce that architecture; I've got quite the unpublished toolbox building up. In particular, explicit mesa-optimizers are actually easier to make equivariant- if each mesa-optimization step is equivariant to a small group, then the optimization process is tyically equivariant to a larger group

There’s an easy way to turn any mathematical answer-based benchmark into a proof-based benchmark and it doesn’t require coq or lean or any human formalization of the benchmark design: just let the model choose whether or not to submit an answer for each question, and score the model zero for the whole benchmark if it submits any wrong answers.

is this Leverage adjacent?

It’s not AGI, but for human labor to retain any long-term value, there has to be an impenetrable wall that AI research hits, and this result rules out a small but nonzero number of locations that wall might’ve been.

“Scaling is over” was sort of the last hope I had for avoiding the “no one is employable, everyone starves” apocalypse. From that frame, the announcement video from openai is offputtingly cheerful.

In this context, instead of using claude to write the joke and then posting it with a disclaimer, I’d love to move to a norm of just posting the prompt without bothering to send it to an LLM at all. Instead of the blue dot parody in italics, the post could just be “Claude please rewrite the pale blue dot story to be about looking at the map.” Same content, faster to read, arguably funnier!

It is intended as a description of Ziz and co, but with a couple caveats: 

1) It was meant as a description that I could hypothetically pattern match to while getting sucked in to one of these, which meant no negative value judgements in the conditions, only in the observed outcomes.

2) It was meant to cast a wide net - hence the tails. When checking if my own activities could be spiraling into yet another rationalist cult, false positives of the form "2% yes- let's look into that" are very cheap. It wasn't meant as a way for me to police the activities of others since that's a setting where false positives are expensive.