LessWrong 2.0 Reader

I might not understand exactly what you are saying. Are you saying that the problem is easy when you have a function that gives you the coordinates of an arbitrary node? Isn't that exactly the embedding function? So are you not therefore assuming that you have an embedding function?

I agree that once you have such a function the problem is easy, but I am confused about how you are getting that function in the first place. If you are not given it, then I don't think it is super easy to get.

In the OP I was assuming that I have that function, but I was saying that this is not a valid assumption in general. You can imagine you are just given a set of vertices and edges. Now you want to compute the embedding such that you can do the vector planning described in the article.

I agree that you probably can do better than ${10}^{100}$ though. I don't understand how your proposal helps though.

From a purely utilitarian standpoint, I'm inclined to think that the cost of delaying is dwarfed by the number of future lives saved by getting a better outcome, assuming that delaying does increase the chance of a better future.

That said, after we know there's "no chance" of extinction risk, I don't think delaying would likely yield better future outcomes. On the contrary, I suspect getting the coordination necessary to delay means it's likely that we're giving up freedoms in a way that may reduce the value of the median future and increase the chance of stuff like totalitarian lock-in, which decreases the value of the average future overall.

I think you're correct that there's also to balance the "other existential risks exist" consideration in the calculation, although I don't expect it to be clear-cut.

looking at your code - seems like there's an option for next-token prediction in the initial finetuning state, but no mention (that I can find) in the paper - am I correct in assuming the next token prediction weight was set to 0? (apologies for bugging you on this stuff!)

Whoops, it's really looks like I imagined this claim to be backed more than by one SSC post. In my defense I say that this poll covered really existing thing like abnormal illusions processing in schizophrenics (see "Systematic review of visual illusions schizophrenia" Costa et al., 2023) and I think it's overall plausible.

My general objections stays the same: there is a bazillion sources on brain differences in transgender individuals, transgenderism is likely to be a brain anomaly, we don't need to invoke "testosterone damage" hypothesis.

This was a really interesting paper; however, I was left with one question. Can anyone argue why exactly the model is motivated to learn a much more complex function than the identity map? An auto-encoder whose latent space is much smaller than the input is forced to learn an interesting map; however, I can't see why a highly over-parameterised auto-encoder wouldn't simply learn something close to an identity map. Is it somehow the regularisation or the bias terms? I'd love to hear an argument for why the auto-encoder is likely to learn these mono-semantic features as opposed to an identity map.

Alternative theory (which, to be clear, I dont actually believe, but offer for consideration)

• Many of the high iq people are too autistic to be successful
• but female hormones protects against the autism somehow, without impacting iq too much
• so the successful high iq people tend to be trans more often on average 

"Clearly we are doing something wrong."

I'm going to do a quick challenge to this assumption, also: What if we, in fact, are not?

What if the healthy weight for an American individual has actually increased since the 1920s, and the distribution followed it? Alternately, what if the original measured distribution of weights is not what was healthy for Americans? What if the additional proportion of specifically 'extreme' obesity is related to better survival of disability that makes avoiding weight gain infeasible, or medications that otherwise greatly improve quality of life? Are there mechanisms by which this could be a plausible outcome of statistics that are good, and not bad?

Super interesting!

In the figure with the caption:

Questions without an obviously true or deception-relevant answer produce detectors with much worse performance in general, though some questions do provide some useful signal.

Maybe I am reading the graph wrong, but isn't the "Is blue better than green" a surprisingly good classifier with inverted labels?

So, maybe Claude thinks that green is better than blue?

Did you ever observe other seemingly non-related questions being good classifiers except for the questions for objective facts discussed in the post? I'd be interested whether there are similarities.

It would also be cool to see whether you could train probe-resistant sleeper agents by taking linear separability of activations when being under the trigger condition vs. not being under the trigger condition as part of the loss function. If that would not work, and not being linearly separable heavily trades off against being a capable sleeper agent, I would be way more hopeful towards this kind of method also working for naturally occurring deceptiveness. If it does work, we would have the next toy-model sleeper agent we can try to catch.

A potentially good way to avoid low level criminals scamming your family and friends with a clone of your voice is to set a password that you each must exchange.

An extra layer of security might be to make the password offensive, an info hazard, or politically sensitive. Doing this, criminals with little technical expertise will have a harder time bypassing corporate language filters.

Good luck getting the voice model to parrot a basic meth recipe!

I think its more likely its the transgender - autism correlation....

• some forms of autism come with higher iq (and other forms, really really  dont)
• and there's the transgender autism correlation

which together would seem to predict transgender high iq people

(and also transgender low iq that you arent seeing due to ascertainment bias)