LessWrong 2.0 Reader

I quit YouTube a few years ago and it was probably the single best decision I've ever made.

However I also found that I naturally substitute it with something else. For example, I subsequently became addictived to Reddit. I quit Reddit and substituted for Hackernews and LessWrong. When I quit those I substituted for checking Slack, Email and Discord.

Thankfully being addicted to Slack does seem to be substantially less harmful than YouTube.

Temporary implies immediately reversible and mild.  

People who are on benzos often have emotional regulation issues, serious withdrawal symptoms (sometimes after very short courses  potentially even a single dose), and cognitive issues that do not resolve quickly.

In an academic sense, this idea is 'fine', but in a very personal way, if someone asked me 'should I take a member of this class of drug for any reason other than a serious issue that is severely affecting my quality of life?', I would answer 'absolutely not, and if you have a severe issue that they might help with, try absolutely everything else first, because once you're on these, you're probably not coming off'.

I think the correct solution to models powerful enough to materially help with, say, bioweapon design, is to not train them, or failing that to destroy them as soon as you find they can do that, not to release them publicly with some mitigations and hope nobody works out a clever jailbreak.

While an odd answer, it is true for me that music helps to install rational thinking. I think I’ve done maybe 3 fermi estimates in my day to day after making and listening to this song.

The Fermi Estimate Jig - LessWrong Inspired https://youtu.be/M_DN3Hl8YzU

Having it stuck in my head has been effective for me. I hope it works for others.

Agreed, it seems less elegant, But one guy on huggingface did a rough plot the cross correlation, and it seems to show that the directions changes with layer https://huggingface.co/posts/Undi95/318385306588047#663744f79522541bd971c919. Although perhaps we are missing something.

Idk. This shows that if you wanted to optimally get rid of refusal, you might want to do this. But, really, you want to balance between refusal and not damaging the model. Probably many layers are just kinda irrelevant for refusal. Though really this argues that we're both wrong, and the most surgical intervention is deleting the direction from key layers only.

Reading a book is certainly less stimulating than hunting. And watching a movie also might be less stimulating than hunting. So maybe stimulation by itself isn't the problem, and instead of "superstimuli" we should be worried about activities that are low effort and/or fruitless. From that perspective, reading a book can be sometimes difficult and fruitful (depending on the book - reading Dostoevsky or Fitzgerald isn't the same as reading a generic romance or young adult novel). And creativity is both difficult and fruitful. So we shouldn't put these things on par with watching tiktok.

Yeah, it'd be helpful to know what heavy lifting is going on there, because I feel like there's a pretty strong distinction between 'frozen burger patties that are otherwise indistinguishable from unfrozen burger patties' and 'TV dinner'.

I wish you had entitled / framed this as “here are some disanalogies between biorisk and AI risk”, rather than suggesting in the title and intro that we should add up the analogies and subtract the disanalogies to get a General Factor of Analogizability between biorisk and AI risk.

We can say that they’re similar in some respects and different in other respects, and if an analogy is leaning on an aspect in which they’re similar, that’s good, and if an analogy is leaning on an aspect in which they’re different, that’s bad. For details and examples of what I mean, see my comments on a different post: here [LW(p) · GW(p)] & here [LW(p) · GW(p)].

Maybe you could reduce the damping, so that when muting you can feel your finger stopping the vibration? It seems to me that more feedback of this kind is usually a good thing for the player. Also the vibration could give you a continuous "envelope" signal to be used later.

Thanks for the idea! I did my own fine-tuning job with the same idea. Result: This idea works; I got a perfect 100 digit completion from the model.

I edited the post to include my experiment here. (I had 1000 examples, batch size 1, LR multiplier 2.)

I now consider this version of the problem solved: one can make GPT-3.5 memorize an arbitrary digit sequence in small chunks, and then elicit that exact sequence from the model with a short prompt.

Thanks again for the contribution!