Holistic believer in serendipitous causality;
newbie to the rationality dance; learning the steps.. one step at a time.
Luiza
Karma: 62
Proposal for alternative to fixing typos:
Come to Hostačov. Break the alignment ideas of the dozens of brains here. Short term maximal impact. We solve your typos. All of them. For life.
edit: sample of typo solving
Small meditation on being/ making/ having sense
for a part of your brain’s world-model to be meaningful doesn’t require that you can explain it in words.
so, hmm, what do we do with this part? how do we model/measure it? also, how much meaning escapes words at an individual/group level?
consciously note the difference between your reply and the post’s reply, including any extra details present or missing, without trying to minimize or maximize the difference.
this is a great prompt & I’m curious if you have some guidance on how one can do that better than one currently does it.
where does one do quick syncs on risk of eow in the now+ε?
Random thought, but at what point does an AGI+ learn to deal with further scaling by rewriting its own physical topology to minimize entropy production? ~ Last Question à la Asimov style
Updated a bit on the self-similar vs non-self similar scaling; I’m more unsure than I previously thought that I understand how scaling works, from individual to different types of collectives + time dynamics.
Short of the redundancy and letdown ending, I like this writing as it captures echoes of reasoning failure (that I find myself fall for at times); seems to be written not just for AI researchers (& adjacents) in 2025, but for many minds across “now and then”.
It strikes me that Humman does grasp truths (reality is complicated and people do have different strengths) but errs “true at this/his resolution” for “true at all scales”. Feels like he assumes self-similar scaling (like a tree) instead of considering the nature of realities that scale in non-self-similar ways (like a snowflake*, with shifts from dendritic to radial structures). More so, he uses his understanding of complexity as a thought-terminating invocation rather than a call to deeper/clearer/coherent-er modeling(s). Both are fairly common failure modes and it would be cool to leave them behind, but I’m unaware of stable ways to do so.
*not meaning to use the snowflake example as a supportive argument for his position ~ “every snowflake is unique and incomparable”, tho I do like that in my best of days.
soo, how does one re-choose & make the choice stick in the middle of a pattern that became part of normality?
One of these quick, cheap & semi-permanent wins for me was to uninstall the apps I didn’t want to use (e.g. instagram) & make it difficult to access the ones I sort of wanted to use (e.g. youtube).
Each time someone tells me that something is not possible, I tend to double the time it takes to it.
Assuming there’s a way of being in which one can half the time instead, what does your mind do then?
bondage play*
This thread needs a bit of counterbalance.
One can consider an alternative in bonding & control play; lots to explore with no brain cells at risk of dying in the process.
If one does not want to consider bonding, maybe a first aid course is due or a refresher would be fitting.
Depending on what the application involved, would it work to anon them, make them public and sort of let the community give feedback?
but I think that’s not what I should spend my remaining words on.
Why not?
~ edit: more sensical ~
I like this one:
I dare you to try to make yourself believe something you know to be false, and to closely observe what actually happens in your mind as you do.
~ more words are appreciated ~
They could be tasked with solving the entropic death of the universe á la Asimov’s Last Question.
What about the gpt oss ones for next weekend?
Soo, what do you see as a better alternative?
Or mini-alternative, with some minimal changes that could create a better future for quality content and access to it?
edit: silly typo
Could you expand on why this is in our favour?
The way I currently see it: by being faced with the same complexities, we get to develop similar (or mappable in-betweens) models. Somewhat like looking at cloud shapes and getting to understand how another saw a different shape.
What unsettles me here is noise and how we could model chaos as noise when the terrain is big enough and the mapper lacks the appropriate tools. (I’m aware there are takes that separate noise and chaos, but I had not been convinced by those that maintain the explainations within the same abstraction layer). By modelling chaos as noise, we either seem to be blind to the ontology, or are wrong. What’s the escape?