Small groups of mammals can already cooperate with each other (wolf’s, lions, monkeys etc.). In mammals, I’d guess having a queen gives a bottleneck in how fast there can be off-spring. Also if there are large returns to division of labor in child-rearing, large animals are smart enough that both parents can do this together, while in wasps the males just die (why actually?). So wasps get higher marginal returns when evolving the first steps towards being eusocial. Also smaller animals have more diverse environments and need fewer years to “locked in” eusociality and workers get born without being fertile (eusocial groups where workers are still fertile are really unstable so prone to evolve away from eusociality again when circumstances aren’t in favor anymore). Also fathers can’t be as sure of their children and the other way around leading to less cooperation if new males join in, which termites overcome by having king and queen, ants just have a queen that stores her sperm, while naked mole rats are just fine with incest?
Morpheus
… that wasn’t enough to learn the pattern, though. Shortly out of college, reality was still hitting me over the head; that time the big idea was an efficient implementation of universal competitively-optimal portfolios. I lost a couple thousand dollars on wildly over-leveraged forex positions.
I am curious what that idea was and where it went wrong.
In that case also consider installing PowerToys and pressing Alt+Space to open applications or files (to avoid unhelpful internet searches etc.).
I like this sequence and am aware it is not finished yet. Here’s my I am understanding so far. After reading the sequence, I think I can predict your response to the first 5 conundrums, so my previous confusion there (why cluster rather than factor) seems resolved. But I think I still disagree with the later examples that I was confused with before reading your sequence. One example of conundrums where I think I get what your reply would be:
-
“Why isn’t factor analysis considered the main research tool?”
Factor analysis doesn’t capture the main bottlenecks (people being depressed for different reasons, people are successful for different reasons etc.)
For others, I don’t see how they connect well. My replies would be:
-
“What is gifted child syndrome/twice-exceptionals?”
I don’t know why you focus on this one? My impression why there’s a focus on this group is because helping them might be worth the investment? Or because the people writing and consuming that theorizing tend to be higher iq. Also, maybe “that phenomenon where desirable trait X and Y tend to be anticorrelated, because the others tend to not want to hang out with you as much, or you don’t want to hang out with them” (writing and math being anticorrelated in the average US college)? I don’t see the relation in the log-normals, other that maybe in your thinking you might want to single out that group, because it might have bottlenecks that are different?
-
“Why would progressivism have paradoxical effects on diversity?”
I am confused? I can see you making the argument that the diversity angle might sometimes be the correct one if it is the bottleneck for a person (black person being arrested for doing drugs in the US? While less of a bottleneck for a lot of other minorities?)
-
“What’s wrong with symptom treatment?”
Do you think people’s intuition here is correctly adjusting for something like the epsilon fallacy? Or to quickly jumping to simplistic conclusions like in this college cost post you link (in a different context), where someone might (in my view accidentally) see the increasing number of small courses as a cause rather than a symptom?
-
“What value does qualitative research provide?”
I am reminded of
and
I feel like so far this sequence has mostly told me what tools not to use and, in practice, I cannot think of a case where reading this sequence has helped pick a better tool, but I was already pretty fond of log normals.
-
So I should look out for that, e.g. by doing some manual fermi estimates or other direct checking about ABC or by investigating the strength of the steelman of reaction XYZ, or by keeping an eye out for people systematically reacting with XYZ without good foundation so I can notice this,
Accusing people in my head of not being numerate enough when this happens has helped, because then I don’t want to be a hypocrite. GPT4o or o1 are good at fermi estimates, making this even easier.
I noticed the tag posts imported from Arbital that haven’t been edited on LW yet can’t be found when searching those tags from the “Add Tags” button above posts. Adding ineffective edits like spaces at the end of a paragraph seems to fix that problem.
I noticed the tag posts imported from Arbital that haven’t been edited on LW yet can’t be found when searching those tags from the “Add Tags” button above posts. Adding ineffective edits like spaces at the end of a paragraph seems to fix that problem.
I didn’t downvote, but my impression is the post seems to hand-wave away a lot of problems and gives the impression you haven’t actually thought clearly and in detail about whether the ideas you propose here are feasible.
Some people have been thinking for quite some time now that an AI that wants to be changed would be great, but that it’s not that easy to create one, so how is your proposal different? Maybe checkout the corrigibility tag. Figuring out which desiderata are actually feasible to implement and how is the hard part. Same goes for your Matroshka bunkers. What useful work are you getting out of your 100% safe Matroshka bunkers? After you thought about that for 5 minutes+, maybe checkout the AI boxing tag and the AI oracle tag. Maybe there is something to the reversibility idea ¯\_(ツ)_/¯.
Also using so many tags gives a bad impression (“AI Timelines”? “Tiling Agents”? “Infinities in Ethics”?). Read the description of the tags.
Finding some some friend (or language model?) to play Zendo (the science game) with makes this really intuitive on a gut level. Guessing a rule based on whether a sequence of 3 integers is either accepted or rejected works pretty well via text.
NOTE: I posted this to LW and I’m new here so I don’t totally know the cross-posting policies. Hope it’s alright that I posted here too!
It seems you posted on LW twice instead or in addition to cross-posting to the EA forum.
I just tried this with o3-mini-high and o3-mini. o3-mini-high identified and prevented the fork correctly, while o3-mini did not even correctly identify it lost.
Yes!
I can only see the image of the 5-d random walk. The other images aren’t rendering.
I was already sold on singularity. For what it’s worth I found the post and comments very helpful for why you would want to take the sun apart in the first place and why it would be feasible and desirable for superintelligent and non-superintelligent civilization (Turning the sun into a smaller sun that doesn’t explode seems nicer than having it explode. Fusion gives off way more energy than lifting the material. Gravity is the weakest of the 4 forces after all. In a superintelligent civilization with reversible computers, not taking apart the sun will make readily available mass a taut constraint).
One thing I am pretty confident about is that methylation patterns are downstream, not upstream. Methyl group turnover time is far too fast to be a plausible root cause of aging. (In principle, there could be some special methyl groups which turn over slowly, but I would find that very surprising.)
My possibly wrong understanding here is that there are histone modifications and other proteins (like CTCF) that make methylation patterns way more stable? Which leads to some methylation patterns like imprinting for genes like IGF2 to be stable in most tissues over ~decades. Nevertheless, loss of imprinting and epigenetic marks still doesn’t necessarily seem like the most likely root cause of aging to me.
This argument against subagents is important and made me genuinely less confused. I love the concrete pizza example and the visual of both agent’s utility in this post. Those lead me to actually remember the technical argument when it came up in conversation.
I found Steven Byrnes valence concept really useful for my own thinking about psychology more broadly and concretely when reading text messages from my contextualizing friend (in that when a message was ambiguous, guessing the correct interpretation based on valence worked surprisingly well for me).
I ended up dodging the bullet of loosing money here, because I was a bit worried that Nate Silvers model might have been behind, because the last poll then was on the 23rd. I was also too busy with other important work to resolve my confusions before the election. My current two best guesses are:
The French whale did not have an edge,
The neighbour polling method is a just-so story to spread confusion, but he actually did have an edge
I don’t understand correctly how this neighbour polling method is supposed to work.
In any case, if Polymarket is still legal in 4 years I expect the prediction market on the election to be efficient relative to me and I will not bet on it.
I had a discussion with @Towards_Keeperhood what we would expect in the world where orcas either are or aren’t more intellectually capable than humans if trained. Main pieces I remember were: Orcas already dominating the planet (like humans do), large sea creatures going extinct due to orcas (similar to how humans drove several species extinct (Megalodon? Probably extinct for different reasons, weak evidence against? Most other large whales are still around)). I argued that @Towards_Keeperhood was also underestimating the intricacies that hunter-gatherers are capable of, and gave the book review for the secret of our success as an example. I think @Towards_Keeperhood did update in that direction after reading that post. I also reread that post and funnily enough stumbled over some evidence that orcas might have fallen into a similar “culture attractor” for intelligence, like humans:
Learn from old people. Humans are almost unique in having menopause; most animals keep reproducing until they die in late middle-age. Why does evolution want humans to stick around without reproducing?
Because old people have already learned the local culture and can teach it to others. Heinrich asks us to throw out any personal experience we have of elders; we live in a rapidly-changing world where an old person is probably “behind the times”. But for most of history, change happened glacially slowly, and old people would have spent their entire lives accumulating relevant knowledge. Imagine a world where when a Silicon Valley programmer can’t figure out how to make his code run, he calls up his grandfather, who spent fifty years coding apps for Google and knows every programming language inside and out.
Quick google search revealed Orcas have menopause too! While chimpanzees don’t! I would not have predicted that.
Not sure what’s going on, but gpt-4o keeps using its search tool when it shouldn’t and telling me about either the weather, or sonic the hedgehog. I couldn’t find anything about this online. Are funny things like this happening to anyone else? I checked both my custom instructions and the memory items and nothing there mentions either of these.