I think, in discarding the simplicity argument, you are underestimating how many zeros are in the ratio gigabytes needed to specify the brain simulation initial conditions:gigabytes needed to store the quantum fields as the simulation runs. The data in the brain is vaguely linear in number of electrons, the ram needed to simulate the brain is vaguely exponential in number of electrons. “Simplest explanation of the state of the GPUs by a factor of 100” and “Simplest explanation of the state of the GPUs by a factor of 10^number of stars in the visible universe” are only quantitatively different, but sometimes quantity has a quality all of its own.
Hastings
Karma: 832
Beauty of notation is an optimization target and so should fail as a metric, but especially compared to other optimization targets I’ve pushed on, in my experience it seems to hold up. The exceptions appear to be string theory and category theory and two failures in a field the size of math is not so bad.
prompts already go through undesigned evolution through reproductive fitness (rendered in 4k artstation flickr 2014)
Sternum and neck for me
Properties of the track I am on are load bearing in this assertion. (Explicitl examples of both cases from the original comment: Tesla worked out how to destroy any structure by resonating it, and took the details to his grave because he was pretty sure that the details would be more useful for destroying buildings than for protecting them from resonating weapons. This didn’t actually matter because his resonating weapon concept was crankish and wrong. Einstein worked out how to destroy any city by splitting atoms, and disclosed this, and it was promptly used to destroy cities. This did matter because he was right, but maybe didn’t matter because lots of people worked out the splitting atoms thing at the same time. It’s hard to tell from the inside whether you are crankish)
Nuclear power has gotten to a point where we can use it quite safely as long as no one does the thing (the thing being chemically separating the plutonium and imploding it in your neighbor’s cities) and we seem to be surviving, as while all the actors have put great effort into being ready do do “the thing,” no one actually does it. I’m beginning to suspect that it will be worth separating alignment into two fields, one of “Actually make AI safe” and another, sadder but easier field of “Make AI safe as long as no one does the thing.” I’ve made some infinitesimal progress on the latter, but am not sure how to advance, use or share it since currently, conditional on me being on the right track, any research that I tell basically anyone about will immediately be used to get ready to do the thing, and conditional on me being on the wrong track (the more likely case by far) it doesn’t matter either way, so it’s all downside. I suspect this is common? This is almost but not quite the same concept as “Don’t advance capabilities.”
I have observed a transition. 12 years ago, the left-right split was based on many loosely correlated factors and strategic/inertial effects, creating bizarre situations like near perfect correlation between opinions on Gay Marriage and privatization of social security. I think at that time you could reason much better if you could recognize that the separation between left and right was not natural. I at least have a ton of cached arguments from this era because it became such a familiar dynamic.
Nowadays, I don’t think this old schema really applies, especially among the actual elected officers and party leadership. The effective left right split is mono-factor: you are right exactly in proportion to your personal loyalty to one Donald J. Trump, resulting in bizarre situations like Dick Cheney being classified as “Left.”
+1 for just throwing your notes up on a website. For example, mine are at https://www.hgreer.com/Reports/ although there is currently a bit of a gap for the last few months as I’ve been working more on synthesizing existing work into a CVPR submission than on exploreing new directions.
The above is a terrible post-hoc justification and I need to get back to note taking.
Organizations and communities can also face hostile telepaths. My pet theory that sort of crystalized while reading this is that p-hacking is academia’s response to a hostile telepath that banned publication of negative results.
This of course sucks for non traditional researchers and especially journalists who don’t even subconsciously know that p=0.05002 r=1e-7 “breakthrough in finding relationship between milk consumption and toenail fungus” is code for “We have conclusively found no effect and want to broadcast to the community that there is no effect here; yet we cannot ever consciously acknowledging that we found nothing because our mortgages depend on fooling a hostile telepath into believing this is something”
Personally I am quite pleased with the field of parapsychology. For example, they took a human intuition and experience (“Wow, last night when I went to sleep I floated out of my body. That was real!”) and operationalized it into a testable hypothesis (“When a subject capable of out of body experiences floats out of their body, they will be able to read random numbers written on a card otherwise hidden to them.”) They went and actually performed this experiment, with a decent deal of rigor, writing the results down accurately, and got an impossible result- one subject could read the card. (Tart, 1968.) A great deal of effort quickly went in to further exploration (including military attention with the men who stare at goats etc) and it turned out that the experiment didn’t replicate, even though everyone involved seemed to genuinely expect it to. In the end, no, you can’t use an out of body experience to remotely view, but I’m really glad someone did the obvious experiments instead of armchair philosophizing.
https://digital.library.unt.edu/ark:/67531/metadc799368/m2/1/high_res_d/vol17-no2-73.pdf is a great read from someone who obviously believes in the metaphysical, and then does a great job designing and running experiments and accurately reporting their observations, and so it’s really only a small ding against them that the author draws the wrong larger conclusions in the end.
Show me a field where replication crises tear through, exposing fraud and rot and an emperor that never had any clothes, a field where replications fail so badly that they result in firings and polemics in the New York Times and destroyed careers- and then I will show you a field that is a little confused but has the spirit and will get there sooner or later.
What you really need to look out for are fields that could never, on a conceptual level, have a devastating replication crisis. Lesswrong sometimes strays a little close to this camp.
Since you’re already in it: do you happen to know if the popular system of epicycles accurately represented the (relative, per body) distance of each planet from earth over time, or just the angle? I’ve been curious about this for a while but haven’t had time to dig in. They’d at minimum have to get it right for the moon and sun for predicting eclipse type.
After reading this, I prompted Claude with
Please write a parody of chapter 3 of the 1926 winnie the pooh, where instead of winnie and piglet searching for a woozle, some bloggers are looking for bloggers similar to matt levine, and not realizing that they are the bloggers who are similar to matt levine. This will be a humorous reply to the attached post.
Arxiv is basically one huge, glacially slow internet comment section, where you reply to an article by citing it. It’s more interactive than it looks- most early career researchers are set up to get a ping whenever they are cited.
Keep in mind that representative democracy as practiced in the US is doing as well as it is while holding up to hundreds of millions of dollars of destructive pessimization effort- any alternative system is going to be hit with similar efforts. Just off the top of my head: we are being hit with about $50 dollars per capita of spending this fall, and that’s plenty to brain-melt a meaningful fraction of the population. Each member of a 500 member sortition body chosing a president, if their identity is leaked, is going to be immediately hit with OOM 30 million dollars of attempts to change their mind. This is a different environment than a calm deliberation and consideration of the issues as examined by the linked studies.
(figures computed by dividing 2024 election spending by targeted population)
What are the odds that Polymarket resolves “Trump yes” and Harris takes office in 2025? If these mystery traders expect to profit from hidden information, the hidden information could be about an anticipated failure of UMA instead of about the election itself.
Are there any mainstream programming languages that make it ergonomic to write high level numerical code that doesn’t allocate once the serious calculation starts? So far for this task C is by far the best option but it’s very manual, and Julia tries and does pretty well but you have to constantly make sure that the compiler successfully optimized away the allocations that you think it optimized away. (Obviously Fortran is also very good for this, but ugh)
To say that most academic research is anything, you’re going to have to pick a measure over research. Uniform measure is not going to be exciting – you’re going to get almost entirely undergraduate assignments and Third World paper mills. If your weighted sampler is “papers linked in articles about how academia is woke” you’re going to find a high %fake. If your weighed measure is “papers read during work hours by employees at F500 companies” you’ll find a lower, nonzero %fake.
Handwringing over public, vitriolic retractions spats is going to fuck your epistemology via sampling bias. There is no replication crisis in underwater basket weaving
Yeah, I definitely oversimplified somewhere. I’m definitely tripped up by “this statement is false” or statements that don’t terminate. Worse, thinking in that direction, I appear to have claimed that the utterance “What color is your t-shirt” is associated with a probability of being true.
I agree that o1 doesn’t have a test time scaling law, at least not in a strong sense, while generatively pretrained transformers seem to have a scaling law in an extremely strong sense,.
I’d put my position like this: if you trained a GPT on a human generated internet a million times larger than the internet of our world, with a million times more parameters, for a million times more iterations, then I am confident that that GPT could beat the minecraft ender dragon zero shot.
If you gave o1 a quadrillion times more thinking time, there is no way in hell it would beat the ender dragon.