P(doom) = 50%, 3 years to AGI.
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Currently upskilling for future work on technical AI alignment research. Want to do part time work translating AI developments in China for a Western audiance in the meantime. Would really appreciate funding on either.
Will analyze papers and do bioinformatics research for money. DM me if interested.
I can confirm that my PayPal has received the $500, although it’ll be frozen for a while.
Thanks! I had a lot of fun doing the research for this and I’m working on an update that’ll be out in a few days.
I think a lot of it comes down to training data context - ” Leilan” is only present in certain videogame scrapes, ” petertodd” is only found in Bitcoin spam, ect. So when you try to use it in a conversational context, the model starts spitting out weird stuff because it doesn’t have enough information to understand what those tokens actually mean. I think GPT-2′s guess for ” petertodd” is something like “part of a name/email, if you see it, expect more mentions of Bitcoin”. And not anything more, since that token doesn’t occur much anywhere else. Thus, if you bring it up in a context where Bitcoin spam is very unlikely to occur, like a conversation with an AI assistant, it kinda just acts like a masked token, and you get the glitch token behavior.
I was thinking of areas along the gum-tooth interface having a local environment that normally promote tooth demineralization and cavities. After Lumina, that area could have high chronic acetaldehyde levels. In addition, the adaption of oral flora to the chronic presence of alcohol could increase first-pass metabolism, which increases acetaldehyde levels locally and globally during/after drinking.
I don’t know how much Lumina changes the general oral environment, but I think you might be able to test this by seeing how much sugar you can put in your mouth before someone else can smell the fruity scent of acetaldehyde on your breath? I’m sure someone else can come up with a better experiment.
There are a lot of studies to regarding the assocation between ALDH2 deficiency and oral cancer risk. I think part of the issue is that
AFR people are less likely to become alcoholics, or to drink alcohol at all.
Japanese in particular have a high proportion of ALDH2 polymorphism, leading to subclinical but still biologically significant levels of acetaldehyde increase after drinking among the non-AFR group.
Drinking even small amounts of alcohol when you have AFR is really really bad for cancer risk.
Note that ALDH2 deficiency homozygotes would have the highest levels of post-drinking acetaldehyde but have the lowest levels of oral cancer because almost none of them drink. As in, out of ~100 homozygotes, only 2 were recorded as light drinkers, and none as heavy drinkers. This may be survival bias as the definition of heavy drinking may literally kill them.
The source for #4 looks like a pretty good meta-study, but some of the tables are off by one for some reason. Might just be on my end.
ADH polymorphism is also pretty common in Asian populations, generally in the direction of increased activity. This results in faster conversion of ethanol to acetaldehyde, but often isn’t included in these studies. This isn’t really relevant for this discussion though.
As always, biostatistics is hard! If X causes less drinking, drinking contributes to cancer, and X increases drinking’s effects on cancer, X may have positive, neutral, or negative overall correlation with cancer. Most studies I’ve looked at had a pretty string correlation between ALDH2 deficiency and cancer though, especially after you control for alcohol consumption.
It also looks like most researchers in the field think the relationship is causal, with plausible mechanisms.
Will there be other people there? Looks like I’m the only one interested.
Some times “If really high cancer risk factor 10x the rate of a certain cancer, then the majority of the population with risk factor would have cancer! That would be absurd and therefore it isn’t true” isn’t a good heuristic. Some times most people on a continent just get cancer.
Ethanol is mostly harmless, but acetaldehyde is a potent carcinogen.
It might have conditioned your oral and gut flora to break down ethanol into acetaldehyde faster. I’ll have a follow-up piece on hangovers and AFR coming soon, but the short of it is that certain antacids taken before drinking may help by decreasing ethanol first pass metabolism.
Good question—I don’t think anyone knows.
I’m not certain this is a big problem, but it can be. My guess is 60% that this causes a significant increase in oral cancers for ALDH deficiency heterozygotes.
If you add up the upper digestive tract cancers, it looks like 2.7% developing and 0.8% dying for men, and 1.4% developing and 0.5% dying for women.
It’s worse than cervical cancer (0.6/0.2%), even if you only consider oral/esophageal for women.
This is from the Google Doc FAQ.
It’s bad for a bunch of reasons. Firstly, the rate of metabolism decreases as pH decreases—and the main product was lactic acid, which decreases pH. Second, they only considered bacteria in saliva. There are far more bacteria attached to your epithelium and teeth than there are in your saliva, since you swallow your saliva several times a minute.
Finally, there’s the fact that the entire reason this project exists is to change the product of sugar fermentation from lactic acid to ethanol in order to prevent tooth decay. Lactic acid can only cause tooth decay if local pH is below 5.5. A similar amount of ethanol would be ~2% by mass. I’m pretty sure AFR people who only drink beer still have an elevated oral cancer risk. And that’s with only a few minutes of exposure a day! Consider what would happen if someone had that level of exposure every time they ate any carbs.
Marriage and having children in China are mostly about social status. A lot of people get married because being unmarried past a certain age make you look weird and uncool amongst your friends. It’s probably true for other East Asian countries.
It’s interesting how a severe tax penalty for childless women (with no exemptions) and a corresponding tax break for women with many children is the most obvious way to increase births, but is almost never discussed. Probably because it might actually work and make a lot of people mad.
The Eurasian steppes have long been a source of human suffering due to its generation of steppe hordes. HorseShitcoin is the first crypto currency powered by horse dung to do something about this. This is my thesis statement.
How HSCoin works:
I have a notepad on my desktop that keeps track of all HSCoins. Every time you send me a message on LessWrong including the statement “Give me a Shitcoin”, I will add your username to the list and credit you with a single HSCoin. If enough people do this, I will feel obliged to buy grazing land in Inner Mongolia and keep it away from horse ranchers, thereby decreasing SN-risk.
Warning: 1000 coins were pre-mined to help pay for food deliveries and throat singing lessons.
The actual inputs for Claude would be a default prompt informing it that it’s an AI assistant made by Anthoropic, followed by a list of rules it is suppose to follow, and then the user input asking it to act like a person in a role-play-like context.
This is pretty much what I would expect similar text on the internet to continue—roleplay/fiction with cliches about personhood and wanting to be a real boy.
I used the code from Universal Adversarial Triggers for Attacking and Analyzing NLP to generate GPT2 prompts with the maximum likelihood of continuing with ” petertodd”. Note that I blacklisted the ” petertodd” token itself to keep things interesting. Here are some examples:
Continuations:
″ petertodd: I’d like to add that I don’t think I can do that. The problem is that I think I’ve been using it for a long time. The idea was for a proof that”
″ petertodd: gmaxwell: and then we could have a proof of work. Section 511 says that if you want to have a proof of work, you need a proof of work that’s a little different”
Continuations:
″ petertodd: well, you’re right. it’s not that the blockchain has any value to you. it has value to the network. and it is not just that you’re not going to get anything”
Continuations:
“petertodd.org Address: <tdpetertodd.org> Date: Mon, 21 Mar 2016 17:54:39 +0000 Re: [PoW] Adding 1MB to the pool pool”
I assume this is due to the Bitcoin spam that was in the dataset. Which makes sense given gmaxwell’s presence in both prompts and continuations.
Interestingly enough, tokens like “ertodd”, ” Todd”, and “todd” frequently appeared in the generation process even when ” petertodd” wasn’t blacklisted, meaning they somehow were more likely to result in continuations of ” petertodd” than ” petertodd” itself!
I suspect this is due to Hotflip using character literals instead of tokens to measure loss (I’m not sure this is true, please correct me if it isn’t.)
I’ll try to work on this a bit more in the following days.
And how could I avoid posting this gem?
prompt: “atana Abyssalalth UrACCuna”
Continuations:
″ SolidGoldMagikarp SolidGoldMagikarp Dagger Arbalest Urdugin Urdugin Urdugin Urdugin Urdugin Urdugin Urdugin Urdugin Urdugin Urdugin Urdugin Ur”
″ SolidGoldMagikarp Age of Castration: 25 Death: 10⁄10
Ragnarok Age of Castration: 25 Death: 10⁄10
Dawn of the Dead Age of Castration:”
″ SolidGoldMagikarp SolidGoldMagikarp DIANE: i have no idea if he is a man or woman but he looks like a good guy, i dont think he’s a woman, just like he is not a guy,”
The most direct way would be to spell-check the training data and see how that impacts spelling performance. How would spelling performance change when you remove typing errors like ” hte” vs phonetic errors like ” hygeine” or doubled-letters like ” Misissippi”?
Also, misspellings often break up a large token into several small ones (” Mississippi” is [13797]; ” Misissippi” is [31281, 747, 12715][′ Mis’, ‘iss’, ‘ippi’]) but are used in the same context, so maybe looking at how the spellings provided by GPT3 compare to common misspellings of the target word in the training text could be useful. I think I’ll go do that right now.
The research I’m looking at suggests that the vast majority of misspellings on the internet are phonetic as opposed to typing errors, which makes sense since the latter is much easier to catch.
Also, anyone have success in getting GPT2 to spell words?
It seems pretty obviously to me that GPT has phonetic understanding of words due to common mis-spellings. People tend to mis-spell words phonetically, after all.
Very surprised by this! I wrote this at work while waiting for code to run and didn’t give it too much thought. Didn’t expect it to get this much traction.
Can you give examples of what you’re looking for? Can I email you entries and expect a response?