Thanks for this! Just to clarify what I meant by "manual distribution", if you've written a dating profile outside of a dating app, you've basically got to share a link if you want anyone to read it (see e.g. this post).

I'd be lying if I said I hadn't considered it 🤣

Advanced AI is a bomb, and we're about to set it off without any safety equipment.

For Policymakers

Suppose you find out that you had a disease will kill you. The prognosis is grim: with luck, you have a decade left. But maybe that's long enough to find a cure! How much is that cure worth to you?

Uncontrolled advanced AI could be the disease that kills you. The prognosis is grim: with luck, you have a decade left. But maybe that's long enough to find a cure! How much is that cure worth to you?

For Policymakers, original-ish

How hard did you think about killing the last cockroach you found in your house? We're the cockroaches, and we are in the AI's house. For policy-makers, variant on the anthill argument, original source unknown

Fair point. It does seems like "pandemic" is a more useful category if it doesn't include a whole bunch of "things that happened but didn't kill a lot of people."

Without aging, COVID-19 would not be a global pandemic, since the death rate in individuals below 30 years old is extremely low.

A pandemic is an epidemic that occurs across multiple continents. Note that we can accordingly envision a pandemic with a death rate of zero, but a pandemic none-the-less. Accordingly, I think you've somewhat overstated the punchline about aging and COVID-19, though I agree with the broader point that if aging were effectively halted at 30, the death rates would be much, much lower.

Fun! I wish I had a lot more time to spend on this, but here's a brief and simple basis for a decision:

library(readr)
library(dplyr)
library(magrittr)

training <- read_csv("https://raw.githubusercontent.com/H-B-P/d-and-d-sci/main/d_and_d_sci.csv")

training %<>%
  dplyr::mutate(outcome = ifelse(result=="succeed", 1, 0))

model <- glm(outcome ~ cha + con + dex + int + str + wis, data = training, family = "binomial")

summary(model)

start <- data.frame(str = c(6), con = c(14), dex = c(13), int = c(13), wis = c(12), cha = c(4))
predict.glm(model, start, type="response")
# > 0.3701247

wise <- data.frame(str = c(6), con = c(15), dex = c(13), int = c(13), wis = c(20), cha = c(5))
predict.glm(model, wise, type="response")
# > 0.7314005

charismatic <- data.frame(str = c(6), con = c(14), dex = c(13), int = c(13), wis = c(12), cha = c(14))
predict.glm(model, charismatic, type="response")
# > 0.6510629

wiseAndCharismatic <- data.frame(str = c(6), con = c(14), dex = c(13), int = c(13), wis = c(20), cha = c(6))
predict.glm(model, wiseAndCharismatic, type="response")
# > 0.73198

It would also be very useful to build some GPT feature "visualization" tools ASAP.

Do you have anything more specific in mind? I see the Image Feature Visualization tool, but in my mind it's basically doing exactly what you're already doing by comparing GPT-2 and GPT-3 snippets.