Great article!
Maybe homologous recombination should be mentioned as the reason why "the newborn cell receives an assemblage of random pieces of each parents' genome". Just mixing chromosomes would not be enough to stop muller's ratchet.
I did train a transformer to predict moves from board positions (not strictly FEN because with FEN positional encodings don't point to the same squares consistently). Maybe I'll get around to letting it compete against the different GPTs.
The game notation is pretty close to a board representation already. For most pieces you just go to their last move to see on which square they are standing. I assume that is very readable for a LLM because they are able to keep all tokens in mind simultaneously.
In my games with ChatGPT and GPT-4 (without the magic prompt) they both seemed to lose track of the position after the opening and completely fell apart. Which might be because by then many pieces have moved several times (so there are competing moves indicating a square) and many pieces have vanished from the board altogether.
The ChessGPT paper does something like that: https://arxiv.org/abs/2306.09200
We collect chess game data from a one-month dump of the Lichess dataset, deliberately distinct from the month used in our own Lichess dataset. we design several model-based tasks including converting PGN to FEN, transferring UCI to FEN, and predicting legal moves, etc, resulting in 1.9M data samples.
You could still offer money up front. Getting $2000 if the stars align is still much more likely than getting $150,000.
I thought about giving the long flu example, but flu is much less contagious than covid and does not infect everyone yearly. That holds even more for SARS or MERS.
People aren't betting with you because the utility of money is not linear.
If you own $150,000 it is very unlikely that $1000 makes any difference to your life whatsoever. But losing $150,000 might ruin it.
Here is a way around that problem: You both wager $1000 (or whatever you like). When the bet is resolved you throw the dice (or rather use a random number generator).
If you win you throw the 99.33% probability "you get paid"-dice.
If your opponent wins he throws the 0.66% probability "he gets paid"-dice.
(If the [0,100] random number is <0.66 your opponent gets $1000 if he wins. If the random number is between 0.66 and 100 you will get $1000 if you win. In the other combinations you both keep your money.)
So instead of wagering a larger amount of money your opponent wagers a larger probability of having to pay in the event of losing the bet.
PS: Yes, you can also just wager small amounts of money but that's kinda boring.
Isn't there also evidence that long covid is partly psychosomatic? (random paper that lists some studies)
The number of people prone to psychosomatic symptoms is probably not going to go up, so your growth rate should be overestimated.
There are also other risk factors involved, same argument applies to those.
In the extreme scenario those people prone to develop long covid already have it and very few other people will get it.
The assumptions in your simulation also seem consistent with that possibility:
Maybe the reinfection long covid probability of 5% is mostly the 60% of the 10% ... ;-)
For what it's worth I know zero people with long covid and I have also never heard anybody mention an acquaintance with long covid.
I think one salient point is the fact that we live in a world where the number of children you have is pretty much directly equivalent to your evolutionary fitness. In the past your evolutionary fitness was bottlenecked by whether you survive childhood, whether your children survive childhood, whether you are able to feed your children, etc - all in a malthusian environment.
This means that the selection pressure for genes that increase your fertility is extremely strong. Much stronger than any selection pressure on any single trait that has been selected for in the past, say light skin or lactase persistence in Europeans.
For what it's worth, I read it when it came out and loved it. I lent it to a friend who never gave it back, which is probably another point in favour. I also enjoyed the follow-up "On the origin of good moves".