FrenziedFelidFanatic

Deep learning doesn’t stop at llms. Honestly, language isn’t a great use case for them. They are—by nature—statistics machines, so if you have a fuck load of data to crunch, they can work very quickly to find patterns. The patterns might not always be correct, but if they are easy to check, then it might be faster to use them and modify the result compared to doing it all yourself.

I don’t know what this person does, though, and it will depend on the specifics of the situation for how they are used.

I want more early vaccine data, actually, so that’s good.

There is a significant decrease in cancer rates among vaccinated compared to unvaccinated, but the early/late divide is less clear. If my statistics is up to snuff (no guarantee there), you can expect an error of ~sqrt(n) in discrete data where n is your count. With the late vaccines, this means an error in the cancer rate of about 2 because they saw ~4 cases (3.2 * 124,000/100,000 ≈ 4). If this is actually overestimating, we could see the rate as 2/124000 or 0.64/40000. In this case, you wouldn’t necessarily expect to see any cases in a sample of 40000.

So it’s not clear from this that early is better than late, though it certainly doesn’t suggest that it’s worse.

The total sample sizes aren’t the problem. It’s the number of people who contracted cervical cancer. I should have been more specific originally: I would want more data to show that early vaccinations are more effective than late ones.

40,000 seems like a lot, but just using data from the late-vaccine group would get an average contraction rate of ~1. That’s enough for an outlier or two to be significant. If 2 of those 40,000 had contracted cervical cancer, it would be a hard sell to say early vaccines cause cancer (though some groups would eat that up). In the same way, I’m not fully convinced here that an early vaccine prevents it more effectively than a later one.

This is just from a cursory overview, but…

N = 40,000 where unvaccinated rates are 8.4 / 100000 or 3.36 per 40,000. Later vaccines brought this down to 3.2 / 100000 or 1.28 / 40000.

So… it’s significant, but I would want more data.

Tropospheric so2 is a problem for reasons beyond warming.

Stratospheric so2 might not be a problem, but geoengineering is always risky.

Plus, since so2 is significantly more reactive than co2, it will be removed from the atmosphere more quickly, meaning that it can only act as a temporary mask without constant maintenance. All-in-all, it’s probably best to see how much damage we are doing early on before we find ourselves in the so2 equivalent of credit card debt and slowly poisoning ourselves to death trying to stay cool.

When those weight loss drugs are actually diabetes drugs that have been co-opted by the market in the same way graphics cards are now used for crypto and ai… yes.

Tenure is—and should be—powerful. UPenn is an R1 institute; if her research is good, it will be hard to do anything until it becomes a significant issue. Like now.

The software is free, but it looks like the trademark is not. So WordPress bans WP engine from some WordPress stuff b/c they aren’t technically WordPress. In other words, they’re free to use (and change) the software, but they can’t (or, rather, shouldn’t) use the name—according to WordPress. WP sues for usage anyway after they are barred from some event or something, but now WordPress is suing back, turning an unofficial dispute to a legal one.

1. that’s why we know it’s a dog whistle and not a coincidence
2. 1488 is one of the more well-known ones insofar as it is used as an example of a dog whistle when explaining the concept more in-depth

It would probably have to be updated in each place it is used, and these articles are unlikely to be frequently updated. It’s only had that name for a few years.