ckarmann

Interesting way to prevent your awesome colleague from being poached by competitors.

I am just speculating, but in many diseases like rabies and chickenpox you can give the vaccine after exposure to help the immune system fight the infection. It's because the period between exposure and symptoms is quite long, often more than a month for rabies for example.

HIV can stay in the body for years before developing AIDS. So you can actually recruit people that have been tested positive recently and have not developed the disease.

I don't know but these fossils are often very fragile and can even collapse under their own weight when unearthed. There is little chance something like this would have survived centuries.

Exactly this. Just google "infrared images of space", it's all false colors but still incredible pictures.

> They likely were, and think of me what you like but I've noticed the same with most Americans I've known.

I am probably misunderstanding what you say, but Cornet has grown up in France, and is probably very aware that health care cost in the US is not a normal thing since he comes from a country where it is considerably cheaper if not free.

His naiveté was about the nature of Google and how he thought a giant corporation basing their revenue on ads and collecting information could be a positive force in the world and not abuse its power. It seems to me his gripe is about the hypocrisy, which in a sense make it even harder to fix the company: if people denies the company ethics problems, you can be sure they are not going to get better.

Twitter in that sense has never promised to not be evil. You can't be naive about its ethics because you just have to doom-scroll the app for an hour to feel bad about it.

Trying to translate in French, I came up first with "Il eût fallu qu'elle eût été surveillée", but mostly because of morphological similarity (it's almost the same amount of verbs, right?). It's also just a more literary form of the simpler "Il aurait fallu qu'elle soit surveillée".

That being said, I have trouble to parse the original english sentence so I may be missing subtleties.

Japan was never a colony, and Vietnam was a french colony for several generations. You are not getting your examples right.

Google (if you're not a work) "Anilingus".

Obviously the primary intent was not loss of life. But there are multiple possible intents, one being obviously to instill fear: "We didn't kill you this time, but we totally could have". Imagine living in the area now.

Anyway, it's better to wait for more information than to speculate.

No known ancestor of Hitler is Jewish. His paternal grandfather is unknown, and that led some people to speculate that he was Jewish.

Hitler had his minions try to clear that up to dispel the rumours, but nobody could find the real father. They concluded of course that he was a pure Aryan.

Source: https://en.wikipedia.org/wiki/Alois_Hitler#Biological_father

I don't think this one has a specific name. There are a lot of unnamed craters on Mars, even much bigger than this one. [0]

[0] https://en.wikipedia.org/wiki/List_of_craters_on_Mars

2000km is the size of the whole Vastitas Borealis region. The photo depicts only a single unnamed crater inside it.

Well, that's the concept of the CrashCourse channel. This video is actually slow-paced compared for example to their World History series.

It's still interesting. After a while you learn to pause the video to let the information sink in.

The Dispossessed - Ursula K. Le Guin

Harry Potter - JK Rowling (re-read it all when the last book came out)

It's hard to get back to Plato's cave.

Does Amazon pretends to do AI? They are just offering a platform to do your own Machine Learning. I don't think they ever said their search engine was doing anything smart.

EDIT: scrap that, I didn't mean Alexa, which is doing AI obviously, but the search engine of Amazon's retail website.

Anyway, NLP is hard and everyone sucks at it. Think about it: just building something that could work with any <N1> <preposition> <N2> or any other way to express the same requests would mean understanding the relationships of every possible combinations of N1 and N2. It means building a generalized world model that is quite different from simply applying ML to a narrow use case. Cracking that would more or less mean solving general AI which probably won't happen soon.

At least in some European countries where COVID cases has submerged the healthcare system, it is a very bad time to have a stroke or a car accident since the ER rooms are full and a lot of hospital resources are diverted to fight the pandemic. So it increases the mortality of these other situations.

You also have to take into account the uncertainty in your risk evaluation. The fatality rate is largely unknown so it's a bit deceptive to quote numbers like 0.37% as if it could be measure up to the second decimal. The German study probably came with error bars that were larger than that. For the age group 50-59, maybe it's 1.3% according to NY state (for both males and females so if the person you responds to is male, it's higher), but it could very well be 2% (or 0.5%) without it being surprising.

There is also a big assumption in considering the risk to be front-loaded. This is not the flu, but it could very well come back next year or in ten years in a different form. We just don't know yet this virus with enough confidence to do this assumption. Anyway, it is a bit strange that the ability for this virus to mutate next year would impact the risk of dying by going out tomorrow which, I think, the person your responded to was thinking about.

Finally, you're saying the flu deaths this year is double what the coronavirus has done, but again the uncertainty is high: the CDC estimation for the flu is from 24,000 to 62,000 deaths this season [1]. So it could be the double, but it could be actually lower. Let's not do things like chosing the estimate that better suits the argument without saying it's a high end of the estimate.

[1] - https://www.kcrg.com/content/news/Americas-2019-2020-flu-sea...

Ah, I understand what you mean. Well, if you know variable X is a proxy for Y, but Y is a lot longer to measure than X, then I guess it makes sense to optimize X knowing that it will increase Y (if you are sure X will bring Y).