thedevil

> not as much as if they also got vaccinated, so it seems like the original point still stands

If previously-infected had as much resistance as vaccinated, they should not be more scared than the vaccinated. Even if they would have more resistance if they were also vaccinated.

Did you mean to say that previously-infected have much less resistance than the vaccinated? I would very grateful for a source on that. I tried to find out numbers on that but couldn't find apples-to-apples comparison.

The statements are logically consistent. You're incorrectly assuming he meant "only" in the first statement.

Those two statements are part of the same thread and LeCun makes it more clear in that thread. And the article says, right after the second statement: "If you read that thread, you’ll see that LeCun seems well aware of everything I’ve laid out in this post"

It sounds like celebrating, not advocating.

That's a very different thing.

It's not just mammals and I wouldn't frame it as them being disposable when propagating the species.

There's a bigger payout at the right tail of the distribution for male genes. Male genes can sometimes have 1000 offspring. Female genes generally can't. Male genes are generally more likely to have 0 offspring. So male genes have less to lose and more to gain by taking risks, behavioral and biological risks. See also male variance hypothesis.

This varies by species and the pattern is likely stronger in more polygynous species and weaker in more monogamous species. See also sexual dimorphism. Human males take more behavioral risks on average and have higher mortality rates throughout life (not only from behavior).

I warn you though, this is a controversial subject because of some of its possible implications.

Also I should disclose that I'm not an expert, just a layperson interested in this and some related topics.

One thought: If you see a resume with Coinbase until October 2020, that's probably someone who prefers political activism in the workplace.

That might be seen in a positive or negative light, depending on your stance.

I personally am afraid to associate myself too publicly with a political stance, lest I be wrong and/or the environment changes.

Out of curiosity, I looked for their policies on misinformation.

I could see some potential risks here but it still seems reasonable to me:

> Demonstrably inaccurate content

> YMYL content that contradicts well-established expert consensus

> Debunked or unsubstantiated conspiracy theories

This part, I imagine, is a bit more controversial:

> pages that potentially spread hate, cause harm, or misinform or deceive users, should receive the Lowest rating.

This is similar to what I've been thinking. I usually say "the brain is more like a board than a CEO".

One additional interesting tidbit I've noticed is that when the brain decides on a belief, it seems to suppress alternative beliefs. For example, with the old-lady-young-lady picture, it's hard to see them both at the same time.

> Politics is not a truth-seeking exercise. It's no science, law or even journalism. It's an allegiance & authority forming exercise.

You've articulated well something I've been trying to say for a while.

I've been saying I try to avoid politics because politics is the opposite of math - the more you learn, the more stupid your thinking becomes.

I feel like my explanation just made me sound like a hater and yours nails the problem on the head.

Rather than separating claims into "covered" and "not covered", I recommend a third bucket "might be covered". I think this goes into that third bucket. Whether or not this particular claim should be paid doesn't mean it's not in the third bucket.

When buying insurance, you shouldn't rely on payments for claims in the third bucket, especially not without a legal fight.

Many consumers don't understand any of this. But I expect (hope) that most large businesses are sophisticated enough to understand this.

I don't think this will hurt Zurich's business, because most of their customers probably understand this.

The part that I found most interesting was the idea that we're moving away from low-capital startups.

Does that mean venture capital is going to have to get much bigger to fund these ventures? Does it suggest the ballooning of worldwide VC funding isn't just a bubble? (There's a really interesting chart in WSJ that I can't find right now). And will a few massive VCs become more powerful while smaller ones fizzle? What about ycombinator?

Does it mean that technical talent will be more or less valuable? Much more talent will be needed, but there's probably less opportunity to build a massive business with a few talented geeks as the low-hanging software fruit is probably plucked. Talented developers might be less critical to business success.

> Typically the only parents that have any control over their child's "opportunities and levels of education..." are those in the uppermost classes.

I'm not in "the uppermost classes". I'm probably at the 80% of household income and went to a state school.

I put my son in a fantastic school in a well-off neighborhood rather than the default assigned school (my mom drives him there and I pay a babysitter/nanny to drive him back). Most of the kids that can read a little bit before entering kindergarten.

He goes to various sports camps and activities classes. His babysitter (more like a part-time nanny) helps teach him to read. We'll probably pay for music lessons and various tutoring later. I also taught my son addition and subtraction of small numbers before entering kindergarten. And we've done a variety of activities to help him learn about science, money, and more.

I tend to rent despite owning real estate.

I tend to move often and only buy real estate if I'm willing to hold it forever. So I buy what I think is a good long term investment and live where I want to live.

I wouldn't be too confident in that. Actuaries are not able to implement the models they'd like to. Regulators don't like complex models and they don't like models with disparate impact. Or models that make some risks (accurately) expensive.

I'm a developer now but by last few years in actuary work were mostly spent helping insurance companies deal with regulation. We did some modeling with with machine learning but ultimately the models that got approved had to be simple (compared to machine learning). My job before that was a lot about internal negotiations with other departments.

Better models were not the main bottleneck.