Tangentially, I wonder: has anyone built a friendly browse/search interface for all-time CVE data [0]? This makes me curious about what the history of SQL injection vulnerability discovery looks like.

0: https://cve.mitre.org/data/downloads/index.html

This armchair level of understanding (even if my summary is not right on the nose) is what we need when we encounter people who actually want to politicize the weather. Will we defeat ignorance, or will ignorance defeat us?

IMO it's simple (and this is of course a common view): healthcare in the United States is a failed market. It's not about free market fundamentalism vs socialism, it's just that in this case, this market is totally broken. Costs are completely out of control, and accordingly, access, that is, access without incurring crippling debt, is a huge problem.

Obama tried to address both cost and access. He went up against the insurance companies, and lost. He got somewhere on access, but even that is just access to insurance; insurance that may leave you with thousands of dollars to pay out-of-pocket if you walk into the ED with a complaint. So maybe you don't go.

Socialized medicine, it seems, certainly won't fly in this country. But socialized insurance, "Medicare for all"? We, the little people, have many reasons to support a multi-decade experiment with that.

There's no ideology here. It's just insane (unless you're profiting!) given history for us to continue imagine that in this case the Invisible Hand is going to fix things.

"Global aviation’s contribution to the climate crisis was growing fast before the Covid-19 pandemic, with emissions jumping by 32% from 2013-18"

Should be:

"Global aviation’s contribution to the climate crisis was growing fast before the Covid-19 pandemic. Aviation's contribution to global CO2 emissions rose from 1.3% to 1.9% from 2013-18"

But the latter doesn't attract clicks so much. Transportation overall accounts for 28% of greenhouse gas emissions [1]. That's the story that matters.

There's certainly a point here about wealthy nations and wealthy people being responsible for a lion's share of the problem, but really, everyone knows that. This article, and indeed the linked paper [2] gloss over the relative size of the aviation problem.

[0] https://ourworldindata.org/co2-emissions-from-aviation

[1] https://www.epa.gov/ghgemissions/sources-greenhouse-gas-emis...

[2] https://www.sciencedirect.com/science/article/pii/S095937802...

Does the distinction matter? I think so. There's a big difference between the provider promising to keep your data secure and to not do anything underhanded with it versus the provider simply not having access to your data.

[0] https://www.theverge.com/2020/3/31/21201234/zoom-end-to-end-...

If I'm teaching an intro class using a general purpose language, it's Ruby or Python. My heart lies with Ruby, but having basic Python chops is such a bigger win downstream.

  for i in range(1, 101):
      if i % 15:
          print ('FizzBuzz')
      elif i % 3 == 0:
          print ('Fizz')
      elif i % 5 == 0:
          print ('Buzz')
      else:
          print (str(i))

  (1..100).each do |i|
    if i % 15 == 0
      puts 'FizzBuzz'
    elsif i % 3 == 0
      puts 'Fizz'
    elsif i % 5 == 0
      puts 'Buzz'
    else
      puts i
    end
  end

The literature on Gödel and philosophy is gargantuan, for some reason. Wasn't it summed up well by Wittgenstein? Paraphrasing: "Who cares about your contradictions?" Well said. Also not the topic Wolchover's article.

Math people can make the same move: "Who cares about your philosophizing?"

Wolchover is masterful here. The layers of abstraction keep piling up, and I had to read the last part more than a couple of times to really get it, but then you have it.

[0] https://en.wikipedia.org/wiki/Splitting_(psychology)

Am I seeing a biased sample or is LW out of fashion these days? If so, why?

Socrates, as we receive him, opened peoples' eyes about causation, too. If anyone did it like a jerk it was him. Leading you down the path of your own ideas into a contradiction, just to show you what you don't know? That's embarrassing and unhelpful. I'd rather be lectured.

Is he the friendliest explainer? Surely not. Who cares? His gifts in communicating deep ideas in science to lay people were beyond compare.

I see a parallel with our healthcare system. Undoubtedly, though things are bad and getting worse, capitalism kinda' works. But in the U.S., healthcare is a failed market and that's not going to change. Time to start over. Same with police unions.

On p-values and linear regression in general, though: when you're new to inferential statistics applied to really complex data, such as anything that relates to human behavior, you go through this "clearly that's not a linear relationship" phase. But that's not really the point. You can choose any sort of function you want to maximize r, the options are endless [0]. But linear regression has a distinct advantage in that you can interpret the model coefficients as meaningful numbers. You can say things like "for every 5% increase in the proportion of binge drinkers in your state you can expect an X% increase in the proportion of the population that will get Covid" ...if the model satisfies some significance parameter threshold, like p<0.05, and, you know, correlation equals causation. Everyone knows that. Anyway, with your great 4th order polynomial, all you can say is "see, it fits!"

About significance thresholds. Yes, they are totally arbitrary, another realization in one's journey with frequentist statistics that is quite deflating. Still, we need a rule of thumb so we use things like p<0.05 and have a bunch of fancy ways to account for things like multiple comparisons, which increase the likelihood that some spurious correlation would have appeared to be significant without such adjustments.

This stuff is all super-useful when used appropriately. That's why, when you need to create a model (outside the Bayesian/ML anything goes world) and you need to get it right, the first thing you do is reach out to your trusty PhD statistician friend. At least, that's what I do. They spend countless hours to get to a place where they can say "in this situation, I would suggest..." I'm glad some people are into it that much.

[0] https://lmfit.github.io/lmfit-py/builtin_models.html

And for not-infrastructure cloud services, aren't they pretty interchangeable? I recently evaluated four different cloud speech-to-text services. Sure the APIs differed, but you know, it's an API call. I see nothing to complain about.