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ossicones

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Data scientist in healthcare.

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Three-Em Dash 3 months ago

Kierkegaard found an application for the three-em dash here: “I have just now come from a party where I was its life and soul; witticisms streamed from my lips, everyone laughed and admired me, but I went away ⸻ yes, the dash should be as long as the radius of the earth's orbit ⸻ and wanted to shoot myself.”

I sympathize with this. I wonder if the author might find it helpful to reimagine the thinking they do as coming up with good questions, rather than good answers. I was inspired to try to do so myself after reading this essay: https://link.springer.com/article/10.1186/s13059-019-1902-1

"[I]f a scientist proposes an important question and provides an answer to it that is later deemed wrong, the scientist will still be credited with posing the question. This is because the framing of a fundamentally new question lies, by definition, beyond what we can expect within our frame of knowledge: while answering a question relies upon logic, coming up with a new question often rests on an illogical leap into the unknown."

If you've ever taken a depression screener at a wellness visit, that's a consequence of this work. This paper describes how unreliable psychiatric diagnosis used to be. There were standards, but they ultimately came down to physician judgment. This created demand for more objective standards, which resulted in the "checklist" approach that we have now.

I shared this with my partner who works in palliative care. He said that it’s rare to hear people expressing deathbed regrets like this. What he hears more of is people saying that their illness is God’s punishment for a behavior pretty universally accepted as bad, especially when there’s substance abuse involved.

Does anyone know how are these patterns were intended to be applied? It seems like they might be block printed, but the fact that they're called "dyeing patterns" makes me think of some kind of resist or shibori.

International students often end up subsidizing US students. Restricting student visas might end up actually increasing tuition.

Also, I disagree that $1m/year for the president of Harvard is ridiculous. That's less than the CEOs of many regional hospital systems are paid, and I think the impact of Harvard is much greater.

With you on restricting student loans for non-STEM programs, though.

If researchers were unable to trust their collaborators, it would mean that they would have to master and oversee every step of every process. This would stop interdisciplinary research completely and massively reduce output.

In software, this would be the equivalent of not importing a package unless you first checked the code line by line.

The system works okay now, but there's a lot of room for improvement. I generally think the best way to improve it is to require more open data so that consumers of research can validate the findings of papers.

I would’ve preferred a less editorialized article about this. In particular, this article has left me wondering who’s actually written fraudulent articles and whose biggest mistake was trusting the wrong collaborators.

The excellent new book, "Modeling Social Behavior" by Paul Smaldino, starts with a quote from Jakob von Uexküll that I've abbreviated a bit here:

"An unbroken description of reality would be simultaneously the truest and most useless thing in the world, and it would certainly not be science. If we want to make reality and therefore truth useful to science, we must do violence to reality. [...] In nature, everything is equally essential. By seeking out the relationships that seem essential to us, we order the material in a surveyable way at the same time. Then we are doing science."

I appreciated how this quote emphasizes that science and modeling are inextricably connected.

Readwise has become essential for me. It's an RSS reader, read later app, and spaced repetition app in one. The idea is that you take in new articles to read, highlight them, and then review the highlights daily via spaced repetition.

It's brought to life for me Richard Feynman's idea that the way to do great work is to keep a dozen questions at the forefront of your mind and wait for inspiration. It's remarkable how often I'll see something in my daily review that helps me reframe my approach to a question.

The way that this book answered its title question was unsatisfying to me. It felt like it was so focused on the data structure of embeddings that it totally glossed over questions about what the structures mean, why they work so well, and emerging research issues in embeddings.

It's like if someone wrote a book called "What is consciousness?" and just provided an annotated list of neurotransmitters. That approach leaves a lot out.