Someone on Twitter just shared this very interesting essay. "Does A=A? I'm not so sure" by James Lindsay
It's a postmodernish musing on the truth of arithmetic statements! π I read it so you don't have to.
It disappeared while I was reading so this tweet is now the only copy!
I know it sounds like I'm making this up but this essay is gone like it never existed! The only reference I could find to the page on the internet is this comment on goodreads. goodreads.com/author_blog_poβ¦
It may (or may not) surprise you to know that this man, James Lindsay, has mocked me mercilessly with all kinds of mean-spirited memes and sneering tweets for my philosophical musings about arithmetic. Portraying me as juvenile and dangerous.
Let me save you reading his whole essay. At the end of the essay he concludes A is not always A if A is not an abstraction. In other words, he makes the same point I made that mathematical statements are ideals that don't necessarily correspond to physical reality.
His argument is things change over time. It sounds like 2013 James Lindsay would agree with me on a lot but disagree with 2021 James Lindsay.
James Lindsay β James Lindsay.
This is the original link to the essay. It seems to be dead as I mentioned.
For folks posting the wayback links, those donβt work for me. I think the page might be deleted but all the servers on wayback arenβt updated yet. This βarchive isβ link seems to work though archive.is/iBGNe
β’ β’ β’
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Many of the biggest tech trends in data analysis can be seen as increasingly sophisticated answers to the question, "How do we monetize data?" π§΅π
The first answer to this question was the buzzword BIG DATA. People thought all you needed was a lot of data, didn't matter what kind, and it would basically monetize itself. Unfortunately, this was incorrect. So the next question became "How do we monetize lots of data?" 2/9
The answer to this question turned out to be the next buzzword. DATA SCIENCE. At this point, people still thought data was inherently easy to monetize so they figured anybody could do it. This turned out to be wrong as well. So the new question became... 3/9
Are you interested in learning statistics or data analysis?
I think learning how to analyze data is tricky because it's actually 3 independent skills.
- Coding
- Applied Knowledge
- Probability Theory π§΅π
When I first started learning data analysis, it was frustrating for me to realize that being good at one of these skills didn't mean I was good at all of the others. So, If you've ever felt that way, you're not alone. 2/8
Coding: Being good at coding allows you to implement your ideas. While it's possible to get by using software, it will limit you as a data analyst. 3/8
I gave up on talking about race on Twitter because I was having the same argument over and over again. In this thread, let me explain THE ANATOMY OF A TWITTER RACE ARGUMENT.
Whenever someone says "X is white supremacy" on Twitter where X is perfectionism or individualism or math worship, there are a constellation of reactions. Many of them predictable.
If X is a genuine point of division, it will often be the case that most white Americans tend to do and like X while most black Americans tend to dislike and not do X. This cultural difference may or may not be problematic.
I've been in science for a while now and as far as I can tell, there are two types of people in this line of work. Those that think we should give everything to science and those that don't.
These two mindsets produce two types of work environments. I'll call them the results-first workplace and the people-first workplace.
In the people-first environment, they prioritize healthy work habits and relationships. Science is a critical piece of a whole and healthy life. In the results-first environment, all that matters is the outcome. People get the job done whatever the cost.
At the beginning of a science, the first step is always to declare the thingness of something that we want to study. This is a star. That is a cow. This is a society. That is a race. This is a mind. That first step is actually a huge step which we rarely ever talk about.
It's just kind of assumed that obviously we can just identify things as clearly being real using our senses and our intuitions and as long as our scientific conclusions seem predictive to us (using the same senses and intuition) then we assume we must be on the right track.
Social phenomena present a real challenge here because we can't perceive social reality directly with our senses and different people have different intuitions which seem to lead to different frameworks which all seem to have some predictive validity.