# Trying out Medium

I have no problems with the Svbtle platform. I have just seen the majority of new writers moving to Medium, and I figure I should give it a fair comparison.

So I published an article there. Check out the profile and the article.

I have no problems with the Svbtle platform. I have just seen the majority of new writers moving to Medium, and I figure I should give it a fair comparison.

So I published an article there. Check out the profile and the article.

Dear producers and directors,

For $100 per scene, I will verify the authenticity of all mathematical lines, props, documents, and boardwork used in that scene. In the event that said math is inauthentic, I will suggest an authentic replacement.

Your friendly neighborhood mathematician,

Jeremy Kun

A lack of authenticity can ruin a tense mood and make a group of supposed experts seem like fools. Designers go through great pains to make costumes or a set authentically French or authentically 1920’s, to avoid anachronisms, and to use contemporary idiom. Medical television shows are applauded by my medical school and resident friends for their accurate jargon. But when it comes to anything mathematical, despite it sometimes being crucial to the plot or characters or setting, television shows and movies hardly seem to try to get it right.

It used to be this way with technology

I have a love-hate relationship with visualizations of mathematical ideas.

Let’s say I’m trying to learn about a difficult mathematical concept. For this example I’ll use Markov chains because I recently saw a highly-appreciated visualization of Markov chains due to Victor Powell and Lewis Lehe. For now I’ll pretend I’m the typical person who claims to be a visual thinker and the only reason I don’t get math is because nobody is patient enough to explain things in a way I can understand. (Such people are everywhere.)

So I’ve heard the mysterious term Markov chain, and tried to learn about it previously by reading a book. Maybe I want to even write a computer program to “do” a Markov Chain, whatever that means. I go check out the Powell-Lehe visualization and at the end I think “Wow! That was so easy to understand! A Markov chain is just a little diagram with a ball bouncing around

Since I started thinking about my own job opportunities, I have always heard and considered Microsoft as the best place for research in industry. Other companies are also considered pretty excellent, but Microsoft tends to make the top of the list in terms of who they hire and how they make it easy for great people to do great work.

For example, when Yahoo closed their New York research lab two years ago, Microsoft offered every fired researcher a job, and even opened a new lab in New York so they didn’t have to move! And though I haven’t verified this, from what I’ve heard Microsoft has never (before now) fired a researcher. They vet their candidates and hire people they intend to keep for the long haul. Microsoft puts people in charge of the research labs who understand that the primary goal is to further the state of the art. And they have a strong track record of doing just that.

In the discussion surrounding a series of recent articles on the question of how mathematics relates to programming (one of my favorite navel-gazing topics), the following question was raised multiple times

If mathematics is so closely related to programming, why don’t professional (research) mathematicians produce great code?

The answer is quite a simple one: they have no incentive to.

It’s pretty ridiculous to claim that a mathematician, someone who typically lives and breathes abstractions, could not learn to write well-organized and thoughtful programs. To give a simple example, I once showed my advisor a little bit about the HTML/CSS logical flow/style separation paradigm for webpages, and he found it extremely natural and elegant. And the next thing he said was along the lines of, “Of course, I would have no time to *really* learn and practice this stuff.” (And he says this as a

You’re right, programming isn’t math. But when someone says this, chances are it’s a programmer misunderstanding mathematics.

I often hear the refrain that programmers don’t need to know any math to be proficient and have perfectly respectable careers. And generally I agree. I happen to think that programming only becomes fun when you incorporate mathematical ideas, and I happen to write a blog about the many ways to do that, but that doesn’t stop me from realizing that the vast majority of programmers completely ignore mathematics because they don’t absolutely need it.

So when Sarah Mei argues in her article “Programming is not Math” that math skills should not be considered the only indicator of a would-be programmer’s potential, I wholeheartedly agree. I’ve never heard anyone make that argument, but I’m much younger than she is. Having faith in Mei’s vast life experience, I’ll

I have been gradually making my way through Scott Aaronson’s wonderful book, “Quantum Computing Since Democritus.” The book is chock-full of deep insights phrased in just-technical-enough language (the kind which I want to relay to the world through an internet megaphone). Scott really has learned how to apply the good and bad attitudes of the past to the problems of today.

For example, did you know that originally computers had so many problems with errors that many people argued fault-tolerant computers would never exist? This was before the transistor, of course, but it was believed that the external world would always have such an adverse interference with the physical machine that one could not reliably use the outputs. John von Neumann proved to the contrary that even with the error-prone hardware of the time it was possible to design perfect fault-tolerance into a machine. But

Though I don’t remember who said it, I once heard a prominent CS researcher say the following:

Reductions are the lifeblood of theoretical computer science.

He was totally right. For those readers who don’t know, a reduction is a systematic way to transform instances of one problem into instances of another, so that solutions to the latter translate back to solutions to the former.

Here’s a simple example. Say you want to generate a zero or a one at random, such you’re equally likely to get either outcome. You can reduce this problem to the problem of generating a zero or a one with some biased probability (that’s not *completely* biased).

In other words, you can simulate a fair coin with a biased coin. How do you do it? You just flip your biased coin twice. If the outcome is “heads then tails,” you call the outcome of the fair coin “heads.” If the outcome is “tails then heads” you

LinkedIn is a weird niche in the internet: it’s a place for recruiters to reach out to candidates without a completely cold-email approach, along with a smattering of other relatively unimportant things going on (lots of “congrats” notes and the occasional unsubstantiated endorsement).

It’s not clear whether it’s a good niche or a bad one, but what is clear is that the most likely person to get their first introduction to me via my LinkedIn profile is a recruiter. So I can target my resume more effectively. I know exactly where to aim in terms of the reader being familiar with me and my work. With that in mind I recently rewrote my profile summary:

If you’re looking at my LinkedIn profile (as opposed to my academic CV [1] or my blog [2]), then chances are you’re a recruiter at a software company. Chances are also good that you haven’t got the first impression most people have of me: I

A few days ago the website CareerCast (Adicio, Inc) released a list of the top jobs in 2014 which put “Mathematician” as number 1. Most news sites have used this as a platform to discuss the centrality of mathematics and technology in the world economy, or the importance of STEM (Science, Technology, Engineering, and Mathematics) in education. I’m not against such discussions — indeed I spend a large fraction of my time writing long and detailed posts explaining mathematics to anyone who will listen — but I do suspect the ranking is misleading.

As every mathematician knows definitions are *extremely important,* so I wonder how mathematician is defined for the purpose of this ranking. After a bit of snooping it appears at least part of their analysis comes directly from the US Bureau of Labor Statistics website, which in turn uses an aggregation of two occupational