Catch of the Month, April 2015

About a month ago, I vaguely recall a discussion on Twitter – if memory serves me, @rickasaurus was involved – around sharing articles. This inspired me to try something. Every morning, I start my day with an espresso first, followed by reading blog posts for half an hour or so. While I get a lot from these quick reading sessions, I rarely go back to the material afterwards, and thought it would be interesting to keep track of a few, and revisit them at the end of the month. I also decided I would primarily focus on slightly out-of-topic areas, that is, pieces with ideas loosely connected to my daily work, but which I found inspiring or stimulating.

Long story short – here is a collection of links I found interesting this month, with minimal commentary on why I found them interesting. I also tried to mention the source when I remembered it; I am always curious to hear how people come across information, I figured others might be interested in my sources.

Lovers and liars: How many sex partners have you really had?

Since my days studying decision analysis, I have been interested by the topic of heuristics and biases, that is, what strategies we use to process information and form decisions – and how far we are from “rational agents”. There is a lot of food for thought in this experiment; one bit I found intriguing was the suggestion that gender had an influence on what strategy is used to produce an estimate, I wish there was more about that.

The best and worst times to have your case reviewed by a judge

Decision making again. I love a well-designed experiment – in this case, the whole story is there, in just one simple chart. Also a reminder that taking regular snacks during your workday is important.

What is the most efficient algorithm to check if a number is a Fibonacci Number? via @hammett

Because every functional programmer loves a Fibonacci sequence :)

Captivating Geometric GIFs by Florian de Looijby via @ptrelford

Beautiful – I need to look into how one creates gifs programmatically!

Data science done well looks easy - and that is a big problem for data scientists via @tggleeson

An interesting discussion on a topic that has been in the back of my mind for a bit: the discourse around data science / machine learning tends to emphasize fancy techniques and algorithm, and not the data work, even though it is an essential part of the job.

Donut math: how donut.c works via @flangy

No comment – pure awesome.

Are we kidding ourselves on competition?

A provocative and intriguing argument: rational investors should diversify, and as a result, firms that act in the best interest of their shareholders have an incentive to avoid competition and collude.

Hacking an epic NHL goal celebration with a hue light show and real-time machine learning via @rasbt

Love it – a gross misuse of brain and computer power, and a very interesting machine learning project.

Parasitic populations solve algorithm problems in half the time

I have a long-standing fascination for optimization techniques that mimic the behavior of populations, mixed together with randomness (ant colonies, bee colonies, swarms…). The idea to introduce a parasite in the system to preserve diversity (and avoid concentrating all the resources on one single search region, I presume) sounds really interesting, I just wish the full article was available – the link merely hints at the idea.

That’s it for April – I’ll keep doing this for myself anyways, if anybody is interested, I’ll be happy to post these once a month.

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