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.