Patterns in shells, cellular automata, knitting and music

Earlier tonight I read the following article (via an NYT piece about Nautilus mag):

Biologists Home in on Turing Patterns: Was Alan Turing right about the mechanism behind tiger stripes?

For the work that led to his 1952 paper, Turing wanted to understand the underlying mechanism that produces natural patterns. He proposed that patterns such as spots form as a result of the interactions between two chemicals that spread throughout a system much like gas atoms in a box do, with one crucial difference. Instead of diffusing evenly like a gas, the chemicals, which Turing called “morphogens,” diffuse at different rates. One serves as an activator to express a unique characteristic, like a tiger’s stripe, and the other acts as an inhibitor, kicking in periodically to shut down the activator’s expression.

And then watched an older video linked from it:

Mathematical Impressions: Shell Games

From the description:

One-dimensional, two-state cellular automata produce a list of bits at discrete time steps, whose output, depending on the parameters, may be trivial or very complex. Surprisingly, this simple mechanism can be Turing complete — that is, capable of calculating anything that any computer can calculate.

The knitting part reminded me of this photo I took of one of my mom’s crocheting pattern books:

"I can read patterns. It's kind of like programming," says @excdinglyrandom while crocheting next to me.

A photo posted by Greg Linch (@greglinch) on

“I can read patterns. It’s kind of like programming,” says @excdinglyrandom while crocheting next to me.

I then went to Hart’s site, which included a link to his daughter’s YouTube page. I hadn’t watched one of Vi Hart‘s videos for a while, so I browsed and immediately clicked the one on Folding Space-Time:

And, of course, it reminded me of Crab Canon on a Möbius Strip:

All of this really just being another reminder that I need to continue reading Gödel, Escher, Bach!

Advancing math and science: Benoit Mandelbroit, fractals and identifying new patterns

An absolutely fascinating episode of NOVA on fractals! Along with my general interest in science and — more recently — math, material like this always makes me think of how we can apply lessons from others fields to journalism. For example, Nate Silver and his statistical models becoming more prominent this election cycle.

Below are some quoted highlights from the show — re-ordered for better flow in this context.


For generations, scientists believed that the wildness of nature could not be defined by mathematics. But fractal geometry is leading to a whole new understanding, revealing an underlying order governed by simple mathematical rules.

Keith Devlin, whom you might recognize as NPR’s “math guy:”

The key to fractal geometry, and the thing that evaded anyone until, really, Mandelbrot sort of said, “This is the way to look at things, is that if you look on the surface, you see complexity, and it looks very non-mathematical.” What Mandelbrot said was that… “think not of what you see, but what it took to produce what you see.”


So this domain of growing, living systems, which I, along with most other mathematicians, had always regarded as pretty well off-limits for mathematics, and certainly off-limits for geometry, suddenly was center stage. It was Mandelbrot’s book that convinced us that this wasn’t just artwork; this was new science in the making. This was a completely new way of looking at the world in which we live that allowed us, not just to look at it, not just to measure it, but to do mathematics and, thereby, understand it in a deeper way than we had before.

On design


If we could understand more about how the eye takes in information, we could do a better job of designing the things that we really need to see.


Look out for one my favorite scientists — Geoffrey West! (Why? In short, read A Physicist Turns the City Into an Equation. I’m slightly obsessed with new ways of quantifying things, such as impact.)

If you can’t see the embed above, watch on You Tube.

When a path of discovery becomes a loop and a mini “eureka” moment

I’m fascinated by paths of discovery. Not just the link you share, but the steps you took to get there. How did you end up at this point?

I experienced one such path tonight that turned into a loop and gave me a mini “eureka!” moment, so I wanted to share:

I met a fellow journalist/geek, Keith Collins, at BarCamp News Innovation Philly on April 28. We were chatting about science and that, of course, led to RadioLab. He mentioned a segment he enjoyed about a pendulum. I did a quick search on my phone and sent myself the link to read later. When I returned to the post, it didn’t seem like I found the right item — this was a post on the Krulwich Wonders blog about a Pendulum Dance. Nonetheless, it fascinated me.

I tweeted it with a hat tip to Keith and he replied with the actual segment he had referenced on the Limits of Science. It did not disappoint. I responded to say that I’d enjoyed it and Keith replied with a link to one of the things mentioned in the segment called Eureqa, which is described as a

“software tool for detecting equations and hidden mathematical relationships in your data. Its goal is to identify the simplest mathematical formulas which could describe the underlying mechanisms that produced the data. “

After downloading the application for later and browsing the page, I happend to scroll down to the “more information section.” A link about symbolic regression, which led to the Wikipedia page on genetic programming, grabbed my interest.

I happened to scroll past the introduction to the history section and read the first line there:

“In 1954, GP [genetic programming] began with the evolutionary algorithms first used by Nils Aall Barricelli applied to evolutionary simulations.”

Baricelli is a prominent figure in the wonderfully insightful book I’m curerntly reading about the origins of the digital universe:  Turing’s Cathedral by George Dyson.


No. Scratch that.


Now that’s what I call finding hidden relationships in your data.

Bonus discovery path: When I tweeted the Pendulum Dance post, Xerox PARC‘s @PARCinc Twitter account favorited it. It seems clear they found it after seeing my reply to a tweet from Scott Klein. That prompted me to look at their recent tweets to see if they were an account I wanted to follow (I did!). In a then-recent tweet, they shared a Wikimedia newsletter that included a summary of a PARC report titled Thermodynamic Principles in Social Collaboration. Gotta love the interwebs!

STEM for kids, teens and me. And my sister.


…programming should be used as a means to introduce kids to ways of thinking and problem solving that will be useful to them in many different spheres of human endeavor. If in the process they get hooked to computer science and end up in careers involving programming, that would not be a very shabby outcome, either!

Shuchi Grover said this in a post about Computational Thinking, Programming…and the Google App Inventor on SmartBean (read other highlights).

I sat down Sunday morning to read that piece (which I found through my handy Google alert for “computational thinking”) and it reminded me of something I’d almost completely forgotten about:

In summer 2000 — before eighth grade — I attended IMACS (no relation to Apple) for a few weeks. IMACS, short for the Institute for Mathematics and Computer Science, offered STEM-related activities in a day-camp format for different age groups.

My faint memories from IMACS include programming some rudimentary commands to control a robot, working with simple electronic circuitry to illuminate small light bulbs and completing various logic/reasoning questions.

So why did I, as 13-year-old who was mainly interested in writing, do this? Honestly, I don’t remember exactly beyond these two basic reasons:

  • My good friend Chris was going to attend
  • I’d had some technical inclinations since elementary school

You see, Chris and I had been aftercare aids at Country Isles. Yes, we sometimes clutched clipboards and walkie-talkies as we deposited toys in classrooms. But we also assisted with tech and AV — even Winterfest in 1997 (I will never forget what it’s like to be a 10-year-old running cables and duct-taping down wires for a school-wide singing show. Oh, and what ever happened to MiniDiscs?).

Earlier in elementary school when people would ask me, “What do you want to be when you grow up?” I would say, “A scientist and inventor.” Surely, even a few years after such a notion, that too factored into my decision to attend IMACS.

My larger point in recapping all this history is that earlier interests, such as from childhood, can stick with us as we grow up and it’s never too late to start appreciating other areas.

Honestly, math was my least favorite subject in high school. I used to think journalists and math didn’t mix. I was young(er) and wrong. In the year or so since I graduated college, I wish I had done at least one stats class (in addition to psychology, but that’s for another post).

So why am I now fascinated by computational thinking and programming? My passion for journalism and how the fields relate, sure. But it’s also clear that my earlier interest and experiences, even one as limited as IMACS, play some role. (I also always have to credit Daniel Bachhuber specifically on the computational thinking front because he shared the first things I read/listened to on that topic.)

All of this is not to say you can’t develop a tech inclination later in life. You certainly can. What I am saying is how it’s helpful to evaluate what and who might have influenced you — and what comes of that.

Case in point, yesterday I talked my sister through setting up a blog on I didn’t succeed earlier in the summer in getting her to host her own cooking blog, but in June she did buy her domain. What changed yesterday? I don’t know. We were just video IM chatting and it happened. Michelle, a rising college sophomore interested in finance and business (she digs math), is now set up to be a creatornot just a consumer.

Even if she never sets up her own hosted blog, never touches a line of code or never goes any further, it has — thus far — certainly been worth my brotherly nudging. And, to borrow from Grover, it wouldn’t be too shabby if she did.

What were some of your most noteworthy technical influences? Where did those influences lead?

Correction: The opening quote, originally attributed to Charles Profitt, has been updated to reflect the actual source — Shuchi Grover.