For some reason, probably due to the coronavirus pandemic, many of us found our way back to various online games.

Previously dead lobbies teeming with gamers, some dying games seeing new life born into them.

My personal poison has always been TrackMania Turbo on PlayStation — I was never the best racer in the world, ranked 26th in Australia, and about 1600th out of about 6 million racers globally, sure I wanted to be on top, but it all came down to milliseconds, and while I had to concede I just didn’t have the consistency to rise much further, I…


As I work through the conceptualisation phase of my second serious game development project, I find myself looking back on my first release, how I ended up at this moment in my life, and how I can improve the outcome.

The beginning

I got my first “real” computer back in the mid 80’s. It was a state of the art Amstrad CPC, replete with Basic 1.0 built into the terminal.

At first I enjoyed playing the games that my parents had purchased along with the machine, and I enjoyed the demos on the system disc that was included. …


Image author unknown — https://knowyourmeme.com/photos/1107046-the-trolley-problem

I’ll preface this by saying that I am not an expert who has mountains of experience in the world of shares and investment. By no means do I offer any advice on what to invest in, this story is simply anecdotes from my recent expedition into the world of ethical investing.

With that out of the way, stock markets have always fascinated me. I see them as “plug-ins” to the economy, where as a whole, investors get to decide which businesses and industries grow and which ones fade. Conceptually, besides their more popular use as a way to theoretically make…


The subject of Elementary Cellular Automata tickles my mind occasionally. These seemingly simple devices are able to spawn as-yet unexplained complexity, especially interesting to me being the output of Rule 30.

In previous articles I’ve described methods to optimise processing effort, profiled the effort of various optimisations, speculated on various patterns, and drawn inconclusive albeit interesting (to me at least) comparisons to logistic functions.

From this I know for a fact that you can skip rows. You don’t have to compute every single cell of every single row to figure out the value of an arbitrary cell on row n.


Photo by Akshay Nanavati on Unsplash

Machine Learning, AI, whatever you want to call it, it’s only getting better with time. Recent developments such as GPT-2 are spookily human-like in the text they generate. The paragraph below was generated by GPT-2 based on this introduction:

This fact alone leaves me with no confidence whatsoever that the algorithms aren’t making their own (and just as likely or even more likely) fact-based inference regarding the article. The advanced methods and distributed architecture of GPT-2 now encourage the experimental use of provably true functions, which might lead to what might be called uncertainty analysis.

Spooky right. It almost makes…


Abstract

Elementary CA Rule 30, when row coefficients are calculated and analysed, appear to express similar characteristics to logistic functions.

Rule 30’s row coefficients appear to resolve to an unstable orbit with a period of 4, hinting at the potential for the existence of a more universal logistic function governing Rule 30’s output.

Logistic functions are used extensively to model population growth. Intuitively, Elementary CA rules can be seen as determining the birth and death rate of a population of cells — some cells cannibalise others, some cells merge together, others create new life as generations progress.

Calculating multiple generations with…


I’ve made it my mission in life to create objectively the world’s least interesting headline. But I think (or sincerely hope) this post is worth the read. I talk a bit here about some new and interesting (to me at least) patterns I’ve found, in addition to summarising my profile testing of the ECA algorithm I’ve been developing.

In my previous post exploring algorithmic reduction in effort for Elementary Cellular Automata (ECA), I explored using a pregenerated lookup table to effectively skip rows when generating ECA output, so you could get from row 1 to 100 in 20 steps rather…


Merry Christmas! (My wife tells me I should call this “The Christmas Tree Rule”)

“DOES COMPUTING THE NTH CELL OF THE CENTER COLUMN REQUIRE AT LEAST O(N) COMPUTATIONAL EFFORT?” — Stephen Wolfram

This question is one of three challenges posed by Stephen Wolfram on the website at https://rule30prize.org/. The problem domain is Elementary Cellular Automata, of which there are 256 unique basic rules, with the computational effort of the generation of values according to Rule 30 being specifically put to the test.

Elementary Cellular automata, broadly speaking, are rules which operate on a row of cells in discrete iterations. These rules are visualised as follows:


I’ve been recently obsessed with Cellular Automata, specifically elementary CA. I stumbled on these while playing around with entropy in random noise (an odd and futile hobby, I know), as some of the transforms I was applying turned out to be identical to some of the elementary cellular automata rules.

Typically, each subsequent generation of a cellular automaton is derived from the repeated application of a single rule, like the famous Rule 30:

http://mathworld.wolfram.com/CellularAutomaton.html

While Rule 30 is well studied for its apparently chaotic and random nature, I wanted to see if there were any meaningful patterns in the relationships between…

Alex Parker

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