Last week I had the honour of both performing with Alex and presenting at Thinking Digital 2014. Suzy O’Hara invited me to represent the intersection of art, science and education of FoAM kernow and present the work I’ve been doing with the Sensory Ecology Group at Exeter University. I did a quick Egglab game demo and related some thoughts on working with scientists and how it connects with my experience teaching programming in the classroom.
It was an interesting and unusual venue for me, organiser Herb Kim is very much developing on the TED theme – so lots of extremely well considered, motivating and inspiring talks. Much of the context was one of venture capital and startup business so it was interesting to see an explosive talk by Aral Balkan on the implications of Facebook and Google’s business models on the future of our society (he included some of the other presenter’s companies too). This reminded me very much of the themes we explored in Naked on Pluto, but coming from a new angle.
Personally his talk was challenging to me as he roundly attacked the free software movement, for essentially providing a great sandbox for enthusiasts and well funded companies – but incapable of doing much more in terms of data security for real people. As a designer, he sees this as essentially a design problem – one that these companies have solved for themselves but is utterly lacking in devices such as the Firefox phone OS. For Aral, this is fundamentally a design problem that needs it’s own movement, and new business models to be developed. These business models need to take into consideration long term usability (for which user privacy is an essential feature) rather than ultra short term profit ‘pump and dump’, selling of people’s information for vast amounts – i.e. silicon valley ‘business as usual’.
Two things are apparent to me following this talk – one is that I have been labouring under the impression that a particular focus on design is somehow implicitly tied with specific business practices – simplification as wallpapering over data harvesting, and other tactics. This is very much a short sighted developer view, and is wrong – they can of course service different types of businesses.
The other point came during his 3 slide explanation of how to start your own social network (1. fork a github repo, 2. set up a server and 3. install it). Clearly even this satirical simplification is beyond all but existing software developers (many of whom are working for companies reliant on user surveillance in some indirect or direct way). The challenge for me is that I can’t ultimately see a way to make ‘interface as user experience’ ever converge on anything other than exploitation. Can ‘user experience’ ever regard people philosophically as anything but consumers – regardless of the underlying business model?
The problem in solving that is that we now have two problems – the terrible state of software engineering preventing accessibility (i.e programming at large still stuck in the 70’s) and the lack of understanding in society of what a computer is and how it works. The second of these problems is being addressed in some part by the activities of CodeClub (Aral is [correction: was] a director of this organisation) and similar education initiatives. Regarding pushing software engineering forward, in some way I think recent livecoding takeup by musicians over programmers is a fascinating development here, in terms of showing us how programming – when it’s taken and twisted into very strange and new forms, can start to make sense and work for ‘real people’.
I’ve been working lately with the Heliconius research group at the University of Cambridge on a game to explain the evolution of mimicry in butterfly wing patterns. It’s for use at the Summer Science Exhibition at the Royal Society in London, where it’ll be run on a large touch screen for school children and visiting academics to play.
The game models biological processes for education purposes (as opposed to the genetic programming as used on the camouflage egg game), and the process of testing this, and deciding what simplifications are required has become a fascinating part of the design process.
In biosciences, genetics are modelled as frequencies of specific alleles in a given population. An allele is a choice (a bit like a switch) encoded by a gene, so a population can be represented as a list of genes where each gene is a list of frequencies of each allele. In this case the genetics consists of choices of wing patterns. The game is designed to demonstrate the evolution of an edible species mimicking a toxic one – we’ll be publishing the game after the event. A disclaimer, my terminology is probably misaligned in the following code, still working on that.
;; an allele is just a string id and a probability value
(define (allele id probability)
(list id probability))
;; a gene is simply a list of alleles
;; return the id of an allele chosen based on probability
(define (gene-express gene)
(let ((v (rndf)))
(lambda (allele r)
(let ((segment (+ (car r) (allele-probability allele))))
(if (and (not (cadr r))
(< v segment))
(list segment allele)
(list segment (cadr r)))))
(list 0 #f)
;; a chromosome is simple list of genes
;; returns a list of allele ids from the chromosome based on probability
(define (chromosome-express chromo)
(map gene-express chromo))
When an individual is removed from the population, we need to adjust the probabilities by subtracting based on the genetics of the eaten individual, and the adding to the other alleles to keep the probabilities summing to one:
;; prevents the probability from 'fixing' at 0% or 100%
(define (calc-decrease p)
(* (min p (- 1 p)) allele-decrease))
;; remove this genome from the population
(define (gene-remove-expression gene genome)
(let ((dec (calc-decrease (allele-probability (car gene)))))
(let ((inc (allele-increase dec (length gene))))
(if (eq? (allele-id allele) genome)
allele (- (allele-probability allele) dec))
allele (+ (allele-probability allele) inc))))
9,000 players, 20,000 games played and 400,000 tested egg patterns later we have over 30 generations complete on most of our artificial egg populations. The overall average egg difficulty has risen from about 0.4 seconds at the start to 2.5 seconds.
Thank you to everyone who contributed their time to playing the game! We spawned 4 brand new populations last week, and we’ll continue running the game for a while yet.
In the meantime, I’ve started working on ways to visualise the 500Mb of pattern generating code that we’ve evolved so far – here are all the eggs for one of the 20 populations, each row is a generation of 127 eggs starting at the top and ordered in fitness score from left to right:
This tree is perhaps more useful. The ancestor egg at the top is the first generation and you can see how mutations happen and successful variants get selected.