biological computation

Reading Terranova’s discussion of biological computation this week reminded me of an article a friend sent me a few weeks ago, “Testing Darwin,” published in Discover by Carl Zimmer in 2005. It describes ‘digital organisms’ that are clearly an example of von Neumann’s cellular automata, which the article agrees have the potential to be a serious alternative to Turing’s universal computing machine.

The article describes a lab at Michigan State that runs a program called Avida, which sets conditions under which fragments of code can self-replicate, mutate, and produce emergent phenomena.

“After more than a decade of development, Avida's digital organisms are now getting close to fulfilling the definition of biological life … One thing the digital organisms do particularly well is evolve. “Avida is not a simulation of evolution; it is an instance of it,” Pennock says”
“physicist Chris Adami of Caltech, set out to create the conditions in which a computer program could evolve the ability to do addition….Within six months, Adami's organisms were addition whizzes. “We were able to get them to evolve without fail,” he says. But when he stopped to look at exactly how the organisms were adding numbers, he was more surprised. “Some of the ways were obvious, but with others I'd say, 'What the hell is happening?”

This is only another example of what Terranova is talking about in her explanation of CAs, but the article does a good job describing the process the researchers use and it helped me understand the lessons of the “biological turn” that she insists upon in the fourth chapter.

What I don’t entirely understand are the implications of the concept of ‘the selfish gene.’ Terranova writes, “The selfish gene is a simple diagram of the apparatuses of subjectification that the abstract machine of soft control distributes and perpetuates not so much among molecules as among collectivities” (126). And, “The selfish gene, however, is not just a metaphor, or a moralization of natural life or an ideological justification of cut-throat competition in the ‘free’ market economy, but more insidiously a technique” (128). Is the selfish gene only a technique of “culture in as much as [it] is also an industry – and hence a mode of labour” (129), or is it also a potentiality of bottom-up organization, whose limits she stresses in the final chapter?

The article also touches on the question of free labour, in an interesting example that I won’t develop here. Creationists threatened by the practical defense of evolution that the Avida program furnishes download its code for free in order to attempt to expose theoretical problems with its initial conditions. The researcher merely says, “We literally have an army of thousands of unpaid bug testers. What more could you want?”

One last thought that Zimmer brings up is that Avida sort of demonstrates how computer viruses could potentially evolve very quickly. The decentralization of the Internet that Terranova harps on is designed to avoid widespread damage, but the example of the virus highlights the interplay between creation and destruction and the sharply anti-utopian nature of network culture that the book is right to emphasize.