I liked The Alternative Science of Computation by Mario Carpo. He opens by proclaiming that the sciences of ‘Galileo and Newton: the science we all studied at school’ is under threat from the ‘alt-right’ — whereas the theories of ‘non-linearity, complexity, chaos, emergence, self-organisation, etc. do not seem to be under any threat at all.’ And he groups these theores together as ‘post-modern ideas of complexity’.
Carpo says that ‘advanced computation follows a post-scientific method that is way closer to Deleuze and Guattari’s worldview than to Newton’s’ which presume means a more Rhizomatic means of operation. He goes on to say that the design professions adoption of these techniques ‘show surprising affinities with the anti-scientific ideology of today’s alt-right.’ I’ll come back this connection to the alt-right towards the end.
Today, we can make and break on a computer screen more models in a minute than a traditional craftsman made and broke in a lifetime. The difference is, we are not expected to learn anything in this process, because the computer does it in our stead. We call this optimization.
This is something which I’ve been interested in in the past. And I’ve noticed my opinions on this “maturing” (if you’ll give me the privilege) over time, and Carpo puts this is reasonably simple terms from here.
Think of the typical environment of many of today’s computational design studios: the idiotic stupor and ecstatic speechlessness of many students confronted with the unmanageable epiphanies of agent-based systems, for example, may be priceless formative experiences when seen as steps in a path of individual discovery, but become questionable when dumbness itself is artfully cultivated as a pedagogical tool.
whether something works, or not, no one can or cares to tell why.
For the same is happening on a much bigger scale in the world at large, out there: as we have been hearing all too often from the truculent prophets of various populist revolutions in recent times, why waste time on theories (or on facts, observation, verification, demonstration, proof, experts, expertise, experience, competence, science, scholarship, mediation, argument, political representation, and so on—in no particular order)? Why argue? Using today’s technology, every complex query can be crowdsourced: just ask the crowds. Or even better, just try that out, and see if it works.
This where the alt-right rejection of factual argument, the ideology of post-modern science, and the new science of computation appear to be preaching the same gospel, all advocating, abetting, or falling prey to the same irrational fascination for a leap in the dark.
I’ve pulled out quite a few quotes there, but essentally what Carpo is saying is that modern methods of computation are ringing the same tune as the ‘anti-expert’ populist idea which is strongly felt at the moment. As humans we have developed science, mathematics, philosophy and so on based on careful considereation and proliferation of theories which Carpo argues ‘condense acquired knowledge in user-friendly, short and simplified statements we can resort to—at some risk—so we do not have to restart from zero every time’. This brute force approach of modern computation goes against these methods and replaces them with try-everyting-until-something-looks-about-right, but with little to no justification, other than the numbers seeming good.
I am not sure I completely agree with Carpo on his analysis as he seems to put this generative approach in the same ball-park as AI, and for me, AI is something different altogether. I agree with his argument that submitted all control to the endless iterations of a super-fast computer program is not in the long run a benefit to the world of design (or otherwise), but this is not the methods of AI. AI is an almagamtion of ideas, theories, concepts which as a whole allows for some new property to emerge. Generative design methods, or efficient sorting algorithms are not in themselves rhizomatic.
A closed loop of knowledge, based one one computational system producing possible outcomes, while another systems mines and alalyses these outcomes to predict.