The Alternative Science of Computation



Volume 3, Issue 09
January 10, 2018

MARIO CARPO (Reyner Banham Professor of Architectural Theory and History at the Bartlett, University College of London)

“The Alternative Science of Computation” was originally published in e-flux Architecture, June 23, 2017

Science cannot get a decent break these days. Scientists around the world have even taken the unusual step of organizing a “March for Science” (on April 22) to defend their work, and the scientific view of the world, against some political ideologies of the far right (or alternative right). They had good reasons to do so, but the contempt of today’s far right for science is not per se a novelty: fascists from all times and places always disparaged science, because fascists believe in violence, not in arguments, and they use force, not facts, to prevail. In that, today’s fascists are not different from their twentieth century predecessors. But let’s forget about them—the fascists—for a moment, and for the sake of the argument, let’s turn our attention to the scientific community instead. Which science is supposed to be under threat, precisely? Modern, inductive, experimental, inferential science—the science of Galileo and Newton: the science we all studied at school—may appear to be the prime target of today’s alt-right. But that is far from being the only science available on the marketplace of ideas. Since the first formulations of Heisenberg’s quantum mechanics in the late 1920s, and more powerfully since the rise of post-modern philosophy in the late 1970s, several alternatives to modern science have been envisaged and discussed by philosophers and scientists alike, and today the theories of non-linearity, complexity, chaos, emergence, self-organization, etc. do not seem to be under any threat at all. In fact, most of these theories never had it so good. That’s because some post-modern ideas of complexity and indeterminacy have been revived, and powerfully vindicated by today’s new science of computation.

Thirty years ago anyone could have argued that Deleuze and Guattari’s theory of science was fake science (and some did say just that). Today, on the contrary, few can deny that advanced computation follows a post-scientific method that is way closer to Deleuze and Guattari’s worldview than to Newton’s. 1 And no one can deny that, when used that way, and specifically when putting to task a range of processes loosely derived from, or akin to, some post-modern ideas of complexity, computers today work splendidly well, and produce valuable, usable, effective results. Let’s face it: what many still like to call Artificial Intelligence, machine learning, or whatever, is nothing artificial at all. It is just a new kind of science—a new scientific method. In fact, if we think of science as modern science exclusively, then computation is a new, revolutionary, post-modern and post-scientific method: it is, in fact, the most drastic alternative to modern science ever, because, unlike many obscure ideological proclamations by any anti-modern wacko, of which the twentieth century produced plenty, computation (or AI) today can be proven to work.

Whether we like it or not, AI already outperforms us and outsmarts us in plenty of cases, and AI can already solve many problems that could not be solved in any other way. But computational machines do not work the way our mind does, and they solve problems following a logic that is different from our logic—the logic of our mind, and of almost all experimental sciences we derived from it. Computers are so fast that they can try almost all options on earth and still find a good one before they run out of time. We can’t work that way because that would take us too long. That’s why, over time, we came up with some shortcuts (which, by the way, is what _method_originally meant in Greek). This is what theories are for: theories 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.

Yet theories today are universally reviled—just like modern science and the modern scientific method in general. 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. Yet plenty of training in digitally empowered architectural studios today extols the magical virtue of computational trial and error. Making is a matter of feeling, not thinking: just do it. Does it break? Try again… and again… and again. Or even better, let the computer try them all (optimize). But the technological hocus-pocus that too often pervades many of today’s computational experiments reflects the incantatory appeal of the whole process: whether something works, or not, no one can or cares to tell why.

Unfortunately, this frolic science of nonchalant serendipity is not limited to design studios—where, after all, it could not do much harm in the worst of cases. This is where our dumbness, whether ingenuous or malicious, appears to be part to a more general spirit of the time. 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. For the fascists, it is the leap of creative destruction, war, and dictatorship; for po-mo philosophers, it is the leap and somersaulting of a non-linear, “jumping” universe; for the alternative science of computation, it is the leap to the wondrous findings of AI, or to the unpredictable “emergence” of supposedly animated, self-organizing material configurations (never mind that the growth of cellular automata, in spite of its mind-blowing complexity, is perfectly deterministic, and never mind that most purpose-built structures made of inorganic materials can be at best as animated as a cuckoo-clock). But if fascism and post-modern vitalism are ideologies, AI is a technology. True, computers work that way, but we don’t; and having humans imitate computers does not seem any smarter than having computers imitate us. Computers can solve problems by repeating the same operation an almost infinite number of times. But as we cannot compete with computers on speed, trial and error is a very ineffective, wasteful, and often dangerous strategy in daily life. Computers don’t need theories to crunch numbers, but we need theories to use computers. Let’s keep post-human science for AI, and all other sciences for us.

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Volume 3, Issue 09
January 10, 2018