A video about neural networks, function approximation, machine learning, and mathematical building blocks. Dennis Nedry did nothing wrong. This is a submission for #SoME3

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Links and Content: On Mathematical Maturity, Thomas Garrity: https://www.youtube.com/watch?v=zHU1xH6Ogs4 Earth Rotation Loop: https://www.youtube.com/watch?v=aiQdLP2mBJE Modeling Shell Surfaces: https://www.geogebra.org/m/xtv7zpn5 Fourier Features Paper: https://arxiv.org/abs/2006.10739 Code for mandelbrot/image approximations: https://github.com/MaxRobinsonTheGreat/mandelbrotnn Code for line/surface approximations: https://github.com/MaxRobinsonTheGreat/ManimApproximations

Music: https://youtube.com/@acolyte-compositions

Timestamps (0:00) Functions Describe the World (3:15) Neural Architecture (5:35) Higher Dimensions (11:55) Taylor Series (15:20) Fourier Series (21:25) The Real World (24:32) An Open Challenge

Casting Code: Reflections on 3D Printing Blog The letterhead of Henri Lebossé announces that his firm uses a ‘mathematically perfected process’ and a ‘special machine’ for ‘reducing and enlarging objects of “art and industry”’.

n 1953, at the dawn of modern computing, Nils Aall Barricelli played God. Clutching a deck of playing cards in one hand and a stack of punched cards in the other, Barricelli hovered over one of the world’s earliest and most influential computers, the IAS machine, at the Institute for Advanced Study in Princeton, New Jersey. During the day the computer was used to make weather forecasting calculations; at night it was commandeered by the Los Alamos group to calculate ballistics for nuclear weaponry. Barricelli, a maverick mathematician, part Italian and part Norwegian, had finagled time on the computer to model the origins and evolution of life.

Inside a simple red brick building at the northern corner of the Institute’s wooded wilds, Barricelli ran models of evolution on a digital computer. His artificial universes, which he fed with numbers drawn from shuffled playing cards, teemed with creatures of code—morphing, mutating, melting, maintaining. He created laws that determined, independent of any foreknowledge on his part, which assemblages of binary digits lived, which died, and which adapted. As he put it in a 1961 paper, in which he speculated on the prospects and conditions for life on other planets, “The author has developed numerical organisms, with properties startlingly similar to living organisms, in the memory of a high speed computer.” For these coded critters, Barricelli became a maker of worlds.

Until his death in 1993, Barricelli floated between biological and mathematical sciences, questioning doctrine, not quite fitting in. “He was a brilliant, eccentric genius,” says George Dyson, the historian of technology and author of Darwin Among The Machines and Turing’s Cathedral, which feature Barricelli’s work. “And the thing about geniuses is that they just see things clearly that other people don’t see.”

Barricelli programmed some of the earliest computer algorithms that resemble real-life processes: a subdivision of what we now call “artificial life,” which seeks to simulate living systems—evolution, adaptation, ecology—in computers. Barricelli presented a bold challenge to the standard Darwinian model of evolution by competition by demonstrating that organisms evolved by symbiosis and cooperation.

Pixar cofounder Alvy Ray Smith says Barricelli influenced his earliest thinking about the possibilities for computer animation.

In fact, Barricelli’s projects anticipated many current avenues of research, including cellular automata, computer programs involving grids of numbers paired with local rules that can produce complicated, unpredictable behavior. His models bear striking resemblance to the one-dimensional cellular automata—life-like lattices of numerical patterns—championed by Stephen Wolfram, whose search tool Wolfram Alpha helps power the brain of Siri on the iPhone. Nonconformist biologist Craig Venter, in defending his creation of a cell with a synthetic genome—“the first self-replicating species we’ve had on the planet whose parent is a computer”—echoes Barricelli.

Barricelli’s experiments had an aesthetic side, too. Uncommonly for the time, he converted the digital 1s and 0s of the computer’s stored memory into pictorial images. Those images, and the ideas behind them, would influence computer animators in generations to come. Pixar cofounder Alvy Ray Smith, for instance, says Barricelli stirred his earliest thinking about the possibilities for computer animation, and beyond that, his philosophical muse. “What we’re really talking about here is the notion that living things are computations,” he says. “Look at how the planet works and it sure does look like a computation.”

Despite Barricelli’s pioneering experiments, barely anyone remembers him. “I have not heard of him to tell you the truth,” says Mark Bedau, professor of humanities and philosophy at Reed College and editor of the journal Artificial Life. “I probably know more about the history than most in the field and I’m not aware of him.”

Barricelli was an anomaly, a mutation in the intellectual zeitgeist, an unsung hero who has mostly languished in obscurity for the past half century. “People weren’t ready for him,” Dyson says. That a progenitor has not received much acknowledgment is a failing not unique to science. Visionaries often arrive before their time. Barricelli charted a course for the digital revolution, and history has been catching up ever since.

Barricelli_BREAKER-02 EVOLUTION BY THE NUMBERS: Barricelli converted his computer tallies of 1s and 0s into images. In this 1953 Barricelli print, explains NYU associate professor Alexander Galloway, the chaotic center represents mutation and disorganization. The more symmetrical fields toward the margins depict Barricelli’s evolved numerical organisms.From the Shelby White and Leon Levy Archives Center, Institute for Advanced Study, Princeton. Barricelli was born in Rome on Jan. 24, 1912. According to Richard Goodman, a retired microbiologist who met and befriended the mathematician in the 1960s, Barricelli claimed to have invented calculus before his tenth birthday. When the young boy showed the math to his father, he learned that Newton and Leibniz had preempted him by centuries. While a student at the University of Rome, Barricelli studied mathematics and physics under Enrico Fermi, a pioneer of quantum theory and nuclear physics. A couple of years after graduating in 1936, he immigrated to Norway with his recently divorced mother and younger sister.

As World War II raged, Barricelli studied. An uncompromising oddball who teetered between madcap and mastermind, Barricelli had a habit of exclaiming “Absolut!” when he agreed with someone, or “Scandaloos!” when he found something disagreeable. His accent was infused with Scandinavian and Romantic pronunciations, making it occasionally challenging for colleagues to understand him. Goodman recalls one of his colleagues at the University of California, Los Angeles who just happened to be reading Barricelli’s papers “when the mathematician himself barged in and, without ceremony, began rattling off a stream of technical information about his work on phage genetics,” a science that studies gene mutation, replication, and expression through model viruses. Goodman’s colleague understood only fragments of the speech, but realized it pertained to what he had been reading.

“Are you familiar with the work of Nils Barricelli?” he asked.

“Barricelli! That’s me!” the mathematician cried.

Notwithstanding having submitted a 500-page dissertation on the statistical analysis of climate variation in 1946, Barricelli never completed his Ph.D. Recalling the scene in the movie Amadeus in which the Emperor of Austria commends Mozart’s performance, save for there being “too many notes,” Barricelli’s thesis committee directed him to slash the paper to a tenth of the size, or else it would not accept the work. Rather than capitulate, Barricelli forfeited the degree.

Barricelli began modeling biological phenomena on paper, but his calculations were slow and limited. He applied to study in the United States as a Fulbright fellow, where he could work with the IAS machine. As he wrote on his original travel grant submission in 1951, he sought “to perform numerical experiments by means of great calculating machines,” in order to clarify, through mathematics, “the first stages of evolution of a species.” He also wished to mingle with great minds—“to communicate with American statisticians and evolution-theorists.” By then he had published papers on statistics and genetics, and had taught Einstein’s theory of relativity. In his application photo, he sports a pyramidal moustache, hair brushed to the back of his elliptic head, and hooded, downturned eyes. At the time of his application, he was a 39-year-old assistant professor at the University of Oslo.

Although the program initially rejected him due to a visa issue, in early 1953 Barricelli arrived at the Institute for Advanced Study as a visiting member. “I hope that you will be finding Mr. Baricelli [sic] an interesting person to talk with,” wrote Ragnar Frisch, a colleague of Barricelli’s who would later win the first Nobel Prize in Economics, in a letter to John von Neumann, a mathematician at IAS, who helped devise the institute’s groundbreaking computer. “He is not very systematic always in his exposition,” Frisch continued, “but he does have interesting ideas.”

Barricelli_BREAKER_2crop PSYCHEDELIC BARRICELLI: In this recreation of a Barricelli experiment, NYU associate professor Alexander Galloway has added color to show the gene groups more clearly. Each swatch of color signals a different organism. Borders between the color fields represent turbulence as genes bounce off and meld with others, symbolizing Barricelli’s symbiogenesis.Courtesy Alexander Galloway Centered above Barricelli’s first computer logbook entry at the Institute for Advanced Study, in handwritten pencil script dated March 3, 1953, is the title “Symbiogenesis problem.” This was his theory of proto-genes, virus-like organisms that teamed up to become complex organisms: first chromosomes, then cellular organs, onward to cellular organisms and, ultimately, other species. Like parasites seeking a host, these proto-genes joined together, according to Barricelli, and through their mutual aid and dependency, originated life as we know it.

Standard neo-Darwinian doctrine maintained that natural selection was the main means by which species formed. Slight variations and mutations in genes combined with competition led to gradual evolutionary change. But Barricelli disagreed. He pictured nimbler genes acting as a collective, cooperative society working together toward becoming species. Darwin’s theory, he concluded, was inadequate. “This theory does not answer our question,” he wrote in 1954, “it does not say why living organisms exist.”

Barricelli coded his numerical organisms on the IAS machine in order to prove his case. “It is very easy to fabricate or simply define entities with the ability to reproduce themselves, e.g., within the realm of arithmetic,” he wrote.

The early computer looked sort of like a mix between a loom and an internal combustion engine. Lining the middle region were 40 Williams cathode ray tubes, which served as the machine’s memory. Within each tube, a beam of electrons (the cathode ray) bombarded one end, creating a 32-by-32 grid of points, each consisting of a slight variation in electrical charge. There were five kilobytes of memory total stored in the machine. Not much by today’s standards, but back then it was an arsenal.

Barricelli saw his computer organisms as a blueprint of life—on this planet and any others.

Inside the device, Barricelli programmed steadily mutable worlds each with rows of 512 “genes,” represented by integers ranging from negative to positive 18. As the computer cycled through hundreds and thousands of generations, persistent groupings of genes would emerge, which Barricelli deemed organisms. The trick was to tweak his manmade laws of nature—“norms,” as he called them—which governed the universe and its entities just so. He had to maintain these ecosystems on the brink of pandemonium and stasis. Too much chaos and his beasts would unravel into a disorganized shamble; too little and they would homogenize. The sweet spot in the middle, however, sustained life-like processes.

Barricelli’s balancing act was not always easygoing. His first trials were riddled with pests: primitive, often single numeric genes invaded the space and gobbled their neighbors. Typically, he was only able to witness a couple of hereditary changes, or a handful at best, before the world unwound. To create lasting evolutionary processes, he needed to handicap these pests’ ability to rapidly reproduce. By the time he returned to the Institute in 1954 to begin a second round of experiments, Barricelli made some critical changes. First, he capped the proliferation of the pests to once per generation. That constraint allowed his numerical organisms enough leeway to outpace the pests. Second, he began employing different norms to different sections of his universes. That forced his numerical organisms always to adapt.

Even in the earlier universes, Barricelli realized that mutation and natural selection alone were insufficient to account for the genesis of species. In fact, most single mutations were harmful. “The majority of the new varieties which have shown the ability to expand are a result of crossing-phenomena and not of mutations, although mutations (especially injurious mutations) have been much more frequent than hereditary changes by crossing in the experiments performed,” he wrote.

When an organism became maximally fit for an environment, the slightest variation would only weaken it. In such cases, it took at least two modifications, effected by a cross-fertilization, to give the numerical organism any chance of improvement. This indicated to Barricelli that symbioses, gene crossing, and “a primitive form of sexual reproduction,” were essential to the emergence of life.

“Barricelli immediately figured out that random mutation wasn’t the important thing; in his first experiment he figured out that the important thing was recombination and sex,” Dyson says. “He figured out right away what took other people much longer to figure out.” Indeed, Barricelli’s theory of symbiogenesis can be seen as anticipating the work of independent-thinking biologist Lynn Margulis, who in the 1960s showed that it was not necessarily genetic mutations over generations, but symbiosis, notably of bacteria, that produced new cell lineages.

Barricelli saw his computer organisms as a blueprint of life—on this planet and any others. “The question whether one type of symbio-organism is developed in the memory of a digital computer while another type is developed in a chemical laboratory or by a natural process on some planet or satellite does not add anything fundamental to this difference,” he wrote. A month after Barricelli began his experiments on the IAS machine, Crick and Watson announced the shape of DNA as a double helix. But learning about the shape of biological life didn’t put a dent in Barricelli’s conviction that he had captured the mechanics of life on a computer. Let Watson and Crick call DNA a double helix. Barricelli called it “molecule-shaped numbers.”

Barricelli_BREAKER

What buried Barricelli in obscurity is something of a mystery. “Being uncompromising in his opinions and not a team player,” says Dyson, no doubt led to Barricelli’s “isolation from the academic mainstream.” Dyson also suspects Barricelli and the indomitable Hungarian mathematician von Neumann, an influential leader at the Institute of Advanced Study, didn’t hit it off. Von Neumann appears to have ignored Barricelli. “That was sort of fatal because everybody looked to von Neumann as the grandfather of self-replicating machines.”

Ever so slowly, though, Barricelli is gaining recognition. That stems in part from another of Barricelli’s remarkable developments; certainly one of his most beautiful. He didn’t rest with creating a universe of numerical organisms, he converted his organisms into images. His computer tallies of 1s and 0s would then self-organize into visual grids of exquisite variety and texture. According to Alexander Galloway, associate professor in the department of media, culture, and communication at New York University, a finished Barricelli “image yielded a snapshot of evolutionary time.”

When Barricelli printed sections of his digitized universes, they were dazzling. To modern eyes they might look like satellite imagery of an alien geography: chaotic oceans, stratigraphic outcrops, and the contours of a single stream running down the center fold, fanning into a delta at the patchwork’s bottom. “Somebody needs to do a museum show and show this stuff because they’re outrageous,” Galloway says.

Barricelli was an uncompromising oddball who teetered between madcap and mastermind.

Today, Galloway, a member of Barricelli’s small but growing cadre of boosters, has recreated the images. Following methods described by Barricelli in one of his papers, Galloway has coded an applet using the computer language Processing to revive Barricelli’s numerical organisms—with slight variation. While Barricelli encoded his numbers as eight-unit-long proto-pixels, Galloway condensed each to a single color-coded cell. By collapsing each number into a single pixel, Galloway has been able to fit eight times as many generations in the frame. These revitalized mosaics look like psychedelic cross-sections of the fossil record. Each swatch of color represents an organism, and when one color field bumps up against another one, that’s where cross-fertilization takes place.

“You can see these kinds of points of turbulence where the one color meets another color,” Galloway says, showing off the images on a computer in his office. “That’s a point where a number would be—or a gene would be—sort of jumping from one organism to another.” Here, in other words, is artificial life—Barricelli’s symbiogenesis—frozen in amber. And cyan and lavender and teal and lime and fuchsia.

Galloway is not the only one to be struck by the beauty of Barricelli’s computer-generated digital images. As a doctoral student, Pixar cofounder Smith became familiar with Barricelli’s work while researching the history of cellular automata for his dissertation. When he came across Barricelli’s prints he was astonished. “It was remarkable to me that with such crude computing facilities in the early 50s, he was able to be making pictures,” Smith says. “I guess in a sense you can say that Barricelli got me thinking about computer animation before I thought about computer animation. I never thought about it that way, but that’s essentially what it was.”

Cyberspace now swells with Barricelli’s progeny. Self-replicating strings of arithmetic live out their days in the digital wilds, increasingly independent of our tampering. The fittest bits survive and propagate. Researchers continue to model reduced, pared-down versions of life artificially, while the real world bursts with Boolean beings. Scientists like Venter conjure synthetic organisms, assisted by computer design. Swarms of autonomous codes thrive, expire, evolve, and mutate underneath our fingertips daily. “All kinds of self-reproducing codes are out there doing things,” Dyson says. In our digital lives, we are immersed in Barricelli’s world.

In what appears to be the first study of its kind, computer scientists report that an algorithm discovered more than 50 years ago in game theory and now widely used in machine learning is mathematically identical to the equations used to describe the distribution of genes within a population of org

KentuckyFC (1144503) writes "One of the most profound advances in science in recent years is the way researchers from a variety of fields are beginning to formulate the problem of consciousness in mathematical terms, in particular using information theory.

“Propaganda is called upon to solve problems created by technology, to play on maladjustments and to integrate the individual into a technological world” (Ellul xvii).

How might future research into digital culture approach a purported “post-digital” age? How might this be understood?

1.

A problem comes from the discourse of ‘the digital’ itself: a moniker which points towards units of Base-2 arbitrary configuration, impersonal architectures of code, massive extensions of modern communication and ruptures in post-modern identity. Terms are messy, and it has never been easy to establish a ‘post’ from something, when pre-discourse definitions continue to hang in the air. As Florian Cramer has articulated so well, ‘post-digital’ is something of a loose, ‘hedge your bets’ term, denoting a general tendency to criticise the digital revolution as a modern innovation (Cramer).

Perhaps it might be aligned with what some have dubbed “solutionism” (Morozov) or “computationalism” (Berry 129; Golumbia 8): the former critiquing a Silicon Valley-led ideology oriented towards solving liberalised problems through efficient computerised means. The latter establishing the notion (and critique thereof) that the mind is inherently computable, and everything associated with it. In both cases, digital technology is no longer just a business that privatises information, but the business of extending efficient, innovative logic to all corners of society and human knowledge, condemning everything else through a cultural logic of efficiency.

In fact, there is a good reason why ‘digital’ might as well be an synonym for ‘efficiency’. Before any consideration is assigned to digital media objects (i.e. platforms, operating systems, networks), consider the inception of ‘the digital’ inception as such: that is information theory. If information was a loose, shabby, inefficient method of vagueness specific to various mediums of communication, Claude Shannon compressed all forms of communication into a universal system with absolute mathematical precision (Shannon). Once information became digital, the conceptual leap of determined symbolic logic was set into motion, and with it, the ‘digital’ became synonymous with an ideology of effectivity. No longer would miscommunication be subject to human finitude, nor be subject to matters of distance and time, but only the limits of entropy and the matter of automating messages through the support of alternating ‘true’ or ‘false’ relay systems.

However, it would be quite difficult to envisage any ‘post-computational’ break from such discourses – and with good reason: Shannon’s breakthrough was only systematically effective through the logic of computation. So the old missed encounter goes: Shannon presupposed Alan Turing’s mathematical idea of computation to transmit digital information, and Turing presupposed Shannon’s information theory to understand what his Universal Turing Machines were actually transmitting. The basic theories of both have not changed, but the materials affording greater processing power, extensive server infrastructure and larger storage space have simply increased the means for these ideas to proliferate, irrespective of what Turing and Shannon actually thought of them (some historians even speculate that Turing may have made the link between information and entropy two years before Bell Labs did) (Good).

Thus a ‘post-digital’ reference point might encompass the historical acknowledgment of Shannon’s digital efficiency, and Turing’s logic but by the same measure, open up a space for critical reflection, and how such efficiencies have transformed not only work, life and culture but also artistic praxis and aesthetics. This is not to say that digital culture is reducibly predicated on efforts made in computer science, but instead fully acknowledges these structures and accounts for how ideologies propagate reactionary attitudes and beliefs within them, whilst restricting other alternatives which do not fit their ‘vision’. Hence, the post-digital ‘task’ set for us nowadays might consist in critiquing digital efficiency and how it has come to work against commonality, despite transforming the majority of Western infrastructure in its wake.

The purpose of these notes is to outline how computation has imparted an unwarranted effect of totalised efficiency, and to label this effect the type of description it deserves: propaganda. The fact that Shannon and Turing had multiple lunches together at Bell labs in 1943, held conversations and exchanged ideas, but not detailed methods of cryptanalysis (Price & Shannon) provides a nice contextual allegory for how digital informatics strategies fail to be transparent.

But in saying this, I do not mean that companies only use digital networks for propagative means (although that happens), but that the very means of computing a real concrete function is constitutively propagative. In this sense, propaganda resembles a post-digital understanding of what it means to be integrated into an ecology of efficiency, and how technical artefacts are literally enacted as propagative decisions. Digital information often deceives us into accepting its transparency, and of holding it to that account: yet in reality it does the complete opposite, with no given range of judgements available to detect manipulation from education, or persuasion from smear. It is the procedural act of interacting with someone else’s automated conceptual principles, embedding pre-determined decisions which not only generate but pre-determine ones ability to make choices about such decisions, like propaganda.

This might consist in distancing ideological definitions of false consciousness as an epistemological limit to knowing alternatives within thought, to engaging with a real programmable systems which embeds such limits concretely, withholding the means to transform them. In other words, propaganda incorporates how ‘decisional structures’ structure other decisions, either conceptually or systematically.

2.

Two years before Shannon’s famous Masters thesis, Turing published what would be a theoretical basis for computation in his 1936 paper “On Computable Numbers, with an Application to the Entscheidungsproblem.” The focus of the paper was to establish the idea of computation within a formal system of logic, which when automated would solve particular mathematical problems put into function (Turing, An Application). What is not necessarily taken into account is the mathematical context to that idea: for the foundations of mathematics were already precarious, way before Turing outlined anything in 1936. Contra the efficiency of the digital, this is a precariousness built-in to computation from its very inception: the precariousness of solving all problems in mathematics.

The key word of that paper, its key focus, was on the Entscheidungsproblem, or decision problem. Originating from David Hilbert’s mathematical school of formalism, ‘decision’ means something more rigorous than the sorts of decisions in daily life. It really means a ‘proof theory’, or how analytic problems in number theory and geometry could be formalised, and thus efficiently solved (Hilbert 3). Solving a theorem is simply finding a provable ‘winning position’ in a game. Similar to Shannon, ‘decision’ is what happens when an automated system of function is constructed in such a sufficiently complex way, that an algorithm can always ‘decide’ a binary, yes or no answer to a mathematical problem, when given an arbitrary input, in a sufficient amount of time. It does not require ingenuity, intuition or heuristic gambles, just a combination of simple consistent formal rules and a careful avoidance of contradiction.

The two key words there are ‘always’ and ‘decide’. The progressive end-game of twentieth century mathematicians who, like Hilbert, sought after a simple totalising conceptual system to decide every mathematical problem and work towards absolute knowledge. All Turing had to do was make explicit Hilbert’s implicit computational treatment of formal rules, manipulate symbol strings and automate them using an ’effective’ or “systematic method” (Turing, Solvable and Unsolvable Problems 584) encoded into a machine. This is what Turing’s thesis meant (discovered independently to Alonzo Church’s equivalent thesis (Church)): any systematic algorithm solved by a mathematical theorem can be computed by a Turing machine (Turing, An Application), or in Robin Gandy’s words, “[e]very effectively calculable function is a computable function” (Gandy).

Thus effective procedures decide problems, and they resolve puzzles providing winning positions (like theorems) in the game of functional rules and formal symbols. In Turing’s words, “a systematic procedure is just a puzzle in which there is never more than one possible move in any of the positions which arise and in which some significance is attached to the final result” (Turing, Solvable and Unsolvable Problems 590). The significance, or the winning position, becomes the crux of the matter for the decision: what puzzles or problems are to be decided? This is what formalism attempted to do: encode everything through the regime of formalised efficiency, so that all of mathematically inefficient problems are, in principle, ready to be solved. Programs are simply proofs: if it could be demonstrated mathematically, it could be automated.

In 1936, Turing had showed some complex mathematical concepts of effective procedures could simulate the functional decisions of all the other effective procedures (such as the Universal Turing Machine). Ten years later, Turing and John von Neumann would independently show how physical general purpose computers, offered the same thing and from that moment on, efficient digital decisions manifested themselves in the cultural application of physical materials. Before Shannon’s information theory offered the precision of transmitting information, Hilbert and Turing developed the structure of its transmission in the underlying regime of formal decision.

Yet, there was also a non-computational importance here, for Turing was also fascinated by what decisions couldn’t compute. His thesis was quite precise, so as to elucidate that if no mathematical problem could be proved, a computer was not of any use. In fact, the entire focus of his 1936 paper, often neglected by Silicon Valley cohorts, was to show that Hilbert’s particular decision problem could not be solved. Unlike Hilbert, Turing was not interested in using computation to solve every problem, but as a curious endeavour for surprising intuitive behaviour. The most important of all, Turing’s halting, or printing problem was influential, precisely as it was undecidable; a decision problem which couldn’t be decided.

We can all picture the halting problem, even obliquely. Picture the frustrated programmer or mathematician starting at their screen, waiting to know when an algorithm will either halt and spit out a result, or provide no answer. The computer itself has already determined the answer for us, the programmer just has to know when to give up. But this is a myth, inherited with a bias towards human knowledge, and a demented understanding of machines as infinite calculating engines, rather than concrete entities of decision. For reasons that escape word space, Turing didn’t understand the halting problem in this way: instead he understood it as a contradictory example of computational decisions failing to decide on each other, on the account that there could never be one totalising decision or effective procedure. There is no guaranteed effective procedure to decide on all the others, and any attempt to build one (or invest in a view which might help build one), either has too much investment in absolute formal reason, or it ends up with ineffective procedures.

Undecidable computation might be looked at as a dystopian counterpart against the efficiency of Shannon’s ‘digital information’ theory. A base 2 binary system of information resembling one of two possible states, whereby a system can communicate with one digit, only in virtue of the fact that there is one other digit alternative to it. Yet the perfect transmission of that information, is only subject to a system which can ‘decide’ on the digits in question, and establish a proof to calculate a success rate. If there is no mathematical proof to decide a problem, then transmitting information becomes problematic for establishing a solution.

3.

What has become clear is that our world is no longer simply accountable to human decision alone. Decisions are no longer limited to the borders of human decisions and ‘culture’ is no longer simply guided by a collective whole of social human decisions. Nor is it reducible to one harmonious ‘natural’ collective decision which prompts and pre-empts everything else. Instead we seem to exist in an ecology of decisions: or better yet decisional ecologies. Before there was ever the networked protocol (Galloway), there was the computational decision. Decision ecologies are already set up before we enter the world, implicitly coterminous with our lives: explicitly determining a quantified or bureaucratic landscape upon which an individual has limited manoeuvrability.

Decisions are not just digital, they are continuous as computers can be: yet decisions are at their most efficient when digitally transferred. Decisions are everywhere and in everything. Look around. We are constantly told by governments and states that are they making tough decisions in the face of austerity. CEOs and Directors make tough decisions for the future of their companies and ‘great’ leaders are revered for being ‘great decisive leaders’: not just making decisions quickly and effectively, but also settling issues and producing definite results.

Even the word ‘decide’, comes from the Latin origin of ‘decidere’, which means to determine something and ‘to cut off.’ Algorithms in financial trading know not of value, but of decision: whether something is marked by profit or loss. Drones know not of human ambiguity, but can only decide between kill and ignore, cutting off anything in-between. Constructing a system which decides between one of two digital values, even repeatedly, means cutting off and excluding all other possible variables, leaving a final result at the end of the encoded message. Making a decision, or building a system to decide a particular ideal or judgement must force other alternatives outside of it. Decisions are always-already embedded into the framework of digital action, always already deciding what is to be done, how it can be done or what is threatening to be done. It would make little sense to suggest that these entities ‘make decisions’ or ‘have decisions’, it would be better to say that they are decisions and ecologies are constitutively constructed by them.

The importance of neo-liberal digital transmissions are not that they become innovative, or worthy of a zeitgeist break: but that they demonstrably decide problems whose predominant significance is beneficial for self-individual efficiency and accumulation of capital. Digital efficiency is simply about the expansion of automating decisions and what sort of formalised significances must be propagated to solve social and economic problems, which creates new problems in a vicious circle.

The question can no longer simply be ‘who decides’, but now, ‘what decides?’ Is it the cafe menu board, the dinner party etiquette, the NASDAQ share price, Google Pagerank, railway network delays, unmanned combat drones, the newspaper crossword, the javascript regular expression or the differential calculus? It’s not quite right to say that algorithms rule the world, whether in algo-trading or in data capture, but the uncomfortable realisation that real entities are built to determine provable outcomes time and time again: most notably ones for cumulating profit and extracting revenue from multiple resources.

One pertinent example: consider George Dantzig’s simplex algorithm: this effective procedure (whose origins began in multidimensional geometry) can always decide solutions for large scale optimisation problems which continually affect multi-national corporations. The simplex algorithm’s proliferation and effectiveness has been critical since its first commercial application in 1952, when Abraham Charnes and William Cooper used it to decide how best to optimally blend four different petroleum products at the Gulf Oil Company (Elwes 35; Gass & Assad 79). Since then the simplex algorithm has had years of successful commercial use, deciding almost everything from bus timetables and work shift patterns to trade shares and Amazon warehouse configurations. According to the optimisation specialist Jacek Gondzio, the simplex algorithm runs at “tens, probably hundreds of thousands of calls every minute” (35), always deciding the most efficient method of extracting optimisation.

In contemporary times, nearly all decision ecologies work in this way, accompanying and facilitating neo-liberal methods of self-regulation and processing all resources through a standardised efficiency: from bureaucratic methods of formal standardisation, banal forms ready to be analysed one central system, to big-data initiatives and simple procedural methods of measurement and calculation. The technique of decision is a propagative method of embedding knowledge, optimisation and standardisation techniques in order to solve problems and an urge to solve the most unsolvable ones, including us.

Google do not build into their services an option to pay for the privilege of protecting privacy: the entire point of providing a free service which purports to improve daily life, is that it primarily benefits the interests of shareholders and extend commercial agendas. James Grimmelmann gave a heavily detailed exposition on Google’s own ‘net neutrality’ algorithms and how biased they happen to be. In short, PageRank does not simply decide relevant results, it decides visitor numbers and he concluded on this note.

With disturbing frequency, though, websites are not users’ friends. Sometimes they are, but often, the websites want visitors, and will be willing to do what it takes to grab them (Grimmelmann 458).

If the post-digital stands for the self-criticality of digitalisation already underpinning contemporary regimes of digital consumption and production, then its saliency lies in understanding the logic of decision inherent to such regimes. The reality of the post-digital, shows that machines remain curiously efficient whether we relish in cynicism or not. Such regimes of standardisation and determined results, were already ‘mistakenly built in’ to the theories which developed digital methods and means, irrespective of what computers can or cannot compute.

4.

Why then should such post-digital actors be understood as instantiations of propaganda? The familiarity of propaganda is manifestly evident in religious and political acts of ideological persuasion: brainwashing, war activity, political spin, mind control techniques, subliminal messages, political campaigns, cartoons, belief indoctrination, media bias, advertising or news reports. A definition of propaganda might follow from all of these examples: namely, the systematic social indoctrination of biased information that persuades the masses to take action on something which is neither beneficial to them, nor in their best interests: or as Peter Kenez writes, propaganda is “the attempt to transmit social and political values in the hope of affecting people’s thinking, emotions, and thereby behaviour” (Kenez 4) Following Stanley B. Cunningham’s watered down definition, propaganda might also denote a helpful and pragmatic “shorthand statement about the quality of information transmitted and received in the twentieth century” (Cunningham 3).

But propaganda isn’t as clear as this general definition makes out: in fact what makes propaganda studies such a provoking topic is that nearly every scholar agrees that no stable definition exists. Propaganda moves beyond simple ‘manipulation’ and ‘lies’ or derogatory, jingoistic representation of an unsubtle mood – propaganda is as much about the paradox of constructing truth, and the irrational spread of emotional pleas, as well as endorsing rational reason. As the master propagandist William J. Daugherty wrote;

It is a complete delusion to think of the brilliant propagandist as being a professional liar. The brilliant propagandist […] tells the truth, or that selection of the truth which is requisite for his purpose, and tells it in such a way that the recipient does not think that he is receiving any propaganda…. (Daugherty 39).

Propaganda, like ideology works by being inherently implicit and social. In the same way that post-ideology apologists ignore their symptom, propaganda is also ignored. It isn’t to be taken as a shadowy fringe activity, blown apart by the democratising fairy-dust of ‘the Internet’. As many others have noted, the purported ‘decentralising’ power of online networks, offer new methods for propagative techniques, or ‘spinternet’ strategies, evident in China (Brady). Iran’s recent investment into video game technology only makes sense, only when you discover that 70% of Iran’s population are under 30 years of age, underscoring a suitable contemporary method of dissemination. Similarly in 2011, the New York City video game developer Kuma Games was mired in controversy when it was discovered that an alleged CIA agent, Amir Mirza Hekmati, had been recruited to make an episodic video game series intending to “change the public opinion’s mindset in the Middle East.” (Tehran Times). The game in question, Kuma\War (2006 – 2011) was a free-to-play First-Person Shooter series, delivered in episodic chunks, the format of which attempted to simulate biased re-enactments of real-life conflicts, shortly after they reached public consciousness.

Despite his unremarkable leanings towards Christian realism, Jacques Ellul famously updated propaganda’s definition as the end product of what he previously lamented as ‘technique’. Instead of viewing propaganda as a highly organised systematic strategy for extending the ideologues of peaceful warfare, he understood it as a general social phenomenon in contemporary society.

Ellul outlined two types: political and sociological propaganda: Political propaganda involves government, administrative techniques which intend to directly change the political beliefs of an intended audience. By contrast, sociological propaganda is the implicit unification of involuntary public behaviour which creates images, aesthetics, problems, stereotypes, the purpose of which aren’t explicitly direct, nor overtly militaristic. Ellul argues that sociological propaganda exists; “in advertising, in the movies (commercial and non-political films), in technology in general, in education, in the Reader’s Digest; and in social service, case work, and settlement houses” (Ellul 64). It is linked to what Ellul called “pre” or “sub-propaganda”: that is, an imperceptible persuasion, silently operating within ones “style of life” or permissible attitude (63). Faintly echoing Louis Althusser’s Ideological State Apparatuses (Althusser 182) nearly ten years prior, Ellul defines it as “the penetration of an ideology by means of its sociological context.” (63) Sociological propaganda is inadequate for decisive action, paving the way for political propaganda – its strengthened explicit cousin – once the former’s implicitness needs to be transformed into the latter’s explicitness.

In a post-digital world, such implicitness no longer gathers wartime spirits, but instead propagates a neo-liberal way of life that is individualistic, wealth driven and opinionated. Ellul’s most powerful assertion is that ‘facts’ and ‘education’ are part and parcel of the sociological propagative effect: nearly everyone faces a compelling need to be opinionated and we are all capable of judging for ourselves what decisions should be made, without at first considering the implicit landscape from which these judgements take place. One can only think of the implicit digital landscape of Twitter: the archetype for self-promotion and snippets of opinions and arguments – all taking place within Ellul’s sub-propaganda of data collection and concealment. Such methods, he warns, will have “solved the problem of man” (xviii).

But information is of relevance here, and propaganda is only effective within a social community when it offers the means to solve problems using the communicative purview of information:

Thus, information not only provides the basis for propaganda but gives propaganda the means to operate; for information actually generates the problems that propaganda exploits and for which it pretends to offer solutions. In fact, no propaganda can work until the moment when a set of facts has become a problem in the eyes of those who constitute public opinion (114).

To understand why it's worth building an almost 200-year-old mechanical computer, it's necessary to first understand what a computer is. Although Babbage's analytical engine is entirely mechanical, it has the same essence as a modern computer. That computer essence is one of the important consequences of another British computing pioneer's work, a century after Babbage. Exactly 99 years after Babbage invented the computer, Alan Turing wrote his now famous paper describing the universal Turing machine. An important mathematical idea arising from Turing's paper and another by American mathematician Alonzo Church is that all computers have the same capabilities, no matter how they are constructed. Because of the Church-Turing thesis, as it is called, we know that Babbage's analytical engine (with its levers and cogs), Turing's theoretical machine and the latest tablet all have the same fundamental limits. Of course, Babbage's machine would by modern standards have been painfully slow.

Interstellar Pulse intothecontinuum:

In July 1967, astronomers at the Cavendish Laboratory in Cambridge, observed an unidentified radio signal from interstellar space, which flashed periodically every 1.33730 seconds. This object flashed with such regularity that it was accurate enough to be used as a clock and only be off by one part in a hundred million. It was eventually determined that this was the first discovery of a pulsar, CP-1919.  This is an object that has about the same mass as the Sun, but is the size of the San Francisco Bay at its widest (~20 kilometers) that is rotating so fast that its emitting a beam of light towards Earth like a strobing light house! Pulsars are neutron stars that are formed from the remnants of a massive star when it experiences stellar death. A hand drawn graph plotted in the style of a waterfall plot, in the Cambridge Encyclopedia of Astronomy, later became renown for its use on the cover of the album “Unknown Pleasures”  by 1970s English band Joy Division. Some even managed to point out the resemblance of this plot to some other waterfall plot gifs. Also, two days ago today was Joy Divisions singer’s, Ian Curtis, birthday! Mathematica code: R[n_] := (SeedRandom[n]; RandomReal[])ListAnimate[ Table[ Show[ Table[ Plot[ 80 - m  + .2Sin[2 PiR[6m] + Sum[4Sin[2 PiR[4m] + t + R[2 nm]2 Pi]* Exp[-(.3x + 30 - 1100R[2 nm])^2/20], {n, 1, 30, 1}]]  + Sum[3(1 + R[3nm])Abs[Sin[t + R[nm]2 Pi]] Exp[-(x - 1100R[nm])^2/20], {n, 1, 4, 1}],  {x, -50, 150},   PlotStyle -> Directive[White, Thick], PlotRange -> {{-50, 150}, {0, 85}}, Background -> Black, Filling -> Axis, FillingStyle -> Black, Axes -> False, AspectRatio -> Full, ImageSize -> {500, 630}], {m, 1, 80, 1}]],{t, 0, 6.318/19, 6.3/19}],AnimationRunning -> False]

It's a testament to Turing's fascination with nearly everything that 76 years since his first major paper, there's still so much to write about his work. Expect this week to offer more events and glimpses into these projects: Neuro-computational studies into the functional basis of cognition. The ever forward march for genuine artificial intelligence. New methods of simulating the complexity of biological forms nearly 60 years after Turing's paper on the chemical basis of morphogenesis (indeed this area of complexity theory is now an established area of major research). The slippery mathematical formalist discoveries which define what can or cannot be computed. And not forgetting key historical developments in cryptography, perhaps the field which Turing is most respected for. Moreover, Turing wasn't just one of the greatest mathematicians of the 20th Century, but also one of the greatest creative engineers; someone who wasn't afraid of putting his ideas into automation, through the ne

Letting microchips make a few mistakes here and there could make them much faster and more energy-efficient.

Managing the probability of errors and limiting where they occur can ensure that the errors do not cause any problems. The result of a mathematical calculation, for example, need not always be calculated precisely—an accuracy of two or three decimal places is often enough. Dr Palem offers the analogy of a person about to cross a big room. Rather than wasting time and energy calculating the shortest path, it’s better just to start walking in roughly the right direction.

In which I string together a series of videos, links and text that use Mario as a base for Science. First is Mario and the Many World Interpretation of Quantum Physics

…So what’s this about quantum physics? Oh, right. Well, I kind of identify the branching-paths effect in the video with the Everett-Wheeler “Many Worlds Interpretation” of quantum physics. Quantum physics does this weird thing where instead of things being in one knowable place or one knowable state, something that is quantum (like, say, an electron) exists in sort of this cloud of potentials, where there’s this mathematical object called a wavefunction that describes the probabilities of the places the electron might be at a given moment. Quantum physics is really all about the way this wavefunction behaves. There’s this thing that happens though where when a quantum thing interacts with something else, the wavefunction “collapses” to a single state vector and the (say) electron suddenly goes from being this potential

We put the question to experts in various fields. Biology is no stranger to large, international collaborations with lofty goals, they pointed out — the race to sequence the human genome around the turn of the century had scientists riveted. But most biological quests lack the mathematical precision, focus and binary satisfaction of a yes-or-no answer that characterize the pursuit of the Higgs. “Most of what is important is messy, and not given to a moment when you plant a flag and crack the champagne,” says Steven Hyman, a neuroscientist at the Broad Institute in Cambridge, Massachusetts.

Nevertheless, our informal survey shows that the field has no shortage of fundamental questions that could fill an anticipatory auditorium. These questions concern where and how life started — and why it ends.

Since the 1930s, the theory of computation has profoundly influenced philosophical thinking about topics such as the theory of the mind, the nature of mathematical knowledge and the prospect of machine intelligence. In fact, it’s hard to think of an idea that has had a bigger impact on philosophy. And yet there is an even bigger philosophical revolution waiting in the wings. The theory of computing is a philosophical minnow compared to the potential of another theory that is currently dominating thinking about computation. @ Technology Review

Gleick makes his case in a sweeping survey that covers the five millenniums of humanity’s engagement with information, from the invention of writing in Sumer to the elevation of information to a first principle in the sciences over the last half-century or so. It’s a grand narrative if ever there was one, but its key moment can be pinpointed to 1948, when Claude Shannon, a young mathematician with a background in cryptography and telephony, published a paper called “A Mathematical Theory of Communication” in a Bell Labs technical journal. For Shannon, communication was purely a matter of sending a message over a noisy channel so that someone else could recover it. Whether the message was meaningful, he said, was “irrelevant to the engineering problem.” Think of a game of Wheel of Fortune, where each card that’s turned over narrows the set of possible answers, except that here the answer could be anything: a common English phrase, a Polish surname, or just a set of license plate numbers

A starry firmament, or sand cascading through one’s open fingers, or weeds springing up time after time: the first conception of infinity, of the uncountable and the unending, is not recorded, but it must have been stimulated by experiences such as these. It may have merged in the mind of an ancient progenitor with thoughts of a God, a possessor of unlimited might, an infinite being itself. But whether or not the idea of God was born with the first thoughts of what cannot be counted, this wonderful book by an American historian of science and a French mathematician teaches us that eons later, the divine and the infinite remain closely entangled. A mathematical understanding of infinity was a conundrum for rationalists, who believed it could be mastered by using only the methods of scientific logic, unsullied by eschatology or religion. But as Jean-Michel Kantor and Loren Graham show, they were wrong. Centuries after Bacon and Descartes, and the birth of the scientific method of the mod

It may sound like the plot of a Dan Brown novel, but an academic at the University of Manchester claims to have cracked a mathematical and musical code in the works of Plato.

Jay Kennedy, a historian and philosopher of science, described his findings as "like opening a tomb and discovering new works by Plato."

Plato is revealed to be a Pythagorean who understood the basic structure of the universe to be mathematical, anticipating the scientific revolution of Galileo and Newton by 2,000 years.

Kennedy's breakthrough, published in the journal Apeiron this week, is based on stichometry: the measure of ancient texts by standard line lengths. Kennedy used a computer to restore the most accurate contemporary versions of Plato's manuscripts to their original form, which would consist of lines of 35 characters, with no spaces or punctuation. What he found was that within a margin of error of just one or two percent, many of Plato's dialogues had line lengths based on round multiples of twel

Little is quite as dull as literary worship; this essay on Borges is thus happily doomed. One finds oneself tempted toward learned-sounding inadequacies like: His work combines the elegance of mathematical proof with the emotionally profound wit of Dostoyevsky. Or: He courts paradox so primrosely, describing his Dupin-like detective character as having “reckless perspicacity” and the light in his infinite Library of Babel as being “insufficient, and unceasing.” But see, such worship is pale.

And problematic as well. More than any other 20th-century figure, Borges is the one designated — and often dismissed as — the Platonic ideal of Writer. His outrageous intellect is cited as proof of either his genius or of his bloodless cerebralism.

But Borges did have some mortal qualities. He lived most of his life with his mother. He loved detective and adventure novels. (His first story in English was published in Ellery Queen Mystery Magazine.) Though he started to go blind in his 30s, he nev

If zombies actually existed, an attack by them would lead to the collapse of civilisation unless dealt with quickly and aggressively.

That is the conclusion of a mathematical exercise carried out by researchers in Canada.

They say only frequent counter-attacks with increasing force would eradicate the fictional creatures.

The scientific paper is published in a book - Infectious Diseases Modelling Research Progress.

In books, films, video games and folklore, zombies are undead creatures, able to turn the living into other zombies with a bite.

But there is a serious side to the work.

In some respects, a zombie "plague" resembles a lethal, rapidly spreading infection. The researchers say the exercise could help scientists model the spread of unfamiliar diseases through human populations.

Speed is the elegance of thought, which mocks stupidity, heavy and slow. Intelligence thinks and says the unexpected; it moves with the fly, with its flight. A fool is defined by predictability… But if life is brief, luckily, thought travels as fast as the speed of light. In earlier times philosophers used the metaphor of light to express the clarity of thought; I would like to use it to express not only brilliance and purity but also speed. In this sense we are inventing right now a new Age of Enlightenment… A lot of… incomprehension… comes simply from this speed. I am fairly glad to be living in the information age, since in it speed becomes once again a fundamental category of intelligence. Michel Serres, Conversations on Science, Culture and Time

(Originally published at 3quarksdaily · Link to Part One) Human beings are often described as the great imitators: We perceive the ant and the termite as part of nature. Their nests and mounds grow out of the Earth. Their actions are indicative of a hidden pattern being woven by natural forces from which we are separated. The termite mound is natural, and we, the eternal outsiders, sitting in our cottages, our apartments and our skyscrapers, are somehow not. Through religion, poetry, or the swift skill of the craftsman smearing pigment onto canvas, humans aim to encapsulate that quality of existence that defies simple description. The best art, or so it is said, brings us closer to attaining a higher truth about the world that remains elusive from language, that perhaps the termite itself embodies as part of its nature. Termite mounds are beautiful, but were built without a concept of beauty. Termite mounds are mathematically precise, yet crawling through their intricate catacombs cannot be found one termite in comprehension of even the simplest mathematical constituent. In short, humans imitate and termites merely are. This extraordinary idea is partly responsible for what I referred to in Part One of this article as The Fallacy of Misplaced Concreteness. It leads us to consider not only the human organism as distinct from its surroundings, but it also forces us to separate human nature from its material artefacts. We understand the termite mound as integral to termite nature, but are quick to distinguish the axe, the wheel, the book, the skyscraper and the computer network from the human nature that bore them. When we act, through art, religion or with the rational structures of science, to interface with the world our imitative (mimetic) capacity has both subjective and objective consequence. Our revelations, our ideas, stories and models have life only insofar as they have a material to become invested through. The religion of the dance, the stone circle and the summer solstice is mimetically different to the religion of the sermon and the scripture because the way it interfaces with the world is different. Likewise, it is only with the consistency of written and printed language that the technical arts could become science, and through which our ‘modern’ era could be built. Dances and stone circles relayed mythic thinking structures, singular, imminent and ethereal in their explanatory capacities. The truth revealed by the stone circle was present at the interface between participant, ceremony and summer solstice: a synchronic truth of absolute presence in the moment. Anyone reading this will find truth and meaning through grapholectic interface. Our thinking is linear, reductive and bound to the page. It is reliant on a diachronic temporality that the pen, the page and the book hold in stasis for us. Imitation alters the material world, which in turn affects the texture of further imitation. If we remove the process from its material interface we lose our objectivity. In doing so we isolate the single termite from its mound and, after much careful study, announce that we have reduced termite nature to its simplest constituent. The reason for the tantalizing involutions here is obviously that intelligence is relentlessly reflexive, so that even the external tools that it uses to implement its workings become ‘internalized’, that is, part of its own reflexive process… To say writing is artificial is not to condemn it but to praise it. Like other artificial creations and indeed more than any other, it is utterly invaluable and indeed essential for the realisation of fuller, interior, human potentials. Technologies are not mere exterior aids but also interior transformations of consciousness, and never more than when they affect the word. Walter J. Ong, Orality and Literacy

Anyone reading this article cannot fail but be aware of the changing interface between eye and text that has taken place over the past two decades or so. New Media – everything from the internet database to the Blackberry – has fundamentally changed the way we connect with each other, but it has also altered the way we connect with information itself. The linear, diachronic substance of the page and the book have given way to a dynamic textuality blurring the divide between authorship and readership, expert testament and the simple accumulation of experience. The main difference between traditional text-based systems and newer, data-driven ones is quite simple: it is the interface. Eyes and fingers manipulate the book, turning over pages in a linear sequence in order to access the information stored in its printed figures. For New Media, for the digital archive and the computer storage network, the same information is stored sequentially in databases which are themselves hidden to the eye. To access them one must commit a search or otherwise run an algorithm that mediates the stored data for us. The most important distinction should be made at the level of the interface, because, although the database as a form has changed little over the past 50 years of computing, the Human Control Interfaces (HCI) we access and manipulate that data through are always passing from one iteration to another. Stone circles interfacing the seasons stayed the same, perhaps being used in similar rituals over the course of a thousand years of human cultural accumulation. Books, interfacing text, language and thought, stay the same in themselves from one print edition to the next, but as a format, books have changed very little in the few hundred years since the printing press. The computer HCI is most different from the book in that change is integral to it structure. To touch a database through a computer terminal, through a Blackberry or iPhone, is to play with data at incredible speed: Sixty years ago, digital computers made information readable. Twenty years ago, the Internet made it reachable. Ten years ago, the first search engine crawlers made it a single database. Now Google and like-minded companies are sifting through the most measured age in history, treating this massive corpus as a laboratory of the human condition… Kilobytes were stored on floppy disks. Megabytes were stored on hard disks. Terabytes were stored in disk arrays. Petabytes are stored in the cloud. As we moved along that progression, we went from the folder analogy to the file cabinet analogy to the library analogy to — well, at petabytes we ran out of organizational analogies. At the petabyte scale, information is not a matter of simple three- and four-dimensional taxonomy and order but of dimensionally agnostic statistics… This is a world where massive amounts of data and applied mathematics replace every other tool that might be brought to bear. Out with every theory of human behavior, from linguistics to sociology. Forget taxonomy, ontology, and psychology. Who knows why people do what they do? The point is they do it, and we can track and measure it with unprecedented fidelity. With enough data, the numbers speak for themselves. Wired Magazine, The End of Theory, June 2008

And as the amount of data has expanded exponentially, so have the interfaces we use to access that data and the models we build to understand that data. On the day that Senator John McCain announced his Vice Presidential Candidate the best place to go for an accurate profile of Sarah Palin was not the traditional media: it was Wikipedia. In an age of instant, global news, no newspaper could keep up with the knowledge of the cloud. The Wikipedia interface allowed knowledge about Sarah Palin from all levels of society to be filtered quickly and efficiently in real-time. Wikipedia acted as if it was encyclopaedia, as newspaper as discussion group and expert all at the same time and it did so completely democratically and at the absence of a traditional management pyramid. The interface itself became the thinking mechanism of the day, as if the notes every reader scribbled in the margins had been instantly cross-checked and added to the content. In only a handful of years the human has gone from merely dipping into the database to becoming an active component in a human-cloud of data. The interface has begun to reflect back upon us, turning each of us into a node in a vast database bigger than any previous material object. Gone are the days when clusters of galaxies had to a catalogued by an expert and entered into a linear taxonomy. Now, the same job is done by the crowd and the interface, allowing a million galaxies to be catalogued by amateurs in the same time it would have taken a team of experts to classify a tiny percentage of the same amount. This method of data mining is called ‘crowdsourcing’ and it represents one of the dominant ways in which raw data will be turned into information (and then knowledge) over the coming decades. Here the cloud serves as more than a metaphor for the group-driven interface, becoming a telling analogy for the trans-grapholectic culture we now find ourselves in. To grasp the topological shift in our thought patterns it pays to move beyond the interface and look at a few of the linear, grapholectic models that have undergone change as a consequence of the information age. One of these models is evolution, a biological theory the significance of which we are still in the process of discerning:

If anyone now thinks that biology is sorted, they are going to be proved wrong too. The more that genomics, bioinformatics and many other newer disciplines reveal about life, the more obvious it becomes that our present understanding is not up to the job. We now gaze on a biological world of mind-boggling complexity that exposes the shortcomings of familiar, tidy concepts such as species, gene and organism. A particularly pertinent example [was recently provided in New Scientist] - the uprooting of the tree of life which Darwin used as an organising principle and which has been a central tenet of biology ever since. Most biologists now accept that the tree is not a fact of nature - it is something we impose on nature in an attempt to make the task of understanding it more tractable. Other important bits of biology - notably development, ageing and sex - are similarly turning out to be much more involved than we ever imagined. As evolutionary biologist Michael Rose at the University of California, Irvine, told us: “The complexity of biology is comparable to quantum mechanics.” New Scientist, Editorial, January 2009

As our technologies became capable of gathering more data than we were capable of comprehending, a new topology of thought, reminiscent of the computer network, began to emerge. For the mindset of the page and the book science could afford to be linear and diachronic. In the era of The Data Deluge science has become more cloud-like, as theories for everything from genetics to neuroscience, particle physics to cosmology have shed their linear constraints. Instead of seeing life as a branching tree, biologists are now speaking of webs of life, where lineages can intersect and interact, where entire species are ecological systems in themselves. As well as seeing the mind as an emergent property of the material brain, neuroscience and philosophy have started to consider the mind as manifest in our extended, material environment. Science has exploded, and picking up the pieces will do no good. Through the topology of the network we have begun to perceive what Michel Serres calls ‘The World Object’, an ecology of interconnections and interactions that transcends and subsumes the causal links propounded by grapholectic culture. At the limits of science a new methodology is emerging at the level of the interface, where masses of data are mined and modelled by systems and/or crowds which themselves require no individual understanding to function efficiently. Where once we studied events and ideas in isolation we now devise ever more complex, multi-dimensional ways for those events and ideas to interconnect; for data sources to swap inputs and output; for outsiders to become insiders. Our interfaces are in constant motion, on trajectories that curve around to meet themselves, diverge and cross-pollinate. Thought has finally been freed from temporal constraint, allowing us to see the physical world, life, language and culture as multi-dimensional, fractal patterns, winding the great yarn of (human) reality: The advantage that results from it is a new organisation of knowledge; the whole landscape is changed. In philosophy, in which elements are even more distanced from one another, this method at first appears strange, for it brings together the most disparate things. People quickly crit[cize] me for this… But these critics and I no longer have the same landscape in view, the same overview of proximities and distances. With each profound transformation of knowledge come these upheavals in perception. Michel Serres, Conversations on Science, Culture and Time

Deep in the deluge of knowledge that poured forth from science in the 20th century were found ironclad limits on what we can know. Werner Heisenberg discovered that improved precision regarding, say, an object’s position inevitably degraded the level of certainty of its momentum. Kurt Gödel showed that within any formal mathematical system advanced enough to be useful, it is impossible to use the system to prove every true statement that it contains. And Alan Turing demonstrated that one cannot, in general, determine if a computer algorithm is going to halt.