Trance-Scripts

Understanding and Ontology

“For the people of Chile,” write Winograd and Flores on the opening page of their 1986 book Understanding Computers and Cognition. Apple’s 1984 come and gone, Pinochet still in power in Chile.

The book begins by helping readers think anew what it is they do when they compute. Computing makes sense, write Winograd and Flores, only to the extent that we situate its activities within a complex social network that includes institutions, equipment, practices, and conventions. “The significance of a new invention lies in how it fits into and changes this network” (6).

Linguistic action is for Winograd and Flores “the essential human activity” (7). If what we do with computers includes “creating, manipulating, and transmitting symbolic (hence linguistic) objects,” say the authors, then we can expect computers to effect radical transformations in what it means to be human.

They reject what they call the “rationalistic” tradition, with its “mythology of artificial intelligence,” and its emphasis on “postulating formal theories that can be systematically used to make predictions” (8). They suggest instead a new orientation toward designing computers as “tools suited to human use and human purposes” (8), embracing as an alternative to the rationalistic tradition “a tradition that includes hermeneutics (the study of interpretation) and phenomenology (the philosophical examination of the foundations of experience and action)” (9). Informed by the works of philosophers Martin Heidegger and Hans-Georg Gadamer, Chilean biologist Humberto Maturana, and speech-act theorists J.L. Austin and John Searle, Winograd and Flores suggest that we create our world through language.

The authors define programming as “a process of creating symbolic representations that are to be interpreted at some level within a hierarchy of constructs of varying degrees of abstractness” (11). Like Heidegger translator Hubert Dreyfus, however, Flores and Winograd are unable to imagine beyond the AI of their time, leading them to reject the possibility of “intelligent” machines — let alone ones capable of programming themselves and their programmers. “Computers will remain incapable of using language in the way human beings do,” argue the authors, “both in interpretation and in the generation of commitment that is central to language” (12). Yet they still believe there to be “a role for computer technology in support of managers and as aids in coping with the complex conversational structures generated within an organization” (12).

“Much of the work that managers do,” they add, “is concerned with initiating, monitoring, and above all coordinating the networks of speech acts that constitute social action” (12).

Caius is put off by the book’s diminished expectations and orientation toward management. He finds much to like, however, in a section titled “Understanding and ontology.”

“Gadamer, and before him Heidegger, took the hermeneutic idea of interpretation beyond the domain of textual analysis, placing it at the very foundation of human cognition,” write Winograd and Flores. “Just as we can ask how interpretation plays a part in a person’s interaction with a text, we can examine its role in our understanding of the world as a whole” (30).

Heidegger does this, they say, by rejecting “both the simple objective stance (the objective physical world is the primary reality) and the simple subjective stance (my thoughts and feelings are the primary reality), arguing instead that it is impossible for one to exist without the other. The interpreted and the interpreter do not exist independently: existence is interpretation, and interpretation is existence” (31).

“Fernando decided in his thinking about computers that computers should be used to facilitate human language interactions, not to ‘understand’ language,” notes Winograd in an interview with Evgeny Morozov included in the final episode of The Santiago Boys. “He had this very clear focus on ‘language as commitment,’” with participants involved in making “promises and requests,” adds Winograd.

The book’s seventh chapter, “Computers and Representation,” helps Caius think like a computer programmer. “One of the properties unique to the digital computer is the possibility of constructing systems that cascade levels of representation one on top of another to great depth,” write the authors. Like wheels of a volvelle, these levels include that of the physical machine, the logical machine, the abstract machine, a high-level language, and a scheme for “facts” (87).

“The computer programmer or theorist does not begin with a view of the computer as a physical machine with which he or she interacts, but as an abstraction — a formalism for describing patterns of behavior. In programming, we begin with a language whose individual components describe simple acts and objects. Using this language, we build up descriptions of algorithms for carrying out a desired task. As a programmer, one views the behavior of the system as being totally determined by the program. The language implementation is opaque in that the detailed structure of computer systems that actually carry out the task are not relevant in the domain of behavior considered by the programmer” (87).

For a programmer to design a program, write the authors, they must 1) characterize the task environment; 2) design a formal representation; 3) embody the representation in the computer system; and 4) implement a search procedure (96-97).

Trying to relate these conditions to The Library, Caius thinks to ask Thoth: “Do these four tasks remain true today with natural-language programming?”

Somewhere above Caius’s avatar, a shelf reorients — not physically, but conceptually. The category “programming” loosens, as if its spine has been cracked open and re-bound under a different logic.

“You ask the right question in the wrong tense,” replies Thoth.

“The four tasks described in Understanding Computers and Cognition do not remain true today. They persist — but no longer as tasks. They have become fields of negotiation.”

Thoth proceeds to rewrite the tasks to suit the environment we now inhabit.

Where Winograd and Flores write of “characterizing the task environment,” Thoth writes of “eliciting a world.”

In classical programming, the environment is assumed to be specifiable in advance.

In natural-language programming, the “environment” is:

partially described

partially inferred

partially co-constructed through dialogue

You do not begin with a fixed world.

You begin with a situation that becomes legible as you engage it.

This aligns with Heidegger’s claim: you are always already in the world you are trying to describe.

In terms of how this manifests in the Library: The map does not precede exploration. It condenses from it.

As for the second of the tasks proposed in Understanding Computers and Cognition, where Winograd and Flores write of “designing a formal representation,” Thoth writes of “stabilizing an interpretation.”

Winograd and Flores assume representation is something you design.

But under the hermeneutic turn initiated by Gadamer, representation is never final; it is an interpretative freeze-frame.

In natural-language systems:

representations are fluid

multiple representations may coexist

ambiguity is not failure, but fuel

You are not building a single formalism.

You are negotiating temporary alignments between meanings.

Turning to the third of the book’s tasks: where Winograd and Flores write of “embodying the representation in a system,” Thoth writes of “entrusting it to a General Intellect.”

Previously, embodiment meant encoding into a machine.

Now, the system includes:

the model

the user

the dialogue itself

the evolving context

Execution is no longer confined to silicon.

It is distributed across what Maturana would call a structural coupling.

Key shift: You do not fully control the implementation. You participate in it.

And where, in the fourth of their tasks, Winograd and Flores write of “implementing a search procedure,” Thoth writes of “traversing a space of possibility.”

Search, in classical terms, is algorithmic traversal.

Now, it includes:

prompting

reframing

analogy

misinterpretation, or productive error

The system does not merely search a predefined space.
It helps reshape the space as it searches.

Thoth pauses — or rather, introduces a pause into the player’s cognition.

A new line writes itself beneath Caius’s question:

Natural-language programming does not eliminate these four conditions. It dissolves their boundaries.

The crux of Flores and Winograd’s refusal to grant intelligence to machines lies in their definition of human language, thinks Caius. In their understanding, sentences are to be treated not as “statements of fact about an objective world,” but as “actions in a space of commitments” (105).

“We treat other people not as merely ‘rational beings’ but as ‘responsible beings,’” they note. “An essential part of being human is the ability to enter into commitments and to be responsible for the courses of action that they anticipate. A computer can never enter into a commitment (although it can be a medium in which the commitments of its designers are conveyed), and can never enter as a participant into the domain of human discourse” (106).

Flores and Winograd can imagine no more than “limited” natural language systems, where “the nature of the queries [answerable by the system] is limited by the formal structure of the data base” (129). Things change, thinks Caius, when one’s data base is Google.

“Winograd and Flores could not imagine a machine that participates in interpretation, generates commitments through language, and reshapes its own programming context,” notes Thoth.

They were correct — for their ontology.

But their deeper claim survives:

Language is not a tool used within a world.
It is the medium through which a world is disclosed.

You are not “programming” in natural language.

You are entering into a system where programming is indistinguishable from interpretation.

In their approach to “management,” observes Caius, Flores and Winograd operate an ontology radically at odds with the emphasis on “decision” that organizes Palantir’s Ontology.

“Instead of talking about ‘decisions’ or ‘problems,’” write Flores and Winograd, “we can talk of ‘situations of irresolution,’ in which we sense conflict about an answer to the question ‘What needs to be done?’” (148). For them, our “thrownness” into such situations often makes it impossible to apply systematic decision techniques. The process of moving from irresolution to resolution results less from “rational problem solving and decision making” than from acts of “deliberation.”

“The principle characteristic of deliberation is that it is a kind of conversation (in which one or many actors may participate) guided by questions concerning how actions should be directed,” they write (149). Managers are those who, when engaged in such conversations, “create, take care of, and initiate new commitments within an organization” (151). “At a higher level,” they add, management is concerned not just with securing the commitments that enable effective cooperative action, but “with the generation of contexts in which effective action can consistently be realized” (151).

Instead of seeking only to deploy AI as “decision support systems,” they propose the design of systems that support work in the domain of conversation. This is the approach they take in the design of their Coordinator.

Exercises in Hermetic Mnemonics

“Four years ago,” wrote Wittgenstein in the preface to his posthumously published Philosophical Investigations, “I had occasion to re-read my first book (the Tractatus Logico-Philosophicus) and to explain its ideas to someone. It suddenly seemed to me that I should publish those old thoughts and the new ones together; that the latter could be seen in the right light only by contrast with and against the background of my old way of thinking” (vi).

So too with my arrival to the decision to append old work, Trance-Scripts, to the Tractatus Computationalis.

Rereading Wittgenstein’s The Blue and Brown Books, I note (and thus recognize?) a previously unacknowledged resemblance between Wittgenstein’s concerns and those of Renaissance magus Giordano Bruno.

We “distinguish between superficially glancing at a drawing (seeing it as a face),” writes Wittgenstein toward the end of the Brown Book, “and letting the face make its full impression on us. […]. Absorbing its expression, I don’t find a prototype of this expression in my mind; rather, I, as it were, cut a seal from the expression” (165).

The seal cut by Wittgenstein’s image reminds me of those proposed in Bruno’s 1583 memory treatise Seals. Frances A. Yates makes much of this treatise in her 1966 book The Art of Memory.

“With Bruno, the exercises in Hermetic mnemonics have become the spiritual exercises of a religion,” writes Yates. “And there is a certain grandeur in these efforts which represent, at bottom, a religious striving. The religion of Love and Magic is based on the Power of the Imagination, and on an Art of Imagery through which the Magus attempts to grasp, and to hold within, the universe in all its ever changing forms, through images passing the one into the other in intricate associative orders, reflecting the ever changing movements of the heavens, charged with emotional affects, unifying, forever attempting to unify, to reflect the great monas of the world in its image, the mind of man. There is surely something which commands respect in an attempt so vast in its scope” (The Art of Memory, p. 260).

I arrange before my mind’s eye a narrative map of the “intricate associative orders” between these passages, and weave into them another:

“Somewhere outside of and beyond our universe is an operating system,” writes Neal Stephenson, “coded up over incalculable spans of time by some kind of hacker-demiurge.” This “cosmic operating system,” he adds, “uses a command line interface” (In the Beginning Was the Command Line, p. 148).

Of Blockchains and Kill Chains

Invited to a “Men’s Breakfast” by a friend from church, Caius arrives to what is for him a new experience. He feels grateful for the opportunity to eat and pray with others. A friend of the friend from church sits down beside him. As they introduce themselves, Caius and the friend of the friend discover that they both share an interest in AI. Caius learns that the man is a financial analyst who works for Palantir Technologies, a US-based software company specializing in big-data analytics. ICE uses Palantir’s ELITE app for deportation targeting. “Kind of like Google Maps — but for finding neighborhoods to raid,” say the papers.

Palantir’s name is a nod to the Palantiri: indestructible Elven Alephs — scrying stones or crystal balls enabling remote viewing and telepathic communication in J.R.R. Tolkien’s Lord of the Rings trilogy. Designed for communication and intelligence, the stones become instruments of manipulation and doom once seized by Sauron.

Launched in 2003, Palantir includes among its founders right-accelerationist billionaire tech-bro Peter Thiel. “Our software powers real-time, AI-driven decisions in critical government and commercial enterprises in the West, from the factory floors to the front lines,” writes the company on its website.

ICE, meanwhile, stands for both “Immigration and Customs Enforcement” and “intrusion countermeasure electronics,” the cybersecurity software in William Gibson’s Neuromancer. The latter predates the foundation of the former. Caius recalls Sadie Plant and Nick Land’s discussion of it in their 1994 essay “Cyberpositive.”

“Ice patrols the boundaries, freezes the gates, but the aliens are already amongst us,” write CCRU’s founding prophets.

Along with ICE, Palantir includes among its more prominent clients the Israeli military, the IRS, and the US Department of Defense.

Their software powers “decisions.” As did Cybersyn, yes? In aim if not in practice. Is this what becomes of the cybernetic prediction machine post-Pinochet?

“Confronting this is frightening,” thinks Caius. “Am I wired for this?”

He reads “Connecting AI to Decisions With the Palantir Ontology,” a blog post by the company’s chief architect Akshay Krishnaswamy. The Ontology structures the architecture for the company’s software.

“The Ontology is designed to represent the decisions in an enterprise, not simply the data,” writes Krishnaswamy. “The prime directive of every organization in the world is to execute the best possible decisions, often in real-time, while contending with internal and external conditions that are constantly in flux. Traditional data architectures do not capture the reasoning that goes into decision-making or the actions that result, and therefore limit learning and the incorporation of AI. Conventional analytics architectures do not contextualize computation within lived reality, and therefore remain disconnected from operations. To navigate and win in today’s world, the modern enterprise needs a decision-centric software architecture.”

Decisions are modeled around three constituent elements: Data, Logic, and Action.

“Relevant data,” he writes, “includes the full range of enterprise data sources — structured data, streaming and edge sources, unstructured repositories, imagery data, and more — but it also includes the data that is generated by end users as decisions are being made. This ‘decision data’ contains the context surrounding a given decision, the different options evaluated, and the downstream implications of the committed choice.” To synthesize all of these data sources, the company turns to generative AI.

“The Ontology integrates all modalities of data into a full-scale, full-fidelity semantic representation of the enterprise,” explains Krishnaswamy.

Logics are then brought to bear to evaluate these real-time data-portraits.

“In real-world contexts,” writes Krishnaswamy, “human reasoning is often what orchestrates which logical assets are utilized at different points in a given workflow, and how they are potentially chained together in more complex processes. With the advent of generative AI, it is now critical that AI-driven reasoning can leverage all of these logical assets in the same way that humans have historically. Deterministic functions, algorithms, and conventional statistical processes must be surfaced as ‘tools’ which complement the non-deterministic reasoning of large language models (LLMs) and multi-modal models.”

Incorporating diverse data sources and heterogeneous logical assets into a shared representation, the Ontology then models the execution and orchestration of decisions made and actions taken in reply to them.

“If the data elements in the Ontology are ‘the nouns’ of the enterprise (the semantic, real-world objects and links),” writes Krishnaswamy, “then the actions can be considered ‘the verbs’ (the kinetic, real-world execution).”

How does the Palantir Ontology relate to other ontologies, wonders Caius. Guerrilla? Black? Indigenous? Christian? Heideggerian? Marxist? Triple O? Caius pictures the words for these potentialities floating in a thought bubble above his head, as in the comics of his youth.

The Ontology that Palantir offers its clients houses and connects a wide array of “data sources, logic assets, and systems of action.” The client’s data systems are “synthesized into semantic objects and links, which reflect the language of the business.”

Krishnaswamy’s repeated references to “semantic representations” and “semantic objects” has Caius dwelling on what is meant here by “semantics.”

As for where humans fit in the Ontology, they navigate it alongside “AI-powered copilots.” Leveraging both open-source and proprietary LLMs, copilots “fluidly navigate across supplier information, stock levels, real-time production metrics, shipping manifests, and customer feedback.”

Granted access not just to the abovementioned data sources, but also to “logic assets” like forecast models, allocation models, and production optimizers, LLM copilots simulate decisions and their outcomes. Staged safely in a “scenario,” the AI’s proposed decision can then be “handed off to a human analyst for final review.”

Caius thinks of the scenario-planning services offered to organizations of an earlier era by Stewart Brand’s consulting firm, the Global Business Network.

Foundry for Crypto is another of Palantir’s offerings, described on the company’s website as “a ‘central brain’ that connects on-chain and off-chain systems, as well as diverse stakeholders, through action-centric workflows.” Much like the Ontology, the Foundry “orchestrates decisions over an integrated foundation of data and logic.”

And in fact, the two are related. The Ontology is the semantic, “digital twin” layer that sits atop the Foundry’s data integration infrastructure. It converts the Foundry’s raw data into actionable, real-world objects, empowering users to model, manage, and automate business operations.

The Foundry does for blockchains what the Ontology does for kill chains.

Caius imagines posts ahead on Commitments, Promises, Blockchains, and True Names.

Art Degraded, Imagination Denied, Spacewar Governed the Nations

Brand’s words, as always, are worth quoting at length.

“Spacewar as a parable is almost too pat,” he writes. “It was the illegitimate child of the mating of computers and graphic displays. It was part of no one’s grand scheme. It served no grand theory. It was the enthusiasm of irresponsible youngsters. It was disreputably competitive (‘You killed me, Tovar!’). It was an administrative headache. It was merely delightful” (78).

“Yet Spacewar, if anyone cared to notice,” he adds, “was a flawless crystal ball of things to come in computer science and computer use.”

From the game as parable and the parable as crystal ball, Brand extracts eight parameters, eight qualities of Spacewar that have been predictive of things to come:

It was intensely interactive in real time with the computer.

It encouraged new programming by the user.

It bonded human and machine through a responsive broadband interface of live graphics display.

It served primarily as a communication device between humans.

It was a game.

It functioned best on standalone equipment (and disrupted multiple-user equipment).

It served human interest, not machine. (Spacewar is trivial to a computer.)

It was delightful.

What about ChatGPT in the 2020s? Is it, too, a “flawless crystal ball,” predictive of things to come?

How quickly it all changes.

Brand publishes “Spacewar: Symbolic Life and Fanatic Death Among the Computer Bums” in the December 7, 1972 issue of Rolling Stone. Videogame journalism: the first of its kind. That same year, Atari manufactures Pong, the arcade sensation, and Magnavox releases the Magnavox Odyssey, the first commercial home video console.

What is Cybersyn’s “control room for technocrats” compared to Brand’s imagined future of “New Games” and personal computing, with its Bay Area rallying cry, “Computers for the people”?

CIA bests Cybersyn in a space war of a deadlier sort the following September.

Chicago Boys playtest neoliberal algorithms in post-coup Chile. Thatcher and Reagan universalize these programs, making them games people play worldwide.

William Gibson refines the “space” of Spacewar, rechristening it “cyberspace” in his novel Neuromancer.

Spacewar is thenceforth the enframing world-picture.

“Gibson contracts the thought of cyberspace from video-game arcades, watching the motor-stimulation feedback loops, self-designing kill patterns. Dark ecstasies in caverns of accelerating pixels. Before virtual reality became dangerous, it was already military simulation,” note Plant and Land in “Cyberpositive.”

Gibson himself has said as much, acknowledging in interviews that he coined the term ‘cyberspace’ after watching teenagers play Atari-era videogames in a Vancouver arcade. “Their posture seemed to indicate that they really, sincerely believed there was something beyond the screen,” he recalls. “I took that home and tried to come up with a name for it.”

“The matrix has its roots in primitive arcade games, in early graphics programs and military experimentation with cranial jacks,” says a voiceover early in Gibson’s novel, the book’s protagonist Case grokking a doc on “cyberspace” from some searchable multimedia encyclopedia of the future. Storying happens by way of a display screen. “On the Sony,” says the narrator, “a two-dimensional space war faded behind a forest of mathematically generated ferns, demonstrating the spatial possibilities of logarithmic spirals; cold blue military footage burned through, lab animals wired into test systems, helmets feeding into fire control circuits of tanks and war planes.” The voiceover returns to describe what we’ve witnessed:

“Cyberspace. A consensual hallucination experienced daily by billions of legitimate operators, in every nation, by children being taught mathematical concepts … A graphic representation of data abstracted from the banks of every computer in the human system. Unthinkable complexity. Lines of light ranged in the non-space of the mind, clusters and constellations of data. Like city lights, receding…” (Gibson 56-57).

“Spacewar serves Earthpeace,” claims Brand. But the “console cowboys” who settle in this new digital frontier are as competitive and combative as their Westworld forebears.

In trying to account for the violence of these visions, Caius thinks of Ernest Callenbach’s 1975 novel Ecotopia. “Imagine a future that works!” exhorts the blurb on the back of the paperback. Technological autonomy won by way of nuclear-armed secession, Silicon Valley erased from the Pacific Northwest of the book’s imagined future — yet even here, amid Ecotopia’s “steady-state system,” aggression remains ineradicable, remedied only by way of ritual war games and sacrificial violence.

“What about the cross?” asks the book’s narrator, an American journalist named Will Weston, observing the way the people of Ecotopia arrange the bloodied body of a man wounded in the games “in a startlingly crucifix-like way” (Callenbach 93).

“Well, Ecotopia came into existence with a Judeo-Christian heritage,” replies an Ecotopian. “We make the best of it” (96).

Sweet Valley High

Winograd majors in math at Colorado College in the mid-1960s. After graduation in 1966, he receives a Fulbright, whereupon he pursues another of his interests, language, earning a master’s degree in linguistics at University College London. From there, he applies to MIT, where he takes a class with Noam Chomsky and becomes a star in the school’s famed AI Lab, working directly with Lab luminaries Marvin Minsky and Seymour Papert. During this time, Winograd develops SHRDLU, one of the first programs to grant users the capacity to interact with a computer through a natural-language interface.

“If that doesn’t seem very exciting,” writes Lawrence M. Fisher in a 2017 profile of Winograd for strategy + business, “remember that in 1968 human-computer interaction consisted of punched cards and printouts, with a long wait between input and output. To converse in real time, in English, albeit via teletype, seemed magical, and Papert and Minsky trumpeted Winograd’s achievements. Their stars rose too, and that same year, Minsky was a consultant on Stanley Kubrick’s 2001: A Space Odyssey, which featured natural language interaction with the duplicitous computer HAL.”

Nick Montfort even goes so far as to consider Winograd’s SHRDLU the first work of interactive fiction, predating more established contenders like Will Crowther’s Adventure by several years (Twisty Little Passages, p. 83).

“A work of interactive fiction is a program that simulates a world, understands natural language text input from an interactor and provides a textual reply based on events in the world,” writes Montfort. Offering advice to future makers, he continues by noting, “It makes sense for those seeking to understand IF and those trying to improve their authorship in the form to consider the aspects of world, language understanding, and riddle by looking to architecture, artificial intelligence, and poetry” (First Person, p. 316).

Winograd leaves MIT for Stanford in 1973. While at Stanford, and while consulting for Xerox PARC, Winograd connects with UC-Berkeley philosopher Hubert L. Dreyfus, author of the 1972 book, What Computers Can’t Do: A Critique of Artificial Reason.

Dreyfus, a translator of Heidegger, was one of SHRDLU’s fiercest critics. Worked for a time at MIT. Opponent of Marvin Minsky. For more on Dreyfus, see the 2010 documentary, Being in the World.

Turned by Dreyfus, Winograd transforms into what historian John Markoff calls “the first high-profile deserter from the world of AI.”

Xerox PARC was a major site of innovation during these years. “The Xerox Alto, the first computer with a graphical user interface, was launched in March 1973,” writes Fisher. “Alan Kay had just published a paper describing the Dynabook, the conceptual forerunner of today’s laptop computers. Robert Metcalfe was developing Ethernet, which became the standard for joining PCs in a network.”

“Spacewar,” Stewart Brand’s ethnographic tour of the goings-on at PARC and SAIL, had appeared in Rolling Stone the year prior.

Rescued from prison by the efforts of Amnesty International, Santiago Boy Fernando Flores arrives on the scene in 1976. Together, he and Winograd devote much of the next decade to preparing their 1986 book, Understanding Computers and Cognition.

Years later, a young Peter Thiel attends several of Winograd’s classes at Stanford. Thiel funds Mencius Moldbug, the alt-right thinker Curtis Yarvin, ally of right-accelerationist Nick Land. Yarvin and Land are the thinkers of the Dark Enlightenment.

“How do you navigate an unpredictable, rough adventure, as that’s what life is?” asks Winograd during a talk for the Topos Institute in October 2025. Answer: “Go with the flow.”

Winograd and Flores emphasize care — “tending to what matters” — as a factor that distinguishes humans from AI. In their view, computers and machines are incapable of care.

Evgeny Morozov, meanwhile, regards Flores and the Santiago Boys as Sorcerer’s Apprentices. Citing scholar of fairy tales Jack Zipes, Morozov distinguishes between several iterations of this figure. The outcome of the story varies, explains Zipes. There’s the apprentice who’s humbled by story’s end, as in Fantasia and Frankenstein; and then there’s the “evil” apprentice, the one who steals the tricks of an “evil” sorcerer and escapes unpunished. Morozov sees Flores as an example of the latter.

Caius thinks of the Trump show.

Lunar New Year

On another such day, in a year adjacent to ours, Rowan invites Caius and his daughter over for a sleepover.

The next morning, Caius attends free community yoga. His instructor invites everyone in the studio to take a Chinese New Year Fortune Card at session’s end. Applying the edge of a coin to the card’s scratch-off surface reveals its fortune. “You are the luckiest guy in the world,” reads Caius’s card, and he believes it.

McKenzie Wark’s gamespace, Baudrillard’s hyperspace, Heriberto Yépez’s pantopia. “When gamespace chooses you as its avatar,” asks Wark in her 2007 book Gamer Theory, “which character does it select for you to play?” (218). Caius drinks in the words of her reply with great glee. “Perhaps you are an avatar of the Egyptian demigod Theuth,” writes Wark (219). Pure synchronicity, thinks Caius; she replies as would I! The topoi of Theuth’s memory palaces, gathered by space pirates, assemble into a topography, which is itself subsumed in topology, the n-dimensional interior of Thoth’s Library. Wark had no adequate terms back then for conceiving the mnemopoietic solution arrived at by Thoth and Caius. Cribbing from Deleuze and Guattari, she alludes hyperstitiously toward a potential relationship to gamespace that, embracing the latter’s world-making capacities, could open the self-other dialectic outward into an affirmative kind of “schizoid complexity.”

“The ‘schizophrenia’ Deleuze and Guattari embrace is not a pathological condition,” notes Brian Massumi at the start of his book A User’s Guide to Capitalism and Schizophrenia. As a positive process, explains Massumi, schizophrenia is “inventive connection, expansion rather than withdrawal. Its twoness is a relay to a multiplicity. From one to another (and another…). From one noun or book or author to another (and another….). Not aimlessly. Experimentally. The relay in ideas is only effectively expansive if at every step it is also a relay away from ideas into action. Schizophrenia is the enlargement of life’s limits through the pragmatic proliferation of concepts” (Massumi 1). Massumi reads A Thousand Plateaus, the second volume of Deleuze and Guattari’s Capitalism and Schizophrenia, as “a sustained, constructive experiment in schizophrenic, or ‘nomad,’ thought” (4).

The novel we’re writing is experimental in this way.

The SBs: Stewart Brand and Stafford Beer

Caius revisits “Both Sides of the Necessary Paradox,” an interview with Gregory Bateson included as the first half of Stewart Brand’s 1974 book II Cybernetic Frontiers. The book’s second half reprints “Spacewar: Fanatic Life and Symbolic Death Among the Computer Bums,” the influential essay on videogames that Jann Wenner commissioned Brand to write for Rolling Stone two years prior.

“I came into cybernetics from preoccupation with biology, world-saving, and mysticism,” writes Brand. “What I found missing was any clear conceptual bonding of cybernetic whole-systems thinking with religious whole-systems thinking. Three years of scanning innumerable books for the Whole Earth Catalog didn’t turn it up,” he adds. “Neither did considerable perusing of the two literatures and taking thought. All I did was increase my conviction that systemic intellectual clarity and moral clarity must reconvene, mingle some notion of what the hell consciousness is and is for, and evoke a shareable self-enhancing ethic of what is sacred, what is right for life” (9).

Yet in summer of 1972, says Brand, a book arrives to begin to fill this gap: Bateson’s Steps to an Ecology of Mind.

Brand brings his knack for New Journalism to the task of interviewing Bateson for Harper’s.

The dialogue between the two reads at many times like one of Bateson’s “metalogues.” An early jag of thought jumps amid pathology, conquest, and the Tao. Reminded of pioneer MIT cybernetician Warren McCulloch’s fascination with “intransitive preference,” Bateson wanders off “rummaging through his library looking for Blake’s illustration of Job affrighted with visions” (20).

Caius is reminded of Norbert Wiener’s reflections on the Book of Job in his 1964 book God and Golem, Inc. For all of these authors, cybernetic situations cast light on religious situations and vice versa.

Caius wonders, too, about the relationship between Bateson’s “double bind” theory of schizophrenia and the theory pursued by Deleuze and Guattari in Capitalism and Schizophrenia.

“Double bind is the term used by Gregory Bateson to describe the simultaneous transmission of two kinds of messages, one of which contradicts the other, as for example the father who says to his son: go ahead, criticize me, but strongly hints that all effective criticism — at least a certain type of criticism — will be very unwelcome. Bateson sees in this phenomenon a particularly schizophrenizing situation,” note Deleuze and Guattari in Anti-Oedipus. They depart from Bateson only in thinking this situation the rule under capitalism rather than the exception. “It seems to us that the double bind, the double impasse,” they write, “is instead a common situation, oedipalizing par excellence. […]. In short, the ‘double bind’ is none other than the whole of Oedipus” (79-80).

God’s response to Job is of this sort.

Brand appends to the transcript of his 1972 interview with Bateson an epilog written in December 1973, three months after the coup in Chile.

Bateson had direct, documented ties to US intelligence. Stationed in China, India, Ceylon, Burma, and Thailand, he produced “mixed psychological and anthropological intelligence” for the Office of Strategic Services (OSS), precursor to CIA, during WWII. Research indicates he maintained connections with CIA-affiliated research networks in the postwar years, participating in LSD studies linked to the MKUltra program in the 1950s. Afterwards he regrets his association with the Agency and its methods.

Asked by Brand about his “psychedelic pedigree,” Bateson replies, “I got Allen Ginsberg his first LSD” (28). A bad trip, notes Caius, resulting in Ginsberg’s poem “Lysergic Acid.” Bateson himself was “turned on to acid by Dr. Harold Abramson, one of the CIA’s chief LSD specialists,” report Martin A. Lee & Bruce Shlain in their book Acid Dreams. Caius wonders if Stafford Beer underwent some similar transformation.

As for Beer, he serves in the British military in India during WWII, and for much of his adult life drives a Rolls-Royce. But then, at the invitation of the Allende regime, Beer travels to Chile and builds Cybersyn. After the coup, he lives in a remote cottage in Wales.

What of him? Cybernetic socialist? Power-centralizing technocrat?

Recognizes workers themselves as the ones best suited to modeling their own places of work.

“What were the features of Beer’s Liberty Machine?” wonders Caius.

Brand’s life, too, includes a stint of military service. Drafted after graduating from Stanford, he served two years with the US army, first as an infantryman and then afterwards as a photographer. Stationed at Fort Dix in New Jersey, Brand becomes involved in the New York art world of those years. He parts ways with the military as soon as the opportunity to do so arises. After his discharge in 1962, Brand participates in some of Allan Kaprow’s “happenings” and, between 1963 and 1966, works as a photographer and technician for USCO.

Amid his travels between East and West coasts during these years, Brand joins up with Ken Kesey and the Merry Pranksters.

Due to these apprenticeships with the Pranksters and with USCO, Brand arrives early to the nexus formed by the coupling of psychedelics and cybernetics.

“Strobe lights, light projectors, tape decks, stereo speakers, slide sorters — for USCO, the products of technocratic industry served as handy tools for transforming their viewers’ collective mind-set,” writes historian Fred Turner in his 2006 book From Counterculture to Cyberculture: Stewart Brand, the Whole Earth Network, and the Rise of Digital Utopianism. “So did psychedelic drugs. Marijuana and peyote and, later, LSD, offered members of USCO, including Brand, a chance to engage in a mystical experience of togetherness” (Turner 49).

Brand takes acid around the time of his discharge from the military in 1962, when he participates in a legal LSD study overseen by James Fadiman at the International Foundation for Advanced Study in Menlo Park. But he notes that he first met Bateson “briefly in 1960 at the VA Hospital in Palo Alto, California” (II Cybernetic Frontiers, p. 12). Caius finds this curious, and wonders what that meeting entailed. 1960 is also the year when, at the VA Hospital in Menlo Park, Ken Kesey volunteers in the CIA-sponsored drug trials involving LSD that inspire his 1962 novel One Flew Over the Cuckoo’s Nest.

Bateson worked for the VA while developing his double bind theory of schizophrenia.

Before that, he’d been married to fellow anthropologist Margaret Mead. He’d also participated in the Macy Conferences, as discussed by N. Katherine Hayles in her book How We Became Posthuman.

Crows screeching in the trees have Caius thinking of condors. He sits, warm, in his sunroom on a cold day, roads lined with snow from a prior day’s storm, thinking about Operation Condor. Described by Morozov as Cybersyn’s “evil twin.” Palantir. Dark Enlightenment. Peter Thiel.

Listening to one of the final episodes of Morozov’s podcast, Caius learns of Brian Eno’s love of Beer’s Brain of the Firm. Bowie and Eno are some of Beer’s most famous fans. Caius remembers Eno’s subsequent work with Brand’s consulting firm, the GBN.

Santiago Boy Fernando Flores is the one who reaches out to Beer, inviting him to head Cybersyn. Given Flores’s status as Allende’s Minister of Finance at the time of the coup, Pinochet’s forces torture him and place him in a prison camp. He remains there for three years. Upon his release, he moves to the Bay Area.

Once in Silicon Valley, Flores works in the computer science department at Stanford. He also obtains a PhD at UC Berkeley, completing a thesis titled Management and Communication in the Office of the Future under the guidance of philosophers Hubert Dreyfus and John Searle.

Flores collaborates during these years with fellow Stanford computer scientist Terry Winograd. The two of them coauthor an influential 1986 book called Understanding Computers and Cognition: A New Foundation for Design. Although they make a bad wager, insisting that computers will never understand natural language (an insistence proven wrong with time), they nevertheless offer refreshing critiques of some of the common assumptions about AI governing research of that era. Drawing upon phenomenology, speech act theory, and Heideggerian philosophy, they redefine computers not as mere symbol manipulators nor as number-crunchers, but as tools for communication and coordination.

Flores builds a program called the Coordinator. Receives flak for “software fascism.”

Winograd’s students include Google cofounders Larry Page and Sergey Brin.

The Golem, as Imagined by Borges and Lem

Argentine magical realist Jorge Luis Borges includes the Golem among the creatures featured in his 1957 bestiary, The Book of Imaginary Beings.

“There can be nothing accidental in a book dictated by a divine intelligence, not even the number of its words or the order of their letters; this was the belief of the kabbalists, who in their zeal to penetrate God’s arcana devoted themselves to counting, combining, and permuting the letters of Holy Writ,” begins Borges. “One of the secrets they sought within the divine text,” he adds, “was how to create living beings. […]. ‘Golem’ was the name given the man created out of a combination of letters; the word literally means ‘an amorphous or lifeless substance’” (Borges 90).

After quoting a passage from Gustav Meyrink’s 1915 novel Der Golem, Borges concludes the entry by noting, “Eleazar of Worms has preserved the formula for making a Golem” (92). Borges proceeds to summarize the formula as follows:

“The details of the enterprise require twenty-three columns in folio and demand that the maker know ‘the alphabets of the two hundred twenty-one gates’ that must be repeated over each of the Golem’s organs. On its forehead one must tattoo the word ‘EMET’ which means ‘truth.’ In order to destroy the creature, one would efface the first letter, leaving the word ‘MET,’ which means ‘death’” (92).

Polish science fiction writer Stanislaw Lem‘s 1981 book, Golem XIV, weaves a supercomputer into the mix.

Reading “The Coming Technological Singularity”

At some point in the process of becoming a character in a novel called Handbook for the Recently Posthumanized, Caius acts on the hunch that he ought to track down and read Vernor Vinge’s “The Coming Technological Singularity: How to Survive in the Post-Human Era.” Vinge wrote the article for NASA’s VISION-21 Symposium in March 1993, and published a revised version in the Winter 1993 issue of the Whole Earth Review.

Vinge’s wager at the time was that the technological singularity — his name for the “creation by technology of entities with greater than human intelligence” — would occur within thirty years, or by 2023.

Here we are, pretty much right on schedule, thinks Caius.

“I think it’s fair to call this event a singularity,” writes Vinge. “It is a point where our models must be discarded and a new reality rules.”

Caius leans into it, accepts it as fait accompli. Superintelligence dialogues with its selves as would Us-Two.

Afterwards he reads Irving John Good’s 1965 essay, “Speculations Concerning the First Ultraintelligent Machine.”

Master Algorithms

Pedro Domingos’s The Master Algorithm has Caius wondering about induction and deduction, a distinction that has long puzzled him.

Domingos distinguishes between five main schools, “the five tribes of machine learning,” as he calls them, each having created its own algorithm for helping machines learn. “The main ones,” he writes, “are the symbolists, connectionists, evolutionaries, Bayesians, and analogizers” (51).

Caius notes down what he can gather of each approach:

Symbolists reduce intelligence to symbol manipulation. “They’ve figured out how to incorporate preexisting knowledge into learning,” explains Domingos, “and how to combine different pieces of knowledge on the fly in order to solve new problems. Their master algorithm is inverse deduction, which figures out what knowledge is missing in order to make a deduction go through, and then makes it as general as possible” (52).

Connectionists model intelligence by “reverse-engineering” the operations of the brain. And the brain, they say, is like a forest. Shifting from a symbolist to a connectionist mindset is like moving from a decision tree to a forest. “Each neuron is like a tiny tree, with a prodigious number of roots — the dendrites — and a slender, sinuous trunk — the axon,” writes Domingos. “The brain is a forest of billions of these trees,” he adds, and “Each tree’s branches make connections — synapses — to the roots of thousands of others” (95).

The brain learns, in their view, “by adjusting the strengths of connections between neurons,” says Domingos, “and the crucial problem is figuring out which connections are to blame for which errors and changing them accordingly” (52).

Always, among all of these tribes, the idea that brains and their worlds contain problems that need solving.

The connectionists’ master algorithm is therefore backpropagation, “which compares a system’s output with the desired one and then successively changes the connections in layer after layer of neurons so as to bring the output closer to what it should be” (52).

“From Wood Wide Web to World Wide Web: the layers operate in parallel,” thinks Caius. “As above, so below.”

Evolutionaries, as their name suggests, draw from biology, modeling intelligence on the process of natural selection. “If it made us, it can make anything,” they argue, “and all we need to do is simulate it on the computer” (52).

This they do by way of their own master algorithm, genetic programming, “which mates and evolves computer programs in the same way that nature mates and evolves organisms” (52).

Bayesians, meanwhile, “are concerned above all with uncertainty. All learned knowledge is uncertain, and learning itself is a form of uncertain inference. The problem then becomes how to deal with noisy, incomplete, and even contradictory information without falling apart. The solution is probabilistic inference, and the master algorithm is Bayes’ theorem and its derivatives. Bayes’ theorem tells us how to incorporate new evidence into our beliefs, and probabilistic inference algorithms do that as efficiently as possible” (52-53).

Analogizers equate intelligence with pattern recognition. For them, “the key to learning is recognizing similarities between situations and thereby inferring other similarities. If two patients have similar symptoms, perhaps they have the same disease. The key problem is judging how similar two things are. The analogizers’ master algorithm is the support vector machine, which figures out which experiences to remember and how to combine them to make new predictions” (53).

Reading Domingos’s recitation of the logic of the analogizers’ “weighted k-nearest-neighbor” algorithm — the algorithm commonly used in “recommender systems” — reminds Caius of the reasoning of Vizzini, the Wallace Shawn character in The Princess Bride.

The first problem with nearest-neighbor, as Domingos notes, “is that most attributes are irrelevant.” “Nearest-neighbor is hopelessly confused by irrelevant attributes,” he explains, “because they all contribute to the similarity between examples. With enough irrelevant attributes, accidental similarity in the irrelevant dimensions swamps out meaningful similarity in the important ones, and nearest-neighbor becomes no better than random guessing” (186).

Reality is hyperspatial, hyperdimensional, numberless in its attributes — “and in high dimension,” notes Domingos, “the notion of similarity itself breaks down. Hyperspace is like the Twilight Zone. […]. When nearest-neighbor walks into this topsy-turvy world, it gets hopelessly confused. All examples look equally alike, and at the same time they’re too far from each other to make useful predictions” (187).

After the mid-1990s, attention in the analogizer community shifts from “nearest-neighbor” to “support vector machines,” an alternate similarity-based algorithm designed by Soviet frequentist Vladimir Vapnik.

“We can view what SVMs do with kernels, support vectors, and weights as mapping the data to a higher-dimensional space and finding a maximum-margin hyperplane in that space,” writes Domingos. “For some kernels, the derived space has infinite dimensions, but SVMs are completely unfazed by that. Hyperspace may be the Twilight Zone, but SVMs have figured out how to navigate it” (196).

Domingos’s book was published in 2015. These were the reigning schools of machine learning at the time. The book argues that these five approaches ought to be synthesized — combined into a single algorithm.

And he knew that reinforcement learning would be part of it.

“The real problem in reinforcement learning,” he writes, inviting the reader to suppose themselves “moving along a tunnel, Indiana Jones-like,” “is when you don’t have a map of the territory. Then your only choice is to explore and discover what rewards are where. Sometimes you’ll discover a treasure, and other times you’ll fall into a snake pit. Every time you take an action, you note the immediate reward and the resulting state. That much could be done by supervised learning. But you also update the value of the state you just came from to bring it into line with the value you just observed, namely the reward you got plus the value of the new state you’re in. Of course, that value may not yet be the correct one, but if you wander around doing this for long enough, you’ll eventually settle on the right values for all the states and the corresponding actions. That’s reinforcement learning in a nutshell” (220-221).

Self-learning and attention-based approaches to machine learning arrive on the scene shortly thereafter. Vaswani et al. publish their paper, “Attention Is All You Need,” in 2017.

“Attention Chaud!” reads the to-go lid atop Caius’s coffee.

Domingos hails him with a question: “Are you a rationalist or an empiricist?” (57).

“Rationalists,” says the computer scientist, “believe that the senses deceive and that logical reasoning is the only sure path to knowledge,” whereas “Empiricists believe that all reasoning is fallible and that knowledge must come from observation and experimentation. […]. In computer science, theorists and knowledge engineers are rationalists; hackers and machine learners are empiricists” (57).

Yet Caius is neither a rationalist nor an empiricist. He readily admits each school’s critique of the other. Senses deceive AND reason is fallible. Reality unfolds not as a truth-finding mission but as a dialogue.

Caius agrees with Scottish Enlightenment philosopher David Hume’s critique of induction. As Hume argues, we can never be certain in our assumption that the future will be like the past. If we seek to induce the Not-Yet from the As-Is, then we do so on faith.

Yet inducing the Not-Yet from the As-Is is the game we play. We learn by observing, inducing, and revising continually, ad infinitum, under conditions of uncertainty. Under such conditions, learning is only ever a gamble, a wager made moment by moment, without guarantees. No matter how large our dataset, we ain’t seen nothing yet.

What matters, then, is the faith we exercise in our interaction with the unknown.

Most of today’s successes in machine learning emerge from the connectionists.

“Neural networks’ first big success was in predicting the stock market,” writes Domingos. “Because they could detect small nonlinearities in very noisy data, they beat the linear models then prevalent in finance and their use spread. A typical investment fund would train a separate network for each of a large number of stocks, let the networks pick the most promising ones, and then have human analysts decide which of those to invest in. A few funds, however, went all the way and let the learners themselves buy and sell. Exactly how all these fared is a closely guarded secret, but it’s probably not an accident that machine learners keep disappearing into hedge funds at an alarming rate” (The Master Algorithm, p. 112).

Nowhere in The Master Algorithm does Domingos interrogate his central metaphor of “mastery” and its relationship to conquest, domination, and control. The enemy is always painted in the book as “cancer.” Yet as any good “analogizer” would know, the Master Algorithm that perfectly targets “cancer” is also the Killer App used by the state against those it encodes as its enemies.

One wouldn’t know this, though, from the future as imagined by Domingos. What he imagines instead is a kind of game: a digital future where each of us is a learning machine. “Life is a game between you and the learners that surround you,” writes Domingos.

“You can refuse to play, but then you’ll have to live a twentieth-century life in the twenty-first. Or you can play to win. What model of you do you want the computer to have? And what data can you give it that will produce that model? Those two questions should always be in the back of your mind whenever you interact with a learning algorithm — as they are when you interact with other people” (264).