Posts Tagged ‘computing’
“The first to arrive is the first to succeed”*…
Is China “pulling up the ladder”? In his valuable newsletter, Ben Evans puts two recent news items on high-tech manufacturing into context…
… First, the FT argues that after the ‘China shock’ of cheap low-value manufacturing, there’s now a growing second China shock of high-value, high-tech manufacturing, where the same model of ferocious, Darwinian competition, backed by subsidies and cheap energy, produces a handful of very efficient and capable winners in each space, plus a lot of overcapacity, that then moves to exports. Second, Bloomberg says that Chinese export controls in those high-tech industries are crippling India’s attempt to build its own tech manufacturing base…
Gift article from the FT: “China shock 2.0: the flood of high-tech goods that will change the world“
Gift article from Bloomberg: “China’s Control Over Tech Is Threatening India’s Manufacturing Dreams“
* (先到先得) Chinese proverb
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As we dissect the dynamics of dominance, we might recall that it was on this date in 1981 that the computer mouse became a practical, operating part of the personal computing world, when Xerox released its 1010 (Star) personal computer. The trackball, a related pointing device, had been invented in 1946 by Ralph Benjamin as part of a post-World War II-era fire-control radar plotting system called the Comprehensive Display System (CDS). Then, in the 1960s, Doug Engelbart and Bill English developed the first mouse prototype. They christened the device the mouse as early models had a cord attached to the rear part of the hand-held unit; the cord looked like a tail and made the device resemble a common mouse. (According to Roger Bates, a hardware designer under English, another reason for choosing this name was because the cursor on the screen was also referred to as “CAT” at this time.) In 1968, Engelbart premiered the pointer at what has come to be known as “The Mother of All Demos.” There followed, through the 70’s, a pair of personal computers that used a mouse (the Xerox Alto and the Lilith); but while they served as proof-of-concept, they sold only in the hundreds of units over the next several years. It was the Star that effectively brought the mouse to market… soon to be followed by Steve Jobs’ Apple Lisa, which forshadowed the Mac and the user interface that we’ve all come to know.
Apropos the articles above, computer mice are still a $2 billion business. But while they were invented and originally largely manufactured in the U.S., they are (as of 2025) mostly manufactured in Asia (68%, the lion’s share– 54%– in China); only 8% are made in the U.S.
“I tend to think that most fears about A.I. are best understood as fears about capitalism”*…
Further to Wednesday‘s and yesterday‘s posts (on to other topics again after this, I promise), a powerful piece from Patrick Tanguay (in his always-illuminating Sentiers newsletter).
He begins with a consideration of Peter Wolfendale’s “Geist in the machine“
… Wolfendale argues that the current AI debate recapitulates an 18th-century conflict between mechanism and romanticism. On one side, naive rationalists (Yudkowsky, Bostrom, much of Silicon Valley) assume intelligence is ultimately reducible to calculation; throw enough computing power at the problem and the gap between human and machine closes. On the other, popular romantics (Bender, Noë, many artists) insist that something about human cognition, whether it’s embodiment, meaning, or consciousness, can never be mechanised. Wolfendale finds both positions insufficient. The rationalists reduce difficult choices to optimisation problems, while the romantics bundle distinct capacities into a single vague essence.
His alternative draws on Kant and Hegel. He separates what we loosely call the “soul” into three capacities: wisdom (the metacognitive ability to reformulate problems, not just solve them), creativity (the ability to invent new rules rather than search through existing ones), and autonomy (the capacity to question and revise our own motivations). Current AI systems show glimmers of the first two but lack the third entirely. Wolfendale treats autonomy as the defining feature of personhood: not a hidden essence steering action, but the ongoing process of asking who we want to be and revising our commitments accordingly. Following Hegel he calls this Geist, spirit as self-reflective freedom.
Wolfendale doesn’t ask whether machines can have souls; he argues we should build them, and that the greater risk lies in not doing so. Machines that handle all our meaningful choices without possessing genuine autonomy would sever us from the communities of mutual recognition through which we pursue truth, beauty, and justice. A perfectly optimised servant that satisfies our preferences while leaving us unchanged is, in his phrase, “a slave so abject it masters us.” Most philosophical treatments of AI consciousness end with a verdict on possibility. Wolfendale ends with an ethical imperative: freedom is best preserved by extending it.
I can’t say I agree, unless “we”… end up with a completely different relationship to our technology and capital. However, his argument all the way before then is a worthy reflection, and pairs well with the one below and another from issue No.387. I’m talking about Anil Seth’s The mythology of conscious AI, where he argues that consciousness probably requires biological life and that silicon-based AI is unlikely to achieve it. Seth maps the biological terrain that makes consciousness hard to replicate; Wolfendale maps the philosophical terrain that makes personhood worth pursuing anyway, on entirely different grounds. Seth ends where the interesting problem begins for Wolfendale: even if machines can’t be conscious, the question of whether they can be autonomous persons, capable of self-reflective revision, remains open:
Though GenAI systems can’t usually compete with human creatives on their own, they are increasingly being used as imaginative prosthetics. This symbiosis reveals that what distinguishes human creativity is not the precise range of heuristics embedded in our perceptual systems, but our metacognitive capacity to modulate and combine them in pursuit of novelty. What makes our imaginative processes conscious is our ability to self-consciously intervene in them, deliberately making unusual choices or drawing analogies between disparate tasks. And yet metacognition is nothing on its own. If reason demands revision, new rules must come from somewhere. […]
[Hubert Dreyfus] argues that the comparative robustness of human intelligence lies in our ability to navigate the relationships between factors and determine what matters in any practical situation. He claims that this wouldn’t be possible were it not for our bodies, which shape the range of actions we can perform, and our needs, which unify our various goals and projects into a structured framework. Dreyfus argues that, without bodies and needs, machines will never match us. […]
This is the basic link between self-determination and self-justification. For Hegel, to be free isn’t simply to be oneself – it isn’t enough to play by one’s own rules. We must also be responsive to error, ensuring not just that inconsistencies in our principles and practices are resolved, but that we build frameworks to hold one another mutually accountable. […]
Delegating all our choices to mere automatons risks alienating us from our sources of meaning. If we consume only media optimised for our personal preferences, generated by AIs with no preferences of their own, then we will cease to belong to aesthetic communities in which tastes are assessed, challenged and deepened. We will no longer see ourselves and one another as even passively involved in the pursuit of beauty. Without mutual recognition in science and civic life, we might as easily be estranged from truth and right – told how to think and act by anonymous machines rather than experts we hold to account…
Tanguay then turns to “The Prospect of Butlerian Jihad” by Liam Mullally, in which Mullally uses…
… Herbert’s Dune and the Butlerian Jihad [here] as a lens for what he sees as a growing anti-tech “structure of feeling” (Raymond Williams’s term): the diffuse public unease about AI, enshittification, surveillance, and tech oligarchs that has not yet solidified into coherent politics. The closest thing to a political expression so far is neo-Luddism, which Mullally credits for drawing attention to technological exploitation but finds insufficient. His concern is that the impulse to reject technology wholesale smuggles in essentialist assumptions about human nature, a romantic defence of “pure” humanity against the corruption of machines. He traces this logic back to Samuel Butler’s 1863 essay Darwin Among the Machines, which framed the human-technology relationship as a zero-sum contest for supremacy, and notes that Butler’s framing was “explicitly supremacist,” written from within colonial New Zealand and structured by the same logic of domination it claimed to resist.
The alternative Mullally proposes draws on Bernard Stiegler’s concept of “originary technicity”: the idea that human subjectivity has always been constituted in part by its tools, that there is no pre-technological human to defend. [see here] If that’s right, then opposing technology as such is an “ontological confusion,” a fight against something that is already part of what we are. The real problem is not machines but the economic logic that shapes their development and deployment. Mullally is clear-eyed about this: capital does not have total command over its technologies, and understanding how they work is a precondition for contesting them. He closes by arguing that the anti-tech structure of feeling is “there for the taking,” but only if it can be redirected. The fights ahead are between capital and whatever coalition can form against it, not between humanity and machines. Technology is a terrain in that conflict; abandoning it means losing before the contest begins.
Wolfendale’s Geist in the Machine above arrived at a parallel conclusion from a different direction: where Mullally argues that rejecting technology means defending a false vision of the human, Wolfendale argues that refusing to extend autonomy to machines risks severing us from the self-reflective freedom that makes us persons in the first place. Both reject the romantic position, but for different reasons:
To the extent that neo-Luddites bring critical attention to technology, they are doing useful work. But this anti-tech sentiment frequently cohabitates with something uneasy: the treatment of technology as some abstract and impenetrable evil, and the retreat, against this, into essentialist views of the human. […]
If “humanity” is not a thing-in-itself, but historically, socially and technically mutable, then the sphere of possibility of the human and of our world becomes much broader. Our relationship to the non-human — to technology or to nature — does not need to be one of control, domination and exploitation. […]
As calls for a fight back against technology grow, the left needs to carefully consider what it is advocating for. Are we fighting the exploitation of workers, the hollowing out of culture and the destruction of the earth via technology, or are we rallying in defence of false visions of pure, a-technical humanity? […]
The anti-tech structure of feeling is there for the taking. But if it is to lead anywhere, it must be taken carefully: a fightback against technological exploitation will be found not in the complete rejection of technology, but in the short-circuiting of one kind of technology and the development of another.
As Max Read (scroll down) observes:
… if we understand A.I. as a product of the systems that precede it, I think it’s fair to say ubiquitous A.I.-generated text is “inevitable” in the same way that high-volume blogs were “inevitable” or Facebook fake news pages were “inevitable”: Not because of some “natural” superiority or excellence, but because they follow so directly from the logic of the system out of which they emerge. In this sense A.I. is “inevitable” precisely because it’s not revolutionary…
The question isn’t if we want a relationship with technology; it’s what kind of relationship we want. We’ve always (at least since we’ve been a conscious species) co-existed with, and been shaped by, tools; we’ve always suffered the “friction” of technological transition as we innovate new tools. As yesterday’s post suggested (in its defense of the open web in the face on a voracious attack from powerful LLM companies), “what matters is power“… power to shape the relationship(s) we have with the technologies we use. That power is currently in the hands of a relatively few companies, all concerned above all else with harvesting as much money as they can from “uses” they design to amplify that engagement and ease that monetization. It doesn’t, of course, have to be this way.
We’ve lived under modern capitalism for only a few hundred years, and under the hyper-global, hyper-extractive regime we currently inhabit for only a century-and-a-half or so, during which time, in fits and starts, it has grown ever more rapcious. George Monbiot observed that “like coal, capitalism has brought many benefits. But, like coal, it now causes more harm than good.” And Ursula Le Guin, that “we live in capitalism. Its power seems inescapable. So did the divine right of kings.” In many countries, “divine right” monarchy has been replaced by “constitutional monarchy.” Perhaps it’s time for more of the world to consider “constitutional capitalism.” We could start by learning from the successes and failures of Scandinavia and Europe.
Social media, AI, quantum computing– on being clear as to the real issue: “Geist in the machine & The prospect of Butlerian Jihad,” from @inevernu.bsky.social.
(All this said, David Chalmers argues that there’s one possibility that might change everything: “Could a Large Language Model be Conscious?” On the other hand, the ARC Prize Foundation suggests, we have some time: a test they devised for benchmarking agentic intelligence recently found that “humans can solve 100% of the environments, in contrast to frontier AI systems which, as of March 2026, score below 1%”… :)
* Ted Chiang (gift article; see also here and here and here)
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As we keep our eyes on the prize, we might spare a thought for a man who wrestled with a version of these same issues in the last century, Pierre Teilhard de Chardin; he died on this date in 1955. A Jesuit theologian, philosopher, geologist, and paleontologist, he conceived the idea of the Omega Point (a maximum level of complexity and consciousness towards which he believed the universe was evolving) and developed Vladimir Vernadsky‘s concept of noosphere. Teilhard took part in the discovery of Peking Man, and wrote on the reconciliation of faith and evolutionary theory. His thinking on both these fronts was censored during his lifetime by the Catholic Church (in particular for its implications for “original sin”); but in 2009, they lifted their ban.
“Quantum computation is … nothing less than a distinctly new way of harnessing nature”*…
As the tools in the world around us change, the world– and we– change with them. The onslaught of AI is the change that seems to be grabbing most of our mindshare these days… and with reason. But there are, of course, other changes (in biotech, in materials science, et al.) that are also going to be hugely impactful.
Today, a look at the computing technology stalking up behind AI: quantum computing. As enthusiasts like David Deutsch (author of the quote above) argue, it can have tremendous benefits, perhaps especially in our ability to model (and thus better understand) our reality.
But quantum computing will, if/when it arrives, also present huge challenges to us as individuals and as societies– perhaps most prominently in its threat to the ways in which we protect our systems and our information: We’ve felt pretty safe for decades, secure in the knowledge that we could lose passwords to phising or hacks, but that it would take the “classical” computers we have 1 billion years to break today’s RSA-2048 encryption. A quantum computer could crack it in as little as a hundred seconds.
The technology has been “somewhere on the horizon” for 30 years… so not something that has seemed urgent to confront. But progress has accelerated; a recent Google paper reports on a programming and architectural breakthrough that greatly reduces the computing resources necessary to break classical cryptography… putting the prospect of “Q-Day” (the point at which quantum computers become powerful enough to break standard encryption methods (RSA, ECC), endangering global digital security) much closer, which would put everything from crypto-wallets to our e-banking accounts at risk.
Charlie Wood brings us up to speed…
Some 30 years ago, the mathematician Peter Shor took a niche physics project — the dream of building a computer based on the counterintuitive rules of quantum mechanics — and shook the world.
Shor worked out a way for quantum computers to swiftly solve a couple of math problems that classical computers could complete only after many billions of years. Those two math problems happened to be the ones that secured the then-emerging digital world. The trustworthiness of nearly every website, inbox, and bank account rests on the assumption that these two problems are impossible to solve. Shor’s algorithm proved that assumption wrong.
For 30 years, Shor’s algorithm has been a security threat in theory only. Physicists initially estimated that they would need a colossal quantum machine with billions of qubits — the elements used in quantum calculations — to run it. That estimate has come down drastically over the years, falling recently to a million qubits. But it has still always sat comfortably beyond the modest capabilities of existing quantum computers, which typically have just hundreds of qubits.
However, two different groups of researchers have just announced advances that notably reduce the gap between theoretical estimates and real machines. A star-studded team of quantum physicists at the California Institute of Technology went public with a design for a quantum computer that could break encryption with only tens of thousands of qubits and said that it had formed a company to build the machine. And researchers at Google announced that they had developed an implementation of Shor’s algorithm that is ten times as efficient as the best previous method.
Neither company has the hardware to break encryption today. But the results underscore what some quantum physicists had already come to suspect: that powerful quantum computers may be years away, rather than decades. “If you care about privacy or you have secrets, then you better start looking for alternatives,” said Nikolas Breuckmann, a mathematical physicist at the University of Bristol, who did not work on either of the papers.
While the new results may provide a jolt for the policymakers and corporations that guard our digital infrastructure, they also signal the rapid progress that physicists have made toward building machines that will let them more thoroughly explore the quantum world.
“We’re going to actually do this,” said Dolev Bluvstein, a Caltech physicist and CEO of the new company, Oratomic…
[Wood unpacks the history of the development of the technology and explores the challenges that remain; he concludes…]
… If any group succeeds at building a quantum computer that can realize Shor’s algorithm, it will mark the end an era — specifically, the “Noisy Intermediate Scale Quantum” era, as Preskill dubbed the pre-error-correction period in a 2018 paper. Each researcher has a vision for what to pursue first with a machine in the new “fault-tolerant” era.
[Robert] Huang said he would start by running Shor’s algorithm, just to prove that the device works. After that, he said he would try to use it to speed up machine learning — an application to be detailed in coming work.
Most of the architects building quantum computers, whether at Oratomic or other startups, are physicists at heart. They’re interested in physics, not cryptography. Specifically, they’re interested in all the things a computer fluent in the language of quantum mechanics could teach them about the quantum realm, such as what sort of materials might become superconductors even at warm temperatures. Preskill, for his part, would like to simulate the quantum nature of space-time.
The Caltech group knows it has years of work ahead before any of its dreams have a chance of coming true. But the researchers can’t wait to get started. “Pick a cooler life quest than building the world’s first quantum computer with your friends!” said a jubilant Bluvstein, reached by phone shortly before their paper went live, before rushing off to celebrate…
Eminently worth reading in full: “New Advances Bring the Era of Quantum Computers Closer Than Ever,” from @walkingthedot.bsky.social in @quantamagazine.bsky.social.
* David Deutsch, The Fabric of Reality
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As we prepare, we might take a moment to appreciate just how vastly and deeply the legacy systems challenged by quantum computing run, recalling that on this date in 1959 Mary Hawes, a computer scientist for the Burroughs Corporation held a meeting of computers users, manufacturers, and academics at the University of Pennsylvania aimed at creating a common business oriented programming language. At the meeting, representative Grace Hopper suggested that they ask the Department of Defense to fund the effort to create such a language. Also attending was Charles Phillips who was director of the Data System Research Staff at the DoD and was excited by the possibility of a common language streamlining their operations. He agreed to sponsor the creation of such a language. This was the genesis of what would eventually become the COBOL language.
To this day COBOL is still the most common programming language used in business, finance, and administrative systems for companies and governments, primarily on mainframe systems, with around 200 billion lines of code still in production use… all of which are in question and/or at risk in a world of quantum computing.
“Scuse me while I kiss the sky”*…
In 1967, Jimi Hendrix’s manager, Chas Chandler arranged for Jimi to meet Cream…
There was a particular night when Cream allowed Jimi to join them for a jam at the Regent Street Polytechnic in central London. Meeting Clapton had been among the enticements Chandler had used to lure Hendrix to Britain: “Hendrix blew into a version of [Howlin’ Wolf’s] ‘Killing Floor’,” recalls [Tony] Garland, “and plays it at breakneck tempo, just like that – it stopped you in your tracks.” [Keith] Altham recalls Chandler going backstage after Clapton left in the middle of the song “which he had yet to master himself”; Clapton was furiously puffing on a cigarette and telling Chas: “You never told me he was that fucking good.” – source
Hendrix’s extraodinary virtuosity has, altogether justly, gotten a great deal of attention; less well noted, his incredible mastery of the technology of music making, recording, and performance. Rohan Puranik explains…
3 February 1967 is a day that belongs in the annals of music history. It’s the day that Jimi Hendrix entered London’s Olympic Studios to record a song using a new component. The song was “Purple Haze,” and the component was the Octavia guitar pedal, created for Hendrix by sound engineer Roger Mayer. The pedal was a key element of a complex chain of analog elements responsible for the final sound, including the acoustics of the studio room itself. When they sent the tapes for remastering in the United States, the sounds on it were so novel that they included an accompanying note explaining that the distortion at the end was not malfunction but intention. A few months later, Hendrix would deliver his legendary electric guitar performance at the Monterey International Pop Festival.
“Purple Haze” firmly established that an electric guitar can be used not just as a stringed instrument with built-in pickups for convenient sound amplification, but also as a full-blown wave synthesizer whose output can be manipulated at will. Modern guitarists can reproduce Hendrix’s chain using separate plug-ins in digital audio workstation software, but the magic often disappears when everything is buffered and quantized. I wanted to find out if a more systematic approach could do a better job and provide insights into how Hendrix created his groundbreaking sound.
My fascination with Hendrix’s Olympic Studios’ performance arose because there is a “Hendrix was an alien” narrative surrounding his musical innovation—that his music appeared more or less out of nowhere. I wanted to replace that narrative with an engineering-driven account that’s inspectable and reproducible—plots, models, and a signal chain from the guitar through the pedals that you can probe stage by stage…
[And probe it Puranik does– fascinatingly, stage by stage…]
… Hendrix didn’t speak in decibels and ohm values, but he collaborated with engineers who did—Mayer and Kramer—and iterated fast as a systems engineer. Reframing Hendrix as an engineer doesn’t diminish the art. It explains how one person, in under four years as a bandleader, could pull the electric guitar toward its full potential by systematically augmenting the instrument’s shortcomings for maximum expression.
“Jimi Hendrix Was a Systems Engineer,” from @spectrum.ieee.org.
See also: “The Technology of Jimi Hendrix.”
* Jimi Hendrix, “Purple Haze”
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As we plug in, we might send well-connected birthday greetings to another wizard with wires, Geoff Tootill; he was born on this date in 1922. An electronic engineer and computer scientist, he worked (with Freddie Williams and Tom Kilburn) to design a computer memory. To that end they built the first electronic stored-program computer— the Manchester Baby— at the University of Manchester in 1948.
The Baby was not intended to be a practical computing engine, but was instead designed as a testbed for the Williams tube, the first truly random-access memory. Nonethless, Baby worked: Alan Turing moved to Manchester to use it, and the following year, it inspired the Ferranti Mark 1, the world’s first commercially available electronic general-purpose stored-program digital computer.
“Technology challenges us to assert our human values, which means that first of all, we have to figure out what they are”*…
As we head into the weekend, some food for thought…
A decade ago, the world was, at once, both the seed of today and a very different place: In what was considered one of the biggest political upsets in American political history (and the fifth and most recent presidential election in which the winning candidate lost the popular vote), Donald Trump was elected to his first term. The U.K. chose Brexit. The stock market finished strong, with the Dow Jones, S&P 500, and Nasdaq reaching new highs. (In the 10 years that have followed, the Dow has risen about 150%; the S&P 500, roughly 400%; and the NASDAQ has roughly sextupled.)
It was a big year for pop culture, marked by Beyoncé’s Lemonade, the massive Pokémon Go craze, and the rise of Netflix with Stranger Things, the Rio Olympics, and the loss of icons like David Bowie and Prince.
It was also a big year in tech: Russian hacking and disinfo (especially on Facebook) was a huge story– as was Apple’s elimination of the headphone jack in the iPhone 7. Theranos collapsed; and Wells fargo opened millions of accounts for customers without those customers’ permission (for which they were sunsequently fined $3 Billion). And Virtual Reality was everywhere (in the promises/offers from tech companies), but nowhere in the market. TikTok was launched in 2016, but hadn’t yet become the phenomenon (and avatar of algorithmly-driven feeds) that it has become. And in the course of 2016, artificial intelligence made the leap from “science fiction concept” to “almost meaningless buzzword” (though in fairness, 2016 was the year that Google DeepMind’s AlphaGo program triumphed against South Korean Go grandmaster Lee Sedol).
Back in 2016, the estimable Alan Jacobs was pondering the road ahead. In a piece for The New Atlantis, he coined and discussed a series of aphorisms relevant to the future as then he saw it. He begins…
Aphorisms are essentially an aristocratic genre of writing. The apho-
rist does not argue or explain, he asserts; and implicit in his assertion
is a conviction that he is wiser or more intelligent than his readers.
– W. H. Auden and Louis Kronenberger, The Viking Book of AphorismsAuthor’s Note: I hope that the statement above is wrong, believing that certain adjustments can be made to the aphoristic procedure that will rescue the following collection from arrogance. The trick is to do this in a way that does not sacrifice
the provocative character that makes the aphorism, at its best, such a powerful form of utterance.Here I employ two strategies to enable me to walk this tightrope. The first is to characterize the aphorisms as “theses for disputation,” à la Martin Luther — that is, I invite response, especially response in the form of disagreement or correction. The second is to create a kind of textual conversation, both on the page and beyond it, by adding commentary (often in the form of quotation) that elucidates each thesis, perhaps even increases its provocativeness, but never descends into coarsely explanatory pedantry…
[There follows a series of provocations and discussions that feel as relevant– and important– today as they were a decade ago. He concludes…]
… Precisely because of this mystery, we need to evaluate our technologies according to the criteria established by our need for “conviviality.”
I use the term with the particular meaning that Ivan Illich gives it in Tools for Conviviality [here]:
I intend it to mean autonomous and creative intercourse among per-
sons, and the intercourse of persons with their environment; and this
in contrast with the conditioned response of persons to the demands
made upon them by others, and by a man-made environment. I con-
sider conviviality to be individual freedom realized in personal inter-
dependence and, as such, an intrinsic ethical value. I believe that, in
any society, as conviviality is reduced below a certain level, no amount
of industrial productivity can effectively satisfy the needs it creates
among society’s members.In my judgment, nothing is more needful in our present technological moment than the rehabilitation and exploration of Illich’s notion of conviviality, and the use of it, first, to apprehend the tools we habitually employ and, second, to alter or replace them. For the point of any truly valuable critique of technology is not merely to understand our tools but to change them — and us…
Eminently worth reading in full, as its still all-too-relevant: “Attending to Technology- Theses for Disputation,” from @ayjay.bsky.social.
Pair with a provocative piece from another fan of Illich, L. M. Sacasas (@lmsacasas.bsky.social): “Surviving the Show: Illich And The Case For An Askesis of Perception.”
[Image above: source]
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As we think about tech, we might recall that it was on this date in 1946 that an ancestor of today’s social networks, streaming services, and AIs, the ENIAC (Electronic Numerical Integrator And Computer), was first demonstrated in operation. (It was announced to the public the following day.) The first general-purpose computer (Turing-complete, digital, and capable of being programmed and re-programmed to solve different problems), ENIAC was begun in 1943, as part of the U.S’s war effort (as a classified military project known as “Project PX“); it was conceived and designed by John Mauchly and Presper Eckert of the University of Pennsylvania, where it was built. The finished machine, composed of 17,468 electronic vacuum tubes, 7,200 crystal diodes, 1,500 relays, 70,000 resistors, 10,000 capacitors and around 5 million hand-soldered joints, weighed more than 27 tons and occupied a 30 x 50 foot room– in its time the largest single electronic apparatus in the world. ENIAC’s basic clock speed was 100,000 cycles per second (or Hertz). Today’s home computers have clock speeds of 3,500,000,000 cycles per second or more.
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