Today’s AI saw the light of day eighty-two years ago, when Warren S. McCulloch and Walter Pitts published “A logical calculus of the ideas immanent in nervous activity” in the December 1943 issue of Bulletin of Mathematical Biophysics.

The paper, in which they discussed networks of idealized and simplified artificial “neurons” and how they might perform simple logical functions, became the inspiration for computer-based “artificial neural networks” (later renamed “deep learning”) and their popular description as “mimicking the brain.”

The journey from that paper to ChatGPT was full of trials and tribulations, troughs (also known as “AI Winter”) and peaks (also known as “AI Bubble”).

In 1949, going beyond McCulloch and Pitts’ conjectures, psychologist Donald Hebb speculated about neural networks’ involvement in human learning. Hebb’s theory is often summarized as “neurons that fire together wire together,” describing how synapses—the connections between neurons—strengthen or weaken over time. It paved the way for the development of computer algorithms that were presumed to emulate the cognitive processes of the human brain. The manipulation of “weights”—numerical values representing the strength of the connection between two nodes in an artificial neural network—became the main preoccupation of researchers working in the approach to machine learning and AI called “Connectionism.”

In 1957, psychologist Frank Rosenblatt, sponsored by the Office of Naval Research, developed the Perceptron, an artificial neural network for image identification. The previous year, however, a different approach to AI had its birth event, a summer workshop at Dartmouth College. This was Symbolic AI—defining formal rules for manipulating symbols (e.g., words, numbers) and expressing in code human reasoning, i.e., drawing inferences and arriving at logical conclusions. It became the dominant approach to AI for the next several decades.

With the Perceptron failing to meet its sponsor’s expectations of human-level intelligence and astute marketing by Symbolic AI proponents, funding for neural networks research evaporated and Connectionism went into hibernation that lasted for more than 15 years.

Japanese computer scientist Kunihiko Fukushima proposed in 1979 the neocognitron, a hierarchical, multilayered artificial neural network, first used for Japanese handwritten character recognition and other pattern recognition tasks. The neocognitron served as the inspiration for the development of convolutional neural networks (CNN) which automatically select the properties or characteristics of the data that are important for the task at hand and are less dependent on humans for their selection compared to other image classification algorithms.

The most important breakthrough in the evolution of artificial neural networks came in 1986 when Geoffrey Hinton, David Rumelhart, and Ronald Williams published a pair of landmark papers popularizing “backpropagation” and showing its positive impact on the performance of neural networks. The term reflected a phase in which the algorithm propagated backwards through its neurons’ measures of the errors produced by the network’s guesses, starting with those directly connected to the outputs. This allowed networks with intermediate “hidden” neurons between input and output layers to learn efficiently.

Three years later, Yann LeCun and other researchers at AT&T Bell Labs successfully applied a backpropagation algorithm to a multi-layer artificial neural network, recognizing handwritten ZIP codes. But given the hardware limitations at the time, it took about 3 days (still a significant improvement over earlier efforts) to train the network.

Computer hardware, however, continued to improve following the trajectory of “Moore’s Law.” Richard Sutton, this year’s winner of the Turing Award, attributed in The Bitter Lesson (2019) all past AI breakthroughs to Moore’s Law “or rather its generalization of continued exponentially falling cost per unit of computation.”

Sutton made this observation to support his attack on Symbolic AI (who, for years, ignored or belittled his seminal work on reinforcement learning) and he was right in his conclusion: “We have to learn the bitter lesson that building in how we think we think does not work in the long run.” But he was wrong about Moore’s Law and the falling cost of computation—it does not explain the 2012 triumph of Connectionism/Deep Learning, what we now call “AI.”

AlexNet, a GPU-supported artificial neural network designed by Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton, achieved in October 2012 an error rate of 15.3% in the ImageNet Large Scale Visual Recognition Challenge, compared to the 26.2% error rate achieved by the second-best entry. The combination of special-purpose GPUs, a different type of chip than the general-purpose CPUs that followed Moore’s Law, and neural network algorithms, managed to process more efficiently and accurately the 1.43 million labeled images retrieved from the Web and organized in the ImageNet database.

On December 13, 1999, Nvidia released the GeForce 256 DDR graphics card. It was the first graphics card to be marketed as a “Graphics Processing Unit” (GPU). Six years earlier, Nvidia’s three cofounders identified the emerging market for specialized chips that would generate faster and more realistic graphics for video games. But they also believed that these graphics processing units could solve new challenges that general-purpose computer chips could not because of their ability to break data into multiple components and process them simultaneously.

A key factor in Nvidia’s success was the release in 2007 of its Compute Unified Device Architecture or CUDA. This parallel processing programming language allows developers to leverage the power of GPUs for general-purpose computing. On December 13, 2011, Nvidia announced the opening of the CUDA platform by releasing the compiler source code to enable broader adoption and innovation.

Nvidia’s “accelerated computing” (massively parallel computing) has been applied to many domains, but the rapid development of GPU-based artificial neural networks since 2012—processing images, video, audio, and text—dramatically elevated its fortunes. Nvidia became the world’s most valuable company because the performance of the artificial neural networks at the heart of today’s AI depends on the parallelism of the hardware they are running on, specifically the GPU’s ability to process many linear algebra multiplications simultaneously.

Parallelism is important not just because of the nature of the algorithms, but because what they process is vast quantities of data, quantities that choke and slow down traditional CPUs. “Scientific visualization,” representing numerical and spatial data as images to better understand complex scientific phenomena, was the field that coined the term “Big Data” in the late 1990s. The first Communications of the ACM article mentioning big data, published in August 1999, observed: “…fast computations spew out massive amounts of data. Where megabyte data sets were once considered large, we now find data sets from individual simulations in the 300GB range.”

The gigabytes turned into terabytes which turned into exabytes of data.

In 1986, 99.2% of all storage capacity in the world was analog, but in 2007, 94% of storage capacity was digital, a complete reversal of roles. The Web drove this digitization and data explosion and the development of new data management software and algorithms specially designed to take advantage of big data, or algorithms, such as artificial neural networks, that benefited from big data. It turned out that “learning” by machines powered by artificial neural networks is highly correlated with the quantity of examples presented to them.

The digital data that trains by examples the artificial neural network that is helping ChatGPT “think,” was born at the same time as the concept of artificial neural networks. On December 8, 1943, the prototype of the Mark 1, the world’s first electronic computer, was successfully tested at Dollis Hill, United Kingdom. It was taken apart and shipped to Bletchley Park, where it was delivered and re-assembled on January 18, 1944. As it was a large structure, it was dubbed Colossus by its operators, who used it and the subsequent improved models to obtain a vast amount of high-level military intelligence from radiotelegraphy messages sent between the German High Command and their army commands throughout occupied Europe. By the end of the war, 63 million characters of high-grade German communications had been decrypted by 550 people helped by the ten Colossus computers.

On December 2, 1954, the Naval Ordnance Research Calculator (NORC) was presented by IBM to the United States Navy at the Naval Surface Weapons Center in Dahlgren, Virginia. At the presentation ceremony, this one-of-a-kind, first-generation vacuum tube computer, calculated pi to 3,089 digits, which was a record at the time, and the calculation took only 13 minutes.

Fast calculations, those that “spew out massive amounts of data,” were the first indicator of machine “intelligence” or even “superintelligence,” the definition of “AI” as computers that are as smart or smarter than humans.

As computer technology continued to evolve, it expanded to other “cognitive” functions beyond calculations, but also in ways that drove the expansion and growth of digital data. The networking of computers was an important stage in this evolution.

On December 6, 1967, the Advanced Research Projects Agency (ARPA) at the United States Department of Defense issued a four-month contract to Stanford Research Institute (SRI) for the purpose of studying the “design and specification of a computer network.”

SRI was expected to report on the effects of selected network tasks on Interface Message Processors (today’s routers) and “the communication facilities serving highly responsive networks.” In August of 1968, ARPA sent out a RFQ to 140 companies, and in December 1968, awarded the contract for building the first 4 IMPs to Bolt, Beranek and Newman (BBN). These will become the first nodes of the network we know today as the Internet.

On December 9, 1968, Doug Engelbart demonstrated the oNLine System (NLS) to about one thousand attendees at the Fall Joint Computer Conference held by the American Federation of Information Processing. The presentation later became known as “the mother of all demos,” defined the potential of computer networks as tools for collaboration and the “augmentation of our collective intelligence.”

On December 13, 1977, Bob Metcalfe, David Boggs, Charles Thacker, and Butler Lampson received a patent for the Ethernet, titled “Multipoint Data Communication System with Collision Detection.” The Ethernet became the dominant standard for local PC networks. The first successful application of these networks was email, substituting paper-based memos with digital ones.

The shift from analog to digital was not limited to text. It happened in all types of information including video, images, and audio.

On December 24, 1877, Thomas Edison applied for a patent for a Phonograph that used tin foil cylinders to write and playback music. On December 3, 1998, the Rio portable player was released to stores by Diamond Multimedia. The Rio was the second MP3 player on the market, but the first one to be commercially successful. The device ran on a single AA battery and featured 32 megabytes of storage, enough for about half an hour of music encoded in the MP3 compression format. It retailed for $200.

Special-purpose chips and lots of data brought us modern AI and ChatGPT. The latter, like all Large Language Models, is infamously known for its “hallucinations.” On December 27, 1948, the New York Times published Norbert Wiener’s solution for curing “psychotic calculators,” albeit one based on false premises. From the Times’s review of Wiener’s Cybernetics:

By copying the human brain, says Professor Wiener, man is learning how to build better calculating machines. And the more he learns about calculators, the better he understands the brain. The cyberneticists are like explorers pushing into a new country and finding that nature, by constructing the human brain, pioneered there before them.

Psychotic Calculators. If calculators are like human brains, do they ever go insane? Indeed, they do, says Professor Wiener. Certain forms of insanity in the brain are believed to be caused by circulating memories which have got out of hand. Memory impulses (of worry or fear) go round & round, refusing to be suppressed. They invade other neuron circuits and eventually occupy so much nerve tissue that the brain, absorbed in its worry, can think of nothing else.

The more complicated calculating machines, says Professor Wiener, do this too. An electrical impulse, instead of going to its proper destination and quieting down dutifully, starts circulating lawlessly. It invades distant parts of the mechanism and sets the whole mass of electronic neurons moving in wild oscillations.

The cures administered to psychotic calculators are weirdly like the modern cures for insanity. One method is to overload the calculator with an extra strong electrical impulse in hope that the shock will stop the machine’s oscillations. This is rather like the shock treatment given to human psychotics. Another cure is to isolate part of the calculator’s mechanism, hoping to cut off the source of trouble. This is “like the lobotomies which brain surgeons perform. Lobotomies sometimes work (for both machine and brain) but are apt to reduce, in both cases, the subject’s judgment.

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The Weather Bureau’s 1870 experiment was an early example of how the economic value of information increases when it’s networked, when information from one source is combined with information stored in another source, or when it is collected from disparate sources and stored in a single repository.

This potential increase in the economic value of networked information is often overlooked in what is known as the “network effect,” or the notion that a product or service becomes more valuable to its users as more people use it. This principle was first associated with the telephone—the larger the number of people owning telephones, the more valuable the telephone is to each owner. Since the emergence of a widely-used global network—the internet running the World Wide Web—it has been translated into a Silicon Valley mantra, ”get big fast,” prioritizing the acquisition of many non-paying customers over profits. It has been applied to all types of social networks and websites or apps catering to all internet users, with the growth in the number of “Monthly Active Users” (or weekly or daily) becoming the only metric used to value the success of online startups, and most recently, online AI chatbots such as ChatGPT.

During the 1990s dot-com bubble, George Gilder popularized the concept of network effect as “Metcalfe’s Law.” Ethernet inventor Bob Metcalfe co-founded 3Com to develop and sell Ethernet cards. In 1983, answering his sales force’s need to convince customers that they should buy additional $1,000 Ethernet cards, he argued that “if a network is too small, its cost exceeds its value; but if a network gets large enough to achieve critical mass, then the sky’s the limit.” He even came up with a mathematical formulation, claiming that the value of a network is proportional to the square of the number of “compatibly communicating devices.”

That made sense in an era when there was not much information sent over the network and the motivation to connect PCs in a network was the economic benefit of sharing a printer rather than the sharing (and analysis) of information. Today, the economic benefit—of any network connecting people, organizations, and devices—lies in what is done with the information that is created and shared by all the “nodes” of the network.

Hear are a few November milestones in the history of networked information:

On November 13, 1990, Tim Berners-Lee published what came to be known as “the world’s first web page,” marking the birth of the global network that made information sharing the focal point of increasing economic value.

As he recounted in Weaving the Web, Berners-Lee started developing in November 1990 “a client program—a point-and-click browser/editor—which I just called WorldWideWeb… I started the global hypertext Web page, on the info.cern.ch server, with my own notes, specifications of HTTP, URI, and HTML, and all the project-related information… At long last I could demonstrate what the Web would look like.”

Almost a decade later, a college student realized that people worldwide could use this global network to share their personal repositories of digital music. On November 17, 1999, Slashdot, the “News for Nerds. Stuff that Matters” website, reported: “There is a cool new tool out there called Napster that allows anyone to become a publicly accessible FTP site – tapping into that huge resource of personal MP3 collections that everyone has, but have not been able to share… RIAA [Recording Industry Association of America] should be scared out of their minds because users are not logged on permanently, so it’s hard to track them down to take legal action.” The RIAA filed a lawsuit against Napster on December 7, 1999, and, as a result, the service shut down in July 2001.

Using the Web for transforming the exchange of physical goods (in addition to digital ones), however, has made the most significant impact on the economy. A crucial aspect of the success of e-commerce, and especially of Amazon in its early days, was the exchange of information not just about the products sold, but also between buyers, in the form of reviews.

On November 21, 2005, Cyber Monday was born with Shop.org announcing in a press release, “‘Cyber Monday’ Quickly Becoming One of the Biggest Online Shopping Days of the Year.” According to Shop.org/BizRate Research 2005 eHoliday Mood Study, 77% of online retailers said that their sales “increased substantially” the previous year on the Monday after Thanksgiving.

The information sent on networks is not always beneficial to the multitude receiving it. Call it Negative Network Effects. On November 3, 1983, Fred Cohen, a student at the University of Southern California School of Engineering, conceived of the first computer virus, or the spreading of an infected host file, as an experiment to be presented at a weekly seminar on computer security. Four years later, Cohen demonstrated that no algorithm can perfectly detect all possible viruses.

On November 2, 1988, Robert T. Morris, a computer science graduate student at Cornell University, released a self-replicating computer worm on ARPANET (a predecessor of the internet). The worm was part of a research project meant to determine the size of the internet by infecting UNIX systems in order to count the number of existing connections. As a result of a programming error, the worm began infecting machines repeatedly, causing clogged networks and system crashes. It became the first worm to spread extensively “in the wild,” the first worm to receive extensive media coverage, and one of the first programs to exploit a buffer overrun vulnerability. Morris was dismissed from Cornell, sentenced to three years’ probation, and fined $10,000. He eventually became a tenured professor at MIT.

Cohen and Morris were known at the time as “hackers,” godfathers of today’s $285 billion cybersecurity market. On November 20, 1963, the MIT student newspaper reported that many telephone services had been curtailed “because of so-called hackers,” the earliest known public use of the term.

According to Steven Levy in Hackers, members of MIT’s Tech Model Railroad Club in the late 1950s used the term a hack to describe “a project undertaken or a product built not solely to fulfill some constructive goal but with some wild pleasure taken in mere involvement… The most productive people working on the S&P [the Signals and Power Subcommittee of the club] called themselves ‘hackers’ with great pride.”

The 1963 article in The Tech described some of the wild, pleasure-filled projects:

The hackers have accomplished such things as tying up all the tie-lines between Harvard and MIT, or making long-distance calls by charging them to a local radar installation. One method involved connecting the PDP-1 computer to the phone system to search the lines until a dial tone, indicating an outside line, was found…. To quote one accomplished hacker, “the field is always open for experimentation.”

Constant experimentation has led to increasingly larger quantities of information being sent over networks and collected in vast data pools. “Big Data” and its statistical analysis—“Data Science”—became the New New Thing, quickly to be replaced by “AI.” It turned out that computers learn much better from examples with the right “deep learning” algorithms, but most importantly, with vast quantities of data and an extensive amount of computer processing power. The network effect became a new mantra—“at scale,” and “scaling laws,” the mathematical proof, as valid as “Metcalfe’s Law,” of the eventual arrival of “superintelligent” machines. I would venture to guess that the economic value of networked information or AI will manifest itself not in robots keeping humans as pets, but in computers augmenting human intelligence and assisting (or intruding) in all manner of human activity, just as they have done for the last seven-plus decades.

On October 26, 2012, antiquarian bookseller Justin Croft wrote about the earliest surviving English paper book, the 1307 King’s Lynn Red Register:

The register is a collection of town documents written up in this book from the year 1307… In the early fourteenth century, England’s writers habitually wrote, as we all know, with quill pens on sheets of parchment or vellum. And that’s true of writers in a variety of fields: in the church, the law courts, or in royal and local government. Here in Lynn, in 1307 we find a medieval writer (or probably writers) writing on quires of paper with the clear intention of binding them up as a book. The paper could not, of course, have been English: the first recorded English paper mill dates from only the 1490s, when a Hertford paper maker supplied paper to the printer William Caxton… To experiment with paper as a record-keeping technology was a brave and forward-looking move in 1307. Probably before its time, since it didn’t catch on for so long. Paper would turn out to have all kinds of advantages over parchment: it can be less bulky, it allows for much more rapid writing in new, faster hands, and ultimately it would become cheaper. By 1500, except for deeds and charters, paper would become dominant in record-keeping of all kinds. Perhaps most significantly, in the long run, it provided a perfect surface for printing.

I’m fascinated by the people who made medieval administrative records. It seems to me that, just like the IT wizards of the 21^st century, it is they who were experimenting with new technologies for recording day-to-day experience.

Paper has been used in China for administrative records since the early 2nd century BC. Anthony Grafton in Information: A Historical Companion: “The Chinese were the first to realize that plant fibers, now known as cellulose, could be beaten, mixed with water, and then left on a screen to drain until a sheet of paper remained… Made from inexpensive materials, paper could be produced in sufficient quantities to be used for printing.”

Numerous innovations throughout the centuries have made the production of “sufficient quantities” of paper progressively more efficient and affordable. “By 1702, paper was so cheap, it was used to make a product explicitly designed to be thrown away after twenty-four hours: the Daily Courant, the world’s first daily newspaper” (Tim Harford, Fifty Things that Made the Modern Economy).

At the same time, technological innovations also ensured that demand for paper would match—and drive—the increasing quantities produced. The output of the electronic transmission of information, starting with the telegraph, and continuing with the telephone, radio/wireless, and today’s internet, more often than not materialized on paper. In many instances, both the input and output of electronic/digital information transmission used paper. A prominent and ubiquitous example of this was, for a long time, the fax machine.

Other innovations ensured that whatever was recorded on paper would be recorded multiple times. In 1803, John Isaac Hawkins invented and obtained a patent for the polygraph, a mechanism for producing a duplicate copy of a handwritten original. On October 7, 1806, Ralph Wedgwood received a patent for carbon paper, which he described as an “apparatus for producing duplicates of writings.” Wedgwood, like his Italian contemporary and fellow carbon paper inventor Pellegrino Turri, was trying to help blind people write with the “black paper” being a substitute for ink.

In “What’s Smudgy, Crinkly and Still Sold in New York? Carbon Paper,” James Barron reported in the New York Times (April 2023) that author Robert Caro still uses carbon paper. “I’m a newspaper man,” Barron quoted Caro, “So every time I put a piece of paper in the typewriter, I put a carbon and another piece in.”

Caro dreads taking the first sheet out of a box he has just opened and finding it so dried out that the copy will be too faint to read easily. The problems do not end with finding carbon paper he can use. He has to find typewriter ribbons, too, he said in an interview.

“That’s actually harder,” he said.

As late as 1955, Chester Carlson was still making carbon copies of his own letters. He invented, however, a new technology that made the production of copies effortless and smudge-free. On October 6, 1942, Carlson received a patent for electrophotography, which he invented four years earlier. Searching for a buyer for his invention between 1939 and 1944, Carlson was turned down by more than 20 companies, including IBM, GE, Eastman Kodak and RCA. In 1946, Haloid, a small photo-paper maker in Rochester, New York, agreed to license electrophotography. Haloid changed the name of the technology to xerography in 1948 and, in 1961, changed the name of the company to Xerox. In 1959, the company introduced the 914, a cheap and convenient office copier, the first to use plain paper. It was estimated that the model might sell 5,000 units in three years, but by the end of 1962, 10,000 units had already been sold.

In 1955, four years before the introduction of the 914, the world produced approximately 20 million copies, almost all of them by non-xerographic means; in 1964, five years after the introduction of the 914, it produced nine and a half billion copies, almost all of them xerographically. Five hundred and fifty billion in 1984. Seven hundred billion in 1985. And in 2004, the world produced more than three trillion xerographic copies.

Two years earlier, Abigail Sellen and Richard Harper published The Myth of the Paperless Office: “The paperless office is a myth not because people fail to achieve their goals, but because they know too well that their goals cannot be achieved without paper. This held true over thirty years ago when the idea of the paperless office first gained some prominence, and it holds true today at the start of the twenty-first century.”

Also in 2002, Malcolm Gladwell wrote in “The Social Life of Paper”: “This is one of the great puzzles of the modern workplace. Computer technology was supposed to replace paper. But that hasn’t happened. Every country in the Western world uses more paper today, on a per-capita basis, than it did ten years ago. The consumption of uncoated free-sheet paper, for instance—the most common kind of office paper—rose almost fifteen per cent in the United States between 1995 and 2000.”

The technology that “was supposed to replace paper” was born equipped with a writing instrument and a place to store its calculations. Recording information digitally in the “memory” of a computer was a key challenge for the developers of the first modern computers.

In his 1947 lecture describing the design proposal for the Automatic Computing Engine (ACE), Alan Turing argued that “speed is necessary if the machine is to work fast enough for the machine to be commercially valuable, but a large storage capacity is necessary if it is to be capable of anything more than rather trivial operations. The storage capacity is therefore the more fundamental requirement.” Turing described “three main types of storage which have been developed recently,” and noted that the ACE would use mercury-based acoustic delay lines, a memory technology that “is due I believe to Eckert of Philadelphia University, who was the engineer chiefly responsible for the Eniac.”

On October 31, 1947, John Presper Eckert and John Mauchly filed for a U.S. patent on a delay-line electronic memory system. From the patent application: “Memory systems… are required in a large variety of devices for carrying out logical procedures wherein they have the function of receiving information, holding it, and transmitting it when and if required.”

In A New History of Modern Computing, Thomas Haigh and Paul Ceruzzi wrote:

The [1944] proposal to build EDVAC… centered on a new kind of memory… the delay line… It took years of engineering frustrations to make the delay line memory work reliably, but the idea was simple and compelling. Pulses representing several hundred digits moved through a fluid-filled tube. Signals received at one end were immediately retransmitted at the other end, so that the same sequence was cycling constantly. Whenever a number reached the end of the tube, it was available to be copied to the computer’s processor if needed.”

Patent 2,629,827 was granted in February 1953 for “Digital stores in which the information circulates continuously using electromechanical delay lines.” By 1953, however, magnetic-core memory took over as the preferred technology and, in 1956, IBM introduced a new “mass storage,” random access technology, the disk drive. It became the dominant storage technology over the subsequent decades, only to be eclipsed somewhat by flash memory in the 1990s. Competing storage technologies, such as optical storage, failed to replace disk storage for most uses, including in PCs.

On October 12, 1988, Steve Jobs introduced the NeXT Computer at Symphony Hall in San Francisco, telling the audience that they are witnessing an event “that occurs only once or twice in a decade—a time when a new architecture is rolled out that is going to change the face of computing.”

Walter Isaacson in Steve Jobs:

One of Job’s management philosophies was that it is crucial, every now and then, to roll the dice and “bet the company” on some new idea or technology. At the NeXT launch, he boasted of an example that, as it turned out, would not be a wise gamble: having a high-capacity (but slow) optical read/write disk and no floppy disk as a backup. “Two years ago we made a decision,” he said. “We saw some new technology and made a decision to risk our company.”

NeXT Computer, Inc. went on to sell only 50,000 beautifully-designed “cubes,” getting out of the hardware business altogether in 1993. For many years, I have kept in my office the “Computing advances to the NeXT level” poster I got at the NeXT launch (a day or two later in Boston) as a reminder that forecasting the next big thing in technology is tough, even impossible.

And yet, many people believe that technology marches according to some “laws” or pre-defined trajectory and that all we have to do is decipher the “evolutionary” path technology (or the economy or society) is destined to follow.

Jobs went on to introduce the iPod on October 23, 2001, with its 5 gigabytes 1.8-inch hard drive delivering “1,000 songs in your pocket.” The iPod and the iPad were industry-changing devices whose invention was made possible, among other things, by a tiny disk drive. The possibility of a significant boost to the simultaneous shrinking (of size) and enlarging (of capacity) of disk drives was known since the discovery of the giant magnetoresistance effect in the very same year, 1988, the NeXT Computer was introduced. Still, no one predicted the iPod.

We cannot predict the future, but a few, like Steve Jobs, insist on trying, sometimes fail, and sometimes succeed in inventing it. Many creative people, however, have made their fortunes by selling fortune-telling to an anxious humanity, eager to learn what’s coming.

On October 26, 1985, Dr. Emmett “Doc” Brown arrived with his time machine to persuade Marty McFly and his girlfriend, Jennifer Parker, to travel to 2015 and help their future children.

Back to the Future II was written in 1989. The crystal glass of its creators produced several correct predictions about 2015 technologies (or at least the concepts behind them, if not the exact products): “Smart homes,” video-based phone calls, wearables, tablet computers, voice-controlled devices, and biometrics. Optical storage, however, in the form of CDs and Laser Discs features prominently. So are fax machines, a ubiquitous communication device in the future of Back to the Future II. And the news was still delivered via paper-based newspapers.

The film was produced the same year another invention that “changed everything” (to use Silicon Valley’s favorite silly phrase) was imagined by Tim Berners-Lee. A few years later, the World Wide Web, while it did not change “everything,” did change significantly how we store, consume, and transmit information.

The most remarkable future-related blunder of the movie, however, has little to do with the existence of a 20-year-old Web in 2015. Given its correct predictions of tablets and other digital devices, why did it present the most important protagonist of the movie as a paper-based device, similar to the 1307 Red Register?

Back to the Future II is the best presentation on film that I’m familiar with of the argument for historical contingency, that individual choices and actions, as well as specific ideas or events, can significantly influence historical outcomes and alter the trajectory of history.

In 2015, Marty purchases an almanac containing the results of major sporting events from 1950 to 2000. Biff, the villain of the movie, gets hold of the almanac, steals the time machine, and gives the almanac to his 1955 self. When Doc and Marty return to 1985, their hometown has completely changed. Biff, having used the almanac to secure a fortune, is now one of the country’s wealthiest and most corrupt men. He has turned their town into a chaotic dystopia, secretly killed Marty’s father, George, in 1973, and forced Marty’s mother, Lorraine, to marry him. Meanwhile, in this alternate history, Doc has been committed to a mental hospital, while Marty was sent to a Swiss boarding school.

Marty then travels to 1955, watches the 2015 Biff gives the almanac to the 1955 Biff, and after some tense chases, finally gets it back. Marty burns the almanac and puts history back on its proper course. In a 1989 movie about time machines and future technologies, the most critical item is a storage technology invented some two millennia earlier.

On October 10, 1995, the Media Laboratory at MIT documented the World Wide Web in its “A Day in the Life of Cyberspace” project. Ten years later, the Media Lab invited submissions on a variety of issues related to technology and the internet, to “create a mosaic of life at the dawn of the digital revolution that is transforming our planet.” I would argue that our world has indeed been somewhat transformed by 2005, albeit not by anything “digital.”

The head of the Media Lab, Nicholas Negroponte, published Being Digital in January 1995. The review of the book in The New York Times provided a handy summary of his predictions:

[Negroponte] predicts that within 10 years, Americans will spend more time on the Internet than watching network television, and video-on-demand will have put videocassette rental stores out of business. In the not too distant future we will all have private multimedia “butlers,” programmed (eventually by voice command) to screen our telephone calls, schedule our days and select our entertainment. The bits of information that stream into our homes will be converted into customized newspapers, featuring our favorite columnists. Raw data about the weather -- or about a football game -- will be converted at our whim into a printout chart, a verbal report, a video picture or a miniature re-creation in our living rooms.

The reviewer, Samuel C. Florman, found this “technically fascinating” but asked a question which is still very relevant today: “Do we really want slave machines organizing our lives? According to the view from M.I.T.’s Media Lab, these developments -- for reasons that I totally fail to grasp -- are supposed to make for a better world, ‘decentralizing, globalizing, harmonizing and empowering.’ “

One possible solution is to destroy containers of information that are deemed misleading, useless, or frivolous. In The Year 2440: A Dream If Ever There Was One, Louis-Sébastien Mercier’s time traveler from 1771 discovers that the Royal Library in 2440 contains only a few volumes. The librarians of the future burned all the books they did not consider valuable.

This was the type of solution that involved a drastic—and destructive—decision-making, implying discernment, good taste, and moral authority. Other types of information overload-reduction solutions call for organizing information in a specially structured container. They still imply selection according to some criteria, but focus on providing handy access to the desired information. In many instances of this type of solution, an added dimension consists of highlighting the connections between various pieces of information, conceived as an aid to discovery.

For example, medieval florilegia were reference works designed to help writers find eloquent, authoritative quotations from venerable authors. On September 5, 1483, the Manipulus florum ('A Handful of Flowers') was first printed in Piacenza, Italy, by Jacobus de Tyela. It was a collection of around 6,000 quotations from authoritative sources, compiled by Thomas of Ireland or Thomas Hibernicus in 1306 at the Sorbonne. Thomas assigned unique reference letters to each entry under a specific topic to support his cross-referencing system. At the end of nearly all of the topics, he provided a reference list which includes similar topics and, more usefully, individual entries of related interest under unrelated topics.1

The Manipulus Florum stands out as an early example of a compilation that is not only ordered in a predetermined way (such as alphabetical order), providing access (findability, traceability) to specific information, but also attempts to link its components to aid in discovery (in finding what you were not looking for).

I mentioned cross-references in last month’s entry, highlighting Ephraim Chambers’ 1728 Cyclopaedia. Chambers declined an invitation in 1739 to publish a French edition of his work; however, it became the starting point for L’Encyclopédie, the French encyclopedia (1751-1772) edited by the philosopher, writer, and translator Denis Diderot and mathematician Jean d’Alembert, and written by a group of prominent French men of letters.2

A contemporary of Mercier, Diderot also faced the specter of information overload and presented his encyclopedia as a solution:

As long as the centuries continue to unfold, the number of books will grow continually, and one can predict that a time will come when it will be almost as difficult to learn anything from books as from the direct study of the whole universe. It will be almost as convenient to search for some bit of truth concealed in nature as it will be to find it hidden away in an immense multitude of bound volumes.

In his Encyclopedia entry on “Encyclopedia,” Diderot explained that the Greek origin of “this word signifies a chain of knowledge.” One of the primary aims of the project, he wrote, was information preservation: “…the purpose of an encyclopedia is to collect knowledge disseminated around the globe; to set forth its general system to the men with whom we live, and transmit it to those who will come after us, so that the work of preceding centuries will not become useless to the centuries to come; and so that our offspring, becoming better instructed, will at the same time become more virtuous and happy, and that we should not die without having rendered a service to the human race.”

The Encyclopedia, however, was not perceived by Diderot as merely a repository for information. Its cross-references served to expand the reader’s knowledge and lead to new and improved contributions to knowledge:

Thanks to encyclopedic ordering, the universality of knowledge, and the frequency of references, the connections grow, the links go out in all directions, the demonstrative power is increased, the word list is complemented, fields of knowledge are drawn closer together and strengthened; we perceive either the continuity or the gaps in our system, its weak sides, its strong points, and at a glance on which objects it is important to work for one's own glory, or for the greater utility to humankind. If our dictionary is good, how many still better works it will produce!

For d’Alembert, in his Preliminary Discourse, the Encyclopedia presented the “order and connection of the parts of human knowledge.” It consists of

collecting knowledge into the smallest a rea possible and of placing the philosopher at a vantage point, so to speak, high above this vast labyrinth, whence he can perceive the principal sciences and the arts simultaneously. From there, he can see at a glance the objects of their speculations and the operations which can be made on these objects; he can discern the general branches of human knowledge, the points that separate or unite them; and sometimes he can even glimpse the secrets that relate them to one another. It is a kind of world map which is to show the principal countries, their position and their mutual dependence, the road that leads directly from one to the other. This road is often cut by a thousand obstacles, which are known in each country only to the inhabitants or to travelers, and which cannot be represented except in individual, highly detailed maps. These individual maps will be the different articles of the Encyclopedia and the Tree or Systematic Chart will be its world map.

Here’s how d’Alembert described his knowledge tree:

The Physical beings act on the senses. The impressions of these beings stimulate perceptions of them in the understanding. The understanding is concerned with its perceptions in only three ways, according to its three principal faculties: memory, reason, and imagination. Either the understanding makes a pure and simple enumeration of its perceptions through memory, or it examines them, compares them, and digests them by means of reason; or it chooses to imitate them, and reproduce them through imagination. Whence results the apparently rather well-founded general distribution of human knowledge into history, which is related to memory; into philosophy, which emanates from reason; and into poetry, which arises from imagination.

Trees of knowledge have been the preoccupation of many scholars before d’Alembert, all attempting to structure and categorize available information in a coherent and educational map.

For example, on September 29, 1295, the Catalan poet and theologian Ramon Llull began publishing Arbor Scientiae (Tree of Science), presenting sixteen trees of knowledge, ranging from earthly and moral to divine and pedagogical. Each tree was divided into seven parts (roots, trunk, branches, twigs, leaves, flowers, fruits). The roots consist of the Lullian divine principles, and from there, the tree grows into the differentiated aspects of its respective category of reality. In 1308, Llull completed Ars Generalis Ultima (The Ultimate General Art), further refining his method of using paper-based mechanical means to create new knowledge through the combination of concepts.

Knowledge trees and cross-references, while serving an educational purpose, failed to provide an adequate solution to the continuing frustration with finding or discovering the right piece of information in the ever-growing mountain of information.

In 1944, Fremont Rider, the librarian at Wesleyan University, estimated that university libraries were doubling in size every sixteen years. Given this growth rate, Rider speculated that the Yale Library in 2040 will have “approximately 200,000,000 volumes, which will occupy over 6,000 miles of shelves… [requiring] a cataloging staff of over six thousand persons.”

A year later, Vannevar Bush proposed a solution: The Memex (a portmanteau of "memory" and "index"), an electromechanical device for storing personal libraries on microfilm. Mimicking the associative processes of the human mind, the device would facilitate the creation and retention of associative links, allowing the user to find the right piece of information by overcoming “the artificiality” of existing indexing systems.” “One cannot hope to equal the speed and flexibility with which the mind follows an associative trail,” Bush wrote in As We May Think, “but it should be possible to beat the mind decisively in regard to the permanence and clarity of the items resurrected from storage.”

Another year passed, and digital information emerged in the form of ENIAC, the first all-purpose digital computer. Constructing the “memory” of this information-processing and producing machine was a key challenge for the designers and developers of what they called “thinking machines” or “giant brains.” Several technologies and approaches were tested and deployed, but only one, providing random access to computer memory, became the dominant computer storage technology over the subsequent decades.

On September 14, 1956, IBM announced the 305 and 650 RAMAC (Random Access Memory Accounting) “data processing machines,” incorporating the first-ever disk storage product. The 305 came with fifty 24-inch disks for a total capacity of 5 megabytes, weighed 1 ton, and could be leased for $3,200 per month.

In 1953, Arthur J. Critchlow, a young member of IBM’s advanced technologies research lab in San Jose, California, was assigned the task of finding a better information storage medium than punch cards. In his meetings with IBM’s customers, Critchlow learned that punch-card equipment performed well when information processing could be done in batches or when information was stored sequentially, but became problematic when random access was required.

Inventory control was such an activity. In warehouse operations, for example, each order typically required several cards to be manually located, removed from a stack of cards, the inventory information updated, and the updated cards returned to their original locations. To facilitate this activity, drawers of cards were set out on work tables so that several people could access cards from the same file. This manner of organizing and processing information, widely known as the “tub file,” was time-consuming and error-prone.

The IBM project’s staff evaluated every existing storage technology in an attempt to find the best technological solution to the loss of productivity and poor quality associated with “tub files.” In addition to superior capacity and reliability, the storage technology eventually selected, magnetic disks, could provide random access to information. A new method (encoded in software) for finding stored information when its physical location on the disk was unknown ensured the success of the new way to store, organize, and share business records.

The IBM storage system was exhibited in the 1958 Brussels World’s Fair, where visitors could query “Professor RAMAC” using a keyboard and get answers in any of ten languages. This public relations coup heralded a day when millions of people would access and retrieve information from the largest tub file ever assembled – the World Wide Web.

The Web drove a new information explosion, with digital information being easily produced, copied, and shared. Information overload acquired new dimensions, and several of the first ventures “born digital” attempted to provide a solution. Information retrieval, what Bush called “selection,” now became “search.”

In 1991, Tim Berners-Lee introduced the World Wide Web, software that facilitates cross-references (initially linking documents) running on the internet, which was launched in 1969 to become a worldwide computer network. On September 10, 1990, Archie, the first internet search engine, was launched.

The program downloaded the directory listings of all files located on public Anonymous FTP (File Transfer Protocol) sites, creating a searchable database of file names. However, Archie did not index the contents of these sites, as the amount of information was so limited that it could be easily searched manually.

When Web pages replaced FTPs, the “amount of information” became limitless. “Search engines” proliferated, mostly following good-old-fashioned indexing and knowledge tree hierarchies.

As I wrote last month, “Google’s founders were the first to seize on Berners-Lee’s insight and build their information retrieval business on tracking closely cross-references (i.e., links between pages) as they were happening and correlate relevance with quantity of cross-references (i.e., popularity of Web pages as judged by how many other pages linked to them).”

On September 4, 2001, Google co-founder Larry Page was granted a patent for the PageRank search algorithm—“The rank assigned to a document is calculated from the ranks of documents citing it… The method is particularly useful in enhancing the performance of search engine results for hypermedia databases, such as the World Wide Web, whose documents have a large variation in quality.”

In In the Plex, Steven Levy quotes Google co-founder Sergey Brin: “We actually developed a lot of math to solve that problem…we convert the entire web into a big equation with several hundred million variables, which are the PageRanks of all the web pages, and billions of terms, which are all the links.”

Developing a model with millions and billions of variables and applying it to billions and trillions of documents and files of all types is what we today call “AI.” Artificial intelligence is helping conjure new texts, images, and videos, resulting in a new phenomenon known as “internet slop.” According to the Merriam-Webster dictionary, slop is “used to refer to unwanted and unasked for AI-generated content, a narrower development of a broader sense of slop currently defined in our dictionary as 'a product of little or no value,' as in 'watching the usual slop on TV.”

What’s to be done about information slop?

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This newsletter primarily tracks the history of the large family of tools and technologies used to create, process, share, store, organize, and analyze data and information. “AI” is today’s most talked-about such tool, but its history covers only the seven-plus decades since the advent of modern computing. Maps—providing information to help organize the world around us—have been guiding humans since they started communicating, teaching, and learning.

Here are a few random cartography coordinates:

The Saint-Bélec slab was found in Finistère, France, in 1900. This 4,000-year-old stone slab is believed to be a representation of the Odet Valley and the surrounding area in early Bronze Age Brittany. It is considered the oldest cartographic representation of a known territory in Europe, possibly serving as a tool for planning hunting excursions or as an aid in storytelling.

The oldest known world map dates to 500-700 BC. It is inscribed on a clay tablet discovered in the city of Sippar in southern Iraq. Centered around Babylon, it shows the world as a disc, surrounded by a ring of water labeled as the "Bitter River.”

In 300 BC, Dicaearchus, a student of Aristotle, placed an orientation on the world map, running east and west through Gibraltar and Rhodes. His reference-line principle was developed later by others into a comprehensive system of parallels and meridians.

Around 150, Claudius Ptolemaeus (Ptolemy) produced the Geography (Greek for “guide for drawing the Earth” or “description of the Earth”), also known by its Latin names as the Geographia and the Cosmographia, a geographical dictionary, an atlas, and a treatise on cartography, compiling the geographical knowledge of the 2nd-century Roman Empire. It included a list of 8,000 places and their approximate latitudes and longitudes.

In 1270, a sea chart was shown to Louis IX, king of France, on the occasion of his participation in the Eighth Crusade. This is the earliest record of the existence of a sea chart. The earliest surviving chart, known as Carta Pisana, dates from about 1275 and was drawn on sheepskin, depicting the entire Mediterranean Sea.

On August 5, 1475, the first printed maps were published in Lübeck, Germany, in the 474-sheet Weltchronik Rudimentum novitiorum, by printer Lucas Brandis. The book was intended to serve as a textbook on the history of the world for novice monks.

In 1507, Martin Waldseemuller’s world map in Cosmographiae Introductio was the first to label the New World “America,” after the explorer Amerigo Vespucci.

The earliest known example of a map designed to portray a fictitious land described in a literary work was a woodcut map of Thomas More’s Utopia. Made by an unknown artist, the first edition was published in Louvain in 1516.

The first atlas: “Aegidius Hooftman, [an Antwerp] merchant, complained to his assistant Radermaker how cumbersome it was to have to assemble a collection of various maps and charts in order to conduct his business overseas. [Abraham] Ortelius spotted a product opportunity and began to assemble a group of maps that could all be printed and bound together in a single volume. The trick was for it to be large enough to contain plenty of useful detail, but small enough to be convenient to carry on ships or keep in an office.

Apparently, Ortelius knew Mercator, who was older and more established, and discussed with him his idea of a book of maps. Mercator happened to be working on the exact same concept, though was magnanimous enough to encourage the younger cartographer. It took Ortelius a full decade’s worth of work, but in 1570 he was finally ready to publish his Theatrum Orbis Terrarum (Theatre of the Globe of the World). The first edition contained 53 maps, which Ortelius had compiled from the works of some 87 of Europe’s finest mapmakers…” [M.E. Rothwell in Cosmographia].

In 1748, the first systematic national topographical survey in France (and the first of its kind in Europe) culminated in the publication of the 182-sheet Carte géographique de la France. On inspection, Louis XV remarked that the more accurate data captured in the survey resulted in a “loss” of more territory than his wars of conquest had gained.

On August 18, 1966, Lunar Orbiter 1 began circling the Moon to map its surface in unprecedented detail.

Mapping the universe with “the largest map in human history”: Began in 2000, the Sloan Digital Sky Survey is the most ambitious astronomical survey project ever undertaken. The survey will map in detail one-quarter of the entire sky, determining the positions and absolute brightness of hundreds of millions of celestial objects. It will also measure the distances to more than a million galaxies and quasars.

On August 22, 2007, Google launched Google Sky, a virtual telescope and “an exciting way to browse and explore the universe.” It was discontinued by Google in 2012, but “you can continue to view the Moon, Mars, and Sky in Google Earth Pro. To view the Moon, Mars, and other planets, go to Google Maps in Space.”

From carving a rock representing their immediate physical environment to creating readily accessible online images covering the universe, humans have used maps for (at least) four millennia to imagine and document their world and their place in it.

Only three decades ago, however, the nature of what we mean by “place” and even “the world” changed. The shift from physical to virtual, from analog to digital, resulted from the invention of one man who strove to create a better way to find and link information (initially, scientific research references). His idea was to develop computer software that would assign a unique address, a virtual latitude and longitude (Uniform Resource Locator or URL), to every piece of online information, in addition to standards defining how to build the containers of information and how these containers would exchange information (HTML and HTTP).

On August 6, 1991, Tim Berners-Lee launched the world’s first website. He also posted a brief summary of his idea for the World Wide Web to the alt.hypertext Usenet newsgroup. It was the first public mention of the project.

The Web replaced an information tool invented in 1780, when Baron Gottfried Freiherr van Swieten, the prefect of the Imperial Library in Vienna, introduced the world's first card catalog. Van Swieten's innovation enabled the addition of new entries in a conveniently searchable order.

Berners-Lee wanted to liberate information organization from what he saw as the “hierarchical straight-jacket” of card catalogs and similar tools, where new entries were added to the most appropriate place in a pre-determined hierarchical map of the information landscape as it was known at a given time. Hierarchical, as in creating “ontologies,” maps for defining key subjects and navigating among their subordinate concepts and the pre-determined relationships between them.

Berners-Lee substituted the rigid classification systems previously dominating the organization of information with a flexible, associative reference scheme: “By being able to reference everything with equal ease, the web could also represent associations between things that might seem unrelated but for some reason did actually share a relationship. This is something the brain can do easily, spontaneously.”

Berners-Lee’s insight is frequently linked to Vannevar Bush’s “As We May Think,” published in 1945: “The human mind… operates by association. With one item in its grasp, it snaps instantly to the next that is suggested by the association of thoughts, in accordance with some intricate web of trails carried by the cells of the brain… Our ineptitude at getting at the record is largely caused by the artificiality of systems of indexing… Selection [i.e., information retrieval] by association, rather than by indexing may yet be mechanized.”

The earliest use of cross-references, to the best of my knowledge, was by Ephraim Chambers in his Cyclopaedia, published in London in 1728. While lacking the worldwide platform for “crowd-sourcing” references that Berners-Lee invented, Chambers shared with him (and Bush) a dislike for hierarchical, alphabetical, indexing systems. Here’s how Chambers explained in the Preface to the Cyclopaedia his innovative system of cross-references: “Former lexicographers have not attempted anything like Structure in their Works; nor seem to have been aware that a dictionary was in some measure capable of the Advantages of a continued Discourse. Accordingly, we see nothing like a Whole in what they have done…. This we endeavoured to attain, by considering the several Matters [i.e., topics] not only absolutely and independently, as to what they are in themselves; but also relatively, or as they respect each other.”

Working at CERN (the European Organization for Nuclear Research), Berners-Lee's immediate goal was to provide his colleagues (and their colleagues in other research institutions) with an online tool that would replace another very old information tool, the citation index. The earliest known citation index was an index of biblical citations in rabbinic literature, the Mafteah ha-Derashot, attributed to Maimonides and probably dating to the 12th century. In 1955, Eugene Garfield published "Citation indexes for science" in the journal Science, and in 1964, the first version of the book Science Citation Index, to “quickly locate key papers from reliable scientific journals and explore a comprehensive citation network to discover related research” (to quote the current Web-based version).

Getting acquainted with citation indexes at the Stanford University library as PhD students, Google’s founders were the first to seize on Berners-Lee’s insight and build their information retrieval business on tracking closely cross-references (i.e., links between Web pages) as they were happening and correlate relevance with quantity of cross-references (i.e., popularity of Web pages as judged by how many other pages linked to them). This set Google apart from its competitors, especially Yahoo, which at the time led the new business of producing maps for navigating the rapidly expanding online information landscape. Yahoo employed many people to organize the content of the Web into a neat taxonomy, and even had a “Chief Ontologist” on staff.

Web pages were proliferating at the time (late 1990s) due to another invention: an easy-to-use “Web Browser,” which facilitated both the viewing of online information (in all its forms—text, audio, images, and videos) and the creation of such information by anyone with access to the internet. Including creating maps, online maps. For example:

On September 8, 2007, during Rome’s all-night festival of Notte Bianca, Wiki City Rome was launched. Using data from cell phones and other wireless technologies, it maps the city’s pulse in real-time. Anyone with an internet connection could view the movement of crowds, event locations, the whereabouts of well-known Roman personalities, and the real-time positions of city buses and trains.

The nonprofit Reinvestment Fund launched PolicyMap.com in May 2008, a website that allows users to access neighborhood-specific demographic information and create custom maps comparing selected areas on variables ranging into the hundreds, including mortgage types, political contributions, occupations, home values, crime, race, incomes, and education levels.

Epidemiologist John Brownstein, along with computer scientist Clark Freifeld, created HealthMap in 2008, which tracks disease in real-time. Scraping news aggregators such as Google News and global electronic infectious and toxin reporting systems, HealthMap matches infectious disease terms with words related to geography. Results are superimposed over Google satellite maps.

“[Mobile phone location log data] may lead to a new kind of cartography. Maps will identify places not by their roads, forests, or mountain ranges, but by what kinds of people spend time there and by what they do.” —BusinessWeek, February 26, 2009. “One of the themes of my profession right now is the democratization of cartography. Mapping used to be a job of the elite …. Now people are taking it upon themselves to map their passions.” —Mark Harrower, Professor of Geography, University of Wisconsin-Madison.

Now communicating their virtual coordinates and pursuits, humans continue to create and use maps to organize their world and their place in it. The online world Berners-Lee and followers created has allowed us to benefit from what Chambers called the “Advantages of a continued Discourse,” the constant updating of information, now placed at our fingertips and always accessible.

The invention of a virtual map of information, however, has transformed into a new world, a new reality for humans to inhabit, for better and for worse. It broke time and space barriers, facilitating improved connections, human and otherwise, but also highlighted the disadvantages of the continuing discourse and enabled the proliferation of useless, inaccurate, and misleading information (now supercharged by “AI”).

Information tools are as good or as bad as the humans creating and using them.

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Steven Lubar in InfoCulture: “Mark Twain, who lost a great deal of money investing in an automatic typesetting machine, suggested its value when he wrote that a Linotype ‘could work like six men and do everything but drink, swear, and go out on strike.’” A competitor to the Linotype, the Monotype, was invented in 1887 by Tolbert Lanston. It produced higher quality type and was controlled by a punched paper tape.

On July 7, 1752, Joseph-Marie Jacquard, French inventor of the Jacquard loom, was born. The Jacquard loom pioneered the use of punched tape or cards to give instructions to a machine, in this case, a loom weaving rugs and linens.

On July 9, 1836, Charles Babbage wrote in his notebook: “My notion is that cards (Jacquards) of the Calc. engine direct a series of operations and then recommence with the first so it might perhaps be possible to cause the same cards to punch others equivalent to any given number of repetitions. But their hole[s] might perhaps be small pieces of formulae previously made by the first cards.”

This passage, says Brian Randell in The Origins of Digital Computers, “puts beyond doubt the fact that Babbage had thought of using the Analytical Engine to what would today be described as ‘computing its own programs.’” In 1952, Arthur Samuel developed the first computer checkers playing program, the first to learn on its own, and in 1959, he coined the term “machine learning,” defining it as “programming of a digital computer to behave in a way which, if done by human beings or animals, would be described as involving the process of learning.”

In The Information: A History, A Theory, A Flood, James Gleick quotes Edgar Allan Poe on Babbage’s machine: “What shall we think of an engine of wood and metal which can… render the exactitude of its operations mathematically certain through its power of correcting its possible errors?” and Oliver Wendell Holmes: “What a satire is that machine on the mere mathematician! A Frankenstein-monster, a thing without brains and without heart, too stupid to make a blunder; which turns out results like a corn-sheller, and never grows any wiser or better, though it grinds a thousand bushels of them!”

On July 1, 1890, two thousand clerks began processing the results of the 1890 U.S. Census, employing ninety-six of Herman Hollerith’s tabulating machines, using a punched card system where a hole punched in a specific place on the card signified a fact about an individual. The data collected on the population of the United States (62,947,714 in 1890) was processed in one year, compared to the eight years it took to process the 1880 Census.

On July 2, 1953, International Business Machines (IBM) announced the IBM 650, the world’s first mass-produced computer. The IBM 650 ran programs from punch cards and stored numbers up to ten digits long on a rotating magnetic drum. It dominated the computer market through the early sixties.

Kevin Maney in Making the World Work Better: “Hollerith gave computers a way to sense the world through a crude form of touch. Subsequent computing and tabulating machines would improve on the process, packing more information unto cards and developing methods for reading the cards much faster. Yet, amazingly, for six more decades computers would experience the outside way no other way.”

This was indeed “amazing,” since keyboards as input mechanisms were a century-plus-old invention. On July 23, 1829, William Austin Burt, a surveyor from Mount Vernon, Michigan, received a patent for the typographer, the earliest forerunner of the typewriter. On July 31, 1961, IBM introduced the IBM Selectric, replacing typebars and the moving carriage with a spherical printing element.

In 2006, a Boston Globe article described the fate of typewriters in the digital age: “When Richard Polt, a professor of philosophy at Xavier University, brings his portable Remington #7 to his local coffee shop to mark papers, he inevitably draws a crowd. ‘It’s a real novelty,’ Polt said. ‘Some of them have never seen a typewriter before … they ask me where the screen is or the mouse or the delete key.’”

On July 4, 1956, MIT researchers connected a keyboard to the Whirlwind computer as an experiment, demonstrating the feasibility of using keyboards for direct input. The Whirlwind was the first computer (1951) to operate in real time. One of the designers of the Whirlwind, Jay W. Forrester, was born on July 14, 1918. Forrester was also one of the inventors of magnetic-core memory, which remained the computer memory technology of choice until the early 1970s. In 1956, believing that all the major innovations in the field had been made, he moved to MIT’s business school, where he developed the field of system dynamics, computer modeling of complex systems, with applications in business and socioeconomics.

Punched cards were used as the primary input method for early computers until the keyboard replaced them. On July 4, 1971, Michael Hart, a freshman at the University of Illinois, entered the United States’ Declaration of Independence into the mainframe he was using, all in upper case, because there was no lower case yet on the mainframe’s keyboard. The first document in what will become Project Gutenberg was downloaded by six users. “I knew what the future of computing, and the internet, was going to be,” Hart later said.

What the expectations from the internet were going to be could have been learned from earlier reactions to the establishment of global networks.

On July 12, 1846, the New York Herald celebrated the inauguration of the telegraph line between Boston and New York: “When a telegraph network spanned the globe, war would be no more, and cannonballs and mortars would be locked up in museums as curiosities and remnants of a barbarous age.”

Ten years later, Ralph Waldo Emerson wrote in English Traits: “Nations are getting obsolete, we go and live where we will. Steam has enabled men to choose what law they will live under. Money makes place for them. The telegraph is a limp band that will hold the Fenris Wolf of war. For now, that a telegraph line runs through France and Europe, from London, every message it transmits makes stronger by one thread, the band which war will have to cut.”

On July 27, 1866, the installation of the Atlantic Cable, connecting the U.S. with Europe, was completed. The first working cable, completed in 1858, failed within a few weeks. Before it did, however, it prompted the biggest parade New York had ever seen and accolades that described the cable, as one newspaper said, as “next only in importance to the ‘Crucifixion.’”

Steven Lubar in InfoCulture: “For merchants, fresh news meant the difference between profit and loss… Information was the key, and more and fresher information was better. These merchants lived in an Information Age. Their hopes and fears and expectations and beliefs about the new information technology were, in some ways, not too far removed from ours.”

Tom Standage in The Victorian Internet: “The hype soon got going again once it became clear, that this time, the transatlantic link was here to stay… [The cable] was hailed as ‘the most wonderful achievement of our civilization’… Edward Thornton, the British ambassador, emphasized the peacemaking potential of the telegraph. ‘What can be more likely to effect [peace] than a constant and complete intercourse between all nations and individuals in the world?’ He asked.

Ever since, the belief that global networks of any kind can abolish war and topple tyrants has stubbornly persisted:

“The Dell Theory of Conflict Prevention argues that no two countries that are both part of the same global supply chain will ever fight a war as long as they are each part of that supply chain” –Thomas Friedman, The World Is Flat, 2005

“This generation will determine if the world can avoid the apocalypse that will come if the fear-ridden establishments continue to dominate global politics, motivated by terror, armed with nukes, and playing old but now far too dangerous games. This generation will not bypass existing institutions and methods: look at the record turnout in Iran and the massive mobilization of the young and minority vote in the US. But they will use technology to displace old modes and orders” –Andrew Sullivan, “The Revolution Will Be Twittered,” The Atlantic Monthly, June 13, 2009

“We live at a moment when the majority of people in the world have access to the internet or mobile phones—the raw tools necessary to start sharing what they're thinking, feeling and doing with whomever they want. …By giving people the power to share, we are starting to see people make their voices heard on a different scale from what has historically been possible. These voices will increase in number and volume. They cannot be ignored. Over time, we expect governments will become more responsive to issues and concerns raised directly by all their people rather than through intermediaries controlled by a select few”—Mark Zuckerberg, Facebook’s IPO filing, 2012

A more accurate description of the “information age” and the actual impact of the internet is found in New Yorker cartoons. On July 5, 1993, Peter Steiner’s cartoon declared, “On the Internet, nobody knows you’re a dog.” A dozen years later, another cartoon depicted two dogs discussing contemporary conversations: “I had my own blog for a while, but I decided to go back to just pointless, incessant barking.”

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I also learned one key lesson, articulated by one of my bosses as “marketing is the selling of ideas.” I’ll take it further: Business success and technological trajectories are the result of the timely selling of ideas, ideas that motivate investors, employees, and customers.

On June 16, 1911, the Computing-Tabulating-Recording Company was incorporated. It changed its name to IBM in 1924.

In “Ideas make IBM 100 years young,” IBM’s Bernard Meyerson writes: “…if you really think about what keeps a company going, it’s that you have to keep reinventing yourself. You cannot reinvent yourself in the absence of great ideas. You have to have the great ideas, and you have to follow them through.” Meyerson equates the great ideas that sustain the life of a company with great technological innovations, but in “1100100 and counting,” The Economist quotes Forrester Research’s George Colony, “IBM is not a technology company, but a company solving business problems using technology” and concludes:

Over time [the close relationships between IBM and its customers] became IBM’s most important platform—and the main reason for its longevity. Customers were happy to buy electric “calculating machines,” as Thomas Watson senior insisted on calling them, from the same firm that had sold them their electromechanical predecessors. They hoped that their trusted supplier would survive in the early 1990s. And they are now willing to let IBM’s services division tell them how to organise their businesses better.

Kevin Maney lists five lessons he drew from his close study of IBM’s history (The Maverick and His Machine: Thomas Watson, Sr. and the Making of IBM), the first one being “At the start, convince the troops you’re a company of destiny, even if that seems crazy.” Thomas Watson Sr. did this and more. In a 1917 speech, he said: “My duty is not the building of this business; it is rather the building of the organization. … I [know] only one definition of good management; that is, good organization. So, as I see it, my work consists in trying to build a bigger and better organization. The organization, in its turn, will take care of the building of the business.”

So what was the Big IBM Idea? A trusted supplier? A focus on destiny and longevity? Building a bigger and better organization? All of the above?

In a 1994 Harvard Business Review article titled “The theory of the business,” management guru Peter Drucker argued that great businesses revolve around a specific idea or “a theory of the business,” articulating the company’s assumptions about its environment, its mission, and its core competencies.

In response, I discussed in a letter-to-the-editor the similarities and dissimilarities between scientists and managers:

Managers [like scientists] must articulate their theories and how they can be refuted and then seek data that proves their theories wrong. That will prevent them from falling into the trap of discarding successful theories… the theory of the business may not just explain reality or past business success; it may also define it by communicating and convincing employees and customers that the company is unique. A business theory, then, unlike a scientific theory, can be true and false at the same time. That is how, as Drucker has illustrated, IBM and General Motors could both succeed and fail when they applied the same business theory to two different businesses.

In short, an idea or a set of ideas may explain past business success. But, business school education and management gurus notwithstanding, one cannot extract from history “management lessons,” prescriptions, and predictions about the future of this or any other business. Even if we had a perfect understanding of the reasons for IBM’s longevity (and its relative decline after 1990), that would not tell us anything about the future of Apple or Google or Facebook. There is no one explanation or theory of business success; the reasons for success in one case can be the very same reasons for failure in another or even for the same business.

I didn’t know it in 1994, but it turns out I was channeling Thomas Watson Sr. who said in another speech, this one in January 1915, shortly after he joined C-T-R:

We all know there have been numerous books written on scientific factory management, scientific sales management, the psychology of selling goods, etc. Many of us have read some of those books. Some of them are good; but we can’t accept any of them as a basis for us to work on. Neither can you afford to accept my ideas as whole and attempt to carry them out, because I do not believe in a fixed method–in any fixed way of selling goods, or of running a business.

Watson Sr. was a great marketer, a great motivator, a great salesman of the idea of building “a bigger and better organization,” with IBM successfully transitioning from an office equipment supplier to the dominant supplier of computer technology, or what today we call artificial intelligence or AI. The narrowly-defined (“thinking machines”) AI marketing campaign launched in 1955, and the subsequent AGI marketing campaign (“superintelligence”) launched in 2007, mask the historical reality of “artificial intelligence,” the steady expansion of the capabilities and functionality of modern computers.

In A New History of Modern Computing, Thomas Haigh and Paul E. Ceruzzi trace the transformation of computing from scientific calculations to administrative assistance to personal appliances to a communications medium. Starting with “superintelligence” calculators surpassing humans in speed and complexity eighty years ago, this constant reinvention continues with today’s “AI,” the perfect storm of GPUs, statistical analysis models, and big data, adding content analysis and processing (not content “generation”) to what a computer can do.

This evolution has been punctuated by peaks of inflated expectations, troughs of disillusionment, and plateaus of productivity. It has been defined by various marketing campaigns and by specific “moments” describing a new stage of where computing is done and its impact on how we live and work.

The first such milestone, the IT moment, highlighted the shift from scientific calculations to business use. In 1958, Harold Leavitt and Thomas Whisler published “Management in the 1980s” in the Harvard Business Review, inventing the term “Information Technology.” Stating that “over the last decade, a new technology has begun to take hold in American business, one so new that its significance is still difficult to evaluate,” they predicted IT “will challenge many long-established practices and doctrines.”

Leavitt and Whisler delineated three major IT components. The first described what they observed at the time—high-speed computers processing large amounts of information. The second was just emerging—applying statistical and mathematical methods to decision-making problems. The third was a prediction based on the authority of leading contemporary AI researchers—the “simulation of higher-order thinking through computer programs,” including programming “much of the top job [in corporations] before too many decades have passed.”

The article focused on the organizational implications of this new technology, such as the centralization (or recentralization) of authority and decision-making facilitated by IT and IT’s negative impact on the middle manager's job. While the pros and cons of “automation” continued to be debated in the following years, the main topic of discussion, admiration, and trepidation, was the constantly increasing speed of the modern computer and its implications for new computing devices and their applications.

The idea of constantly increasing speed as the key driver of modern computing was captured by Gordon E. Moore in “Cramming more components onto integrated circuits,” published in Electronics in 1965, predicting that the number of components that could be placed on a chip would double every year, doubling the speed of computers. It was a very specific prediction, couched in quantitative, “scientific” terms, with the convincing appearance of a law of nature.

“Moore’s Law,” as this prediction came to be known, is not about physics, not even about economics. And it is much more than a self-fulfilling prophecy guiding an industry as it is generally perceived to be. Moore’s Law is a marketing campaign promoting a specific way of designing computer chips, selling this innovation to investors, potential customers, and future employees.

Like other marketing slogans—and unlike the laws of physics—Moore’s Law was revised to fit with evolving market realities. In a 2006 article in the IEEE Annuals of the History of Computing, Ethan Mollick has convincingly showed that the “law” was adjusted periodically in response to changing competitive conditions (e.g., the rise of the Japanese semiconductor industry): “The semiconductor industry has undergone dramatic transformations over the past 40 years, rendering irrelevant many of the original assumptions embodied in Moore’s Law. These changes in the nature of the industry have coincided with periodic revisions of the law, so that when the semiconductor market evolved, the law evolved with it.”

As the power and miniaturization of computers steadily advanced, another milestone arrived: the networking moment. In 1973, Bob Metcalfe and David Boggs invented Ethernet and implemented the first local-area network or LAN at Xerox Parc. Metcalfe explained later that what became known as “Metcalfe’s Law” is a “vision thing.” It helped him jump over a big hurdle: The first Ethernet card Metcalfe sold went for $5000 in 1980 and he used the “law” to “convince early Ethernet adopters to try LANs large enough to exhibit network effects,” in effect promising them that the value of their investment will grow as more people get connected to the office network. Metcalfe’s law—the value of a network is proportional to the square of the number of its users—encapsulates a brilliant marketing concept, engineered to get early adopters—and their accountants—over the difficulty of calculating the ROI for a new, expensive, unproven technology. It provided the ultimate promise: This technology gets more “valuable” the more you invest in it.

The networking moment made scale—how many people and devices are connected—even more important than speed—how fast is the processing of data. Together with the PC moment (1982) and the internet moment (1993) and the mobile moment (2007), it begat Big Data and today’s version of AI. It also turned into the rallying cry of an energized Silicon Valley—"At Scale” became the guiding light of internet startups. And it also led to yet another marketing campaign—the one about the “new economy”—and to the dot-com bubble.

In 2006, IEEE Spectrum published “Metcalfe’s Law is Wrong.” Its authors convincingly corrected Metcalfe’s mathematical formulation of network effects: “…for a small but rapidly growing network, it may be a decent approximation for a while,” said one of the authors in 2023. “But our correction applies after you hit scale.”

The IEEE Spectrum published my response to the article, which started with the following: “Asking whether Metcalfe’s Law is right or wrong is like asking whether ‘reach out and touch someone’ is right or wrong. A successful marketing slogan is a promise, not a verifiable empirical statement. Metcalfe’s Law – and Moore’s – proved only one thing: Engineers, or more generally, entrepreneurs, are the best marketers.”

Moore’s and Metcalfe’s “laws” are two prominent examples of the remarkable marriage of engineering and marketing ingenuity that has made many American entrepreneurs successful. However, the most successful marketing and branding campaign invented by an engineer (John McCarthy) has been promoting “artificial intelligence” since 1955. Today’s entrepreneurs, asking for billions of dollars of venture capital and seeking to distinguish what they develop from the somewhat tarnished image of old-fashioned AI, are promoting the new AGI campaign, promising to finally deliver what’s been promised many times in the past.

By doing so, they obscure the true distinction between what they have achieved and the various practical approaches taken in the 1950s (and later) to achieving computers' “simulation of higher-order thinking.” Most importantly, they mislead the public with this disinformation campaign, diverting attention from the potential impact of their machine learning models on how we work.

Researchers and business executives not occupied with raising billions of dollars have started to provide empirical indicators of the organizational implications of AI, similar to what was captured by the IT moment of 1958. For example, a recent randomized controlled trial of the use of AI by 776 professionals at Procter and Gamble found that “AI effectively replicated the performance benefits of having a human teammate—one person with AI could match what previously required two-person collaboration,” reports Ethan Mollick.

As a professor at the Wharton Business School (and author of a popular guide to the new AI), Mollick is sensitive to the real-world impact of AI on how work is done in organizations: “AI sometimes functions more like a teammate than a tool… The most exciting implication may be that AI doesn't just automate existing tasks, it changes how we can think about work itself. The future of work isn't just about individuals adapting to AI, it's about organizations reimagining the fundamental nature of teamwork and management structures themselves.”

To paraphrase a re-engineering guru of the 1990s, don’t automate; recreate with AI. We may be arriving at a new milestone, the AI moment, with new, empirically validated insights into new ways to refashion the organization of work. We may also have arrived at the peak of the AI bubble, or it may still have a year or two before deflation. A very safe prediction, however, is that the marketing campaign about creating superintelligence will never expire.

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On May 1, 1957, the film Desk Set was released. “They can’t build a machine to do our job; there are too many cross-references in this place,” the head librarian (Katharine Hepburn) tells her anxious colleagues in the research department when a “methods engineer” (Spencer Tracy) is hired to “improve workman-hour relationship” in a large corporation. By the film’s end, she proves her point by winning, not only the engineer’s heart, but also a contest with the ominous-looking EMERAC, a room-sized “electronic brain” (aka a computer).

The film, particularly the statement about cross-references, has been a source of inspiration and a rallying cry for librarians ever since. I learned about it when, in 1988, I joined the corporate research department (which was part of a magnificent network of libraries) at Digital Equipment Corporation (DEC), a leading purveyor of “electronic brains. "

In 2002, Cheryl Knott Malone published an article on Desk Set in the IEEE Annals of the History of Computing, calling it “the lone film [at the time] to offer a vision of the future of computing outside the realm of science fiction.” The film was “prophetic in its depiction of automated information retrieval by an electronic computer capable of storing the contents of an entire library, processing natural-language input, and prompting users to formulate more precise queries.”

More broadly, I would argue, Desk Set was about the promise and threat of new technology, its perceived potential to mimic (and replace) the human brain. That meant not only being able to think and reason like human beings but also suffering from similar memory lapses and the failure of overworked circuits.

By anthropomorphizing the computer (called “Emmy” by the engineers working with it), the movie puts the fears of the new technology to rest. After all, it is fallible, and it certainly cannot replace the librarians; it can only assist with mechanical, repetitive work, such as processing the payroll. That depiction of the computer was in the interest of IBM, which sponsored the film, and its attempts at the time to quiet down the growing anxiety of its customers and their trepidation about the new “artificial intelligence.”

However, the anxiety about the new technology did not go away. In surveying librarians’ attitudes in the 1950s and later, Malone quotes Jesse Shera, dean of the library school at Western Reserve University and one of the first proponents of the synergy between information science and library science. Shera was the chief sponsor of The Librarian and the Machine, written in the early 1960s (published 1965) by Paul Wasserman, a study of the “promise and threat” of computers in libraries.

The book opens with the following declaration: “As if it were not already a problem enough for library administrators striving to respond to the myriad pressures and infinite complexities…. A newer and even more foreboding terror [my italics]… was beginning to emerge more clearly. I refer to the computer and its attendant supporting apparatus.” But, like Desk Set, his conclusion at the end of his year-long study is comforting: “Machines today can do much of man’s work more rapidly and efficiently; but they cannot do his intellectual work as well.”

Do ChatGPT and other Large Language Models or LLMs present a new reality in which computers, like the fictional EMERAC, finally manage to “converse” with their users? Do they succeed in prompting them to formulate more precise queries? How much of man’s “intellectual work” (and at what quality) can AI do today and what tasks and jobs will it replace in the future?

A few years before the first “electronic brains” started automating work, Fremont Rider, Wesleyan University Librarian, published The Scholar and the Future of the Research Library (1944). He estimated that American university libraries were doubling in size every sixteen years. Given this growth rate, Rider speculated that the Yale Library in 2040 will have “approximately 200,000,000 volumes, which will occupy over 6,000 miles of shelves… [requiring] a cataloging staff of over six thousand persons.”

That was somewhat similar to the prediction made in the 1930s, when automatic telephone switchboards replaced operator-assisted switchboards, that before long, more operators would be needed than young girls suitable for the job.

That “prediction” served as a justification for automation, as AT&T had to explain to its customers why they were required to do the work previously performed by another human. Rider’s prediction about libraries and librarians was correct about the growth in the volume of knowledge stored on paper, but did not foresee that “electronic brains” would automate some knowledge-work and provide digital storage for the ever-growing volume of information. Most importantly, they will provide better means of finding relevant information, by librarians, and later, by anyone with access to the internet, a giant digital library.

The quest to provide knowledge seekers with the knowledge they seek—and the links between one information item and another—is much older than the internet or the first computers. In 1728, Ephraim Chambers, a London globe-maker, published the Cyclopaedia, or, An Universal Dictionary of Arts and Sciences. It was probably the earliest attempt to link all the articles in an encyclopedia or, more generally, all the components of human knowledge by association. In the Preface, Chambers explained his innovative system of cross-references:

“Former lexicographers have not attempted anything like Structure in their Works; nor seem to have been aware that a dictionary was in some measure capable of the Advantages of a continued Discourse. Accordingly, we see nothing like a Whole in what they have done…. This we endeavoured to attain, by considering the several Matters [i.e., topics] not only absolutely and independently, as to what they are in themselves; but also relatively, or as they respect each other. They are both treated as so many Wholes, and so many Parts of some greater Whole; their Connexion with which, is pointed out by a Reference… A Communication is opened between the several parts of the Work; and the several Articles are in some measure replaced in their natural Order of Science, out of which the Technical or Alphabetical one had remov’d them.”

New concepts, like new technologies, raise questions about their assumed benefits and concerns about their presumed drawbacks. The supplement to the 1758 edition of the Cyclopaedia says:

Some few however condemn the use of all such dictionaries, on the first pretence, that, by lessening the difficulties of attaining knowledge, they abate our diligence in the pursuit of it; and by dazzling our eyes with superficial shew, seduce us from digging solid riches in the mine itself.

Regardless, the quest for better organization of information and knowledge continued. Just before the advent of “electronic brains,” Vannevar Bush wrote in As We May Think (1945): “Our ineptitude at getting at the record is largely caused by the artificiality of systems of indexing… Selection [i.e., information retrieval] by association, rather than by indexing may yet be mechanized.”

Will “electronic brains” help librarians or serve to replace them? Is ChatGPT a brilliant answer to Chambers’ attempt to create a knowledge-inquiry tool with the “Advantages of a continued Discourse”? Why was it—and still is—even conceivable that a computer can do man’s thinking, or more narrowly, replace a librarian? Because most of us fervently subscribe to the hallucination of “artificial general intelligence” or AGI, that computer engineers will develop (next year, we are told) a machine more intelligent than humans? Because of our “morbid fascination with the latest form of technology,” to use another Wasserman observation from 1965?

The organization I worked for when I joined DEC in 1988 had not only reference librarians and lots of paper-based knowledge but also some digitized information. A team of information retrieval (what we started calling “search” ten years later) experts developed and managed a database of digitized news articles (“competitive information system” or CIS). Among other innovations, they developed database search software—what we today call “AI”—that could distinguish between “DEC” as the company’s name and “Dec” as an abbreviation for December.

In those days, the information retrieval world was ruled by “taxonomies” and “ontologies,” or attempts to organize information in hierarchies of concepts or categories, defining their relations. This paradigm was so strong that even later, in the early attempts to organize the rapidly growing information on the Web, the first “search engines” such as Yahoo! had a Chief Ontologist on staff.

Tim Berners-Lee, the inventor of the Web, troubled like Bush and Chambers before him by the way information was organized, was excited to escape from the “straightjacket of hierarchical documentation systems.” Berners-Lee wrote in Weaving the Web: “By being able to reference everything with equal ease, the web could also represent associations between things that might seem unrelated but for some reason did actually share a relationship.”

Google triumphed over taxonomy-obsessed Yahoo! and others because it got the true spirit of the Web. Google’s founders were the first to seize on Berners-Lee’s insight and build their information retrieval business on tracking closely cross-references (i.e., links between pages) as they were happening and correlate relevance with quantity of cross-references (i.e., popularity of pages as judged by how many other pages linked to them).

Chambers wrote about “the Advantages of a continued Discourse.” He meant the “conversation” between different concepts and themes, and how they relate. ChatGPT, and now “AI research agents,” speedily mine the vast troves of valuable, inaccurate, misleading, and outdated information on the Web. Just like the earlier “crowd-sourcing” perfected by Google, the clever mining of what people value and link to, these AI agents’ “reasoning” reflects how people ask questions online and search for information. Their “intelligence” is the sum of observations of online searches by thousands or millions of people and the information available, for better or worse, at any given moment on the Web. They simply regurgitate this summary; they can’t “converse” with the concepts or themes they find. Just like Chambers’ Cyclopaedia they are a helpful tool that requires human intelligence to make sense of, build upon, and create new (hopefully valuable) information and knowledge.

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The film 2001: A Space Odyssey had its world premiere at the Uptown Theater in Washington, D.C., on April 2, 1968. Reflecting the mixed reactions to the film, Renata Adler wrote in The New York Times that it was "somewhere between hypnotic and immensely boring.” Pauline Kael was more decisive, calling the movie "monumentally unimaginative." Reacting more favorably were “the potheads and pill-poppers of the late 1960s counterculture,” observed Paul Whitington fifty years later, reporting that “one particularly enthusiastic hippie is said to have charged at the screen shouting ‘it's God!’”

The “God” of this anecdote may have referred to HAL 9000, the artificially intelligent computer killing one after the other the astronauts on a space mission to Jupiter. Or it may have referred to the God-like humans capable of creating sentient machines, expressing the core belief of the Zeitgeist, the modern religion worshipping tool-wielding humans as masters of the universe. Four months after the movie's release, Stewart Brand opened the first issue of the Whole Earth Catalog with the statement “We are as gods and might as well get good at it.”

The 160-minute film with only 40 minutes of dialogue became “the movie that changed all movies forever,” as the poster for its 50th-anniversary re-release modestly, perhaps accurately, proclaimed. There is no doubt, however, of its influence on popular culture, specifically on the perception of sentient machines and their killer instincts. HAL's chilling admonishment to the last surviving astronaut, “I'm sorry, Dave. I’m afraid I can't do that,” entered the popular lexicon.

The science fiction writer and futurist Arthur C. Clarke, who worked with the director Stanley Kubrick on the plot for the movie (and later published a novel with the same title), said in a later interview:

Of course the key person in the expedition was the computer HAL, who as everyone said is the only human character in the movie. HAL developed slowly. At one time we were going to have a female voice. Athena, I think was a suggested name. I don't know again when we changed to HAL. I've been trying for years to stamp out the legend that HAL was derived from IBM by the transmission of one letter. But, in fact, as I've said, in the book, HAL stands for Heuristic Algorithmic, H-A-L. And that means that it can work on a program's already set up, or it can look around for better solutions and you get the best of both worlds. So, that's how the name HAL originated.

That both Clarke and Kubrick denied the allusion to IBM may had to do with the fact that IBM was one of the many organizations and individuals consulted while creating the film. They started the four-year process in 1964, the year IBM introduced its own masterpiece, the computer that sealed the company’s domination of the industry for the next quarter of a century.

On April 7, 1964, IBM announced the System 360, the first family of computers spanning the performance range of all existing (and incompatible) IBM computers. Thomas J. Watson Jr., IBM’s CEO at the time, wrote in his autobiography Father, Son, & Co.:

By September 1960, we had eight computers in our sales catalog, plus a number of older vacuum-tube machines. The internal architecture of each of these computers was quite different; different software and different peripheral equipment, such as printers and disk drives, had to be used with each machine. If a customer’s business grew and he wanted to shift from a small computer to a large one, he had to get all new everything and rewrite all his programs often at great expense. …

[The] new line was named System/360—after the 360 degrees in a circle—because we intended it to encompass every need of every user in the business and the scientific worlds. Fortune magazine christened the project “IBM’s $5,000,000,000 Gamble” and billed it as “the most crucial and portentous—as well as perhaps the riskiest—business judgment of recent times.”… It was the biggest privately financed commercial project ever undertaken. The writer at Fortune pointed out that it was substantially larger than the World War II effort that produced the atom bomb.

Like nuclear energy, the System/360 and all the computers and networks of computers that came after it could be used for creation or destruction. It was a tool, and as 2001: A Space Odyssey depicts in the first part of the movie, tools can be used to help humanity or as weapons in humanity’s wars.

In Profiles of the Future, published in 1962, Arthur Clarke wrote:

The old idea that Man invented tools is… a misleading half-truth; it would be more accurate to say that tools invented Man. They were very primitive tools… yet they led to us—and to the eventual extinction of the apeman who first wielded them… The tools the apemen invented caused them to evolve into their successor, Homo sapiens. The tool we have invented is our successor. Biological evolution has given way to a far more rapid process—technological evolution. To put it bluntly and brutally, the machine is going to take over.

Talk of the machine taking over has risen to the surface of public discourse over the last 50-plus years each time computer engineers have added yet another “human-like” capability to the tools they create. After IBM’s Watson AI defeated Jeopardy-champion Ken Jennings in 2011, he wrote in Slate:

I understood then why the engineers wanted to beat me so badly: To them, I wasn’t the good guy, playing for the human race. That was Watson’s role, as a symbol and product of human innovation and ingenuity. So my defeat at the hands of a machine has a happy ending, after all. At least until the whole system becomes sentient and figures out the nuclear launch codes…

The fear that machines will figure out the nuclear codes and destroy their creators was called “absurd” by one of 2001’s creators who went on to co-create a few years later, Clarke wrote in Profiles of the Future:

The popular idea, fostered by comic strips and the cheaper forms of science fiction, that intelligent machines must be malevolent entities hostile to man, is so absurd that it is hardly worth wasting energy to refute it. I am almost tempted to argue that only unintelligent machines can be malevolent… Those who picture machines as active enemies are merely projecting their own aggressive instincts, inherited from the jungle, into a world where such things do not exist. The higher the intelligence, the greater the degree of cooperativeness. If there is ever a war between men and machines, it is easy to guess who will start it.

According to this materialistic fantasy, which many computer and AI engineers adhere to today, tools create us and will surpass us, but will do no harm or possibly wipe out humanity.

Here’s Piers Bizony in Nature in 2018:

Certainly, in the film, the surviving astronaut’s final conflict with HAL prefigures a critical problem with today’s artificial-intelligence (AI) systems. How do we optimize them to deliver good outcomes? HAL thinks that the mission to Jupiter is more important than the safety of the spaceship’s crew. Why did no one program that idea out of him? Now, we face similar questions about the automated editorship of our searches and news feeds, and the increasing presence of AI inside semi-autonomous weapons…

Should we watch out for superior “aliens” closer to home, and guard against AI systems one day supplanting us in the evolutionary story yet to unfold? Or does the absence of anything like HAL, even after 50 years, suggest that there is, after all, something fundamental about intelligence that is impossible to replicate inside a machine?

Yes, our imagination, for example.

The imagination, creativity, and drive behind the evolution of modern computing, or “artificial intelligence,” constantly expanding the functionality and capabilities of the original giant digital calculator of the late 1940s, taking advantage of the steadily increasing power of computation, or what has come to be known as “Moore’s Law.”

On April 19, 1965, Gordon E. Moore published “Cramming more components onto integrated circuits” in Electronics, predicting that the number of components that could be placed on a chip would double every year, doubling the speed of computers.

Four years earlier, on April 25, 1961, Robert Noyce was granted a patent for a “Semiconductor Device-and-Lead Structure,” a type of integrated circuit made of Silicon. Integrating large numbers of tiny transistors into a small chip was an enormous improvement over the manual assembly of circuits using electronic components. The integrated circuit’s mass production capability, reliability, and building-block approach to circuit design ensured the rapid adoption of standardized ICs instead of designs based on discrete transistors.

Three years after the publication of Moore’s paper, he and Noyce left Fairchild Semiconductor to establish Intel with $2.5 million in funding arranged by Arthur Rock who coined the term venture capitalist. Everything we associate with today’s Silicon Valley was already there: audacious risk taking, willing investors, creating (temporary) monopolies, and lots of luck. Arthur Rock was a personal friend (another attribute of today’s Silicon Valley), and he convinced others, with a 3-page memo, to invest in the “nebulous” idea of the two entrepreneurs. Moore’s article and prediction probably also helped convince the other investors that they are betting on a technology with a guaranteed exponential future.

It was a very specific prediction, couched in quantitative, “scientific” terms, with the convincing appearance of a law of nature. There was no better way to sell a new industry to a bunch of fellows with money who were also creating a new one, the venture capital industry. And there was competition for the funds provided by the nascent VC industry: some twenty-six new semiconductor firms were established between 1967 and 1970, observes George Gilder in Microcosm.

More important than the VCs, however, were the potential customers for Moore’s and Noyce’s (and a few other tinkerers’) innovations, at Fairchild and Intel. Moore’s 1965 article was written earlier as an internal document titled “The Future of Integrated Electronics” to encourage his company’s customers to adopt the most advanced technology in their new computer designs.

In Understanding Moore’s Law, David C. Brock writes: “While the market for silicon integrated circuits was growing in the early 1960s, Moore and others in the semiconductor industry experienced customer resistance to and skepticism of the new microchips… In addition to advancing the new technology itself, Moore was getting his message across to potential customers and the semiconductor industry. Finally, in early 1965 came the opportunity to publicize and advance the cause.”

Moore told Jeffrey Zygmont in 2001: “When we tried to sell these things, we did not run into a receptive audience.” Writes Zygmont: “[by 1965], under assault by competing approaches to circuit miniaturization, feeling their product poorly appreciated, IC advocates felt a competitive urgency to popularize the concept. Therefore, they proselytized… [Moore’s] prophecy was desperate propaganda.”

One of the industry leaders proselytizing, for example, was C. Harry Knowles, manager of Westinghouse’s molecular electronics division, writing in June 1964 in IEEE Spectrum: “Speed has doubled every year over the past seven years on average.” Moore did not “discover” his law, as many commentators write. Moore brilliantly came up with the best formulation of a marketing slogan.

Like other marketing slogans—and unlike the laws of physics—Moore’s Law was revised to fit the changing competitive and technological environment. In a 2006 IEEE Annuals of the History of Computing article, Ethan Mollick has convincingly showed that the “law” and the prediction were adjusted periodically in response to changing conditions (e.g., the rise of the Japanese semiconductor industry): “The semiconductor industry has undergone dramatic transformations over the past 40 years, rendering irrelevant many of the original assumptions embodied in Moore’s Law. These changes in the nature of the industry have coincided with periodic revisions of the law, so that when the semiconductor market evolved, the law evolved with it.”

Humans are not machines, and their intelligence is adaptable, flexible, and creative. Our imagination can follow many paths: inducing anxiety and fear, driving ambition and desire, or exaggerating consequences and possibilities. Some people envision malevolent machines, others hope to see or create rational and perfect thinking machines devoid of human foibles and emotions, others are worried about hype and anticipated disappointments, and others easily imagine all of the above.

In the must-see Korean TV series My Mister, one of the protagonists (played by Kwon Nara) imagines living in a future “AI world” when AI will be smarter than lawyers and doctors and other educated people so no one will be able “to boast” and we will all be liberated and be able to just love one another.

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In 1589, William Lee, an English clergyman, invented the first stocking frame knitting machine, which, after many improvements by other inventors, drove the spread of automated lace-making at the end of the 18th century. Legend has it that Lee had invented his machine to get revenge on a lover who had preferred to concentrate on her knitting rather than attend to him (as depicted by Alfred Elmore in the 1846 painting The Invention of the Stocking Loom).

Lee demonstrated his machine to Queen Elizabeth I, hoping to obtain a patent, but she refused to grant one, fearing the impact on the work of English artisans: "Thou aimest high, Master Lee. Consider thou what the invention could do to my poor subjects. It would assuredly bring to them ruin by depriving them of employment, thus making them beggars" (quoted in Why Nations Fail by Daron Acemoglu and James Robinson).

Another accidental inventor was Alexander Graham Bell. His father, grandfather, and brother had all been associated with work on elocution and speech, and his mother and wife were deaf, which influenced Bell's research interests and inventions throughout his life. Bell’s research on hearing and speech led him to experiment with the transmission of sound using electricity, culminating on March 7, 1876, when he received a U.S. patent for “improvement in telegraphy,” his invention of what later would be called the telephone. Three days later, on March 10, 1876, Bell said into his device: “Mr. Watson, come here, I want you.” Thomas Watson, his assistant, sitting in an adjacent room at 5 Exeter Place, Boston, answered: “Mr. Bell, do you understand what I say?”

Later that day, Bell wrote to his father (as Edwin S. Grosvenor and Morgan Wesson recount in Alexander Graham Bell):

Articulate speech was transmitted intelligibly this afternoon. I have constructed a new apparatus operated by the human voice. It is not, of course, complete yet—but some sentences were understood this afternoon… I feel I have at last struck the solution of a great problem—and the day is coming when telegraph wires will be laid to houses just like water or gas—and friends converse with each other without leaving home.

The telephone was adopted enthusiastically in the U.S., but there were doubters elsewhere, questioning its potential to re-engineer how businesses communicated. In 1879, William Henry Preece, inventor and consulting engineer for the British Post Office, could not see the phone succeeding in Britain because he thought the new technology could not compete with cheap labor: “…there are conditions in America which necessitate the use of instruments of this kind more than here. Here we have a superabundance of messengers, errand boys, and things of that kind.”

The telephone not only ended the careers of numerous messenger boys worldwide but also led to the total demise of the telegraph operator. On January 25, 1915, Bell inaugurated the first transcontinental telephone service in the United States with a phone call from New York City to Thomas Watson in San Francisco. Bell repeated the words of his first-ever telephone call on March 10, 1876. In 1915, Watson replied, “It would take me a week to get to you this time.”

While the telephone destroyed some jobs, it created other new occupations, such as the telephone operator. But this very popular job among young girls also eventually became the victim of yet another accidental inventor.

On March 10, 1891, Almon Brown Strowger, an American undertaker, was issued a patent for his electromechanical switch to automate telephone exchanges. Steven Lubar in InfoCulture: “…a Kansas City undertaker, Strowger had a good practical reason for inventing the automatic switchboard. Legend has it that his telephone operator was the wife of a business rival, and he was sure that she was diverting business from him to her husband. And so he devised what he called a ‘girl-less, cuss-less’ telephone exchange.”

The first automatic switchboard was installed in La Porte, Indiana, in 1892, but automated switchboards did not become widespread until the 1930s. Anticipating future reactions to some of the inventions of the computer age, shifting work to the users was not received enthusiastically by them. But AT&T’s top-notch propaganda machine got over that inconvenience by predicting that more operators would be needed before long than there were young girls suitable for the job.

AT&T and its users were ambivalent about switching to automatic switching. While users were not happy about working for no pay for the phone company, they also valued the privacy accorded to them by the automatic switchboard. AT&T, for its part, was interested in preserving its vast investment in operator-assisted switching equipment. Richard John in Network Nation:

To rebut the presumption that Bell operating companies were wedded to obsolete technology, Bell publicists lauded the female telephone operator as a faithful servant… The telephone operator was the “most economical servant”—the only flesh-and-blood servant many telephone users could afford…. The idealization of the female telephone operator had a special allure for union organizers intent on protecting telephone operators from technological obsolescence. Electromechanical switching… testified a labor organizer in 1940… was “inanimate,” “unresponsive,” and “stupid,” and did “none of the things which machinery is supposed to do in industry”—making it a “perfect example of a wasteful, expensive, inefficient, clumsy, anti-social device.”

The transistor, invented in 1946 at AT&T to improve switching, led to the rise and spread of computerization and made the switching system essentially a computer. By 1982, almost half of all calls were switched electronically. The transistor also took computerization from the confines of deep-pocketed corporations and put it in the hands of hobbyists.

On March 5, 1975, The Homebrew Computer Club met for the first time, with 32 “enthusiastic people” attending. Apple’s co-founder Steve Wozniak:

Without computer clubs there would probably be no Apple computers. Our club in the Silicon Valley, the Homebrew Computer Club, was among the first of its kind. It was in early 1975, and a lot of tech-type people would gather and trade integrated circuits back and forth. You could have called it Chips and Dips…

The Apple I and II were designed strictly on a hobby, for-fun basis, not to be a product for a company. They were meant to bring down to the club and put on the table during the random access period and demonstrate: Look at this, it uses very few chips. It’s got a video screen. You can type stuff on it. Personal computer keyboards and video screens were not well established then. There was a lot of showing off to other members of the club. Schematics of the Apple I were passed around freely, and I’d even go over to people’s houses and help them build their own.

The Apple I and Apple II computers were shown off every two weeks at the club meeting. “Here’s the latest little feature,” we’d say. We’d get some positive feedback going and turn people on. It’s very motivating for a creator to be able to show what’s being created as it goes on. It’s unusual for one of the most successful products of all time, like the Apple II, to be demonstrated throughout its development.

The first Apple computers were not designed as a “product for a company.” In the 1970s, business executives were not entirely clear about the potential impact of computers on work and workers. On March 1, 1971, Time magazine reviewed the state of the computer industry in “A Growth Industry Grows Up”:

Computer technology has raced ahead of the ability of many customers to make good use of it. Not long ago, the Research Institute of America found that only half of 2,500 companies questioned felt that their present machines were paying for themselves in increased efficiency… For all the change that it has already wrought, the computer has barely begun to transform the methods of business and very probably the character of civilization.

Apple and other PC makers greatly impacted workers and how work gets done, and later, how consumers live their lives (although, I would argue, computers certainly did transform “the character of civilization”). However, it was difficult for “experts” to predict the exact nature of which workers will be impacted and how. See January in the History of Information Technologies for more on failed predictions, specifically SRI’s 1976 report “Office of the Future,” authored by an expert working not far from the Homebrew Computer Club.

Recent advances in artificial intelligence have led many commentators to declare that we have entered “the second machine age,” in which automation will impact knowledge workers rather than blue-collar workers, who were the type of workers replaced by computers—and knitting frames—before. This is not an entirely accurate description of the past and possibly the future.

In 1953, John Diebold wrote in “AUTOMATION-The new Technology”:

The effect of automation on the information handling functions of business will probably be more spectacular and far-reaching [than its impact on factory workers]. Repetitive office work, when in sufficient bulk as in insurance companies, will be put on at least a partially automatic basis. Certain functions, such as filing and statistical analysis on the lower management levels, will be performed by machines. Yet very few offices will be entirely automatic. Much day-to-day work—answering correspondence and the like—will have to be done by human beings.

Two years after publishing the book that popularized the term “automation,” Diebold established one of the first consulting companies advising businesses on how to adopt this new technology. An entirely new industry and an entirely new breed of knowledge workers followed in his path. “Automation” has created many new jobs, and there is no reason why AI and robots will not give rise to new knowledge worker jobs—in consulting, servicing, help-desking, observing, counting, talking, analyzing, researching, marketing, selling, etc.

More importantly, as Diebold has predicted, but in a much larger sense, computers have altered and augmented knowledge work since the 1950s. To begin with, early computers replaced the original “computers,” human calculators (mostly women) that had already been replaced to some extent by mechanical calculators. Their work was certainly “knowledge work,” and their skills and education served well those who transitioned into the new jobs of “computer operators” and “programmers.”

How about that quintessential knowledge work, that of a lawyer? Sure, we now have AI programs leading to “expensive lawyers replaced by cheaper software.” But how many expensive lawyers were “replaced” by the advent of LexisNexis in the 1970s?

Diebold predicted that the “day-to-day work” would not be automated, and what he probably had in mind was that secretaries would keep their jobs. The fact that they didn’t show how difficult it is to predict what impact computers will have (or not have) on knowledge work. Secretaries were not replaced by computers but by managers who accepted the new social norm that it was not beneath them to “answer correspondence and the like,” as long as they did it with the new status symbol—the personal computer.

View the current projections of how many jobs will be destroyed by artificial intelligence with healthy skepticism. As in the past, many occupations will undoubtedly be affected by increased computerization and automation. However, many current occupations will thrive, and new ones will be created as the way work is done continues to change.

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My new newsletter AI: An Offbeat History is live and calling for readers! Please take a look and subscribe. I plan to write about episodes in the history of AI, including my own experiences over the last fifty years.

This month’s milestones in the history of information technologies highlight the mechanization and computerization of data collection and information organization, storage, and distribution and their impact on human experience.

On February 15, 2011, IBM’s question-answering system Watson “commented” on the results of its successful match the previous night with two Jeopardy! champions: “There is no way I’m going to let these simian creatures defeat me. While they’re sleeping, I’m processing countless terabytes of useless information.”

The last bit, no doubt, was stored in Watson’s memory under the category Oscar Wilde. In “A Few Maxims for the Instruction of the Over-Educated,” Wilde lamented that “it is a very sad thing that nowadays there is so little useless information.”

Wilde wrote this in 1894 when the processing of very useful information became very efficient with the invention of the tabulating machine by Herman Hollerith.

Hollerith was born in Buffalo, New York, on February 29, 1860. In 1884, he filed a patent application for his invention of “a certain new and useful Improvement in the Art of Compiling Statistics.” In 1886, Hollerith started a business to rent out the tabulating machines he had invented. “Taking a page from train conductors, who then punched holes in tickets to denote passengers’ observable traits (e.g., that they were tall or female) to prevent fraud, he developed a punch card that held a person’s data and an electric contraption to read it,” wrote The Economist in 2011, commemorating IBM’s 100^th anniversary.

James Cortada in Before the Computer: “The U.S. Census of 1890… was a milestone in the history of modern data processing…. No other occurrence so clearly symbolized the start of the age of mechanized data handling…. Before the end of that year, [Hollerith’s] machines had tabulated all 62,622,250 souls in the United States. Use of his machines saved the bureau $5 million over manual methods while cutting sharply the time to do the job. Additional analysis of other variables with his machines meant that the Census of 1890 could be completed within two years, as opposed to nearly ten years taken for fewer data variables and a smaller population in the previous census.”

Hollerith’s company later merged with two other ventures to form in 1911 the Computing-Tabulating-Recording Company (CTR). On February 14, 1924, CTR changed its name to International Business Machines Corporation (IBM).

“IBM” was first used for CTR’s subsidiaries in Canada and South America, but after “several years of persuading a slow-moving board of directors,” Thomas and Marva Belden note in The Lengthening Shadow, Thomas J. Watson Sr. succeeded in applying it to the entire company: “International to represent its big aspirations and Business Machines to evade the confines of the office appliance industry.” As Kevin Maney observes in The Maverick and His Machine, IBM “was still an upstart little company” in 1924, when “revenues climbed to $11 million—not quite back to 1920 levels.”

The upstart, according to Watson Sr., was going to live forever. In a talk he gave on June 21, 1924, at the first meeting of IBM’s Quarter Century Club, he said: “The opportunities of the future are bound to be greater than those of the past, and to the possibilities of this business there is no limit so long as it holds the loyal cooperation of men and women like yourselves.”

The most significant future opportunity for IBM emerged with the rapid growth of a new market for generating, processing, organizing, storing, and distributing digitized data and information, the market for computers.

On February 16, 1946, the Electronic Numerical Integrator And Computer (ENIAC) was formally dedicated at the University of Pennsylvania. It was the only fully electronic computer working in the U.S. from its inception during World War II to 1950 when other computers successfully joined the race to create a new industry.

Another important milestone in the evolution of the new market occurred on February 6, 1959, when Jack Kilby filed a patent application for a “Method of making miniaturized electronic circuit” or the integrated circuit. “The University of Illinois gave him only average grades in electrical engineering, a disappointment to his father, who ran an electrical company, and he failed to get into MIT,” reports Harold Evans in They Made America. In 2000, Kilby was awarded the Nobel Prize in Physics for “his part in the invention and development of the integrated circuit, the chip. Through this invention, microelectronics has grown to become the basis of all modern technology.”

Computer-driven digitization has grown to become the basis of all types and forms of modern information. But where do you put all the 0s and 1s?

Already on February 20, 1947, Alan Turing argued that “the provision of proper storage is the key to the problem of the digital computer” in his presentation to the London Mathematical Society in which he described the Automatic Computing Engine (ACE), an early British stored-program computer.

One answer to the storage challenge came on February 28, 1956, when Jay Forrester of MIT was awarded a patent for his invention of magnetic core memory. It became the standard for computer memory until it was supplanted by solid-state RAM in the mid-seventies.

Forrester came up with the idea of 3D storage of computer data while working on MIT’s Whirlwind computer, which required a fast memory system for real-time aircraft tracking. Forrester’s was not the only patent granted to magnetic core memory inventions. The patent dispute continued until February 1964, when IBM (which had acquired patent rights from other core memory inventors, including An Wang) agreed to pay MIT $13 million. Forrester summarized the experience: “It took about seven years to convince people in the industry that magnetic core memory would work. And it took the next seven years to convince them that they had not all thought of it first.”

The challenge of where and how you keep information has always been accompanied by the challenge of distributing it and getting it to other destinations.

The first technology for transmitting information by electrical impulses was the telegraph, or “writing at a distance.” On February 3, 1837, the U.S. House of Representatives passed a resolution requesting the Treasury Secretary, Levi Woodbury, to report to the House at its next session, “upon the propriety of establishing a system of telegraphs for the United States.” Of the eighteen responses that Woodbury received, writes Richard John in Network Nation: Inventing American Telecommunications, “seventeen assumed that the telegraph would be optical and that its motive power would be human… The only respondent to envision a different motive power was Samuel F. B. Morse… [who] proposed, instead, a new kind of telegraph of his own devising that would transmit information not by sight but, rather, by electrical impulses transmitted by wire.”

For more than 100 years, the telegraph was the principal means of transmitting information by wire or radio waves. By the late 20^th century, the telegraph was replaced by digital data-transmission systems based on computer technology. "Telegram services” are still available in many countries, but the transmission is usually done via a computer network.

The funding source for the first wide-area computer networks was born on February 7, 1958, when the U.S. Department of Defense issued Directive 5105.15, establishing the Advanced Research Projects Agency (ARPA). The agency, later renamed DARPA, was created because “The Soviet Union’s launch of Sputnik showed that a fundamental change was needed in America’s defense science and technology programs.” One of the “frontiers of technology” created by the agency was the ARPANET, the forerunner of the internet.

The internet connected large computers, first in the U.S. and then around the world. In 1989, Tim Berners-Lee invented the World Wide Web, software running on top of the internet that linked documents stored on these computers and, later, all types and forms of information. All the digitized information, with no limits, no space constraints.

On February 1, 1884, the first part (or fascicle) of the Oxford English Dictionary was published. A 352-page volume, it defined words from “A to Ant,” corroborated by quotations from historical sources. The 11th edition of the Encyclopaedia Britannica (1910-1911) noted that “the chief difficulty in the way of this use of quotations – after the difficulty of collection – is that of finding space for them in a dictionary of reasonable size.” Today’s online edition of the OED contains 3.5 million quotations (and Britannica’s 11^th edition is freely available on the Web).

The most successful Web application for connecting people was launched on February 4, 2004, when Thefacebook.com went live.

Its home screen read, says David Kirkpatrick in The Facebook Effect, “Facebook is an online directory that connects people through social networks at colleges.” Four days after the launch, more than 650 students had registered, and by the end of May, it was operating in 34 schools and had almost 100,000 users. “The nature of the site,” Mark Zuckerberg told the Harvard Crimson on February 9, “is such that each user’s experience improves if they can get their friends to join in.”

The first social network, or “virtual community,” as it was called then, was launched on February 17, 1978, when the first public dial-up bulletin board system, the Computerized Bulletin Board System, or CBBS, went online. Sparking the creation of tens of thousands of bulletin board systems all over the world, CBBS was developed by Ward Christensen and Randy Suess and connected more than 250,000 users before it was retired in the 1990s.

Computers could not only add to human experience but also subtract from it, automating and replacing what only humans could do, enjoy, and create.

On February 5, 1850, the first U.S. patent for a push-key operated adding machine was issued to Dubois D. Parmelee of New Paltz, New York. In 1886, William Burroughs founded the American Arithmometer Company to commercialize his “adding machine” that “could do the work of two or three active clerks,” according to The Bankers’ Magazine in 1894. The HP-65, the first programmable handheld calculator, was introduced as a $795 “personal computer” by Hewlett-Packard in 1974.

Calculating machines, however, went much further than automating addition and subtraction. On February 10, 1996, IBM’s Deep Blue became the first computer to win a chess game against a reigning world champion, Garry Kasparov, under regular time controls.

Deep Blue won one game, tied two and lost three. The next year, Deep Blue defeated Kasparov in a six-game match—the first time a reigning world champion lost a match to a computer opponent in tournament play. Deep Blue was a combination of special-purpose hardware and software, a system capable of examining 200 million moves per second, or 50 billion positions, in the three minutes allocated for a single move in a chess game.

On February 14, 2011, when IBM’s Watson won the second match of Jeopardy! against two of the most successful contestants on the show, Ken Jennings and Brad Rutter, Jennings added to his Final Jeopardy! response, "I, for one, welcome our new computer overlords.”

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On January 3, 1983, Time magazine put the PC on its cover as “machine of the year.” Time’s publisher John A. Meyers: “Several human candidates might have represented 1982, but none symbolized the past year more richly, or will be viewed by history as more significant, than a machine: the computer.”

The Time magazine cover appeared just a few years after the arrival of the first PCs and the development of software that automated previous work and expanded its scope to include non-specialists. For example, turning some accounting work—specifically, management accounting, the ongoing internal measurement of a company’s performance—into a task that employees without an accounting degree could accomplish. This “democratization” of accounting not only automated previous work but greatly augmented management work, giving business executives easy access to data about their companies and new ways to measure and predict future performance.

On January 2, 1979, Software Arts was incorporated by Dan Bricklin and Bob Frankston to develop VisiCalc, the world’s first electronic spreadsheet program. VisiCalc came to be widely regarded as the first “killer app” that turned the PC into a serious business tool. Dan Bricklin:

The idea for the electronic spreadsheet came to me while I was a student at the Harvard Business School, working on my MBA degree, in the spring of 1978. Sitting in Aldrich Hall, room 108, I would daydream. "Imagine if my calculator had a ball in its back, like a mouse..." (I had seen a mouse previously, I think in a demonstration at a conference by Doug Engelbart, and maybe the Alto). And "...imagine if I had a heads-up display, like in a fighter plane, where I could see the virtual image hanging in the air in front of me. I could just move my mouse/keyboard calculator around on the table, punch in a few numbers, circle them to get a sum, do some calculations, and answer '10% will be fine!'" (10% was always the answer in those days when we couldn't do very complicated calculations...)

In the summer of 1978, between the first and second year of the MBA program, while riding a bike along a path on Martha's Vineyard, I decided that I wanted to pursue this idea and create a real product to sell after I graduated.

Bricklin understood the potential of the “democratization” and automation of accounting, of eliminating ledgers that required specialized skills and manual recalculation of the entire spreadsheet when a single item changed.

Steve Jobs knew how to exploit the marketing potential of the “democratization” of computing. On January 22, 1984, the Apple Macintosh was introduced with the "1984" television commercial which aired during Super Bowl XVIII. The commercial was later called by Advertising Age “the greatest commercial ever made.” A few months earlier, before showing a preview of the commercial, Steve Jobs said:

It is now 1984. It appears IBM wants it all. Apple is perceived to be the only hope to offer IBM a run for its money. Dealers initially welcoming IBM with open arms now fear an IBM dominated and controlled future. They are increasingly turning back to Apple as the only force that can ensure their future freedom. IBM wants it all and is aiming its guns on its last obstacle to industry control: Apple. Will Big Blue dominate the entire computer industry? The entire information age? Was George Orwell right about 1984?

Following the airing of the “1984” TV commercial, the Macintosh was launched on January 24, 1984. It was the first mass-market personal computer featuring a graphical user interface and a mouse, offering two applications, MacWrite and MacPaint, designed to show off its innovative interface. By April 1984, 50,000 Macintoshes were sold.

Steven Levy announced in Rolling Stones “This [is] the future of computing.” Levy quoted Steve Jobs: “I don't want to sound arrogant, but I know this thing is going to be the next great milestone in this industry. Every bone in my body says it's going to be great, and people are going to realize that and buy it.” And Mitch Kapor, developer of Lotus 1-2-3, a best-selling spreadsheet program for the IBM PC: “The IBM PC is a machine you can respect. The Macintosh is a machine you can love.”

When Steve Jobs introduced the Macintosh, the Mac said: “Never trust a computer you cannot lift.”

On January 24, 1984 (give or take a day or two), I started working for NORC, a social science research center at the University of Chicago. Over the next year, I experienced firsthand the shift from large, centralized computers to personal ones and the move from a command line to a graphical user interface.

My responsibilities included managing $2.5 million in survey research budgets. At first, I used the budget management application running on the University’s VAX mini-computer (mini, as opposed to mainframe). I would log on using a remote terminal, type some commands, and enter the new numbers I needed to record. Then, after an hour or two of hard work, I pressed a key on the terminal, ordering the VAX to re-calculate the budget with the new data I entered. To this day, I remember my great frustration and dismay when the VAX responded by indicating something was wrong with the data I entered. Telling me what exactly was wrong was beyond what the VAX—or any other computer program at that time—could do (this was certainly true in the case of the mini-computer accounting program I used).

I had to start the work from the beginning and hope that on the second or third try, I will get everything right and a new budget spreadsheet will be created. This, by the way, was no different from my experience working for a bank a few years before, where I totaled by hand the transactions for the day on an NCR accounting machine. Quite often I would get to the end of the pile of checks only to find out that the accounts didn’t balance because somewhere I entered a wrong number and I would have to enter all the data again.

Visicalc changed forever this linear approach to accounting. At NORC, I benefited from that idea when I started managing budgets with Visicalc, running on an Osborne laptop. Soon thereafter I migrated to an IBM PC which ran the invention of another HBS student, Mitch Kapor, who was also frustrated with re-calculation and other delights of paper-based accounting or electronic spreadsheets running on large computers.

Lotus 1-2-3 was an efficient tool for managing budgets that managers could use themselves without wasting time on figuring out what data entry mistake they made or waiting for the accounting department to deliver the goods. Now they had complete control of the processing of the data, to say nothing, of course, about modeling or what-if scenarios, the entire range of budgeting and forecasting functions at their fingertips.

The role of managers in the “PC revolution” serves as an illustration of how the way people use new technology and how they perceive it is as important as the technology itself. Cloudy crystal balls or failed predictions about information technologies suffer from ignoring humans—their motivations, aspirations, attitudes—and ignoring how society works.

The best example of such failure I’m familiar with is “The Office of the Future,” a report published in 1976 by the Long-Range Planning Service of the Stanford Research Institute (SRI). The manager of 1985, the report predicted, will not have a personal secretary. Instead, he (decidedly not she) will be assisted, along with other managers, by a centralized pool of assistants (decidedly and exclusively, according to the report, of the female persuasion). He will contact the “administrative support center” whenever he needs to dictate a memo to a “word processing specialist,” find a document (helped by an “information storage/retrieval specialist”), or rely on an “administrative support specialist” to help him make decisions.

Unlike many similar forecasts, this report does consider sociological factors, in addition to organizational, economic, and technological trends. But it could only see what was in the air at the time—the “women’s liberation” movement—and it completely missed its future implications: “Working women are demanding and receiving increased responsibility, fulfillment, and opportunities for advancement. The secretarial position as it exists today is under fire because it usually lacks responsibility and advancement potential… In the automated office of the future, repetitious and dull work is expected to be handled by personnel with minimal education and training. Secretaries will, in effect, become administrative specialists, relieving the manager they support of a considerable volume of work.”

Regardless of the women’s liberation movement of his day, the author could not see beyond the creation of a 2-tier system in which some women would continue to perform dull and unchallenging tasks, while other women would be “liberated” into a fulfilling new job category of “administrative support specialist.” In this 1976 forecast, there are no women managers.

But this is not the only sociological factor the report missed. The most interesting sociological revolution of the office in the 1980s – and one missing from most accounts of the PC revolution – is what managers (male and female) did with their new word processing, communicating, and calculating machine. They took over some of the “dull” secretarial tasks that no self-respecting manager would deign to perform before the 1980s.

This was the real revolution: The typing of memos (later emails), the filing of documents, the creation of more and more data in digital form. In short, a large part of the management of office information, previously exclusively in the hands of secretaries, became in the 1980s (and progressively more so in the 1990s and beyond) an integral part of managerial work.

It was a question of status. No manager would type before the 1980s because it was perceived as work that was not commensurate with his status. Many managers started to type in the 1980s because now they could do it with a new “cool” tool, the PC, which conferred on them the leading-edge, high-status image of this new technology. (See the Osborne ad above). What mattered was that you were important enough to have one of these cool things, not that you performed with it tasks that were considered beneath you just a few years before.

Status matters. People matter. What we do with technology—and why and how—is an important factor in its adoption and its evolution.

Steve Jobs and the failed Lisa (introduced on January 19, 1983) and Macintosh teams changed that and gave us an interface that made computing easy, intuitive, and fun. NORC purchased 80 Macs when I worked there, mostly used for computer-assisted interviewing. I don’t think there was any financial software available for the Mac at the time and I continued to use Lotus 1-2-3 on the IBM PC. But I played with the Mac at any opportunity I got. Indeed, there was nothing like it at the time.

It took a few years before the software running on most PCs adapted to the new personal approach to computing, but eventually, Microsoft Windows came along and icons and folders ruled the day. Microsoft also crushed all other electronic spreadsheets with Excel and did the same to other pioneering word-processing and presentation tools. But Steve Jobs triumphed in the end with yet another series of innovations.

Introducing the iPhone on January 9, 2007, Jobs said:

Every once in a while, a revolutionary product comes along that changes everything… today, we're introducing three revolutionary products... The first one is a widescreen iPod with touch controls. The second is a revolutionary mobile phone. And the third is a breakthrough Internet communications device…. An iPod, a phone, and an internet communicator…These are not three separate devices, this is one device, and we are calling it iPhone.

Jobs should have said (or let the iPhone say): “Never trust a computer you cannot put in your pocket.” Or “Never trust a computer you cannot touch.” Today, he might have said, “Never trust a computer you cannot talk to.”

Explaining to the audience the new concept of “internet communicator,” Jobs mentioned email, browser, Google Maps, and detecting Wi-Fi. He did not mention and possibly had not envisioned what happened next: the widespread use of the iPhone (and the mobile phones that followed it) as a camera replacement.

Long before the invention of the PC and the iPhone, a new ingenious device was applied to the work of painters and illustrators, people with unique and rare skills for capturing and preserving reality.

On January 7, 1839, the Daguerreotype photography process was presented to the French Academy of Sciences by Francois Arago, a physicist and politician. Arago told the Academy that it was “…indispensable that the government should compensate M. Daguerre, and that France should then nobly give to the whole world this discovery which could contribute so much to the progress of art and science.”

On March 5, 1839, another inventor, looking (in the United States, England, and France) for government sponsorship of his invention of the telegraph, met with Daguerre. A highly impressed Samuel F. B. Morse wrote to his brother: “It is one of the most beautiful discoveries of the age… No painting or engraving ever approached it.”

In late September 1839, as Jeff Rosenheim writes in Art and the Empire City, shortly after the French government (on August 19) publicly released the details of the Daguerreotype process, “…a boat arrived [in New York] with a published text with step-by-step instructions for creating the plates and making the exposures. Morse and others in New York, Boston, and Philadelphia immediately set about to build their cameras, find usable lenses, and experiment with the new invention.”

New Yorkers were ready for the Daguerreotype, already alerted to the “new discovery” by articles in the local press, such as the one in The Corsair on April 13, 1839, titled “The Pencil of Nature”: “Wonderful wonder of wonders!! … Steel engravers, copper engravers, and etchers, drink up your aquafortis, and die! There is an end to your black art… All nature shall paint herself — fields, rivers, trees, houses, plains, mountains, cities, shall all paint themselves at a bidding, and at a few moment’s notice.”

In 1838, Charles Wheatstone invented the first mass-produced photographic images, the stereographs, a pair of photographs mounted side-by-side that create the illusion of a three-dimensional image when viewed through a stereoscope. In “The Stereoscope and the Stereograph” (The Atlantic Monthly, June 1859), Oliver Wendell Holmes captured the wonders of photography in yet another memorable phrase, “the mirror with a memory”:

The Daguerreotype… has fixed the most fleeting of our illusions, that which the apostle and the philosopher and the poet have alike used as the type of instability and unreality. The photograph has completed the triumph, by making a sheet of paper reflect images like a mirror and hold them as a picture… [it is the] invention of the mirror with a memory…

The time will come when a man who wishes to see any object, natural or artificial, will go to the Imperial, National, or City Stereographic Library and call for its skin or form, as he would for a book at any common library… we must have special stereographic collections, just as we have professional and other special libraries. And as a means of facilitating the formation of public and private stereographic collections, there must be arranged a comprehensive system of exchanges, so that there may grow up something like a universal currency of these bank-notes, or promises to pay in solid substance, which the sun has engraved for the great Bank of Nature.

Let our readers fill out a blank check on the future as they like—we give our indorsement to their imaginations beforehand. We are looking into stereoscopes as pretty toys, and wondering over the photograph as a charming novelty; but before another generation has passed away, it will be recognized that a new epoch in the history of human progress dates from the time when He who

never but in uncreated light
Dwelt from eternity—

took a pencil of fire from the hand of the “angel standing in the sun,” and placed it in the hands of a mortal.

“Daguerreotypes introduced to Americans a new realism, a style built on close observation and exact detail, so factual it no longer seemed an illusion,” William Howarth painted in Civilization (March/April 1996) the larger picture of a newly born industry in America: “Hawthorne’s one attempt at literary realism, The House of the Seven Gables (1851), features a daguerreotypist who uses his new art to dispel old shadows: ‘I make pictures out of sunshine,’ he claims, and they reveal ‘the secret character with a truth that no painter would ever venture upon.’… By 1853 the American photo industry employed 17,000 workers, who took over 3 million pictures a year.”

A hundred and fifty years after what Holmes called the moment of the “triumph of human ingenuity,” the Metropolitan Museum of Art mounted an exhibition on the early days of Daguerreotypes in France. Said Philippe de Montebello, the director of the museum at the time: “The invention of the daguerreotype—the earliest photographic process—forever altered the way we see and understand our world. No invention since Gutenberg’s movable type had so changed the transmission of knowledge and culture, and none would have so great an impact again until the informational revolution of the late twentieth century.”

In the same year of the Metropolitan’s exhibition, 2003, more digital cameras than traditional film cameras were sold for the first time in the U.S. The “informational revolution” has replaced analog with digital, and digital images captured by smartphones and shared on the internet have animated the triumph of Deep Learning (shortly to be renamed “AI”) at the ImageNet competition of 2012, ushering in a new—and so far very successful and quite “democratic”—stage in the evolution of “artificial intelligence.” Today’s worldwide “Stereographic Library,” the Web, supplied the labeled images needed to “train” a computer to identify and categorize objects, people, places, text, and actions in digital images and videos, in a wide range of domains from healthcare to banking to nature.

But these innovations—and the new image identification proficiency of computers—did not alter the idea of photography as invented by Nicéphore Niépce in 1822, and captured so well by the inimitable Ambrose Bierce in his definition of “photograph” (The Devil’s Dictionary, 1911): “A picture painted by the sun without instruction in art.”

The Note reported on a “state-of-the art survey on information selection systems that retrieve replica copies of selected items as the product of search… 15 specific systems employing search-type selection principles are described.”

History of information:

Though we typically view information searching through a digital lens, as late as 1961… systems remained in place, and under development, for the searching and retrieval of images stored on the photographic media of microfilm and microfiche. The was very much in the tradition of Vannevar Bush's Rapid Selector Project, and remains still another example of the overlap of media for information storage and distribution, with the persistence of the use of analog photographic methodology that originated in the 1930s paralleling the early development of digital storage and retrieval modalities thirty years later… At least one microfilm rapid selector as originally conceived and developed by Vannevar Bush was in operation for the U.S. Navy in 1961.

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The range of offerings has expanded to include variety of music concerts, live sport events, documentaries and lectures, faith broadcasts, stand-up comedy, museum and gallery exhibitions, and TV specials such as the record-breaking Doctor Who fiftieth anniversary special The Day of The Doctor.

Theatre, ballet, sport, exhibitions, TV specials and documentaries are now established forms of what became to be known as “Event Cinema.”

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The screening featured ten short films, including their first film, Sortie des Usines Lumière à Lyon (Workers Leaving the Lumière Factory). Each film was 17 meters long, which, when hand cranked through a projector, ran approximately 50 seconds. By 1900, the Lumière brothers had produced 1,299 short movies. For the World Fair that year, they developed their new Lumière Wide format which, at 75 mm wide, has held the record for over 100 years as the widest film format.

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Once in a great while a scientific book is published that sets bells jangling wildly in a dozen different sciences. Such a book is Cybernetics… It bristles with difficult mathematics; its text is a curious mixture of charm and opacity. But for those who can penetrate it (and thousands are trying), the book is intensely exciting…

Most remarkable are the computing machines, Professor Wiener's own specialty. They are growing with fearful speed. They started by solving mathematical equations with flash-of-lightning rapidity. Now they are beginning to act like genuine mechanical brains. Dr. Wiener sees no reason why they can't learn from experience, like monstrous and precocious children racing through grammar school. One such mechanical brain, ripe with stored experience, might run a whole industry, replacing not only mechanics and clerks but many of the executives too…

As men construct better calculating machines, explains Wiener, and as they explore their own brains, the two seem more & more alike. Man, he thinks, is recreating himself, monstrously magnified, in his own image.

The early calculators contained gears, scales and measuring devices. The best modern ones are built chiefly of electron tubes which give a simple "yes or no" answer when stimulated by electrical impulses. This is roughly what the neurons (nerve cells) do in the human brain.

Some modern calculators "remember" by means of electrical impulses circulating for long periods around closed circuits. One kind of human memory is believed to depend on a similar system: groups of neurons connected in rings. The memory impulses go round & round and are called upon when needed. Some calculators use "scanning" as in television. So does the brain. In place of the beam of electrons which scans a television tube, many physiologists believe, the brain has "alpha waves": electrical surges, ten per second, which question the circulating memories.

By copying the human brain, says Professor Wiener, man is learning how to build better calculating machines. And the more he learns about calculators, the better he understands the brain. The cyberneticists are like explorers pushing into a new country and finding that nature, by constructing the human brain, pioneered there before them.

Psychotic Calculators. If calculators are like human brains, do they ever go insane? Indeed they do, says Professor Wiener. Certain forms of insanity in the brain are believed to be caused by circulating memories which have got out of hand. Memory impulses (of worry or fear) go round & round, refusing to be suppressed. They invade other neuron circuits and eventually occupy so much nerve tissue that the brain, absorbed in its worry, can think of nothing else.

The more complicated calculating machines, says Professor Wiener, do this too. An electrical impulse, instead of going to its proper destination and quieting down dutifully, starts circulating lawlessly. It invades distant parts of the mechanism and sets the whole mass of electronic neurons moving in wild oscillations.

The cures administered to psychotic calculators are weirdly like the modern cures for insanity. One method is to overload the calculator with an extra strong electrical impulse in hope that the shock will stop the machine's oscillations. This is rather like the shock treatment given to human psychotics. Another cure is to isolate part of the calculator's mechanism, hoping to cut off the source of trouble. This is "like the lobotomies which brain surgeons perform. Lobotomies sometimes work (for both machine and brain) but are apt to reduce, in both cases, the subject's judgment.

…Many times throughout his book Dr. Wiener stops in a cold sweat and looks a few years ahead: "Long before Nagasaki and the public awareness of the atomic bomb," he says, "it had occurred to me that we were here in the presence of another social potentiality of unheard-of importance for good and for evil . . . The first industrial revolution . . . was the devaluation of the human arm by the competition of machinery . . . The modern industrial revolution is similarly bound to devalue the human brain at least in its simpler and routine decisions . . . The human being of mediocre attainments or less [will have] nothing to sell that is worth anyone's money to buy."

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This Day in Data, AI and Learning will change to This Month in the History of Information Technologies on January 1, 2025. It will be reborn as a monthly essay inspired by each month’s milestones in the history of technologies of information generation and diffusion.

My new newsletter AI: An Offbeat History is live and calling for readers! Please take a look and subscribe. I plan to write about episodes in the history of AI, including my own experiences over the last fifty years.

Million thanks for subscribing and have a great 2025!

Brian Randell in the Origins of Digital Computers:

In the space of perhaps three years since the start of his work on the Analytics Engine in 1834, Babbage had arrived at the concept of a general purpose digital computer consisting of a store, arithmetic unit, punched cad input and output, and a card-controlled sequencing mechanism that provided iteration and conditional branching.

Henry Babbage (Charles’ youngest son):

It is a machine to calculate the numerical value or values of any formula or function of which the mathematician can indicate the method of solution. It is to perform the ordinary rules of arithmetic in any order as previously settled by the mathematician, and any number of times and on any quantities. It is to be absolutely automatic, the slave of the mathematician, carrying out his orders and relieving him from the drudgery of computing. It must print the results, or any intermediate result arrived at.

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The Seiko-Quartz Astron 35SQ was an 18-karat gold watch that replaced the traditional mechanics of the wristwatch—hairspring, balance wheel, and escapement—with a quartz crystal that oscillated at a rate of 8,192 cycles per second. Its miniature silver battery lasted more than a year. But it was a luxury product, retailing for $1,250. Nevertheless, the “quartz revolution” had begun, and within a few years the technology became affordable and ubiquitous. By 1976, Texas Instruments was selling a twenty-dollar watch line, and within a few years, it could no longer compete with models selling for less than ten dollars.

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