Reacting to New Technologies: AI, Fake News, and Regulation


A number of this week’s [February 19, 2018] milestones in the history of technology demonstrate society’s reactions to new technologies over the years: A discussion of AI replacing and augmenting human intelligence, a warning about the abundance of misinformation on the internet, and government regulation of a communication platform, suppressing free speech in the name of public interest.

On February 20, 1947, Alan Turing gave a talk at the London Mathematical Society in which he declared that “what we want is a machine that can learn from experience.” Anticipating today’s enthusiasm about machine learning and deep learning, Turing declared that “It would be like a pupil who had learnt much from his master, but had added much more by his own work.  When this happens, I feel that one is obliged to regard the machine as showing intelligence.”

Turing also anticipated the debate over the impact of artificial intelligence on jobs: Does it destroy jobs (automation) or does it help humans do their jobs better and do more interesting things (augmentation)? Turing speculated that digital computers will replace some of the calculation work done at the time by human computers. But “the main bulk of the work done by these [digital] computers will however consist of problems which could not have been tackled by hand computing because of the scale of the undertaking.” (Here he was also anticipating today’s most popular word in Silicon Valley: “scale.”)

Advancing (and again, anticipating) the augmentation argument in the debate over AI and jobs, Turing suggested that humans will be needed to assess the accuracy of the calculations done by digital computers. At the same time, he also predicted the automation of high-value jobs (held by what he called “masters” as opposed to the “slaves” operating the computer) and the possible defense mechanisms invented by what today we call “knowledge workers”:

The masters are liable to get replaced because as soon as any  technique becomes  at  all  stereotyped  it  becomes  possible  to  devise  a  system  of  instruction tables which will enable the electronic computer to do it for itself…

They may be unwilling to let their jobs be stolen from them in this way. In that case they would surround the whole of their work  with  mystery  and  make  excuses,  couched  in  well-chosen  gibberish,  whenever  any  dangerous  suggestions  were  made.

Turing concluded his lecture with a plea for expecting intelligent machines to be no more intelligent than humans:

One must therefore not expect a machine to do a very great deal of building up of instruction tables on its own. No man adds very much to the body of knowledge, why should we expect more of a machine? Putting the same point differently, the machine must be allowed to have contact with human beings in order that it may adapt itself to their standards.

What Turing did not anticipate was that digital computers will be used by individuals (as opposed to organizations) to pursue their personal goals, including adding to “the body of knowledge” inaccurate information, intentionally or unwittingly.

It was impossible for Turing to anticipate the evolution of the digital computers of his day into personal computers, smart phones, and the internet. That may have prevented him from seeing the possible parallels with radio broadcasting—a new technology that, only two decades before his lecture, allowed individuals to add to the body of knowledge and transmit whatever they were adding to many other people.

On February 23, 1927, President Calvin Coolidge signed the 1927 Radio Act, creating the Federal Radio Commission (FRC), forerunner of the Federal Communications Commission (FCC), established in 1934.

In “The Radio Act of 1927 as an Act of Progressivism,” Mark Goodman writes:

The technology and growth of radio had outpaced existing Congressional regulation, written in 1912 when radio meant ship-to-shore broadcasting. In the 1920s, by mailing a postcard to Secretary of Commerce Herbert Hoover, anyone with a radio transmitter could broadcast on the frequency chosen by Hoover. The airwaves were an open forum where anyone with the expertise and equipment could reach 25 million listeners.

By 1926, radio in the United States included 15,111 amateur stations, 1,902 ship stations, 553 land stations for maritime use, and 536 broadcasting stations. For those 536 broadcasting stations, the government allocated only eighty-nine wave lengths. The undisciplined and unregulated voice of the public interfered with corporate goals of delivering programming and advertising on a dependable schedule to a mass audience.

Congress faced many difficulties in trying to write legislation. No precedent existed for managing broadcasting except the powerless Radio Act of 1912. No one knew in 1926 where the technology was going nor what radio would be like even the next year, so Congress was trying to write the law to cover all potentialities.

Senator Key Pittman of Nevada expressed his frustration to the Senate chair: “I do not think, sir, that in the 14 years I have been here there has ever been a question before the Senate that in the very nature of the thing Senators can know so little about as this subject.”

Nor was the public much better informed, Pittman noted, even though he received telegrams daily urging passage. “I am receiving many telegrams from my State urging me to vote for this conference report, and informing me that things will go to pieces, that there will be a terrible situation in this country that cannot be coped with unless this report is adopted. Those telegrams come from people, most of whom, I know, know nothing on earth about this bill.”

The Radio Act of 1927 was based on a number of assumptions: That the equality of transmission facilities, reception, and service were worthy political goals; the notion that the spectrum belonged to the public but could be licensed to individuals; and that the number of channels on the spectrum was limited when compared to those who wanted access to it.

Concluding his study of the Radio Act, Mark Goodman writes:

Congress passed the Radio Act of 1927 to bring order to the chaos of radio broadcasting. In the process, Congressional representatives had to deal with several free speech issues, which were resolved in favor of the Progressive concepts of public interest, thereby limiting free speech… Congressmen feared radio’s potential power to prompt radical political or social reform, spread indecent language, and to monopolize opinions.  Therefore, the FRC was empowered to protect listeners from those who would not operate radio for “public interest, convenience, and necessity.”

Regulation stopped the use of the new communication platform by the masses, but six decades later, a new technology gave rise to a new (and massive) communication platform. The invention of the Web and its rapid proliferation in the 1990s, running as a software layer on top of the 20-year-old internet, connecting all computers (and eventually, smart phones) and facilitating greatly the creation and dissemination of information and mis-information, has brought about another explosion in the volume of additions to the body of knowledge, but this time by millions of people worldwide.

On February 25, 1995, Astronomer and author Clifford Stoll wrote in Newsweek:

After two decades online, I’m perplexed. It’s not that I haven’t had a gas of a good time on the Internet. I’ve met great people and even caught a hacker or two. But today, I’m uneasy about this most trendy and oversold community. Visionaries see a future of telecommuting workers, interactive libraries and multimedia classrooms. They speak of electronic town meetings and virtual communities. Commerce and business will shift from offices and malls to networks and modems. And the freedom of digital networks will make government more democratic.

Baloney. Do our computer pundits lack all common sense? The truth in no online database will replace your daily newspaper, no CD-ROM can take the place of a competent teacher and no computer network will change the way government works…

Every voice can be heard cheaply and instantly. The result? Every voice is heard. The cacophony more closely resembles citizens band radio, complete with handles, harassment, and anonymous threats. When most everyone shouts, few listen…

While the Internet beckons brightly, seductively flashing an icon of knowledge-as-power, this nonplace lures us to surrender our time on earth. A poor substitute it is, this virtual reality where frustration is legion and where—in the holy names of Education and Progress—important aspects of human interactions are relentlessly devalued.

Stoll’s was a lonely voice, mostly because the people with the loudest and most dominant voices at the time, those who owned and produced the news, believed like Stoll that “the truth in no online database will replace your daily newspaper.”

They did not anticipate how easy it will be for individuals to start their own blogs and that some blogs will grow into “new media” publications. They did not anticipate that an online database connecting millions of people will not only become a new dominant communication platform but will also start functioning like a daily newspaper.

Nicholas Thompson and Fred Vogelstein just published in Wired a blow-by-blow account of the most recent two years in the tumultuous life of Facebook, a company suffering from a split-personality disorder: Is it a “dumb pipe” (as dominant communication platforms seeking to avoid responsibility for the content they carry liked to call themselves in the past) or a newspaper?

One manifestation of the clash between Facebook’s “religious tenet” that it is “an open, neutral platform” (as Wired describes it) and its desire to crush Twitter (which is why it “came to dominate how we discover and consume news”), was the hiring and firing of a team of journalists, first attempting to use humans to scrub the news and then trusting the machine to do a better job. In a passage illustrating how prescient Turing was, Thompson and Vogelstein write:

…the young journalists knew their jobs were doomed from the start. Tech companies, for the most part, prefer to have as little as possible done by humans—because, it’s often said, they don’t scale. You can’t hire a billion of them, and they prove meddlesome in ways that algorithms don’t. They need bathroom breaks and health insurance, and the most annoying of them sometimes talk to the press. Eventually, everyone assumed, Facebook’s algorithms would be good enough to run the whole project, and the people on Fearnow’s team—who served partly to train those algorithms—would be expendable.

Most of the Wired article, however, is not about AI’s impact on jobs but about Facebook’s impact on the “fake news” meme and vice versa. Specifically, it is about the repercussions of Mark Zuckerberg’s assertion two days after the 2016 presidential election that the idea that fake news on Facebook influenced the election in any way “is a pretty crazy idea.”

The article goes from the dismay of Facebookers of their leader’s political insensitivity to a trio of a security researcher, a venture capitalist (and early investor in Facebook), and a design ethicist, banding together to lead the charge against the evil one and talk “to anyone who would listen about Facebook’s poisonous effects on American democracy,” and to the receptive audiences with “their own mounting grievances” against Facebook—the media and Congress. The solution, for some, is regulation, just like with radio broadcasting in the 1920s: “The company won’t protect us by itself, and nothing less than our democracy is at stake,” says Facebook’s former privacy manager.

All this social pressure was too much for Zuckerberg who made a complete about-face during the company Q3 earnings call last year: “I’ve expressed how upset I am that the Russians tried to use our tools to sow mistrust. We build these tools to help people connect and to bring us closer together. And they used them to try to undermine our values.”

After going through a year of “coercive persuasion” (i.e., indoctrination), Zuckerberg has bought into the fake news that fake news can influence elections and destroy democracy. The Wired article never questions this meme, this dogma, this “filter bubble” where everybody accepts something as a given and never questions it. Most (all?) journalists, commentators, and politicians today never question it.

Wired explains Zuckerberg’s politically incorrect dismissal of the idea that fake news on Facebook can influence elections by describing him as someone who likes “to form his opinions from data.” The analysis given to him before he uttered his inconceivable opinion, according to Wired, “was just an aggregate look at the percentage of clearly fake stories that appeared across all of Facebook. It didn’t measure their influence or the way fake news affected specific groups.”

Excellent point. But the article, like most (all?) articles on the subject, does not provide any data showing the influence of fake news on voters’ actions.

I have good news for Zuckerberg, real news that may help jump-start his recovery from fake news brainwashing, on the way to, yet again, forming his opinions (and decisions) on the basis of facts. The January 2018 issue of Computer, published by the IEEE Computer Society, leads with an article by Wendy Hall, Ramine Tinati, and Will Jennings of the University of Southampton, titled “From Brexit to Trump: Social Media’s Role in Democracy.” It summarizes their study (and related work) of the role of social media in political campaigns. Their conclusion?

“Our ability to understand the impact that social networks have had on the democratic process is currently very limited.”

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The Undaunted Ambition of American Entrepreneurs and Inventors and the Incredible Hype They Generate

A number of this week’s [February 12, 2018] milestones in the history of technology link the rise of IBM, the introduction of the ENIAC, and the renewed fascination with so-called artificial intelligence.


On February 14, 1924, the Computing-Tabulating-Recording Company (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. (An $11 million company in 1924 was equivalent to a company in 2001 with revenues of $113 million…).”

The upstart, according to Watson, was going to live forever. From a talk he gave at the first meeting of IBM’s Quarter Century Club (employees who have served the company for 25 years), on June 21, 1924:

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.

And in January 1926, at the One Hundred Percent Club Convention:

This business has a future for your sons and grandsons and your great-grandsons, because it is going on forever.  Nothing in the world will ever stop it. The IBM is not merely an organization of men; it is an institution that will go on forever.

On February 14, 1946, the New York Times announced the unveiling of “an amazing machine that applies electronic speeds for the first time to mathematical tasks hitherto too difficult and cumbersome for solution… Leaders who saw the device in action for the first time,” the report continued “heralded it as a tool with which to begin to rebuild scientific affairs on new foundations.”

With those words, the Electronic Numerical Integrator and Computer (ENIAC), the world´s first large-scale electronic general-purpose digital computer, developed at The Moore School of Electrical Engineering at the University of Pennsylvania in Philadelphia, emerged from the wraps of secrecy under which it had been constructed in the last years of World War II.

“The ENIAC weighed 30 tons, covered 1,500 square feet of floor space, used over 17,000 vacuum tubes (five times more than any previous device), 70,000 resistors,10,000 capacitors, 1,500 relays, and 6,000 manual switches, consumed 174,000 watts of power, and cost about $500,000,” says C. Dianne Martin in her 1995 article “ENIAC: The Press Conference That Shook the World.” After reviewing the press reports about the public unveiling of the ENIAC, she concludes:

Like many other examples of scientific discovery during the last 50 years, the press consistently used exciting imagery and metaphors to describe the early computers. The science journalists covered the development of computers as a series of dramatic events rather than as an incremental process of research and testing. Readers were given hyperbole designed to raise their expectations about the use of the new electronic brains to solve many different kinds of problems.

This engendered premature enthusiasm, which then led to disillusionment and even distrust of computers on the part of the public when the new technology did not live up to these expectations.

The premature enthusiasm and exciting imagery was not confined only to ENIAC or the press. In the same vain, and the same year (1946), Waldemar Kaempffert reported in The New York Times:

Crude in comparison with brains as the computing machines may be that solve in a few seconds mathematical problems that would ordinarily take hours, they behave as if they had a will of their own. In fact, the talk at the meeting of the American Institute of Electrical Engineers was half electronics, half physiology. One scientist excused the absence of a colleague, the inventor of a new robot, with the explanation that ‘he couldn’t bear to leave the machine at home alone’ just as if it were a baby.

On February 15, 1946, The Electronic Numerical Integrator And Computer (ENIAC) was formally dedicated at the University of Pennsylvania. Thomas Haigh, Mark Priestley and Crispin Rope in ENIAC in Action: Making and Remaking the Modern Computer:

ENIAC established the feasibility of high-speed electronic computing, demonstrating that a machine containing many thousands of unreliable vacuum tubes could nevertheless be coaxed into uninterrupted operation for long enough to do something useful.

During an operational life of almost a decade ENIAC did a great deal more than merely inspire the next wave of computer builders. Until 1950 it was the only fully electronic computer working in the United States, and it was irresistible to many governmental and corporate users whose mathematical problems required a formerly infeasible amount of computational work. By October of 1955, when ENIAC was decommissioned, scores of people had learned to program and operate it, many of whom went on to distinguished computing careers.

Reviewing ENIAC in Action, I wrote:

Today’s parallel to the ENIAC-era big calculation is big data, as is the notion of “discovery” and the abandonment of hypotheses. “One set initial parameters, ran the program, and waited to see what happened” is today’s The unreasonable effectiveness of data.  There is a direct line of scientific practice from the ENIAC pioneering simulations to “automated science.” But is the removal of human imagination from scientific practice good for scientific progress?

Similarly, it’s interesting to learn about the origins of today’s renewed interest in, fascination with, and fear of “artificial intelligence.” Haigh, Priestley and Rope argue against the claim that the “irresponsible hyperbole” regarding early computers was generated solely by the media, writing that “many computing pioneers, including John von Neumann, [conceived] of computers as artificial brains.”

Indeed, in his A First Draft of a Report on the EDVAC—which became the foundation text of modern computer science (or more accurately, computer engineering practice)—von Neumann compared the components of the computer to “the neurons of higher animals.” While von Neumann thought that the brain was a computer, he allowed that it was a complex one, following McCulloch and Pitts (in their 1943 paper “A Logical Calculus of the Ideas Immanent in Nervous Activity”) in ignoring “the more complicated aspects of neuron functioning,” he wrote.

Given that McCulloch said about the “neurons” discussed in his and Pitts’ seminal paper that they “were deliberately as impoverished as possible,” what we have at the dawn of “artificial intelligence” is simplification squared, based on an extremely limited (possibly non-existent at the time) understanding of how the human brain works.

These mathematical exercises, born out of the workings of very developed brains but not mimicking or even remotely describing them, led to the development of “artificial neural networks” which led to “deep learning” which led to general excitement today about computer programs “mimicking the brain” when they succeed in identifying cat images or beating a Go champion.

In 1949, computer scientist Edmund Berkeley wrote in his book, Giant Brains or Machines that Think: “These machines are similar to what a brain would be if it were made of hardware and wire instead of flesh and nerves… A machine can handle information; it can calculate, conclude, and choose; it can perform reasonable operations with information. A machine, therefore, can think.”

Haigh, Priestley and Rope write that “…the idea of computers as brains was always controversial, and… most people professionally involved with the field had stepped away from it by the 1950s.” But thirty years later, Marvin Minsky famously stated: “The human brain is just a computer that happens to be made out of meat.”

Most computer scientists by that time were indeed occupied by less lofty goals than playing God, but only very few objected to these kind of statements, and to Minsky receiving the most prestigious award in their profession (for establishing the field of artificial intelligence). The idea that computers and brains are the same thing, today leads people with very developed brains to conclude that if computers can win in Go, they can think, and that with just a few more short steps up the neural networks evolution ladder, computers will reason that it’s in their best interests to destroy humanity.

On February 15, 2011, IBM’s computer Watson commented on the results of his 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, of course, is stored in “his” memory under the category “Oscar Wilde.”

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Deep Blue and Deep Learning: It’s all About Brute Force

RobotHumanThere are interesting parallels between one of this week’s milestones in the history of technology and the current excitement and anxiety about artificial intelligence (AI).

On February 10, 1996, IBM’s Deep Blue became the first machine to win a chess game against a reigning world champion, Garry Kasparov. Kasparov won three and drew two of the following five games, defeating Deep Blue by a score of 4–2.  In May 1997, an upgraded version of Deep Blue won the six-game rematch 3½–2½ to become the first computer to defeat a reigning world champion in a match under standard chess tournament time controls.

Deep Blue was an example of so-called “artificial intelligence” achieved through “brutforce,” the super-human calculating speed that has been the hallmark of digital computers since they were invented in the 1940s. Deep Blue was a specialized, purpose-built computer, the fastest to face a chess world champion, 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.

To many observers, this was another milestone in man’s quest to build a machine in his own image and another indicator that it’s just a matter of time before we create a self-conscious machine complex enough to mimic the brain and display human-like intelligence or even super-intelligence.

An example of such “the mind is a meat machine” (to quote Marvin Minsky) philosophy is Charles Krauthammer’s “Be Afraid” in the Weekly Standard, May 26, 1997. To Krauthammer, Deep Blue’s win in the 1996 match was due to “brute force” calculation, which is not artificial intelligence, he says, just faster calculation of a much wider range of possible tactical moves.

But one specific move in Game 2 of the 1997 match, a game that Kasparov based not on tactics, but on strategy (where human players have a great advantage over machines), was “the lightning flash that shows us the terrors to come.” Krauthammer continues:

What was new about Game Two… was that the machine played like a human. Grandmaster observers said that had they not known who was playing they would have imagined that Kasparov was playing one of the great human players, maybe even himself. Machines are not supposed to play this way… To the amazement of all, not least Kasparov, in this game drained of tactics, Deep Blue won. Brilliantly. Creatively. Humanly. It played with — forgive me — nuance and subtlety.

Fast forward to March 2016, to Cade Metz writing in Wired on Go champion Lee Sedol’s loss to AlphaGo at the Google DeepMind Challenge Match. In “The AI Behind AlphaGo Can Teach Us About Being Human,” Metz reported on yet another earth-shattering artificial-intelligence-becoming-human-intelligence move:

Move 37 showed that AlphaGo wasn’t just regurgitating years of programming or cranking through a brute-force predictive algorithm. It was the moment AlphaGo proved it understands, or at least appears to mimic understanding in a way that is indistinguishable from the real thing. From where Lee sat, AlphaGo displayed what Go players might describe as intuition, the ability to play a beautiful game not just like a person but in a way no person could.

AlphaGo used 1,920 Central Processing Units (CPU) and 280 Graphics Processing Units (GPU, according to The Economist, and possibly additional proprietary Tensor Processing Units, for a lot of hardware power, plus brute force statistical analysis software known as Deep Neural Networks, or more popularly as Deep Learning.

Still, Google’s programmers have not dissuaded anyone from believing they are creating human-like machines and often promoted the idea (the only Google exception I know of is Peter Norvig, but he is neither a member of the Google Brain nor of the Google DeepMind teams, Google’s AI avant-garde).

IBM’s programmers, in contrast, were more modest. Krauthammer quotes Joe Hoane, one of Deep Blue’s programmers, answering the question “How much of your work was devoted specifically to artificial intelligence in emulating human thought?” Hoane’s answer: “No effort was devoted to [that]. It is not an artificial intelligence project in any way. It is a project in — we play chess through sheer speed of calculation and we just shift through the possibilities and we just pick one line.”

So the earth-shattering moves may have been just a bug in the software. But that explanation escaped observers, then and now, preferring to believe that humans can create intelligent machines (“giant brains” as they were called in the early days of very fast calculators) because the only difference between humans and machines is the degree of complexity, the sheer number of human or artificial neurons firing. Here’s Krauthammer:

You build a machine that does nothing but calculation and it crosses over and creates poetry. This is alchemy. You build a device with enough number-crunching algorithmic power and speed—and, lo, quantity becomes quality, tactics becomes strategy, calculation becomes intuition… After all, how do humans get intuition and thought and feel? Unless you believe in some metaphysical homunculus hovering over (in?) the brain directing its bits and pieces, you must attribute our strategic, holistic mental abilities to the incredibly complex firing of neurons in the brain.

We are all materialists now. Or almost all of us. Read here and (especially) here for a different take.

If you are not interested in philosophical debates (and prefer to ignore the fact that the dominant materialist paradigm affects—through government policies, for example—many aspects of your life), at least read Tom Simonite excellent Wired article “AI Beat Humans at Reading! Maybe not” in which he shows how exaggerated are recent various claims for AI “breakthroughs.”

Beware of fake AI news and be less afraid.

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From Social Security to Social Anxiety


Ida M. Fuller with the first social security check.

Two of this week’s milestones in the history of technology highlight the evolution in the use of computing machines from supporting social security to boosting social cohesion and social anxiety.

On January 31, 1940, Ida M. Fuller became the first person to receive an old-age monthly benefit check under the new Social Security law. Her first check was for $22.54. The Social Security Act was signed into law by Franklin Roosevelt on August 14, 1935. Kevin Maney in The Maverick and His Machine: “No single flourish of a pen had ever created such a gigantic information processing problem.”

But IBM was ready. Its President, Thomas Watson, Sr., defied the odds and during the early years of the Depression continued to invest in research and development, building inventory, and hiring people. As a result, according to Maney, “IBM won the contract to do all of the New Deal’s accounting – the biggest project to date to automate the government. … Watson borrowed a common recipe for stunning success: one part madness, one part luck, and one part hard work to be ready when luck kicked in.”

The nature of processing information before computers is evident in the description of the building in which the Social Security administration was housed at the time:

The most prominent aspect of Social Security’s operations in the Candler Building was the massive amount of paper records processed and stored there.  These records were kept in row upon row of filing cabinets – often stacked double-decker style to minimize space requirements.  One of the most interesting of these filing systems was the Visible Index, which was literally an index to all of the detailed records kept in the facility.  The Visible Index was composed of millions of thin bamboo strips wrapped in paper upon which specialized equipment would type every individual’s name and Social Security number.  These strips were inserted onto metal pages which were assembled into large sheets. By 1959, when Social Security began converting the information to microfilm, there were 163 million individual strips in the Visible Index.

On January 1, 2011, the first members of the baby boom generation reached retirement age. The number of retired workers is projected to grow rapidly and will double in less than 30 years. People are also living longer, and the birth rate is low. As a result, the ratio of workers paying Social Security taxes to people collecting benefits will fall from 3.0 to 1 in 2009 to 2.1 to 1 in 2031.

In 1955, the 81-year-old Ida Fuller (who died on January 31, 1975, aged 100, after collecting $22,888.92 from Social Security monthly benefits, compared to her contributions of $24.75) said: “I think that Social Security is a wonderful thing for the people. With my income from some bank stock and the rental from the apartment, Social Security gives me all I need.”

Sixty-four years after the first social security check was issued, paper checks were replaced by online transactions and letters as the primary form of person-to-person communications were replaced by web-based social networks.

On February 4, 2004, Facebook was launched when went live at Harvard University. Its home screen read, says David Kirkpatrick in The Facebook Effect, “Thefacebook is an online directory that connects people though social networks at colleges.” Zuckerberg’s classmate Andrew McCollum designed a logo using an image of Al Pacino he’s found online that he covered with a fog of ones and zeros.


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,” 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.” In late 2017, Facebook had 2.07 billion monthly active users.

Successfully connecting more than a third of the world’s adult (15+) population brings a lot of scrutiny. “Is Social Media the Tobacco Industry of the 21st Century?” asked an article on recently, summing up the current sentiment about Facebook.

The most discussed complaint is that Facebook is bad for democracy, aiding and abetting the rise of “fake news” and “echo chambers.”

Why blame the network for what its users do with it? And how exactly what American citizens do with it impact their freedom to vote in American elections?

Consider the social network of the 18th century. On November 2, 1772, the town of Boston established a Committee of Correspondence as an agency to organize a public information network in Massachusetts. The Committee drafted a pamphlet and a cover letter which it circulated to 260 Massachusetts towns and districts, instructing them in current politics and inviting each to express its views publicly. In each town, community leaders read the pamphlet aloud and the town’s people discussed, debated, and chose a committee to draft a response which was read aloud and voted upon.

When 140 towns responded and their responses published in the newspapers, “it was evident that the commitment to informed citizenry was widespread and concrete” according to Richard D. Brown (in Chandler and Cortada, eds., A Nation Transformed by Information). But why this commitment?

In Liah Greenfeld‘s words (in Nationalism: Five Roads to Modernity), “Americans had a national identity… which in theory made every individual the sole legitimate representative of his own interests and equal participant in the political life of the collectivity. It was grounded in the values of reason, equality, and individual liberty.”

The Internet is not “inherently” democratizing, no more than the telegraph ever was and no matter how much people have always wanted to believe in the power of technology to transform society. Believing in and upholding the right values for a long period of time—individual liberty and responsibility of informed citizenry making its own decisions while debating, discussing, and sharing information (and mis-information)—is what makes societies democratic.

More than a century after the establishment of the first social network in the U.S., the citizenry was informed (and mis-informed) by hundreds of newspapers, mostly sold by newspaper boys on the streets. After paying a visit to the United States, Charles Dickens described (in Martin Chuzzlewit, 1844) the newsboys greeting a ship in New York Harbor: “’Here’s this morning’s New York Stabber! Here’s the New York Family Spy! Here’s the New York Private Listener! … Here’s the full particulars of the patriotic loco-foco movement yesterday, in which the whigs were so chawed up, and the last Alabama gauging case … and all the Political, Commercial and Fashionable News. Here they are!’ … ‘It is in such enlightened means,’ said a voice almost in Martin’s ear, ‘that the bubbling passions of my country find a vent.’”

Another visitor from abroad, the Polish writer Henryk Sienkiewicz, could discern (in Portrait of America, 1876) in the mass circulation of newspapers, the American belief about the universal need for information: “In Poland, a newspaper subscription tends to satisfy purely intellectual needs and is regarded as somewhat of a luxury which the majority of the people can heroically forego; in the United States a newspaper is regarded as a basic need of every person, indispensable as bread itself.”

Basic need for information, of all kinds, as Mark Twain observed (in Pudd’nhead Wilson, 1897): “The old saw says, ‘Let a sleeping dog lie.’ Right. Still, when there is much at stake, it is better to get a newspaper to do it.”

Blaming Facebook for fake news is like blaming the newspaper boys for the fake news the highly partisan 19th century newspapers were in the habit of publishing. Somehow, American democracy survived.

Consider a more recent fact: According to a Gallup/Knight Foundation survey, the American public divides evenly on the question of who is primarily responsible for ensuring people have an accurate and politically balanced understanding of the news—48% say the news media (“by virtue of how they report the news and what stories they cover”) and 48% say individuals themselves (“by virtue of what news sources they use and how carefully they evaluate the news”).

Half of the American public believes someone else is responsible for feeding them the correct “understanding of the news.” Facebook had little to do with the erosion of the belief in individual responsibility, but it certainly feeling the impact of drifting away from the values upheld by the users of the 18th century social network.

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How Steve Jobs and Thomas Watson Sr. Sold AI to the Public

ssec5A number of this week’s milestones in the history of technology showcase two prominent computer industry showmen, Steve Jobs and Thomas Watson Sr., their respective companies, Apple and IBM, and how they sold smart machines to the general public.

On January 22, 1984, the Apple Macintosh was introduced in the “1984” television commercial aired during Super Bowl XVIII. The commercial was later called by Advertising Age “the greatest commercial ever made.” A few months earlier, Steve Jobs said this before showing a preview of the commercial:

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?

Thirty-six years earlier, another master promoter, the one who laid the foundation for big blue domination, intuitively understood the importance of making machines endowed with artificial intelligence (or “giant brains” as they were called at the time) palatable to the general public.

On January 27, 1948, IBM announced the Selective Sequence Electronic Calculator (SSEC) and demonstrated it to the public. “The most important aspect of the SSEC,” according to Brian Randell in the Origins of Digital Computers, “was that it could perform arithmetic on, and then execute, stored instructions – it was almost certainly the first operational machine with these capabilities.”

As Kevin Maney explains in The Maverick and his Machine, IBM’s CEO, Thomas Watson Sr., “didn’t know much about how to build an electronic computer,” but in 1947, he “was the only person on earth who knew how to sell” one. Maney:

The engineers finished testing the SSEC in late 1947 when Watson made a decision that forever altered the public perception of computers and linked IBM to the new generation of information machines. He told the engineers to disassemble the SSEC and set it up in the ground floor lobby of IBM’s 590 Madison Avenue headquarters. The lobby was open to the public and its large windows allowed a view of the SSEC for the multitudes cramming the sidewalks on Madison and 57th street. … The spectacle of the SSEC defined the public’s image of a computer for decades. Kept dust-free behind glass panels, reels of electronic tape ticked like clocks, punches stamped out cards and whizzed them into hoppers, and thousands of tiny lights flashed on and off in no discernable pattern… Pedestrians stopped to gawk and gave the SSEC the nickname “Poppy.” … Watson took the computer out of the lab and sold it to the public.

The SSEC ran at 590 Madison Ave. until July 1952 when it was replaced by a new IBM computer, the first to be mass-produced. According to Columbia University’s website for the SSEC, it “inspired a generation of cartoonists to portray the computer as a series of wall-sized panels covered with lights, meters, dials, switches, and spinning rolls of tape.”

As IBM was one of a handful of computer pioneers establishing a new industry, Watson’s key selling point to the general public was not challenging the alleged thought control of a dominant competitor as Steve Jobs will do more than three decades later, but extolling computer-aided thought expansion: “…to explore the consequences of man’s thought to the outermost reaches of time, space, and physical conditions.” Watson was the first to see that “AI” stood not only for “artificial intelligence” but also for human “augmented intelligence.”

Like his better-known successor more than three decades later, Thomas Watson Sr. was a perfectionist. When he reviewed the SSEC “exhibition” prior to the public unveiling, he remarked that “The sweep of this room is hindered by those large black columns down the center. Have them removed before the ceremony.” But since they supported the building, the columns stayed. Instead, the photo in the brochure handed out at the ceremony (see image at the top of this article) was carefully retouched to remove all traces of the offending columns.

IBM became the dominant computer company and, because it “wanted it all,” entered the new PC market in August 1981. Apple failed in its initial response, the Apple Lisa, but following the airing of the “1984” TV commercial, the Apple 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.” The magazine’s 1984 article is full of quotable quotes. From 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.

From Bill Gates

People concentrate on finding Jobs’ flaws, but there’s no way this group could have done any of this stuff without Jobs. They really have worked miracles.

From Mitch Kapor, developer of Lotus 1-2-3, a best-selling program for the IBM PC:

The IBM PC is a machine you can respect. The Macintosh is a machine you can love.

Here’s Steve Jobs introducing the Macintosh at the Apple shareholders meeting on January 24, 1984. And the Mac said: “Never trust a computer you cannot lift.”

In January 1984, I started working for NORC, a social science research center at the University of Chicago. Over the next 12 months or so, I’ve experienced the shift from large, centralized computers to personal ones and the shift from a command-line to a graphical user interface.

I was responsible, among other things, for 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 logon 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, telling the VAX to re-calculate the budget with the new data I’ve entered. To this day, I remember my great frustration and dismay when the VAX came back telling me 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 the 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 on an NCR accounting machine the transactions for the day. 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.

This linear approach to accounting and finance changed in 1979 when Dan Bricklin and Bob Frankston invented Visicalc, the first electronic spreadsheet and the first killer app for personal computers.

Steven Levy has described the way financial calculations were done at the time (on paper!) and Brickiln’s epiphany in 1978 when he was a student at the Harvard Business School:

The problem with ledger sheets was that if one monthly expense went up or down, everything – everything – had to be recalculated. It was a tedious task, and few people who earned their MBAs at Harvard expected to work with spreadsheets very much. Making spreadsheets, however necessary, was a dull chore best left to accountants, junior analysts, or secretaries. As for sophisticated “modeling” tasks – which, among other things, enable executives to project costs for their companies – these tasks could be done only on big mainframe computers by the data-processing people who worked for the companies Harvard MBAs managed.

Bricklin knew all this, but he also knew that spreadsheets were needed for the exercise; he wanted an easier way to do them. It occurred to him: why not create the spreadsheets on a microcomputer?

At NORC, I experienced first-hand the power of that idea when I started managing budgets with Visicalc, running on an Osborne laptop. Soon thereafter I migrated to the first IBM PC at NORC which ran the invention of another HBS student, Mitch Kapor, who was also frustrated with re-calculation and other delights of paper 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. You had complete control of the numbers and the processing of the data, you didn’t have to wait for a remote computer to do the calculations only to find out you need to enter the data again. To say nothing, of course, about modeling, what-if scenarios, the entire range of functions at your fingertips.

But in many respects, the IBM PC (and other PCs) was a mainframe on a desk. Steve Jobs and the Lisa and Macintosh teams changed that and brought us an interface that made computing easy, intuitive, and fun. NORC got 80 Macs that year, 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 any opportunity I got. Indeed, there was nothing like it at the time.

It took sometime 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 word-processing and presentation tools.

But Steve Jobs triumphed at the end with yet another series of inventions. At the introduction of the iPhone in 2007, he should have said (or let the iPhone say): “Never trust a computer you cannot put it in your pocket.” Or “Never trust a computer you cannot touch.” Today, he might have said “Never trust a computer you cannot talk to.” What he would have said ten years from now? “Never trust a computer you cannot merge with”?

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Inventing and Fumbling the Future

apple_lisa_werbungThirty-five years ago this week, Apple introduced a computer that changed the way people communicated with their electronic devices, using graphical icons and visual indicators rather than punched cards or text-based commands.

On January 19, 1983, Apple introduced Lisa, a $9,995 PC for business users. Many of its innovations such as the graphical user interface, a mouse, and document-centric computing, were taken from the Alto computer developed at Xerox PARC, introduced as the $16,595 Xerox Star in 1981.

Steve Jobs recalled (in Walter Isaacson’s Steve Jobs) that he and the Lisa team were very relieved when they saw the Xerox Star: “We knew they hadn’t done it right and that we could–at a fraction of the price.” Isaacson says that “The Apple raid on Xerox PARC is sometimes described as one of the biggest heists in the chronicles of industry” and quotes Jobs on the subject: “Picasso had a saying–‘good artists copy, great artists steal’—and we have always been shameless about stealing great ideas… They [Xerox management] were copier-heads who had no clue about what a computer could do… Xerox could have owned the entire computer industry.”


The story of how Xerox invented the future but failed to realize it has become a popular urban legend in tech circles, especially after the publication in 1998 of Fumbling the Future: How Xerox Invented, Then Ignored, the First Personal Computer by D.K. Smith and R.C. Alexander. Moshe Vardi, the former Editor-in-Chief of the Communications of the ACM (CACM), recounted a few years ago his own story of fumbling the future, as a member of a 1989 IBM research team that produced a report envisioning an “information-technology-enabled 21st-century future.”

The IBM report got right some of the implications of its vision of a “global, multi-media, videotext-like utility.” For example, it predicted a reduced need for travel agents, a flood of worthless information, and how “fast dissemination of information through a global information utility” will increase the volatility of politics, diplomacy, and “other aspects of life.”

Vardi also brought to his readers’ attention a video on the future produced by AT&T in 1993 “with a rather clear vision of the future, predicting what was then revolutionary technology, such as paying tolls without stopping and reading books on computers.”

What can we learn from these yesterday’s futures? Vardi correctly concluded that “The future looks clear only in hindsight. It is rather easy to practically stare at it and not see it. It follows that those who did make the future happen deserve double and triple credit. They not only saw the future, but also trusted their vision to follow through, and translated vision to execution.”

But what exactly those who “fumbled the future” did not see? More important, what is it that we should understand now about how their future has evolved?

The IBM report and the AT&T video look prescient today but they repeated many predictions that were made years before 1989 and 1993. These predictions eventually became a reality but it is how we got there that these descriptions of the future missed. To paraphrase Lewis Carroll, if you know where you are going, it matters a lot which road you are taking.

The IBM report says: “In some sense, the proposed vision may not appear to be revolutionary: the envisioned system might be dismissed as a safely predictable extrapolation from and merging of existing information tools that it may complement or even replace.” I would argue that the vision, for both IBM and AT&T, was not just an “extrapolation of existing information tools,” but also an extrapolation of their existing businesses—what they wanted the future to be. Their vision was based on the following assumptions:

  1. The business/enterprise market will be the first to adopt and use the global information utility; the consumer/home market will follow. IBM: “the private home consumer market would probably be the last to join the system because of yet unclear needs for such services and the initial high costs involved.” And: “An important vehicle to spur the development of home applications will be business applications.”
  2. The global information utility will consist of a “global communications network” and “information services” riding on top of it. It will be costly to construct and the performance and availability requirements will be very high. IBM: “Once an information utility is meant to be used and depended on as a ‘multi-media telephone’ system, it must live up to the telephone system RAS [Reliability, Availability, and Serviceability] requirements, which go far beyond most of today’s information systems.” And: “Without 24-hour availability and low MTTR [Mean Time To Repair/Restore] figures, no subscriber will want to rely on such a utility.”
  3. Information will come from centralized databases developed by established information providers (companies) and will be pushed over the network to the users when they request it on a “pay-as-you-go” basis.

When Vardi wrote that “it is practically easy to stare at [the future] and not see it,” he probably meant the Internet, which no doubt all of the authors of the IBM report were familiar with (in 1989, a 20-year-old open network connecting academic institutions, government agencies and some large corporations). But neither IBM nor AT&T (nor other established IT companies) cared much about it because it was not “robust” enough and would not answer the enterprise-level requirements of their existing customers. Moreover, they did not control it, as they controlled their own private networks.

Now, before you say “innovator’s dilemma,” let me remind you (and Professor Christensen) that there were many innovators outside the established IT companies in the 1980s and early 1990s that were pursuing the vision that is articulated so beautifully in the IBM report. The most prominent examples – and for a while, successful – were CompuServe and AOL. A third, Prodigy, was a joint venture of IBM, CBS, and Sears. So, as a matter of fact, even the established players were trying to innovate along these lines and they even followed Christensen’s advice (which he gave about a decade later) that they should do it outside of their “stifling” corporate walls. Another innovator, previously-successful and very-successful-in-the-future, who followed the same vision, was the aforementioned Steve Jobs, launching in 1988 his biggest failure, the NeXT Workstation (the IBM report talks about NeXT-like workstations as the only access device to the global information utility, never mentioning PCs, or laptops, or mobile phones).

The vision of “let’s-use-a-heavy-duty-access-device-to-find-or-get-costly-information-from-centralized-databases-running-on-top-of-an-expensive-network” was thwarted by one man, Tim Berners-Lee, and his 1989 invention, the World Wide Web.

Berners-Lee put the lipstick on the pig, lighting up with information the standardized, open, “non-robust,” and cheap Internet (which was – and still is – piggybacking on the “robust” global telephone network). The network and its specifications were absent from his vision which was focused on information, on what the end results of the IBM and AT&T visions were all about, i.e., providing people with easy-to-use tool for creating, sharing, and organizing information. As it turned out, the road to letting people plan their travel on their own was not through an expensive, pay-as-you-go information utility, but through a hypermedia browser and an open network only scientists (and other geeks such as IBM researchers) knew about in 1989.

The amazing thing is that the IBM researchers understood well the importance of hypermedia. The only computer company mentioned by name in the report is Apple and its Hypercard. IBM: “In the context of a global multi-media information utility, the hypermedia concept takes on an enhanced significance in that global hypermedia links may be created to allow users to navigate through and create new views and relations from separate, distributed data bases. A professional composing a hyper-document would imbed in it direct hyperlinks to the works he means to cite, rather than painfully typing in references. ‘Readers’ would then be able to directly select these links and see the real things instead of having to chase them through references. The set of all databases maintained on-line would thus form a hypernet of information on which the user’s workstation would be a powerful window.”

Compare this to Tim Berners-Lee writing in Weaving the Web: “The research community has used links between paper documents for ages: Tables of content, indexes, bibliographies and reference sections… On the Web… scientists could escape from the sequential organization of each paper and bibliography, to pick and choose a path of references that served their own interest.” There is no doubt that the future significance of hypermedia was an insanely great insight by the IBM researchers in 1989, including hinting at Berners-Lee’s great breakthrough which was to escape from (in his words) “the straightjacket of hierarchical documentation systems.”

But it was Berners-Lee, not IBM, that successfully translated his vision into a viable product (or, more accurately, three standards that spawned millions of successful products). Why? Because he looked at the future through different lens than IBM’s (or AOL’s).

Berners-Lee’s vision did not focus on the question of how you deliver information – the network – but on the question of how you organize and share it.  This, as it turned out, was the right path to realizing the visions of e-books, a flood of worthless information, and the elimination of all kinds of intermediaries. And because this road was taken by Berners-Lee and others, starting with Mosaic (the first successful browser), information became free and its creation shifted in big way from large, established media companies to individuals and small “new media” ventures. Because this road was taken, IT innovation in the last thirty years has been mainly in the consumer space, and the market for information services has been almost entirely consumer-oriented.

I’m intimately familiar with IBM-type visions of the late 1980s because I was developing similar ones for my employer at the time, Digital Equipment Corporation, most famously (inside DEC) my 1989 report, “Computing in the 1990s.” I predicted that the 1990s will give rise to “a distributed network of data centers, servers and desktop devices, able to provide adequate solutions (i.e., mix and match various configurations of systems and staff) to business problems and needs.” Believe me, this was quite visionary for people used to talk only about “systems.” (My report was incorporated in the annual business plan for the VAX line of mini-computers, the plan referring to them as “servers” for the first time).

In another report, on “Enterprise Integration,” I wrote: “Successful integration of the business environment, coupled with a successful integration of the computing environment, may lead to data overload. With the destruction of both human and systems barriers to access, users may find themselves facing an overwhelming amount of data, without any means of sorting it and capturing only what they need at a particular point in time. It is the means of sorting through the data that carry the potential for true Enterprise Integration in the 1990s.”  Not too bad, if I may say so myself, predicting Google when Larry Page and Sergey Brin were still in high-school.

And I was truly prescient in a series of presentations and reports in the early 1990s, arguing that the coming digitization of all information (most of it was in analog form at the time), is going to blur what were then rigid boundaries between the computer, consumer electronics, and media “industries.”

But I never mentioned the Internet in any of these reports. Why pay attention to an obscure network which I used a few times to respond to questions about my reports by some geeks at places with names like “Argonne National Laboratory,” when Digital had at the time the largest private network in the world, Easynet, and more than 10,000 communities of VAX Notes (electronic bulletin boards with which DEC employees – and authorized partners and customers and friendly geeks – collaborated and shared information)?

Of course, the only future possible was that of a large, expensive, global, multi-media, high-speed, robust network. Just like Easynet. Or IBM’s and AT&T’s private networks or the visions from other large companies of how the future of computing and telecommunications will be a nice and comforting extrapolation of their existing businesses.

The exception to these visions of computing of the late 1980s and early 1990s was the one produced by Apple in 1987, The Knowledge Navigator. It was also an extrapolation of the company’s existing business, and because of that, it portrayed a different future.

In contrast to IBM’s and AT&T’s (and DEC’s), it was focused on information and individuals, not on communication utilities and commercial enterprises. It featured a university professor, conducting his research work, investigating data and collaborating with a remote colleague, assisted by a talking, all-knowing “smart agent.” The global network was there in the background, but the emphasis was on navigating knowledge and a whole new way of interacting with computers by simply talking to them, as if they were humans.

We are not there yet, but Steve Jobs and Apple moved us closer in 2007 by introducing a new way (touch) for interacting with computers, packaged as phones, which also turned out to be the perfect access devices—much better than NeXT Workstations—to the global knowledge network, the Web.

Back in 1983, the Lisa failed to become a commercial success, the second such failure in a row for Apple. The innovative, visionary company almost fumbled the future. But then it found the right packaging, the right market, and the right pricing for its breakthrough human-computer interface: The Macintosh.

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From Tabulating Machines to Machine Learning to Deep Learning


A Census Bureau clerk tabulates data using a Hollerith Machine (Source: US Census Bureau)

This week’s milestone in the history of technology is the patent that launched the ongoing quest to get machines to help us and them know more about our world, from tabulating machines to machine learning to deep learning (or today’s “artificial intelligence”).

On January 8, 1889, Herman Hollerith was granted a patent titled the “Art of Compiling Statistics.” The patent described a punched card tabulating machine which launched a new industry and the fruitful marriage of statistics and computer engineering—called “machine learning” since the late 1950s, and reincarnated today as “deep learning” (also popularly known today as “artificial intelligence”).

Commemorating IBM’s 100th anniversary in 2011, The Economist wrote:

In 1886, Herman Hollerith, a statistician, started a business to rent out the tabulating machines he had originally invented for America’s census. 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. The technology became the core of IBM’s business when it was incorporated as Computing Tabulating Recording Company (CTR) in 1911 after Hollerith’s firm merged with three others.

In his patent application, Hollerith explained the use of his machine in the context of a population survey, highlighting its usefulness in the statistical analysis of “big data”:

The returns of a census contain the names of individuals and various data relating to such persons, as age, sex, race, nativity, nativity of father, nativity of mother, occupation, civil condition, etc. These facts or data I will for convenience call statistical items, from which items the various statistical tables are compiled. In such compilation the person is the unit, and the statistics are compiled according to single items or combinations of items… it maybe required to know the numbers of persons engaged in certain occupations, classified according to sex, groups of ages, and certain nativities. In such cases persons are counted according to combinations of items. A method for compiling such statistics must be capable of counting or adding units according to single statistical items or combinations of such items. The labor and expense of such tallies, especially when counting combinations of items made by the usual methods, are very great.

James Cortada in Before the Computer quotes Walter Wilcox of the U.S. Bureau of the Census:

While the returns of the Tenth (1880) Census were being tabulated at Washington, John Shaw Billings [Director of the Division of Vital Statistics] was walking with a companion through the office in which hundreds of clerks were engaged in laboriously transferring data from schedules to record sheets by the slow and heartbreaking method of hand tallying. As they were watching the clerks he said to his companion, “there ought to be some mechanical way of doing this job, something on the principle of the Jacquard loom.”

Says Cortada: “It was a singular moment in the history of data processing, one historians could reasonably point to and say that things had changed because of it. It stirred Hollerith’s imagination and ultimately his achievements.” Cortada describes the results of the first large-scale machine learning project:

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.

But the efficient output of the machine was considered by some as “fake news.” In 1891, the Electrical Engineer reported (quoted in Patricia Cline Cohen’s A Calculating People):

The statement by Mr. Porter [the head of the Census Bureau, announcing the initial count of the 1890 census] that the population of this great republic was only 62,622,250 sent into spasms of indignation a great many people who had made up their minds that the dignity of the republic could only be supported on a total of 75,000,000. Hence there was a howl, not of “deep-mouthed welcome,” but of frantic disappointment.  And then the publication of the figures for New York! Rachel weeping for her lost children and refusing to be comforted was a mere puppet-show compared with some of our New York politicians over the strayed and stolen Manhattan Island citizens.

A century later, no matter how efficiently machines learned, they were still accused of creating and disseminating “fake news.” On March 24, 2011, the U.S. Census Bureau delivered “New York’s 2010 Census population totals, including first look at race and Hispanic origin data for legislative redistricting.” In response to the census data showing that New York has about 200,000 less people than originally thought, Sen. Chuck Schumer said, “The Census Bureau has never known how to count urban populations and needs to go back to the drawing board. It strains credulity to believe that New York City has grown by only 167,000 people over the last decade.” Mayor Bloomberg called the numbers “totally incongruous” and Brooklyn borough president Marty Markowitz said “I know they made a big big mistake.” [the results of the 1990 census were also disappointing and were unsuccessfully challenged in court, according to the New York Times].

Complaints by politicians and other people not happy with learning machines have not slowed down the continuing advances in using computers in ingenious ways for increasingly sophisticated statistical analysis. But for many years after Hollerith’s invention and after tabulating machines became digital computers, the machines interacted with the world around them in a very specific, one-dimensional way. 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 world no other way.

Deep learning, the recently successful variant of machine learning (giving rise to the buzz around “artificial intelligence”), opened up new vistas for learning machines. Now they can “see” and “hear” the world around them, driving a worldwide race for producing the winning self-driving car and for planting everywhere virtual assistants—new applications in the age-old endeavor of combining statistical analysis with computer engineering, of getting machines to assist us in the processing, tabulating, and analysis of data.

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