Posts Tagged ‘computers’
“What might once have been called advertising must now be understood as continuous behavior modification on a titanic scale, but without informed consent”*…
“Which category have they put you in?”
This sinister question—at least, it was meant to sound sinister—headlined the advertising copy for The 480, a 1964 novel by Eugene Burdick. His previous best sellers, The Ugly American and Fail-Safe, had caused sensations in political circles, and the new one promised to do the same. Its jacket featured the image of a punched card. The title referred to 480 categories of voter, defined by region, religion, age, and other demographic characteristics, such as “Midwestern, rural, Protestant, lower income, female.” Many readers recoiled from the notion of being sorted into one of these boxes. The New York Times’s reviewer called The 480 a “shock novel” and found it implausible.
What was so shocking? What was implausible? The idea that a company might use computer technology and behavioral science to gather and crunch data on American citizens, with the nefarious goal of influencing a presidential election.
In the 1950s and 1960s this seemed like science fiction. Actually, The 480 was a thinly disguised roman à clef, based on a real-life company called Simulmatics, which had secretly worked for the 1960 campaign of John F. Kennedy. Burdick had been a political operative himself and knew the Simulmatics founders well. The company’s confidential reports and memoranda went straight into his prose. And the 480 categories—listed in an appendix to the novel—were the real Simulmatics voter types, the creation of what one of its founders called “a kind of Manhattan Project gamble in politics.”
Simulmatics was founded in 1959 and lasted eleven years. Jill Lepore mentioned its involvement in the Kennedy campaign in These Truths (2018), her monumental history of the United States; she was already on the trail of the story she tells in her new book, If Then. Lepore is a brilliant and prolific historian with an eye for unusual and revealing stories, and this one is a remarkable saga, sometimes comical, sometimes ominous: a “shadow history of the 1960s,” as she writes, because Simulmatics stumbled through the decade as a bit player, onstage for the Vietnam War, the civil rights movement, the Great Society, the riots and protests. It began with grand ambitions to invent a new kind of predictive behavioral science, in a research environment increasingly tied to a rising defense establishment amid the anxiety of the cold war. It ended ignominiously, in embarrassment and bankruptcy.
Irving Kristol, the future architect of neoconservativism, dismissed Simulmatics in 1964 as “a struggling little company which, despite the fact that it worked on a few problems for the Kennedy organization in 1960, has since had a difficult time making ends meet,” and he wasn’t wrong. Today it is almost completely forgotten. Yet Lepore finds in it a plausible untold origin story for our current panopticon: a world of constant surveillance, if not by the state then by megacorporations that make vast fortunes by predicting and manipulating our behavior—including, most insidiously, our behavior as voters…
The ever-illuminating James Gleick (@JamesGleick) unpacks the remarkable Jill Lepore‘s new history, If Then: How the Simulmatics Corporation Invented the Future: “Simulating Democracy.”
See also: this week’s Bloomberg Businessweek, and for historical perspective, “Age of Invention: The Tools of Absolutism.”
* Jaron Lanier (see, e.g., here and here)
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As we think about the targets painted on our chests, we might recall that it was on this date in 2011 that Facebook introduced the Timeline as the design of a user’s main Facebook page.
“Plans are worthless, but planning is everything”*…
We’re living through a real-time natural experiment on a global scale. The differential performance of countries, cities and regions in the face of the COVID-19 pandemic is a live test of the effectiveness, capacity and legitimacy of governments, leaders and social contracts.
The progression of the initial outbreak in different countries followed three main patterns. Countries like Singapore and Taiwan represented Pattern A, where (despite many connections to the original source of the outbreak in China) vigilant government action effectively cut off community transmission, keeping total cases and deaths low. China and South Korea represented Pattern B: an initial uncontrolled outbreak followed by draconian government interventions that succeeded in getting at least the first wave of the outbreak under control.
Pattern C is represented by countries like Italy and Iran, where waiting too long to lock down populations led to a short-term exponential growth of new cases that overwhelmed the healthcare system and resulted in a large number of deaths. In the United States, the lack of effective and universally applied social isolation mechanisms, as well as a fragmented healthcare system and a significant delay in rolling out mass virus testing, led to a replication of Pattern C, at least in densely populated places like New York City and Chicago.
Despite the Chinese and Americans blaming each other and crediting their own political system for successful responses, the course of the virus didn’t score easy political points on either side of the new Cold War. Regime type isn’t correlated with outcomes. Authoritarian and democratic countries are included in each of the three patterns of responses: authoritarian China and democratic South Korea had effective responses to a dramatic breakout; authoritarian Singapore and democratic Taiwan both managed to quarantine and contain the virus; authoritarian Iran and democratic Italy both experienced catastrophe.
It’s generally a mistake to make long-term forecasts in the midst of a hurricane, but some outlines of lasting shifts are emerging. First, a government or society’s capacity for technical competence in executing plans matters more than ideology or structure. The most effective arrangements for dealing with the pandemic have been found in countries that combine a participatory public culture of information sharing with operational experts competently executing decisions. Second, hyper-individualist views of privacy and other forms of risk are likely to be submerged as countries move to restrict personal freedoms and use personal data to manage public and aggregated social risks. Third, countries that are able to successfully take a longer view of planning and risk management will be at a significant advantage…
From Steve Weber and @nils_gilman, an argument for the importance of operational expertise, plans for the long-term, and the socialization of some risks: “The Long Shadow Of The Future.”
* Dwight D. Eisenhower
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As we make ourselves ready, we might recall that it was on this date in 1822 that Charles Babbage [see almanac entry here] proposes a Difference Engine in a paper to the Royal Astronomical Society (which he’d helped found two years earlier).
In Babbage’s time, printed mathematical tables were calculated by human computers… in other words, by hand. They were central to navigation, science, and engineering, as well as mathematics– but mistakes occurred, both in transcription and in calculation. Babbage determined to mechanize the process and to reduce– indeed, to eliminate– errors. His Difference Engine was intended as precisely that sort of mechanical calculator (in this instance, to compute values of polynomial functions).
In 1833 he began his programmable Analytical Machine (AKA, the Analytical Engine), the forerunner of modern computers, with coding help from Ada Lovelace, who created an algorithm for the Analytical Machine to calculate a sequence of Bernoulli numbers— for which she is remembered as the first computer programmer.
A portion of the difference engine
“A better world won’t come about simply because we use data; data has its dark underside.”*…
Data isn’t the new oil, it’s the new CO2. It’s a common trope in the data/tech field to say that “data is the new oil”. The basic idea being – it’s a new resource that is being extracted, it is valuable, and is a raw product that fuels other industries. But it also implies that data in inherently valuable in and of itself and that “my data” is valuable, a resource that I really should tap in to.
In reality, we are more impacted by other people’s data (with whom we are grouped) than we are by data about us. As I have written in the MIT Technology Review – “even if you deny consent to ‘your’ data being used, an organisation can use data about other people to make statistical extrapolations that affect you.” We are bound by other people’s consent. Our own consent (or lack thereof) is becoming increasingly irrelevant. We won’t solve the societal problems pervasive data surveillance is causing by rushing through online consent forms. If you see data as CO2, it becomes clearer that its impacts are societal not solely individual. My neighbour’s car emissions, the emissions from a factory on a different continent, impact me more than my own emissions or lack thereof. This isn’t to abdicate individual responsibility or harm. It’s adding a new lens that we too often miss entirely.
We should not endlessly be defending arguments along the lines that “people choose to willingly give up their freedom in exchange for free stuff online”. The argument is flawed for two reasons. First the reason that is usually given – people have no choice but to consent in order to access the service, so consent is manufactured. We are not exercising choice in providing data but rather resigned to the fact that they have no choice in the matter.
The second, less well known but just as powerful, argument is that we are not only bound by other people’s data; we are bound by other people’s consent. In an era of machine learning-driven group profiling, this effectively renders my denial of consent meaningless. Even if I withhold consent, say I refuse to use Facebook or Twitter or Amazon, the fact that everyone around me has joined means there are just as many data points about me to target and surveil. The issue is systemic, it is not one where a lone individual can make a choice and opt out of the system. We perpetuate this myth by talking about data as our own individual “oil”, ready to sell to the highest bidder. In reality I have little control over this supposed resource which acts more like an atmospheric pollutant, impacting me and others in myriads of indirect ways. There are more relations – direct and indirect – between data related to me, data about me, data inferred about me via others than I can possibly imagine, let alone control with the tools we have at our disposal today.
Because of this, we need a social, systemic approach to deal with our data emissions. An environmental approach to data rights as I’ve argued previously. But first let’s all admit that the line of inquiry defending pervasive surveillance in the name of “individual freedom” and individual consent gets us nowhere closer to understanding the threats we are facing.
Martin Tisné argues for an “environmental” approach to data rights: “Data isn’t the new oil, it’s the new CO2.”
Lest one think that we couldn’t/shouldn’t have seen this (and related issues like over dependence on algorithms, the digital divide, et al.) coming, see also Paul Baran‘s prescient 1968 essay, “On the Future Computer Era,” one of the last pieces he did at RAND, before co-leading the spin-off of The Institute for the Future.
* Ethics and Data Science
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As we ponder privacy, we might recall that it was on this date in 1981 that IBM released IBM model number 5150– AKA the IBM PC– the original version and progenitor of the IBM PC compatible hardware platform. Since the machine was based on open architecture, within a short time of its introduction, third-party suppliers of peripheral devices, expansion cards, and software proliferated; the influence of the IBM PC on the personal computer market was substantial in standardizing a platform for personal computers (and creating a market for Microsoft’s operating system– first PC DOS, then Windows– on which the PC platform ran). “IBM compatible” became an important criterion for sales growth; after the 1980s, only the Apple Macintosh family kept a significant share of the microcomputer market without compatibility with the IBM personal computer.
“Big Data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it”*…
You’ve probably heard of kilobytes, megabytes, gigabytes, or even terabytes.
These data units are common everyday amounts that the average person may run into. Units this size may be big enough to quantify the amount of data sent in an email attachment, or the data stored on a hard drive, for example.
In the coming years, however, these common units will begin to seem more quaint – that’s because the entire digital universe is expected to reach 44 zettabytes by 2020.
If this number is correct, it will mean there are 40 times more bytes than there are stars in the observable universe…
The stuff of dreams, the stuff of nightmares: “How Much Data is Generated Each Day?”
* Dan Ariely
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As we revel in really, really big numbers, we might spare a thought for Edgar Frank “Ted” Codd; he died on this date in 2003. A distinguished computer scientist who did important work on cellular automata, he is best remembered as the father of computer databases– as the person who laid the foundation for for relational databases, for storing and retrieving information in computer records.
“The most fruitful and natural exercise for our minds is, in my opinion, conversation”*…
In this age of Alexa and Siri, it’s worth recalling the (long) history of human-machine conversation. At MIT in 1966 an experiment with an early form of artificial intelligence anticipated just how close we might grow with our machines…
Client: Men are all alike.
ELIZA (a computer): In what way
Client: They’re always bugging us about something or other.
ELIZA: Can you think of a specific example
Client: Well, my boyfriend made me come here.
ELIZA: Your boyfriend made you come here
Client: He says I’m depressed much of the time.
ELIZA: I am sorry to hear you are depressed
Client: It’s true. I am unhappy.
ELIZA: Do you think coming here will help you not to be unhappy
Client: I need some help, that much seems certain.
– Joseph Weizenbaum, from a transcription of “Computational Linguistics.”
Named after George Bernard Shaw’s ingénue in Pygmalion, the ELIZA program operated on the Project MAC computer system at MIT and made natural language exchange possible between man and machine. ELIZA identified key words and phrases and responded with rote answers that simulated a conversation.
“Talking Cure,” via Lapham’s Quarterly.
* Michel de Montaigne, The Essays
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As we lie back on the couch, we might note that it was on this date in 1928 that the Enigma Machine encoded its first message.
A simple German machine the size of a portable typewriter, ENIGMA allowed for security in communications by a process in which typed letters were replaced by a cipher text displayed on illuminated lamps. The cipher was symmetrical so entering the cipher text into another ENIGMA reproduced the original message. Security was provided by a set of rotor wheels and a series of patch cables whose arrangement was agreed upon previously.
ENIGMA was used extensively by the German military during World War II to transmit battle plans and other secret information. By December of 1941, however, British codebreakers managed to decipher the code, allowing them to routinely read most ENIGMA traffic.
[source- Computer History Museum]
