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October 12, 2016

[liveblog] Perception of Moral Judgment Made by Machines

I’m at the PAPIs conference where Edmond Awad [ twitter]at the MIT Media Lab is giving a talk about “Moral Machine: Perception of Moral Judgement Made by Machines.”

NOTE: Live-blogging. Getting things wrong. Missing points. Omitting key information. Introducing artificial choppiness. Over-emphasizing small matters. Paraphrasing badly. Not running a spellpchecker. Mangling other people’s ideas and words. You are warned, people.

He begins with a hypothetical in which you can swerve a car to kill one person instead of stay on its course and kill five. The audience chooses to swerve, and Edmond points out that we’re utilitarians. Second hypothesis: swerve into a barrier that will kill you but save the pedestrians. Most of us say we’d like it to swerve. Edmond points out that this is a variation of the trolley problem, except now it’s a machine that’s making the decision for us.

Autonomous cars are predicted to minimize fatalities from accidents by 90%. He says his advisor’s research found that most people think a car should swerve and sacrifice the passenger, but they don’t want to buy such a car. They want everyone else to.

He connects this to the Tragedy of the Commons in which if everyone acts to maximize their good, the commons fails. In such cases, governments sometimes issue regulations. Research shows that people don’t want the government to regulate the behavior of autonomous cars, although the US Dept of Transportation is requiring manufacturers to address this question.

Edmond’s group has created the moral machine, a website that creates moral dilemmas for autonomous cars. There have been about two million users and 14 million responses.

Some national trends are emerging. E.g., Eastern countries tend to prefer to save passengers more than Western countries do. Now the MIT group is looking for correlations with other factors, e.g., religiousness, economics, etc. Also, what are the factors most crucial in making decisions?

They are also looking at the effect of automation levels on the assignment of blame. Toyota’s “Guardian Angel” model results in humans being judged less harshly: that mode has a human driver but lets the car override human decisions.


In response to a question, Edmond says that Mercedes has said that its cars will always save the passenger. He raises the possibility of the owner of such a car being held responsible for plowing into a bus full of children.

Q: The solutions in the Moral Machine seem contrived. The cars should just drive slower.

A: Yes, the point is to stimulate discussion. E.g., it doesn’t raise the possibility of swerving to avoid hitting someone who is in some way considered to be more worthy of life. [I’m rephrasing his response badly. My fault!]

Q: Have you analyzed chains of events? Does the responsibility decay the further you are from the event?

This very quickly gets game theoretical.

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October 11, 2016

[liveblog] Bas Nieland, Datatrix, on predicting customer behavior

At the PAPis conference Bas Nieland, CEO and Co-Founder of Datatrics, is talking about how to predict the color of shoes your customer is going to buy. The company tries to “make data science marketeer-proof for marketing teams of all sizes.” IT ties to create 360-degree customer profiles by bringing together info from all the data silos.

NOTE: Live-blogging. Getting things wrong. Missing points. Omitting key information. Introducing artificial choppiness. Over-emphasizing small matters. Paraphrasing badly. Not running a spellpchecker. Mangling other people’s ideas and words. You are warned, people.

They use some machine learning to create these profiles. The profile includes the buying phase, the best time to present choices to a user, and the type of persuasion that will get them to take the desired action. [Yes, this makes me uncomfortable.]

It is structured around a core API that talks to mongoDB and MySQL. They provide “workbenches” that work with the customer’s data systems. They use BigML to operate on this data.

The outcome are models that can be used to make recommendations. They use visualizations so that marketeers can understand it. But the marketeers couldn’t figure out how to use even simplified visualizations. So they created visual decision trees. But still the marketeers couldn’t figure it out. So they turn the data into simple declarative phrases: which audience they should contact, in which channel, what content, and when. E.g.:

“To increase sales, çontact your customers in the buying phase with high engagement through FB with content about jeans on sale on Thursday, around 10 o’clock.”

They predict the increase in sales for each action, and quantify in dollars the size of the opportunity. They also classify responses by customer type and phase.

For a hotel chain, they connected 16,000 variables and 21M data points, that got reduced to 75 variables by BigML which created a predictive model that ended up getting the chain more customer conversions. E.g., if the model says someone is in the orientation phase, the Web site shows photos of recommend hotels. If in the decision phase, the user sees persuasive messages, e.g., “18 people have looked at this room today.” The messages themselves are chosen based on the customer’s profile.

Coming up: Chatbot integration. It’s a “real conversation” [with a bot with a photo of an atttractive white woman who is supposedly doing the chatting]

Take-aways: Start simple. Make ML very easy to understand. Make it actionable.


Me: Is there a way built in for a customer to let your model know that it’s gotten her wrong. E.g., stop sending me pregnancy ads because I lost the baby.

Bas: No.

Me: Is that on the roadmap?

Bas: Yes. But not on a schedule. [I’m not proud of myself for this hostile question. I have been turning into an asshole over the past few years.]

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[liveblog] First panel: Building intelligent applications with machine learning

I’m at the PAPIs conference. The opening panel is about building intelligent apps with machine learning. The panelists are all representing companies. It’s Q&A with the audience; I will not be able to keep up well.

NOTE: Live-blogging. Getting things wrong. Missing points. Omitting key information. Introducing artificial choppiness. Over-emphasizing small matters. Paraphrasing badly. Not running a spellpchecker. Mangling other people’s ideas and words. You are warned, people.

The moderator asks one of the panelists (Snejina Zacharia from Insurify) how AI can change a heavily regulated audience such as insurance. She replies that the insurance industry gets low marks for customer satisfaction, which is an opportunity. Also, they can leverage the existing platforms and build modern APIs on stop of them. Also, they can explore how to use AI in existing functions, e.g., chatbots, systems that let users just confirm their identification rather than enter all the data. They also let users pick from an AI-filtered list of carriers that are right for them. Also, personalization: predicting risk and adjusting the questionnaire based on the user’s responses.

Another panelist is working on mapping for a company that is not Google and that is owned by three car companies. So, when an Audi goes over a bump, and then a Mercedes goes over it, it will record the same data. On personalization: it’s ripe for change. People are talking about 100B devices being connected by 2020. People think that RFID tags didn’t live up to their early hype, but 10 billion RFID tags are going to be sold this year. These can provide highly personalized, higher relevant data. This will be the base for the next wave of apps. We need a standards body effort, and governments addressing privacy and security. Some standards bodies are working on it, e.g., Global Standards 1, which manages the barcodes standard.

Another panelist: Why is marketing such a good opportunity for AI and ML? Marketers used to have a specific skill set. It’s an art: writing, presenting, etc. Now they’re being challenged by tech and have to understand data. In fact, now they have to think like scientists: hypothesize, experiment, redo the hypothesis… And now marketers are responsible for revenue. Being a scientist responsible for predictable revenue is driving interest in AI and ML. This panelist’s company uses data about companies and people to segmentize following up on leads, etc. [Wrong place for a product pitch, IMO, which is a tad ironic, isn’t it?]

Another panelist: The question is: how can we use predictive intelligence to make our applications better? Layer input intelligence on top of input-programming-output. For this we need a platform that provides services and is easy to attach to existing processes.

Q: Should we develop cutting edge tech or use what Google, IBM, etc. offer?

A: It depends on whether you’re an early adopter or straggler. Regulated industries have to wait for more mature tech. But if your bread and butter is based on providing the latest and greatest, then you should use the latest tech.

A: It also depends on whether you’re doing a vertically integrated solution or something broader.

Q: What makes an app “smart”? Is it: Dynamic, with rapidly changing data?

A: Marketers use personas, e.g., a handful of types. They used to be written in stone, just about. Smart apps update the personas after ever campaign, every time you get new info about what’s going on in the market, etc.

Q: In B-to-C marketing, many companies have built the AI piece for advertising. Are you seeing any standardization or platforms on top of the advertising channels to manage the ads going out on them?

A: Yes, some companies focus on omni-channel marketing.

A: Companies are becoming service companies, not product companies. They no longer hand off to retailers.

A: It’s generally harder to automate non-digital channels. It’s harder to put a revenue number on, say, TV ads.

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[liveblog] PAPIs: Cynthia Rudin on Regulating Greed

I’m at the PAPIs (Predictive Applications and APIS) [twitter: papistotio] conference at the NERD Center in Cambridge.

NOTE: Live-blogging. Getting things wrong. Missing points. Omitting key information. Introducing artificial choppiness. Over-emphasizing small matters. Paraphrasing badly. Not running a spellpchecker. Mangling other people’s ideas and words. You are warned, people.

The first speaker is Cynthia Rudin, Director of the Prediction Analysis Lab at MIT. Her topic is “Regulating Greed over Time: An Important Lesson for Practical Recommender Systems.” It’s about her Lab’s entry in a data mining competition. (The entry did not win.) The competition was to design a better algorithm for Yahoo’s recommendation of articles. To create an unbiased data set they showed people random articles for two weeks. Your algorithm had to choose to show one of the pool of articles to a user. To evaluate a recommender system, they’d check if your algorithm recommended the same thing that was shown to the user. If the user clicked on it, you could get an evaluation. [I don’t think I got this right.] If so, you sent your algorithm to Yahoo, and they evaluated its clickthrough rate; you never got access to Yahoo’s data.

This is, she says, a form of the multi-arm bandit problem: one arm is better (more likely to lead to a pay out) but you don’t know which one. So you spend your time figuring out which arm is the best, and then you only pull that one. Yahoo and Microsoft are among the companies using multi-arm bandit systems for recommendation systems. “They’re a great alternative to massive A-B testing

] [No, I don’t understand this. Not Cynthia’s fault!.].

Because the team didn’t have access to Yahoo’s data, they couldn’t tune their algorithms to it. Nevertheless, they achieved a 9% clickthrough rate … and still lost (albeit by a tiny margin). Cynthia explains how they increased the efficiency of their algorithms, but it’s math so I can only here play the sound of a muted trumpet. But it involves “decay exploration on the old articles,” and a “peak grabber”: If any articles gets more than 9 clicks out of the last 100 times they show the article, and they keep displaying it: if you have a good article, grab it. The dynamic version of a Peak Grabber had them continuing to showing a peak article if it had a clickthrough rate 14% above the global clickthrough rate.

“We were adjusting the exploration-exploitation tradeoff based on trends.” Is this a phenomenon worth exploring?The phenomenon: you shouldn’t always explore. There are times when you should just stop and exploit the flowers.

Some data supports this. E.g., in England, on Boxing Day you should be done exploring and just put your best prices on things — not too high, not too low. When the clicks on your site are low, you should be exploring. When high, maybe not. “Life has patterns.” The Multiarm Bandit techniques don’t know about these patterns.

Her group came up with a formal way of putting this. At each time there is a known reward multiplier: G(t). G is like the number of people in the store. When G is high, you want to exploit, not explore. In the lower zones you want to balance exploration and exploitation.

So they created two theorems, each leading to an algorithm. [She shows the algorithm. I can’t type in math notation that fast..]

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September 21, 2016

[iab] Robert Scoble

I’m at a IAB conference in Toronto. The first speaker is Robert Scoble, who I haven’t seen since the early 2000s. He’s working at Upload VR that gives him “a front row seat on what’s coming.”

WARNING: Live blogging. Not spellpchecking before posting. Not even re-reading it. Getting things wrong, including emphasis.

The title of his talk is “The Fourth Transformation: How AR and AI change everything.”

First: The PC.

Second: Mac and GUI. Important companies in the first went away.

Third: Mobile and touch. Companies from the second went away.

We’re now getting a taste of the fourth: Virtual Reality and Augmented Reality. Kids take to VR naturally and with enthusiasm, he notes.

“Most people in the world are going to experience with VR with a mobile phone because the cost advantages of doing that are immense.” This Christmas Google will launch its Tango sensors that map the world in 3D. Early games for the Tango phone will give a taste of AR: mapping the physical space and put virtual things into it. Robert shows what’s possible with the Tango phone. Retail 411 is working on bringing you straight to the product you want in a physical store. This tech will let us build new games, but also, for example, put a virtual blue line on a floor to show you where your meeting is. Or, in a furniture store it can show you the items in a vision of your home.

Robert calls AR “Mixed Reality” because he thinks AR refers to the prior generation.

Vuforia was designed for mobile phones, placing virtual objets in real space. But soon we’ll be doing this with glasses, Robert says. Genesis [?] puts a virtual window on your wall. Click on it, and zombies crawl through it and come toward you.

Magic Leap got huge investments because the optics of the glasses they;re building are so good. He points out that the system knows to occlude images by interfering real world objects, e.g., the couch between you and the zombie.

He shows a Hololens app preview. Dokodemo Teleportation Door, made in Unity. You place a door on the ground. Open it. There’s a polygonal world inside it. Walk through the door and you’re in it.

Robert says Apple ditched the headphone jack in order to put advanced audio computing in your head, replacing ambient sound with processed sound that may include virtual audio.

Eyefluence builds sensors for eyes. Robert shows video of someone navigating complex screens of icons solely with his eyes. “Advertisers will be able to build a new kind of billboard in the street and know who looked at it.” [Oh great.]

ActionGram puts holograms into VR. [If you need a tiny George Takei in your living room — and who doesn’t? — this is for you.]

SnapChat bought a company that puts a camera in glasses. SnapChat is going to bring out a connected camera. It could be the size of a sugar cube.

Sephora has an app that shows you how their makeup looks like on your face, color matched.

Robert talks about the effect on sports. E.g, Nascar has 100+ sensors in cars already Researchers are putting sensors in NFL players’ tags for “next gen stats.”

“We’re in the Apple II stage” of this. It wasn’t great but kicked off a trillion dollar industries. Robert’s been told that we’re two years away, but says maybe it’s four years. “The new Ford cards are all built in virtual reality…If you don’t have a team thinking about working in this new world, you’ll be at a disadvantage soon.”

“This is the best educational technology humans have ever invented.”

This is intensely social tech, he says. You can play basketball or ski jumping with your friends over the Internet. He shows a Facebook demo. You can share things with others, things with media inside of them. E.g., go to a physical space and see it together. [Very cool demo. I think this is it:]

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July 13, 2016

Making the place better

I was supposed to give an opening talk at the 9th annual Ethics & Publishing conference put on by George Washington Uinversity. Unfortunately, a family emergency kept me from going, so I sent a very homemade video of the presentation that I recorded at my desk with my monitor raised to head height.

The theme of my talk was a change in how we make the place better — “the place” being where we live — in the networked age. It’s part of what I’ve been thinking about as I prepare to write a book about the change in our paradigm of the future. So, these are thoughts-in-progress. And I know I could have stuck the landing better. In any case, here it is.


June 23, 2016

No more rockstars

Leigh Honeywell [twitter: @hypatiadotca] has posted an important essay — No More Rockstars — written by her, Valerie Aurora (@vaurorapub), and Mary Gardiner (@me_gardiner). There’s a lot in it, and it’s clear and well-written, so it does not need summarizing by me, except to let you know why I think you should read it: It addresses the power imbalance implicit in a conceptual framework that thinks some industry leaders are special and therefore not subject to the same rules as the rest of us. The post analytically describes the phenomenon and suggests ways to avoid the dangers.

Lexi Gill writes a follow-on piece about one particular way that the rockstar culture leads to inequities:

… rock stars are often unofficial gatekeepers to an entire community or industry. They not only get to decide who’s “in” and who’s “out,” but have privileged access to an endless stream of new victims to choose from. Once “in,” the rock star also has special power to manipulate a newcomer’s experience, role and relationships within the community.

Having worked for many people and having observed many more, I can say that for me the best leaders are people whose joy comes from helping people flourish, that is, to discover and become who they are, even if that means developing away from the organization. Those are the women and men who have made the biggest difference in my professional life. I thank them for it.

…All part of the privilege of being a man.

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June 12, 2016

Beyond bricolage

In 1962, Claude Levi-Strauss brought the concept of bricolage into the anthropological and philosophical lexicons. It has to do with thinking with one’s hands, putting together new things by repurposing old things. It has since been applied to the Internet (including, apparently, by me, thanks to a tip from Rageboy). The term “bricolage” uncovers something important about the Net, but it also covers up something fundamental about the Net that has been growing even more important.

In The Savage Mind (relevant excerpt), CLS argued against the prevailing view that “primitive” peoples were unable to form abstract concepts. After showing that they often in have extensive sets of concepts for flora and fauna, he maintains that these concepts go beyond what they pragmatically need to know:

…animals and plants are not known as a result of their usefulness; they are deemed to be useful or interesting because they are first of all known.

It may be objected that science of this kind can scarcely be of much practical effect. The answer to this is that its main purpose is not a practical one. It meets intellectual requirements rather than or instead of satisfying needs.

It meets, in short, a “demand for order.”

CLS wants us to see the mythopoeic world as being as rich, complex, and detailed as the modern scientific world, while still drawing the relevant distinctions. He uses bricolage as a bridge for our understanding. A bricoleur scavenges the environment for items that can be reused, getting their heft, trying them out, fitting them together and then giving them a twist. The mythopoeic mind engages in this bricolage rather than in the scientific or engineering enterprise of letting a desired project assemble the “raw materials.” A bricoleur has what s/he has and shapes projects around that. And what the bricoleur has generally has been fashioned for some other purpose.

Bricolage is a very useful concept for understanding the Internet’s mashup culture, its culture of re-use. It expresses the way in which one thing inspires another, and the power of re-contextualization. It evokes the sense of invention and play that is dominant on so much of the Net. While the Engineer is King (and, all too rarely, Queen) of this age, the bricoleurs have kept the Net weird, and bless them for it.

But there are at least two ways in which this metaphor is inapt.

First, traditional bricoleurs don’t have search engines that let them in a single glance look across the universe for what they need. Search engines let materials assemble around projects, rather than projects be shaped by the available materials. (Yes, this distinction is too strong. Yes, it’s more complicated than that. Still, there’s some truth to it.)

Second, we have been moving with some consistency toward a Net that at its topmost layers replicates the interoperability of its lower layers. Those low levels specify the rules — protocols — by which networks can join together to move data packets to their destinations. Those packets are designed so they can be correctly interpreted as data by any recipient applications. As you move up the stack, you start to lose this interoperability: Microsoft Word can’t make sense of the data output by Pages, and a graphics program may not be able to make sense of the layer information output by Photoshop.

But, over time, we’re getting better at this:

Applications add import and export services as the market requires. More consequentially, more and richer standards for interoperability continue to emerge, as they have from the very beginning: FTP, HTML, XML, Dublin Core,, the many Semantic Web vocabularies, ontologies, and schema, etc.

More important, we are now taking steps to make sure that what we create is available for re-use in ways we have not imagined. We do this by working within standards and protocols. We do it by putting our work into the sphere of reusable items, whether that’s by applying the Creative Commons license, putting our work into a public archive, , or even just paying attention to what will make our work more findable.

This is very different from the bricoleur’s world in which objects are designed for one use, and it takes the ingenuity of the bricoleur to find a new use for it.

This movement continues the initial work of the Internet. From the beginning the Net has been predicated on providing an environment with the fewest possible assumptions about how it will be used. The Net was designed to move anyone’s information no matter what it’s about, what it’s for, where it’s going, or who owns it. The higher levels of the stack are increasingly realizing that vision. The Net is thus more than ever becoming a universe of objects explicitly designed for reuse in unexpected ways. (An important corrective to this sunny point of view: Christian Sandvig’s brilliant description of how the Net has incrementally become designed for delivering video above all else.)

Insofar as we are explicitly creating works designed for unexpected reuse, the bricolage metaphor is flawed, as all metaphors are. It usefully highlights the “found” nature of so much of Internet culture. It puts into the shadows, however, the truly transformative movement we are now living through in which we are explicitly designing objects for uses that we cannot anticipate.

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March 1, 2016

[berkman] Dries Buytaert

I’m at a Berkman [twitter: BerkmanCenter] lunchtime talk (I’m moderating, actually) where Dries Buytaert is giving a talk about some important changes in the Web.

NOTE: Live-blogging. Getting things wrong. Missing points. Omitting key information. Introducing artificial choppiness. Over-emphasizing small matters. Paraphrasing badly. Not running a spellpchecker. Mangling other people’s ideas and words. You are warned, people.

He begins by recounting his early days as the inventor of Drupal, in 2001. He’s also the founder of Acquia, one of the fastest growing tech companies in the US. It currently has 750 people working on products and services for Drupal. Drupal is used by about 3% of the billion web sites in the world.

When Drupal started, he felt he “could wrap his arms” around everything going on on the Web. Now that’s impossible, he says. E.g, Google AdWords were just starting, but now AdWords is a $65B business. The mobile Web didn’t exist. Social media didn’t yet exist. Drupal was (and is) Open Source, a concept that most people didn’t understand. “Drupal survived all of these changes in the market because we thought ahead” and then worked with the community.

“The Internet has changed dramatically” in the past decade. Big platforms have emerged. They’re starting to squeeze smaller sites out of the picture. There’s research that shows that many people think that Facebook is the Internet. “How can we save the open Web?,” Dries askes.

What do we mean by the open or closed Web? The closed Web consists of walled gardens. But these walled gardens also do some important good things: bringing millions of people online, helping human rights and liberties, and democratizing the sharing of information. But, their scale is scary . FB has 1.6B active users every month; Apple has over a billion IoS devices. Such behemoths can shape the news. They record data about our behavior, and they won’t stop until they know everything about us.

Dries shows a table of what the different big platforms know about us. “Google probably knows the most about us” because of gMail.

The closed web is winning “because it’s easier to use.” E.g., After Dries moved from Belgium to the US, Facebook and etc. made it much easier to stay in touch with his friends and family.

The open web is characterized by:

  1. Creative freedom — you could create any site you wanted and style it anyway you pleased

  2. Serendipity. That’s still there, but it’s less used. “We just scroll our FB feed and that’s it.”

  3. Control — you owned your own data.

  4. Decentralized — open standards connected the pieces

Closed Web:

  1. Templates dictate your creative license

  2. Algorithms determine what you see

  3. Privacy is in question

  4. Information is siloed

The big platforms are exerting control. E.g., Twitter closed down its open API so it could control the clients that access it. FB launched “Free Basics” that controls which sites you can access. Google lets people purchase results.

There are three major trends we can’t ignore, he says.

First, there’s the “Big Reverse of the Web,” about which Dries has been blogging about. “We’re in a transformational stage of the Web,” flipping it on its head. We used to go to sites and get the information we want. Now information is coming to us. Info, products, and services will come to us at the right time on the right device.”

Second, “Data is eating the world.”

Third, “Rise of the machines.”

For example, “content will find us,” AKA “mobile or contextual information.” If your flight is cancelled, the info available to you at the airport will provide the relevant info, not offer you car rentals for when you arrive. This creates a better user experience, and “user experience always wins.”

Will the Web be open or closed? “It could go either way.” So we should be thinking about how we can build data-driven, user-centric algorithms. “How can we take back control over our data?” “How can we break the silos” and decentralize them while still offering the best user experience. “How do we compete with Google in a decentralized way. Not exactly easy.”

For this, we need more transparency about how data is captured and used, but also how the algorithms work. “We need an FDA for data and algorithms.” (He says he’s not sure about this.) “It would be good if someone could audit these algorithms,” because, for example, Google’s can affect an election. But how to do this? Maybe we need algorithms to audit the algorithms?

Second, we need to protect our data. Perhaps we should “build personal information brokers.” You unbundle FB and Google, put the data in one place, and through APIs give apps access to them. “Some organizations are experimenting with this.”

Third, decentralization and a better user experience. “For the open web to win, we need to be much better to use.” This is where Open Source and open standards come in, for they allow us to build a “layer of tech that enables different apps to communicate, and that makes them very easy to use.” This is very tricky. E.g., how do you make it easy to leave a comment on many different sites without requiring people to log in to each?

It may look almost impossible, but global projects like Drupal can have an impact, Dries says. “We have to try. Today the Web is used by billions of people. Tomorrow by more people.” The Internet of Things will accelerate the Net’s effect. “The Net will change everything, every country, every business, every life.” So, “we have a huge responsibility to build the web that is a great foundation for all these people for decades to come.”

[Because I was moderating the discussion, I couldn’t capture it here. Sorry.]

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February 17, 2016

Oculus Time Shift: Virtual Reality in the 1850s

From On Time: Technology and Temporality in Modern Egypt, by On Barak (Univ. of California Press, 2013):

Dioramas were given their definitive form by Louis Daguerre, the inventor of photography, in the early 1820s. They consisted of massive, realistic landscape paintings, suspended from a theater ceiling and moving in sequence on a wire, with shifting light effects projected from behind. Alternatively, pictures might be stationed around a revolving platform.

Throughout the 1850s, after the diorama of the Overland Mail debuted in London, various other dioramas and panoramas showcased Egypt. “The Great Moving Panorama of the Nile” had been exhibited in England over 2,500 times by 1852. The new photographic “Cairo Panorama” debuted in 1859. In 1860 “London to Hong Kong in Two Hours” took spectators to the Far East via Egypt along the Overland Route.

…A typical description, taken from a review of the 1847 “City of Cairo Panorama,” reveals how Eurocentrism was performed in these spectacles: “The visitor standing on the circular platform is in the very center of the locality represented, as real to the eye as if he were on the spot itself. (Kindle Locations 789-802)

BTW, Barak’s book is about the history of the difference between the Western colonists’ view of time and the local Egyptian understanding:

…means of transportation and communication did not drive social synchronization and standardized timekeeping, as social scientists conventionally argue. Rather, they promoted what I call “countertempos” predicated on discomfort with the time of the clock and a disdain for dehumanizing European standards of efficiency, linearity, and punctuality. (Kindle locations 209-212)

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