I  typed my doctoral dissertation in 1978 on my last electric typewriter, a sturdy IBM Model B.

My soon-to-be wife was writing hers out long hand, which I was then typing up.

Then one day we took a chapter to a local typist who was using a Xerox word processor which was priced too high for grad students or for most offices. When I saw her correcting text, and cutting and pasting, my eyes bulged out like a Tex Avery wolf.

As soon as Kay-Pro II’s were available, I bought one from my cousin who had recently opened a computer store.

The moment I received it  and turned it on, I got curious about how the characters made it to the screen, and became a writer about tech. In fact, I became a frequent contributor to the Pro-Files KayPro magazine, writing ‘splainers about the details of how these contraptions. worked.

I typed my wife’s dissertation on it — which was my justification for buying it — and the day when its power really hit her was when I used WordStar’s block move command to instantly swap sections 1 and 4 as her thesis advisor had suggested; she had unthinkingly assumed it meant I’d be retyping the entire chapter. 

People noticed the deeper implications early on. E.g., Michael Heim, a fellow philosophy prof (which I had been, too), wrote a prescient book, Electric Language, in the early 1990s (I think) about  the metaphysical implications of typing into an utterly malleable medium. David Levy wrote Scrolling Forward about the nature of documents in the Age of the PC. People like Frode Hegland are still writing about this and innovating in the text manipulation space.

A small observation I used to like to make around 1990 about the transformation that had already snuck into our culture: Before word processors, a document was a one of a kind piece of writing like a passport, a deed, or an historic map used by Napoleon; a document was tied to its material embodiment. Then the word processing folks needed a way to talk about anything you could write using bits, thus severing “documents” from their embodiment. Everything became a document as everything became a copy.

In any case, word processing profoundly changed not only how I write, but how I think, since I think by writing. Having a fluid medium lowers the cost of trying out ideas, but also makes it easy for me to change the structure of my thoughts, and since thinking is generally  about connecting ideas, and those connections almost always assume a structure that changes their meaning — not just a linear scroll of one-liners — word processing is a crucial piece of “scaffolding” (in Clark and Chalmer‘s sense) for me and I suspect for most people.

In fact, I’ve come to recognize I am not a writer so much as a re-writer of my own words.

Figures

1. Norsk Teknisk Museum – Teigen fotoatelier, CC BY-SA 4.0
   https://creativecommons.org/licenses/by-sa/4.0, via Wikimedia Commons
2. By Autopilot – Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=39098108

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I just stumbled across an open access archive of 146 issues of The Realist, Paul Krassner’s 1960s political and cultural satire magazine. Thanks, JSTOR!

I read it when I was in high school and college in the 1960s and early 1970s. It was far more savage than MAD magazine, more explicit in topics and language, and went after riskier targets. The epitome of this was his parody of William Manchester’s book about the JFK assassination, The Death of a President — a parody that ended with an act by LBJ on the plane carrying Kennedy’s body to Washington that is still so crude and shocking that I’d have to use euphemisms to describe it. Instead, here’s an article that puts it in context.

That was Krassner pulping a topic with a meat hammer, but The Realist was often more clever and addressed very real issues: craven politicians, the abuse of power, the institutionalized oppression of the vulnerable, the US as a warmonger, the heartlessness of capitalism. To be clear, the LBJ article also addressed real issues: The growing JFK hagiography, LBJ’s lust for power and crude lack of empathy, the masculine all-consuming and sexualized power dynamic, the media’s genteel cowardice, etc. It just did so atypically in the form of a short story

Krassner was one of the co-founders of the Yippies. He published The Realist until 2001. He died in 2019.

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danah boyd’s 2018 “You think you want media literacy, do you?” remains an essential, frame-changing discussion of the sort of media literacy that everyone, including danah [@zephoria], agrees we need: the sort that usually focuses on teaching us how to not fall for traps and thus how to disbelieve. But, she argues, that’s not enough. We also need to know how to come to belief.

I went back to danah’s brilliant essay because Barbara Fister [@bfister], a librarian I’ve long admired, has now posted “Lizard People in the Library.” Referencing danah’s essay among many others, Barbara asks: Given the extremity and absurdity of many American’s beliefs, what’s missing from our educational system, and what can we do about it? Barbara presents a set of important, practical, and highly sensible steps we can take. (Her essay is part of the Project Information Literacy research program.)

The only thing I’d dare to add to either essay — or more exactly, an emphasis I would add — is that we desperately need to learn and teach how to come to belief together. Sense-making as well as belief-forming are inherently collaborative projects. It turns out that without explicit training and guidance, we tend to be very very bad at it.

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Kate Zwaard (twitter: @kzwa) Chief of National Digital Strategies at the Library of Congress and leader of the LC Lab, is opening MIT Libraries’ Grand Challenge Summit..The next 1.5 days will be about the grand challenges in enabling scholarly discovery.

For context she tells us that the LC is the largest library in the world, with 164M items. It has the world’s largest collection of film, maps, comic books, telephone directories, and more. [Too many for me to keep this post up with.]

• You can wolk for two football fields just in the maps section. The world’s largest collection of recorded sound. The largest collection

• Personal papers from Ben Franklin, Rosa Parks, Groucho Marx, Claude Shannon, and so many more.

• Last year they circulated almost a million physical items.

• Every week 11,000 tangible items come in through the Copyright office.

• Last year, they digitized 4.7M iems, as well 730M documents crawled from the Web, plus much more. File count: 243M and growing every day.

These serve just one of the LC’s goal: “Acquire, preserve, and provide access to a universal collection of knowledge and the record of America’s creativity.” Not to mention serving Congress, and much more. [I can only keep up with a little of this. Kate’s a fantastic presenter and is not speaking too quickly. The LC is just too big!]

Kate thinks of the LC’s work as an exothermic reaction that needs an activation energy or catalyst. She leads the LC Labs, which started a year ago as a place of experimentation. The LC is a delicate machine, which makes it hard for it to change. The Labs enable experimentation. “Trying things that are easy and cheap is the only way forward.”

When thinking about what to do next, she things about what’s feasible and the impact. One way of having impact: demonstrating that the collection has unexplored potentials for research. She’s especially interested in how the Labs can help deal with the problem of scale at the LC.

She talks about some of Lab’s projects.

If you wanted to make stuff with LC data, there was no way of doing that. Now there’s LC for Robots, added documentation, and Jupyter Notebooks: an open source Web app that let you create open docs that contain code, running text, etc. It lets people play with the API without doing all the work from scratch.

But it’s not enough to throw some resources onto a Web page. The NEH data challenge asked people to create new things using the info about 12M newspapers in the collection. Now the Lab has the Congressional Data Challenge: do something with with Congressional data.

Labs has an Innovator in Residence project. The initial applicants came from LC to give it a try. One of them created a “Beyond Words” crowdsourcing project that asks them to add data to resources

Kate likes helping people find collections they otherwise would have missed. For ten years LC has collaborated wi the Flickr Commons. But they wanted to crowdsource a transcription project for any image of text. A repo will be going up on GitHub shortly for this.

In the second year of the Innovator in Residence, they got the artist Jer Thorp [Twitter: @blprnt] to come for 6 months. Kate talks about his work with the papers of Edward Lorenz, who coined the phrase “The Butterfly Effect.” Jer animated Lorenz’s attractor, which, he points out, looks a bit like a butterfly. Jer’s used the attractor on a collection of 3M words. It results in “something like a poem.” (Here’s Jer’s Artist in the Archive podcast about his residency.)

Jer wonders how we can put serendipity back into the LC and into the Web. “How do we enable our users to be carried off by curiousity not by a particular destination.” The LC is a closed stack library, but it can help guide digital wanderers. ”

Last year the LC released 25M catalog records. Jer did a project that randomly pulls the first names of 20 authors in any particular need. It demonstrates, among other things, the changing demographics of authors. Another project: “Birthy Deathy” that displays birthplace info. Antother looks for polymaths.

In 2018 the Lab will have their first open call for an Innovator in Residence. They’ll be looking for data journalists.

Kate talks about Laura Wrubel
‘s work with the Lab. “Library of Congress Colors” displays a graphic of the dominant colors in a collection.

Or Laura’s Photo Roulette: you guess the date of a photo.

Kate says she likes to think that libraries not just “book holes.” One project: find links among items in the archives. But the WARC format is not amenable to that.

The Lab is partnering with lots of great grops, including JSONstor and WikiData.

They’re working on using machine learning to identify place names in their photos.

What does this have to do with scale, she asks, nothng that the LC has done pretty well with scale. E.g., for the past seven years, the size of their digital collection has doubled every 32 months.

The Library also thinks about how to become a place of warmth and welcome. (She gives a shout out to MIT Libraries’ Future of Libraries
report). Right now, visitors and scholars go to different parts of the building. Visitors to the building see a monument to knowledge, but not a living, breathing place. “The Library is for you. It is a place you own. It is a home.”

She reads from a story by Ann Lamott.

How friendship relates to scale. “Everything good that has happened in my life has happened because of friendship.” The average length of employment of a current employee is thirty years. — that’s not the average retirement year. “It’s not just for the LC but for our field.” Good advice she got: “Pick your career by the kind of people you like to be around.” Librarians!

“We’ve got a tough road ahead of us. We’re still in the early days of the disruption that computation is going to bring to our profession.” “Friendship is what will get us through these hard times. We need to invite peopld into the tent.” “Everything we’ve accomplished has been through the generosity of our friends and colleagues.” This 100% true of the Labs. It’s ust 4 people, but everything they do is done in collaboration.

She concludes (paraphrasing badly): I don’t believe in geniuses, and i don’t believe in paradigm shirts. I believe in friendship and working together over the long term. [She put this far better.]

Q&A

Q: How does the Lab decide on projects?

A: Collaboratively

Q: I’m an archivist at MIT. The works are in closed stack, which can mislead people about the scale. How do we explain the scale in an interesting way.

A: Funding is difficult because so much of the money that comes is to maintain and grow the collection and services. It can be a challenge to carve out funding for experimentation and innovation. We’ve been working hard on finding ways to help people wrap their heads around the place.

Q: Data science students are eager to engage, e.g., as interns. How can academic institutions help to make that happen?

A: We’re very interested in what sorts of partnerships we can create to bring students in. The data is so rich, and the place is so interesting.

Q: Moving from models that think about data as packages as opposed to unpacking and integrating. What do you think about the FAIR principle: making things Findable, Accesible Interoperable, and Reusable? Also, we need to bring in professionals thinking about knowledge much more broadly.

I’m very interested in Hathi Trust‘s data capsules. Are there ways we can allow people to search through audio files that are not going to age into the commons until we’re gone? You’re right: the model of chunks coming in and out is not going to work for us.

Q: In academia, our focus has been to provide resources efficiently. How can weave in serendipity without hurting the efficiency?

A: That’s hard. Maybe we should just serve the person who has a specific purpose. You could give ancillary answers. And crowdsourcing could make a lot more available.

[Great talk.]

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Steve Thomas [twitter: @stevelibrarian] of the Circulating Ideas podcast interviews me about the messiness of meaning, library innovation, and educating against fake news.

You can listen to it here.

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Harvard metaLab is giving an informal Berkman Klein talk about their work on designing for ethical AI. Jeffrey Schnapp introduces metaLab as “an idea foundry, a knowledge-design lab, and a production studio experimenting in the networked arts and humanities.” The discussion today will be about metaLab’s various involvements in the Berkman Klein – MIT MediaLab project on ethics and governance of AI. The conference is packed with Fellows and the newly-arrived summer interns.

Matthew Battles and Jessica Yurkofsky begin by talking about Curricle, a “new platform for experimenting with shopping for courses.” How can the experience be richer, more visual, and use more of the information and data that Harvard has? They’ve come up with a UI that has three elements: traditional search, a visualization, and a list of the results.

“They’ve been grappling with the ethics of putting forward new search algorithms. ”They’ve been grappling with the ethics of putting forward new search algorithms. The design is guided by transparency, autonomy, and visualization. Transparency means that they make apparent how the search works, allowing students to assign weights to keywords. If Curricle makes recommendations, it will explain that it’s because other students like you have chosen it or because students like you have never done this, etc. Visualization shows students what’s being returned by their search and how it’s distributed.

Similar principles guide a new project, AI Compass, that is the entry point for information about Berkman Klein’s work on the Ethics and Governance of AI project. It is designed to document the research being done and to provide a tool for surveying the field more broadly. They looked at how neural nets are visualized, how training sets are presented, and other visual metaphors. They are trying to find a way to present these resources in their connections. They have decided to use Conway’s Game of Life [which I was writing about an hour ago, which freaks me out a bit]. The game allows complex structures to emerge from simple rules. AI Compass is using animated cellular automata as icons on the site.

metaLab wants to enable people to explore the information at three different scales. The macro scale shows all of the content arranged into thematic areas. This lets you see connections among the pieces. The middle scale shows the content with more information. At the lowest scale, you see the resource information itself, as well as connections to related content.

Sarah Newman talks about how AI is viewed in popular culture: the Matrix, Ahnuld, etc. “We generally don’t think about AI as it’s expressed in the tools we actually use”We generally don’t think about AI as it’s expressed in the tools we actually use, such as face recognition, search, recommendations, etc. metaLab is interested in how art can draw out the social and cultural dimensions of AI. “What can we learn about ourselves by how we interact with, tell stories about, and project logic, intelligence, and sentience onto machines?” The aim is to “provoke meaningful reflection.”

One project is called “The Future of Secrets.” Where our email and texts be in 100 years? And what does this tell us about our relationship with our tech. Why and how do we trust them? It’s an installation that’s been at the Museum of Fine Arts in Boston and recently in Berlin. People enter secrets that are printed out anonymously. People created stories, most of which weren’t true, often about the logic of the machine. People tended to project much more intelligence on the machine than was there. Cameras were watching and would occasionally print out images from the show itself.

From this came a new piece (done with fellow Rachel Kalmar) in which a computer reads the secrets out loud. It will be installed at the Berkman Klein Center soon.

Working with Kim Albrecht in Berlin, the center is creating data visualizations based on the data that a mobile phone collects, including the accelerometer. “These visualizations let us see how the device is constructing an image of the world we’re moving through”These visualizations let us see how the device is constructing an image of the world we’re moving through. That image is messy, noisy.

The lab is also collaborating on a Berlin exhibition, adding provocative framing using X degrees of Separation. It finds relationships among objects from disparate cultures. What relationships do algorithms find? How does that compare with how humans do it? What can we learn?

Starting in the fall, Jeffrey and a co-teacher are going to be leading a robotics design studio, experimenting with interior and exterior architecture in which robotic agents are copresent with human actors. This is already happening, raising regulatory and urban planning challenges. The studio will also take seriously machine vision as a way of generating new ways of thinking about mobility within city spaces.

Q&A

Q: me: For AI Compass, where’s the info coming from? How is the data represented? Open API?

Matthew: It’s designed to focus on particular topics. E.g., Youth, Governance, Art. Each has a curator. The goal is not to map the entire space. It will be a growing resource. An open API is not yet on the radar, but it wouldn’t be difficult to do.

Q: At the AI Advance, Jonathan Zittrain said that organizations are a type of AI: governed by a set of rules, they grow and learn beyond their individuals, etc.

Matthew: We hope to deal with this very capacious approach to AI is through artists. What have artists done that bear on AI beyond the cinematic tropes? There’s a rich discourse about this. We want to be in dialogue with all sorts of people about this.

Q: About Curricle: Are you integrating Q results [student responses to classes], etc.?

Sarah: Not yet. There’s mixed feeling from administrators about using that data. We want Curricle to encourage people to take new paths. The Q data tends to encourage people down old paths. Curricle will let students annotate their own paths and share it.

Jeffrey: We’re aiming at creating a curiosity engine. We’re working with a century of curricular data. This is a rare privilege.

me: It’d enrich the library if the data about resources was hooked into LibraryCloud.

Q: kendra: A useful feature would be finding a random course that fits into your schedule.

A: In the works.

Q: It’d be great to have transparency around the suggestions of unexpected courses. We don’t want people to be choosing courses simply to be unique.

A: Good point.

A: The same tool that lets you diversify your courses also lets you concentrate all of them into two days in classrooms near your dorm. Because the data includes courses from all the faculty, being unique is actually easy. The challenge is suggesting uniqueness that means something.

Q: People choose courses in part based on who else is choosing that course. It’d be great to have friends in the platform.

A: Great idea.

Q: How do you educate the people using the platform? How do you present and explain the options? How are you going to work with advisors?

A: Important concerns at the core of what we’re thinking about and working on.

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I went to see my friend Jeff Goldenson — we worked together at the Harvard Library Innovation Lab — at Olin College, where he’s director of the library. Jeff’s taken a library that was an under-utilized resource and, with full Administrative backing, turned it into a playground and a lab…by learning some lessons from community theater. Most importantly, he’s turned it into a place that the community feels it owns.

Olin’s got 350 students, all engineers, half of whom are women. It’s a school that stresses hands-on learning, which turns out to work well for Jeff’s approach. The library’s got two floors, neither of them particularly large, and 15,000 volumes. (Here’s a banana for scale: My local community library has about ten times that many. Yes, it is an affluent community. Nevertheless, please keep in mind that I’m still looking for work.)

Here’s some of what Jeff — who’s background is in architecture and design — has done:

First, he has done the expected things to make the library more inviting — a place as well as a resource, as Jeff puts it. These include a media tools library, maker spaces, coffee spots, some very cool events. (Ask Jeff about the Awkward Family Photobooth :)

Second, he has encouraged students to participate in coming up with new ideas for the library and, since it is a hands-on engineering school, building them.

Third, he has taken some fantastic steps to make the library re-configurable, well beyond the usual putting wheels on everything. For example, he is not only putting things on shelves in the stacks that you won’t find in most libraries, he’s coming up with ways of enabling shelves to be generally repurposable.

Fourth, Jeff being Jeff, everything he thinks of or builds is done in open, shareable ways. (Jeff undoubtedly doesn’t want me to be as cagey as I’m being in this post.)

Fifth, when you have a chance, ask Jeff about cardboard. And vinyl. And other materials that lets him and others alter the physicality of the library — the library as place — the way a local theater company creates sets. For example, once a week the Library turns a structure in the lobby into a coffee shop. It’s very popular, but it still looks like a library structure repurposed as a coffee shop. But with the magic of some cardboard, paint, and just a few inexpensive touches — e.g., some cheap hanging lamps — the structure and the space are transformed. It’s set design, with the library as the theater. This way of thinking lowers the cost and risk of altering the perceived meaning and feel of the place.

The result is not just a supercool library but a model for how existing libraries without lots of resources can give themselves over to their communities…and become a point of pride for them.

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Vinny Senguttuvan is Senior Data Scientist at METIS. Before that, he was at Facebook-based gaming company, High 5 Games, which had 10M users. His talk at PAPIs: “Predicting Customers.”

The main challenge: Most of the players play for free. Only 2% ever spend money on the site, buying extra money to play. (It’s not gambling because you never cash out). 2% of those 2% contribute the majority of the revenue.

All proposed changes go through A/B testing. E.g., should we change the “Buy credits” button from blue to red. This is classic hypothesis testing. So you put up both options and see which gets the best results. It’s important to remember that there’s a cost to the change, so the A-B preference needs to be substantial enough. But often the differences are marginal. So you can increase the sample size. This complicates the process. “A long list of changes means not enough time per change.” And you want to be sure that the change affects the paying customers positively, which means taking even longer.

When they don’t have enough samples, they can bring down the confidence level required to make the change. Or they could bias one side of the hypothesis. And you can assume the variables are independent and run simultaneous A-B tests on various variables. High 5 does all three. It’s not perfect but it works.

Second, there is a poularity metric by which they rank or classify their 100 games. They constantly add games — it went from 15 to 100 in two years. This continuously changes the ranking of the games. Plus, some are launched locked. This complicates things. Vinny’s boss came up with a model of an n-dimensional casino, but it was too complex. Instead, they take 2 simple approaches: 1. An average-weighted spin. 2. Bayesian. Both predicted well but had flaws, so they used a type of average of both.

Third: Survival analysis. They wanted to know how many users are still active a given time after they created their account, and when is a user at risk of discontinuing use. First, they grouped users into cohorts (people who joined within a couple of weeks of each other) and plotted survival rates over time. They also observed return rates of users after each additional day of absence. They also implement a Cox survival model. They found that newer users were more likely to decline in their use of the product; early users are more committed. This pattern is widespread. That means they have to continuously acquire new players. They also alert users when they reach the elbow of disuse.

Fourth: Predictive lifetime value. Lifetime value = total revenue from a user over the entire time the the produced. This is significant because of costs: 10-15% of the rev goes into ads to acquire customers. Their 365 day prediction model should be a time series, but they needed results faster, so they flipped it into a regression problem, predicting the 365 day revenue based on the user’s first month data: how they spent, purchase count, days of play, player level achievement, and the date joined. [He talks about regression problems, but I can’t keep up.] At that point it cost $2 to acquire a customer from FB ad, and $6 from mobile apps. But when they tested, the mobile acquisitions were more profitable than those that came from through FB. It turned out that FB was counting as new users any player who hadn’t played in 30 days, and was re-charging them for it. [I hope I got that right.]

Fifth: Recommendation systems. Pandora notes the feature of songs and uses this to recommend similarities. YouTube makes recommendations made based on relations among users. Non-matrix factorization [I’m pretty sure he just made this up] gives you the ability to predict the score for a video that you know nothing about in terms of content. But what if the ratings are not clearly defined? At High 5, there are no explicit ratings. They calculated a rating based on how often a player plays it, how long the session, etc. And what do you do about missing values: use averages. But there are too many zeroes in the system, so they use sparse matrix solvers. Plus, there is a semi-order to the games, so they used some human input. [Useful for library Stackscores
?]

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On books and knowledge, from Sylvie and Bruno by Lewis Carroll, 1889:

“Which contain the greatest amount of Science, do you think, the books, or the minds?”

“Rather a profound question for a lady!” I said to myself, holding, with the conceit so natural to Man, that Woman’s intellect is essentially shallow. And I considered a minute before replying. “If you mean living minds, I don’t think it’s possible to decide. There is so much written Science that no living person has ever read: and there is so much thought-out Science that hasn’t yet been written. But, if you mean the whole human race, then I think the minds have it: everything, recorded in books, must have once been in some mind, you know.”

“Isn’t that rather like one of the Rules in Algebra?” my Lady enquired. (“Algebra too!” I thought with increasing wonder.) “I mean, if we consider thoughts as factors, may we not say that the Least Common Multiple of all the minds contains that of all the books; but not the other way?”

“Certainly we may!” I replied, delighted with the illustration. “And what a grand thing it would be,” I went on dreamily, thinking aloud rather than talking, “if we could only apply that Rule to books! You know, in finding the Least Common Multiple, we strike out a quantity wherever it occurs, except in the term where it is raised to its highest power. So we should have to erase every recorded thought, except in the sentence where it is expressed with the greatest intensity.”

My Lady laughed merrily. “Some books would be reduced to blank paper, I’m afraid!” she said.

“They would. Most libraries would be terribly diminished in bulk. But just think what they would gain in quality!”

“When will it be done?” she eagerly asked. “If there’s any chance of it in my time, I think I’ll leave off reading, and wait for it!”

“Well, perhaps in another thousand years or so—”

“Then there’s no use waiting!”, said my Lady. “Let’s sit down. Uggug, my pet, come and sit by me!”

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I’m very happy with Pres. Obama’s nomination of Carla Hayden to be the next Librarian of Congress.

She’s been a people’s librarian as the head of the Enoch Pratt Free Library in Baltimore — an actual librarian rather than an historian or academic. I expect her to work to make the treasures of the Library of Congress even more accessible to all.

She’s is on the board of the Digital Public Library of America which has a thorough commitment to open access and to the use of technology to unlock the riches of library culture.

It is also worth noting that she is not an elderly white man. Having a black woman as the head of the Library of Congress says something important, starting with “It’s about time.”

We’ll see where she stands on copyright issues. I have some hopes about that.

This looks like a brilliant choice.

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