What’s in a name? AI, LLMs, Chatbots and what we hope our words will accomplish

There’s a lot of debate in academic circles about what to call ChatGPT, the new Bing, Bard, and the set of new technologies that operate on similar principles.

“AI” or “Generative AI” are the terms preferred by industry. These terms are rightly criticized by many scholars in the fields of communications, science and technology studies, and even computer science as not only inaccurate (there is nothing “intelligent” about these systems in the way we usually think about intelligence; they have no understanding of the text they produce) but also as crass marketing ploys. These critics say these terms perpetuate a cynical “AI hype cycle” that’s simply intended to drive attention and investment capital to the companies that make these technologies—and deflect attention from their harms.

But I worry that the use of the term “AI hype cycle” isn’t doing the work its proponents think it is. My understanding is that those proponents hope the term will help people see through the breathless predictions of corporate interests and focus instead on the harms these interests are perpetrating in the present. I worry, however, that it reads, to the casual observer, as “nothing to see here.”

Surely that’s not the outcome we want.

“Chatbot” is another contender for the term of choice. And indeed it describes the kinds of interactions that most people are having with these technologies at the moment. But as Ted Underwood has said, “chatbot” doesn’t capture the range of appliacations that these technologies enable beyond chat through their APIs. They’re clearly more than the chatbots we used 20 years ago on AIM.

“LLM” (“Large Language Model”) seems to be preferred by academics (according to Simon Willison’s Mastodon poll), because it’s more accurate. LLM surely does a better job of describing of how these technologies work—by statistically predicting the next most likely word in a sequence—not by suggesting there’s any kind of deeper understanding at work.

But “LLM” has a serious problem. It’s an acronym, and therefore, it’s jargon. And therefore, it’s boring.

That leads me to my title’s second question: “What do we want our words to do?” Is the purpose of our words to asymptotically approach truth above and against all other considerations? Or do we want to draw people’s attention to that truth, even if the words we use are not quite as precise?

I have very mixed feelings about this. LLM is more accurate. But it’s also easy (for ordinary people, decision makers, regulators, politicians) to ignore. That is, it’s boring. I understand not wanting to amplify Silicon Valley’s hype, but we also don’t want to downplay the likely consequences of this technology. There has to be a way to tell people that something will be transformative (quite probably for the worse), and command their necessary attention, without being a cheerleader for it.

For example, the term “World Wide Web” was both completely hype and totally inaccurate when it was coined by Tim Berners Lee in 1989. But after the Internet being ignored by pretty much the entire world for 20 years, “the Web” surely captured the popular mind and brought the Internet to public attention.

Conversely, the terms “SARS-CoV-2” and “COVID-19” were both sober and accurate. But I worry that the clinical nature of the terms enabled people who were already predisposed to looking the other way to do so more easily. Calling it “Pangolin Flu” or “Raccoon Dog Virus” would have been less accurate, but they would have caught people’s attention. “Bird Flu,” “Swine Flu,” and Zika Virus, which have killed very few people in this country get a ton of attention relative to their impact. Surely terminology wasn’t the root cause of our society’s lazy response to the pandemic. But I don’t think it helped.

Now, I am NOT suggesting we develop intentionally misleading terminology just to get people’s attention. We can leave that to Silicon Valley. But I am suggesting that we think about what we want our words to do. Do we value accuracy to the exclusion of all other considerations? In that case, an acronym of some sort may be in order. Or do we also want people to pay attention to what we’re saying?

I don’t have a good suggestion for a specific term that accomplishes both goals (accuracy and attention), but I think we need one. And if we can’t come up with one that does both jobs, and the public conversation settles on “generative AI” or some other term coined by the industry, I don’t think we should spend too much time banging our heads against it or trying to push alternatives. We’ll be better served if our many and valid and urgent criticisms of “generative AI” and its industry are heard by people than by sticking earnestly with a term that lets people ignore us.

If there’s anything we should have learned from the past 25 years of the history of the internet it’s that academics “calling bullshit” is not a plan for dealing with unwanted technology outcomes.

Academics have two very deeply held and interrelated attachments. One is to accuracy and the truth. The other is to jargon. The one is good. The other can cause trouble. I hope in this case our attachment to the first doesn’t lead us to adopt a jargon-y language that enables people already predisposed to ignore the harms of the tech industry to do so more comfortably.

Teaching and Learning with Primary Sources in the age of Generative AI

The following is a (more or less verbatim) transcript of a keynote address I gave earlier today to the Dartmouth College Teaching with Primary Sources Symposium. My thanks to Morgan Swan and Laura Barrett of the Dartmouth College Library for hosting me and giving me the opportunity to gather some initial thoughts about this thoroughly disorienting new development in the history of information.

Thank you, Morgan, and thank you all for being here this morning. I was going to talk about our Sourcery project today, which is an application to streamline remote access to archival materials for both researchers and archivists, but at the last minute I’ve decided to bow to the inevitable and talk about ChatGPT instead.

Dartmouth College Green on a beautiful early-spring day

I can almost feel the inner groan emanating from those of you who are exhausted and perhaps dismayed by the 24/7 coverage of “Generative AI.” I’m talking about things like ChatGPT, DALL-E, MidJourney, Jasper, Stable Diffusion, and Google’s just released, Bard. Indeed, the coverage has been wall to wall, and the hype has at times been breathless, and it’s reasonable to be skeptical of “the next big thing” from Silicon Valley. After all we’ve just seen the Silicon Valley hype machine very nearly bring down the banking system. In just past year, we’ve seen the spectacular fall of the last “next big thing,” so-called “crypto,” which promised to revolutionize everything from finance to art. And we’ve just lived through a decade in which the social media giants have created a veritable dystopia of teen suicide, election interference, and resurgent white nationalism.

So, when the tech industry tells you that this whatever is “going to change everything,” it makes sense to be wary. I’m wary myself. But with a healthy dose of skepticism, and more than a little cynicism, I’m here to tell you today as a 25-year veteran of the digital humanities and a historian of science and technology, as someone who teaches the history of digital culture, that Generative AI is the biggest change in the information landscape since at least 1994 and the launch of the Netscape web browser which brought the Internet to billions. It’s surely bigger than the rise of search with Google in the early 2000s or the rise of social media in the early 2010s. And it’s moving at a speed that makes it extremely difficult to say where it’s headed. But let’s just say that if we all had an inkling that the robots were coming 100 or 50 or 25 years into the future, it’s now clear to me that they’ll be here in a matter of just a few years—if not a few months.

It’s hard to overstate just how fast this is happening. Let me give you an example. Here is the text of a talk entitled (coincidentally!) “Teaching with primary sources in the next digital age.” This text was generated by ChatGPT—or GPT-3.5—the version which was made available to the public last fall, and which really kicked off this wall-to-wall media frenzy over Generative AI.

You can see that it does a plausible job of producing a three-to-five paragraph essay on the topic of my talk today that would not be an embarrassment if it was written by your ninth-grade son or daughter. It covers a range of relevant topics, provides a cogent, if simplistic, explanation of those topics, and it does so in correct and readable English prose.

Now here’s the same talk generated by GPT-4 which came out just last week. It’s significantly more convincing than the text produced by version 3.5. It demonstrates a much greater fluency with the language of libraries and archives. It correctly identifies many if not most of the most salient issues facing teaching in archives today and provides much greater detail and nuance. It’s even a little trendy, using some of the edu-speak and library lingo that you’d hear at a conference of educators or librarians in 2023.

Now here’s the outline for a slide deck of this talk that I asked GPT-4 to compose, complete with suggestions for relevant images. Below that is the text of speaker notes for just one of the bullets in this talk that I asked the bot to write.

Now—if I had generated speaker notes for each of the bullets in this outline and asked GPT’s stablemate and image generator, DALL-E, to create accompanying images—all of which would have taken the systems about 5 minutes—and then delivered this talk more or less verbatim to this highly educated, highly accomplished, Ivy League audience, I’m guessing the reaction would have been: “OK, seems a little basic for this kind of thing” and “wow, that was talk was a big piece of milktoast.” It would have been completely uninspiring, and there would have been plenty to criticize—but neither would I have seemed completely out of place at this podium. After all, how many crappy, uninspiring, worn out PowerPoints have you sat through in your career? But the important point to stress here is that in less than six months, the technology has gone from writing at a ninth-grade level to writing at a college level and maybe even more.

Much of the discourse among journalists and in the academic blogs and social media has revolved around picking out the mistakes these technologies make. For example, my good friend at Middlebury, Jason Mittell, along with many others, has pointed out that ChatGPT tends to invent citations: references to articles attributed to authors with titles that look plausible in real journals that do not, in fact, exist. Australian literary scholar, Andrew Dean, has pointed out how ChatGPT spectacularly misunderstands some metaphors in poetry. And it’s true. Generative AIs make lots of extremely weird mistakes, and they wrap those mistakes in extremely convincing-sounding prose, which often makes them hard to catch. And as Matt Kirschenbaum has pointed out: they’re going to flood the Internet with this stuff. Undoubtedly there are issues here.

But don’t mistake the fact that ChatGPT is lousy at some things for the reality that it’ll be good enough for lots, and lots, and lots of things. And based on the current trajectory of improvement, do we really think these problems won’t be fixed?

Let me give another couple of examples. Look at this chart, which shows GPT-3.5’s performance on a range of real-world tests. Now look at this chart, which shows GPT-4’s improvement. If these robots have gone from writing decent five-paragraph high school essays to passing the Bar Exam (in the 90th percentile!!) in six months, do we really think they won’t figure out citations in the next year, or two, or five? Keep in mind that GPT-4 is a general purpose model that’s engineered to do everything pretty well. It wasn’t even engineered to take the Bar Exam. Google CEO, Sundar Pichai tells us that AI computing power is doubling every six months. If today it can kill the Bar Exam, do we really think it won’t be able to produce a plausible article for a mid-tier peer reviewed scholarly journal in a minor sub-discipline of the humanities in a year or two? Are we confident that there will be any way for us to tell that machine-written article from one written by a human?

(And just so our friends in the STEM fields don’t start feeling too smug, GPT can write code too. Not perfectly of course, but it wasn’t trained for that either. It just figured it out. Do we really think it’s that long until an AI can build yet another delivery app for yet another fast-food chain? Indeed, Ubisoft and Roblox are starting to use AI to design games. Our students’ parents are going to have to start getting their heads around the fact that “learning to code” isn’t going to be the bulletproof job-market armor they thought it was. I’m particularly worried for my digital media students who have invested blood, sweat, and tears learning the procedural ins and outs of the Adobe suite.)

There are some big philosophical issues at play here. One is around meaning. The way GPT-4 and other generative AIs produce text is by predicting the next word in a sentence statistically based on a model of drawn from an unimaginably large (and frankly unknowable) corpus of text the size of the whole Internet—a “large language model” or LLM—not by understanding the topic they’re given. In this way the prose they produce is totally devoid of meaning. Drawing on philosopher, Harry Frankfurter’s definition of “bullshit” as “speech intended to persuade without regard for truth”, Princeton computer scientists Arvind Narayanan and Sayash Kapoor suggest that these LLMs are merely “bullshit generators.” But if something meaningless is indistinguishable from something meaningful—if it holds meaning for us, but not the machine—is it really meaningless? If we can’t tell the simulation from the real, does it matter? These are crucial philosophical, even moral, questions. But I’m not a philosopher or an ethicist, and I’m not going to pretend to be able to think through them with any authority.

What I know is: here we are.

As a purely practical matter, then, we need to start preparing our students to live in a world of sometimes bogus, often very useful, generative AI. The first-year students arriving in the fall may very well graduate into a world that has no way of knowing machine-generated from human-generated work. Whatever we think about them, however we feel about them (and I feel a mixture of disorientation, disgust, and exhaustion), these technologies are going to drastically change what those Silicon Valley types might call “the value proposition” of human creativity and knowledge creation. Framing it in these terms is ugly, but that’s the reality our students will face. And there’s an urgency to it that we must face.

So, let’s get down to brass tacks. What does all this mean for what we’re here to talk about today, that is, “Teaching with Primary Sources”?

One way to start to answer this question is to take the value proposition framing seriously and ask ourselves, “What kinds of human textual production will continue to be of value in this new future and what kinds will not?” One thing I think we can say pretty much for sure is that writing based on research that can be done entirely online is in trouble. More precisely, writing about things about which there’s already a lot online is in trouble. Let’s call this “synthetic writing” for short. Writing that synthesizes existing writing is almost certainly going to be done better by robots. This means that what has passed as “journalism” for the past 20 years since Google revolutionized the ad business—those BuzzFeed style “listicles” (“The 20 best places in Dallas for tacos!”) that flood the internet and are designed for nothing more than to sell search ads against—that’s dead.

But it’s not only that. Other kinds of synthetic writing—for example, student essays that compare and contrast two texts or (more relevant to us today) place a primary source in the context drawn from secondary source reading—those are dead too. Omeka exhibits that synthesize narrative threads among a group of primary sources chosen from our digitized collections? Not yet, but soon.

And it’s not just that these kinds of assignments will be obsolete because AI will make it too easy for students to cheat. It’s what’s the point of teaching students to do something that they’ll never be asked to do again outside of school? This has always been a problem with college essays that were only ever destined for a file cabinet in the professor’s desk. But at least we could tell ourselves that we were doing something that simulated the kind of knowledge work they would so as lawyers and teachers and businesspeople out in the real world. But now?

(Incidentally, I also fear that synthetic scholarly writing is in trouble, for instance, a Marxist analysis of Don Quixote. When there’s a lot of text about Marx and a lot of text about Don Quixote out there on the Internet, chances are the AI will do a better—certainly a much faster—job of weaving the two together. Revisionist and theoretical takes on known narratives are in trouble.)

We have to start looking for the things we have to offer that are (at least for now) AI-proof, so to speak. We have to start thinking about the skills that students will need to navigate an AI world. Those are the things that will be of real value to them. So, I’m going to use the rest of my time to start exploring with you (because I certainly don’t have any hard and fast answers) some of the shifts we might want to start to make to accommodate ourselves and our students to this new world.

I’m going to quickly run through eight things.

  1. The most obvious thing we can do it to refocus on the physical. GPT and its competitors are trained on digitized sources. At least for now they can only be as smart as what’s already on the Internet. They can’t know anything about anything that’s not online. That’s going to mean that physical archives (and material culture in general) will take on a much greater prominence as the things that AI doesn’t know about and can’t say anything about. In an age of AI, there will be much greater demand for the undigitized stuff. Being able to work with undigitized materials is going to be a big “value add” for humans in the age of these LLMs. And our students do not know how to access it. Most of us were trained on card catalogs, in sorting through library stacks, of traveling to different archives and sifting through boxes of sources. Having been born into the age of Google, our students are much less good at this, and they’re going to need to get better. Moreover, they’re going to need better ways of getting at these physical sources that don’t always involve tons of travel, with all its risks to climate and contagion. Archivists, meanwhile, will need new tools to deal with the increased demand. We launched our Sourcery app, which is designed to provide better connections between researchers and archivists and to provide improved access to remote undigitized sources before these LLMs hit the papers. But tools like Sourcery are going to be increasingly important in an age when the kind of access that real humans need isn’t the digital kind, but the physical kind.
  2. Moreover, we should start rethinking our digitization programs. The copyright issues around LLMs are (let’s say) complex, but currently Open AI, Google, Microsoft, Meta, and the others are rolling right ahead, sucking up anything they can get their hands on, and processing those materials through their AIs. This includes all of the open access materials we have so earnestly spent 30 years producing for the greater good. Maybe we want to start asking ourselves whether we really want to continue providing completely open, barrier-free access to these materials. We’ve assumed that more open meant more humane. But when it’s a robot taking advantage of that openness? We need a gut check.
  3. AIs will in general just be better at the Internet than us. They’ll find, sort, sift, and synthesize things faster. They’ll conduct multi-step online operations—like booking a trip or editing a podcast—faster than us. This hits a generation that’s extremely invested in being good at the Internet, and, unfortunately, increasingly bad at working in the real world. Our current undergraduates have been deeply marked by the experience of the pandemic. I’m sure many of you have seen a drastic increase in class absences and a drastic decrease in class participation since the pandemic. We know from data that more and more of our students struggle with depression and anxiety. Students have difficulty forming friendships in the real world. There are a growing number of students who choose to take all online classes even though they’re living in the dorms. This attachment to the virtual may not serve them well in a world where the virtual is dominated by robots who are better than us at doing things in the digital world. We need to get our students re-accustomed to human-to-human connections.
  4. At the same time, we need to encourage students to know themselves better. We need to help them cultivate authentic, personal interests. This is a generation that has been trained to write to the test. But AIs will be able to write to the test much better than we can. AIs will be able to ascertain much better than we can what they (whomever they is: the school board, the college board, the boss, the search algorithm) want. But what the AI can’t really do is tell us what we want, what we like, what we’re interested in and how to get it. We need to cultivate our students’ sense of themselves and help them work with the new AIs to get it. Otherwise, the AI will just tell them what they’re interested in, in ways that are much more sophisticated and convincing than the Instagram and TikTok algorithms that are currently shoving content at them. For those of us teaching with primary sources this means exposing them to the different, the out of the ordinary, the inscrutable. It means helping them become good “pickers” – helping them select the primary sources that truly hold meaning for them. As educators of all sorts, it means building up their personalities, celebrating their uniqueness, and supporting their difference.
  5. I think we also need to return to teaching names and dates history. That’s an unfashionable statement. The conventional wisdom of at least the last 30 years is that that names, dates, and places aren’t that important to memorize because the real stuff of history are the themes and theories—and anyway, the Google can always give us the names and dates. Moreover, names and dates history is boring and with the humanities in perpetual crisis and on the chopping block in the neoliberal university, we want to do everything we can to make our disciplines more attractive. But memorized names, and dates, and places are the things that allow historians to make the creative leaps that constitute new ideas. The biggest gap I see between students of all stripes, including graduate students, and the privileged few like me who make it into university teaching positions (besides white male privilege) is a fluency with names, dates, and places. The historians that impress most are the ones who can take two apparently disconnected happenings and draw a meaningful connection between them. Most often the thing that suggests that connection to them is a connected name, date, place, source, event, or institution that they have readily at hand. Those connections are where new historical ideas are born. Not where they end, for sure, but where they are born. AI is going to be very good at synthesizing existing ideas. But it may be less good at making new ones. We need students who can birth new ideas.
  6. Related to this is the way we teach students to read. In the last 20 years, largely in response to the demands of testing, but also in response to the prioritization of “critical thinking” as a career skill, we’ve taught students not to read for immersion, for distraction, for imagination, but for analysis. Kids read tactically. They don’t just read. In many cases, this means they don’t read at all unless they have to. Yet, this is exactly how the AI reads. Tactically. Purely for analysis. Purely to answer the question. And they’ll ultimately be able to do this way better than us. But humans can read in another way. To be inspired. To be moved. We need to get back to this. The imaginative mode of reading will set us apart.
  7. More practically, we need to start working with these models to get better at asking them the right questions. If you’ve spent any time with them, you’ll know that what you put in is very important in determining what you get out. Here’s an example. In this chat, I asked GPT-3.5, “How can I teach with primary sources.” OK. Not bad. But then in another chat I asked, “Give me a step-by-step plan for using primary sources in the classroom to teach students to make use of historical evidence in their writing” and I followed it up with a few more questions: “Can you elaborate?” and “Are there other steps I should take?” and then “Can you suggest an assignment that will assess these skills?” You’ll see that it gets better and better as it goes along. I’m no expert at this. But I’m planning on becoming one because I want to be able to show our students how to use it well. Because, don’t fool yourselves, they’re going to use it.
  8. Finally, then, perhaps the most immediate thing we can do is to inculcate good practice around students’ use of AI generated content. We need to establish citation practices, and indeed the MLA has just suggested some guidance for citing generative AI content. Stanford, and other universities, are beginning to issue policies and teaching guidance. So far, these policies are pretty weak. Stanford’s policy basically boils down to, “Students: Don’t cheat. Faculty: Figure it out for yourselves.” It’s a busy time of year and all, but we need urgently to work with administration to make these things better.

I’m nearly out of time, and I really, really want to leave time for conversation, so I’ll leave it there. These are just a couple of thoughts that I’ve pulled together in my few weeks of following these developments. As I’ve said, I’m no expert in computer science, or philosophy, or business, but I think I can fairly call myself an expert in digital humanities and the history of science and technology, and I’m convinced this new world is right around the corner. I don’t have to like it. You don’t have to like it. If we want to stop it, or slow it down, we should advocate for that. But we need to understand it. We need to prepare our students for it.

At the same time, if you look at my list of things we should be doing to prepare for the AI revolution, they are, in fact, things we should have been (and in many cases have been) doing all along. Paying more attention to the undigitized materials in our collections? I’m guessing that’s something you already want to do. Helping students have meaningful, in-person, human connections? Ditto. Paying more attention to what we put online to be indexed, manipulated, sold against search advertising? Ditto. Encouraging students to have greater fluency with names, dates, and places? Helping them format more sophisticated search queries? Promoting better citation practice for born-digital materials and greater academic integrity? Ditto. Ditto. Ditto.

AI is going to change the way we do things. Make no mistake. But like all other technological revolutions, the changes it demands will just require us to be better teachers, better archivists, better humans.

Thank you.

Briefly noted for November 29, 2022

Check out these amazing WPA-style posters created by the Department of Energy to mark the infrastructure achievements made possible under the 2009 stimulus bill. I hope this time around, the government doesn’t wait 10 years to start selling the infrastructure and climate bills that passed earlier this year.

Two takes on this year’s tech industry crash: The first, from Derek Thompson, is cultural (the crash is big tech’s “midlife crisis”). The second, from Matt Yglesias, is financial (higher interest rates are making speculation in technology relatively less attractive).

Steven Johnson on the importance of the cassette tape and the way it changed both the sound and the business of music—in many of the same ways that another low-fidelity technology, the mp3, did.

Finally, if you have been wondering what Post.news is, how it’s different from other social networks, and especially how it plans to make money, here’s a primer from Neiman Journalism Lab.

Why STEM can’t answer today’s hard questions

I recently relistened an interview Ezra Klein did with Danielle Allen (Harvard Edmond J. Safra Center for Ethics) in 2019, in which they discuss how science, technology, and business differ fundamentally from politics because the former disciplines assume a set of values that are already ordered by priority (efficiency, profit, etc.) but politics is essentially all about the setting and the reordering of those values. That’s why engineering and STEM have a hard time “fixing” politics and a hard time “solving” more human questions (and perhaps even why STEM majors vote in much smaller numbers than humanities majors).

This is something the pandemic has thrown into sharp relief in the years since Klein and Allen’s conversation. On one level, STEM can “fix” the pandemic by giving us miracle vaccines. But that’s only if we assume a set of values that are held in common by the populace (the health of the community, safety, trust in expertise, etc.) If the values themselves are at issue, as they are surrounding COVID-19, then STEM doesn’t have much to offer, at least for those communities (red state voters, anti-vaxxers) whose values diverge from those assumed by STEM.

This suggests, as Allen argues, that we need to rebalance the school curriculum in favor of humanities education, including paying a greater attention to language (the primary toolkit of politics) and civics. It also suggests the need for more humanities within the STEM curriculum—not just the three-credit add-on ethics courses that characterize engineering programs and medical school, but a real integration of humanities topics, methods, and thinking as part of what it means to “know” about STEM.

This is, of course, something that’s especially appealing to me as a historian of science, but it’s something that should be just as appealing to engineers, who like to frame their work as “problem solving.” If STEM really wants to solve the big problems facing us today, it is going to have to start further back, to solve for more than just technical questions, but also for the values questions that increasingly precede them.

Looks Like the Internet: Digital Humanities and Cultural Heritage Projects Succeed When They Look Like the Network

A rough transcript of my talk at the 2013 ACRL/NY Symposium last week. The symposium’s theme was “The Library as Knowledge Laboratory.” Many thanks to Anice Mills and the entire program committee for inviting me to such an engaging event.

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When Bill Gates and Paul Allen set out in 1975 to put “a computer on every desk and in every home, all running Microsoft software” it was absurdly audacious. Not only were the two practically teenagers. Practically no one owned a computer. When Tim Berners-Lee called the protocols he proposed primarily for internal sharing of research documents among his laboratory colleagues at CERN “the World Wide Web,” it was equally audacious. Berners-Lee was just one of hundreds of physicists working in relative anonymity in the laboratory. His supervisor approved his proposal, allowing him six months to work on the idea with the brief handwritten comment, “vague, but exciting.”

In hindsight, we now know that both projects proved their audacious claims. More or less every desk and every home now has a computer, more or less all of them running some kind of Microsoft software. The World Wide Web is indeed a world-wide web. But what is it that these visionaries saw that their contemporaries didn’t? Both Gates and Allen and Berners-Lee saw the potential of distributed systems.

In stark contrast to the model of mainframe computing dominant at the time, Gates and Allen (and a few peers such as Steve Jobs and Steve Wozniak and other members of the Homebrew Computing Club) saw that computing would achieve its greatest reach if computing power were placed in the hands of users. They saw that the personal computer, by moving computing power from the center (the mainframe) to the nodes (the end user terminal) of the system, would kick-start a virtuous cycle of experimentation and innovation that would ultimately lead to everyone owning a computer.

Tim Berners-Lee saw (as indeed did his predecessors who built the Internet atop which the Web sits) that placing content creation, linking, indexing, and other application-specific functions at the fringes of the network and allowing the network simply to handle data transfers, would enable greater ease of information sharing, a flourishing of connections between and among users and their documents, and thus a free-flowing of creativity. This distributed system of Internet+Web was in stark contrast to the centralized, managed computer networks that dominated the 1980s and early 1990s, networks like Compuserve and Prodigy, which managed all content and functional applications from their central servers.

This design principle, called the “end-to-end principle,” states that most features of a network should be left to users to invent and implement, that the network should be as simple as possible, and that complexity should be developed at its end points not at its core. That the network should be dumb and the terminals should be smart. This is precisely how the Internet works. The Internet itself doesn’t care whether the data being transmitted is a sophisticated Flash interactive or a plain text document. The complexity of Flash is handled at the end points and the Internet just transmits the data.

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In my experience digital cultural heritage and digital humanities projects function best when they adhere to this design principle, technically, structurally, and administratively. Digital cultural heritage and digital humanities projects work best when content is created and functional applications are designed, that is, when the real work is performed at the nodes and when the management functions of the system are limited to establishing communication protocols and keeping open the pathways along which work can take place, along which ideas, content, collections, and code can flow. That is, digital cultural heritage and digital humanities projects work best when they are structured like the Internet itself, the very network upon which they operate and thrive. The success of THATCamp in recent years demonstrates the truth of this proposition.

Begun in 2008 by my colleagues and I at the Roy Rosenzweig Center for History and New Media as an unfunded gathering of digitally-minded humanities scholars, students, librarians, museum professionals, and others, THATCamp has in five years grown to more than 100 events in 20 countries around the globe.

How did we do this? Well, we didn’t really do it at all. Shortly after the second THATCamp event in 2009, one of the attendees, Ben Brumfield, asked if he could reproduce the gathering and use the name with colleagues attending the Society of American Archivists meeting in Austin. Shortly after that, other attendees organized THATCamp Pacific Northwest and THATCamp Southern California. By early-2010 THATCamp seemed to be “going viral” and we worked with the Mellon Foundation to secure funding to help coordinate what was now something of a movement.

But that money wasn’t directed at funding individual THATCamps or organizing them from CHNM. Mellon funding for THATCamp paid for information, documentation, and a “coordinator,” Amanda French, who would be available to answer questions and make connections between THATCamp organizers. To this day, each THATCamp remains independently organized, planned, funded, and carried out. The functional application of THATCamp takes place completely at the nodes. All that’s provided centrally at CHNM are the protocols—the branding, the groundrules, the architecture, the governance, and some advice—by which these local applications can perform smoothly and connect to one another to form a broader THATCamp community.

As I see it, looking and acting like the Internet—adopting and adapting its network architecture to structure our own work—gives us the best chance of succeeding as digital humanists and librarians. What does this mean for the future? Well, I’m at once hopeful and fearful for the future.

On the side of fear, I see much of the thrust of new technology today to be pointing in the opposite direction, towards a re-aggregation of innovation from the nodes to the center, centers dominated by proprietary interests. This is best represented by the App Store, which answers first and foremost to the priorities of Apple, but also by “apps” themselves, which centralize users’ interactions within wall-gardens not dissimilar to those built by Compuserve and Prodigy in the pre-aeb era. The Facebook App is designed to keep you in Facebook. Cloud computing is a more complicated case, but it too removes much of the computing power that in the PC era used to be located at the nodes to a central “cloud.”

On the other hand, on the side of hope, are developments coming out of this very community, developments like the the Digital Public Library of America, which is structured very much according to the end-to-end principle. DPLA executive director, Dan Cohen, has described DPLA’s content aggregation model as ponds feeding lakes feeding an ocean.

As cultural heritage professionals, it is our duty to empower end users—or as I like to call them, “people.” Doing this means keeping our efforts, regardless of which direction the latest trends in mobile and cloud computing seem to point, looking like the Internet.

[Image credits: Flickr user didbygraham and Wikipedia.]

An Unexpected Honor

Yesterday I received a letter from Google addressed to Robert T. Gunther at Found History. As founder of the Museum of the History of Science at Oxford, where I did my doctoral work, and a major figure in my dissertation, I am very honored to welcome Dr. Gunther to the Found History staff. Despite having passed away in 1940, it is my hope that Dr. Gunther will make significant contribution to this blog’s coverage of the history of scientific instrumentation.

E-Book Readers: Parables of Closed and Open

During a discussion of e-book readers on a recent episode of Digital Campus, I made a comparison between Amazon’s Kindle and Apple’s iPod which I think more or less holds up. Just as Apple revolutionized a fragmented, immature digital music player market in the early 2000s with an elegant, intuitive new device (the iPod) and a seamless, integrated, but closed interface for using it (iTunes)—and in doing so managed very nearly to corner that market—so too did Amazon hope to corner an otherwise stale e-book market with the introduction last year of its slick, integrated, but closed Kindle device and wireless bookstore. No doubt Amazon would be more than happy with the eighty percent of the e-book market that Apple now enjoys of the digital music player market.

In recent months, however, there have been a slew of announcements that seem to suggest that Amazon will not be able to get the same kind of jump on the e-book market that Apple got on the digital music market. Several weeks ago, Sony announced that it was revamping its longstanding line of e-book readers with built-in wifi (one of the big selling points of the Kindle) and support for the open EPUB standard (which allows it to display Google Books). Now it appears that Barnes & Noble is entering the market with its own e-book reader, and in more recent news, that its device will run on the open source Android mobile operating platform.

If these entries into the e-book market are successful, it may foretell of a more open future for e-books than has befallen digital music. It would also suggest that the iPod model of a closed, end-to-end user experience isn’t the future of computing, handheld or otherwise. Indeed, as successful and transformative as it is, Apple’s iPhone hasn’t been able to achieve the kind of dominance of the “superphone” market that the iPod did of the music player market, something borne out by a recent report by Gartner, which has Nokia’s Symbian and Android in first and second place by number of handsets by 2012 with more than fifty percent market share. This story of a relatively open hardware and operating system combination winning out over a more closed, more controlled platform is the same one that played out two decades ago when the combination of the PC and Windows won out over the Mac for leadership of the personal computing market. If Sony, Barnes & Noble, and other late entrants into the e-book game finish first, it will have shown the end-to-end iPod experience to be the exception rather than the rule, much to Amazon’s disappointment I’m sure.

Briefly Noted: FOSS Culture; Digital Humanities Calendar; Guardian API; WWW Turns 20

GNOME Foundation executive director Stormy Peters has some advice on bridging the gap between institutional and open source cultures. Useful reading for digital humanities centers and cultural heritage institutions looking to participate in open source software development.

Amanda French has posted a much-needed open calendar of upcoming events in Digital Humanities, Archives, Libraries, and Museums.

The Guardian newspaper unveils an open API to more than 1,000,000 articles written since 1999.

20 years ago today: Tim Berners-Lee produced his first written description of the Web.

Motto

I came across this old quote last night in finishing up David Post’s In Search of Jefferson’s Moose: Notes on the State of Cyberspace. It seems a fair approximation of how things work (should work?) in the new digital humanities:

“We reject: kings, presidents and voting. We believe in: rough consensus and running code.”

David Clark, “A Cloudy Crystal Ball: Visions of the Future.” Internet Engineering Task Force, July 1992. [PDF].

Briefly Noted for February 10, 2009

Jessica Pritchard of the American Historical Association blog reports on a panel at last month’s annual meeting that asked what it takes to be a public historian. Entitled “Perspectives on Public History: What Knowledge, Skills, and Experiences are Essential for the Public History Professional?” the panel was chaired by George Mason’s own Spencer Crew.

Going back a bit to the December issue of Code4Lib Journal, Dale Askey considers why librarians are reluctant to release their code and suggests some strategies for stemming their reluctance. I have to say I sympathize completely with my colleagues in the library; I think the entire Omeka team will agree with me that putting yourself out there in open source project is no easy feat of psychology.

The Bowery Boys, hosts of the excellent NYC History podcast, give us The History of New York City in Video Games, a thoroughgoing look of how New York has been pictured by game designers from the Brooklyn of the original Super Mario Brothers to the five boroughs of Grand Theft Auto IV’s “Liberty City.”

John Slater, Creative Director of Mozilla, rightly notes that, however unlikely, t-shirts are important to the success of open source software. In his T-Shirt History of Mozilla, Slater shows us 50 designs dating back to late 1990s.