Alexis C. Madrigal

The venerable law school casebook has held sway in American law schools since Christopher Langdell created and popularized them at Harvard Law School in the decades around the turn of the century. At first fiercely resisted, they became the dominant way of presenting legal information to students by World War II -- and now, after a hundred or so years, a team at Harvard wants to revamp the casebook, giving it the most significant formal makeover since those early years.

Casebooks contain condensed and annotated legal cases. They are generally put together by a few professors and published in hardback books. Over the last year, Harvard Law professor Jonathan Zittrain has put together a team to create a casebook for the digital age.

Zittrain, developer Dan Collis-Puro, and project manager Laura Miyakawa, will show off the project at Tuesday's Berkman Center lunch at Harvard. You can watch them live at noon eastern.

"Existing casebooks are pretty big. They are pretty expensive. And they stagnate," Miyakawa told me. "What we've been trying to do is create an online casebook that's free, remixable and that can be used not just for a specific class, but for instructors anywhere."

They created a new tool called Collage that lets professor cut down and annotate cases. It's getting at tryout in Zittrain's torts class. If other professors pick up the casebook, they can add their own annotations and see the annotations made by their colleagues. The system could speed information diffusion as professors can see precisely what others are highlighting in important cases.

Collage pairs with Playlist, a way of packaging groups of links together. It's a coursepack, tool, one might say.

No matter how well the whole suite of tools -- collectively known as H20 -- work, they could face a long battle for acceptance. Langdell created his first coursepack in 1870, but it wasn't until the 1890s that his innovation became broadly accepted.

"The vast majority of students, alumni, and law professors initially derided [the casebook] as an 'abomination,' and for two decades case method and the associated reforms were largely confined to Harvard," Ohio State historian of education Bruce Kimball noted in a paper on the rise of the case method.

Zittrain and Miyakawa do have one thing going for them: their casebooks would be free, a decided advantage over their expensive hardbound competitors.

Update 9/21: An Atlantic reader wrote in to note that there is another digital casebook project from The Center for Computer Assisted Learning, eLangdell.

How to Think About... is a video series that provides you with quick frames for thinking about the world's blizzard of technologies and services. The idea is simple: imagine we're having a beer and you ask me, "What do you think about X?"

I switch on the camera and respond. These videos are informal, extemporaneous affairs and we hope they feel like the start of a talk. We'd love to hear how you think about these things, too.

Add games to the list of media that can be turned into political attack ads. Russ Carnahan, the Democratic incumbent in Missouri's 3rd District, recently released a PacMan take-off to hit his opponent, Republican challenger Ed Martin on various ethics-related issues.

The game mechanics are just like PacMan with the exception that your little avatar is Martin's head, and the ghouls chasing you are actually gavels. As you successfully avoid them, your score is noted in "Tax Dollars Wasted." When they catch you, the gavels smush your head and a little political attack ad is shown with the options to continue or read more about Martin's alleged infelicities.

After a few rounds, I think it's a pretty fun game, as far as little casual browser games go. But I have to wonder if being forced to play Martin could actually generate a little empathy for him. In the game universe, you are Martin and the Carnahan campaign's attack messaging comes up when you lose, not when you win.

Hat tip: Patrick di Justo.

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We attempted to define a canon here, but we don't expect everyone to agree to it.

Our tech canon will change over time as new works come out, old ones assume new importance, and readers convince us that other things deserve more attention. Here's a selection of responses we got through our suggestion form.

Nearly every topic has a canon, a set of classics that you need to know. These works are recognized as key touchpoints of analysis and understanding. Technology, though, seems to resist that sort of thing. We think of it as something that is changing too fast for anything to remain relevant for long.

But it is precisely because technology does change that its lasting works are so important. What remains after round after around of creative destruction has proven its value. Many of the works that reach that threshold are scholarship, but certainly not all of them. We tried to reach deep and wide with this canon. We began with more than 200 suggestions from tech writers and scholars on Twitter and whittled them down to this core group.

Of course, no one will be wholly happy with this list. It may be a little too academic for some. Too focused on the recent past for others. Perhaps it's not digital-centric enough.

That's why we're thinking of this canon as a living document. We want your suggestions, which you can send us through this form. We're also more than open to critique or help. This is the start of the conversation, not the end, and we hope you'll help us sharpen our vision of what works deserve lasting glory.

At this point, the actual rankings are approaching arbitrary. These works all stand as great works that deepen and broaden our conceptions not just of what technology is, but what it means.

The great themes of technological art and literature are represented: the control of nature, the control of electrons, cyborgs, artificial intelligence, network building, Gutenberg, the rise of the digital. Read these books. They are worthwhile.

If there is one thing that stands out to me looking at the entire tech canon, it's that history matters in technology because history is how the world got to be the way that it is. (There are two books in the top ten with the word old in their titles.) We might be inventing the future, but it's out of the rags, riches and remainders of the past. We can't escape history, even by making new things.

And why try? It is how people use and shape technology -- where we intersect with our machines -- that determines what the world's possibilities become. If we left out all we humans already know and have made and bring to newly created things, we'd only know half the story. Our bodies and our brains and our ideas and our laws matter.

We are the software that runs the world's hardware.

Ever since the first extrasolar planet was discovered in 1995, astronomers have had their sights set on a much more difficult target: finding an Earth-like planet.

Now, two scientists have made the fairly bold prediction that we're going to find a watery, warm planet such as our own in the first half of next year!

"In the past decades, the number of known extrasolar planets has ballooned into the hundreds, and with it the expectation that the discovery of the first Earth-like extrasolar planet is not far off," writes Greg Loughlin of the University of California, Santa Cruz and Sam Arbesman of Harvard. "Here we develop a novel metric of habitability for discovered planets, and use this to arrive at a prediction for when the first habitable planet will be discovered. Using a bootstrap analysis of currently discovered exoplanets, we predict the discovery of the first Earth-like planet to be announced in the first half of 2011, with the likeliest date being early May 2011."

Of course, we want to find another Earth because we want to find ourselves, intelligent life -- or failing that, just life. Judging by the organisms we have here on Earth, that knocks out nearly every spot in universe, except for the trillions of planets orbiting stars at just the right distance and with the right elemental composition. The easiest planets to discover turn out to be the worst for hosting life because they are big and very close to their home stars.

It's exceptionally difficult to find the habitable ones that we want to. We generally find planets by detecting the way they distort their star's orbit (the wobble method) or by measuring the very slight dimming that occurs when a planet passes in front of its star (the transiting method).

Neither of these is an easy task under any circumstances, but if you're looking for something like Earth in a solar system with a star like ours, you're trying to detect a planet that is 300,000 times less massive than its star.

Astronomers developed special techniques and telescopes to aid the quest. As the years have gone by, we've gotten better and better at spotting smaller and smaller planets hanging out in orbits more conducive to liquid water's presence. We even launched a space telescope called Kepler with the express mission of finding earth-like planets. The Kepler group will release the data on their 400 best planetary candidates next February, which is awfully close to the May 2011 date the Loughlin and Arbesman came up with, a fact that did not escape them.

"It does seem to accord well with outside considerations," Arbesman told me.

The paper was posted to arXiv, a repository for papers in math and physics, and will be published early next month in the open-access journal PLoS One.

Beyond the stunning topline of the paper, it's fascinating to see the field of scientometrics, which tries to measure science quantitatively, in action. It got me wondering: could we use a similar methodology to predict other scientific discoveries or breakthroughs? A better way of asking the question is really: in what circumstances could we imagine trying to extrapolate from current data to some future date?

"It makes the most sense in pretty carefully delimited and defined areas, where we have a very good sense of the properties of that discovery. We know the contours and shape of that discovery," said paper co-author Arbesman.

For other areas, like "finding a cure for cancer," it's not so easy to know what you'd need to discover or create to have a solution. "When will a cure for cancer occur? It will be a lot of successive small things. It's hard to know how to quantify it," Arbesman said.

[via @Brainpicker]

Image: NASA rendering, i.e. not a real photo.

When we ran the story yesterday about Netflix users who've rated tens of thousands of movies, I was hoping that one or more of them would come out of the woodwork.

And so one has. Meet Brian Dear. He's writing a book about PLATO, which he describes as the first online community in the world. And he's rated 20,348. You can decide for yourself which is the more impressive accomplishment.

In any case, after he posted in the comments about how many movies he'd rated, I got in contact with him, asking for an explanation of his motivation and method. Turns out that one reason he did so much rating was as part of his project to evaluate the Netflix user experience earlier this decade. He documented all that. The idea was to get Netflix to change. So not only was he a power user of the rating system, he sought to change the company that built it, too.

Here's why and how he rated 20,348 movies in his own words -- and what he thinks about Netflix's algorithm after all that effort:

Netflix was relatively new back in 2002, and I felt there were lots of good things and a few not so good things about the user interface, and pointed them out and made suggestions for improvement. It wound up getting the attention of the Netflix execs and product team and we had some good exchanges and I felt "mission accomplished."

I rated movies I'd never seen to tell Netflix "no interest" -- in the hopes that if it knew what I did *not* like, as well as what I *did* like, it could only help in terms of recommendations. Another major motivation was that back in those days, one was not able to tell Netflix that one did not want to see **anything** in a particular genre. You can block entire genres now (thanks to my prodding, perhaps). So the only way back then -- so I thought -- to drill home the point to the Netflix recommendation engine that I didn't, say, want sports movies, or TV programs, was to say "not interested" to everything in the genre.

It didn't help, amazingly. I might say "not interested" to 500, 1000 movies or programs in a particular genre, and it would STILL recommend stuff from that genre, which only emboldened me more :-) And to my surprise, all that effort wound up breaking their recommendation engine! To date, they've never fixed it. Something I once pointed out to Reid Hastings, when I bumped into him at a conference. He laughed and told me forget it, they'll never fix it. And so, years go by and I keep renting occasionally from Netflix but I never get any recommendations from 'em.

I haven't rated many movies since 2005. Prolly a dozen or two. In other words, this isn't an ongoing preoccupation.

Take a look at the attached screen grab -- note that Netflix is broken. I've rated 20348 movies, but it cannot make any suggestions, and tells me to rate more "so we can help you find movies you'll love".

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James Watson, credited with the co-discovery of the structure of DNA, is a bombthrower. He loves to cause a stir and make a scene. In his golden years, he's sometimes put that impulse to startlingly off-key effect, such as his comments about race a few years ago. But back in 1971 Watson took a look at the possibilities for human cloning and called on his fellow scientists to step up and come up with a way to deal with its social implications. The subheadline put forward the key question of his Atlantic piece, "Is this what we want?"

The top of this story is a bit of a tough read, peppered with hard nuggets of genetic jargon. But plow through and you'll be rewarded with some deliciously weird stuff about the possibility of the Shah of Iran genetically cloning himself.

Looking back, though, what's most fascinating about Watson's story is that human cloning has not really been an issue. To many people of the time, it seemed damn near inevitable that people would try to clone themselves (or someone). And here we are decades later with a de facto moratorium, despite spotty legal treatment around the globe.

Activation of such eggs to divide to become blastocysts, followed by implantation into suitable uteri, should lead to the development of healthy fetuses, and subsequent normal-appearing babies.

The growing up to adulthood of these first clonal humans could be a very startling event, a fact already appreciated by many magazine editors, one of whom commissioned a cover with multiple copies of Ringo Starr, another of whom gave us overblown multiple likenesses of the current sex goddess, Raquel Welch. It takes little imagination to perceive that different people will have highly different fantasies, some perhaps imagining the existence of countless people with the features of Picasso or Frank Sinatra or Walt Frazier or Doris Day. And would monarchs like the Shah of Iran, knowing they might never be able to have a normal male heir, consider the possibility of having a son whose genetic constitution would be identical to their own?

Clearly, even more bizarre possibilities can be thought of, and so we might have expected that many biologists, particularly those whose work impinges upon this possibility, would seriously ponder its implication, and begin a dialogue which would educated the world's citizens and offer suggestions which our legislative bodies might consider in framing national science policies. On the whole, however, this has not happened. Though a number of scientific papers devoted to the problem of genetic engineering have casually mentioned that clonal reproduction may someday be with us, the discussion to which I am party has been so vague and devoid of meaningful time estimates as to be virtually soporific.

Does this effective silence imply a conspiracy to keep the general public unaware of a potential threat to their basic way of life? Could it be motivated by fear that the general reaction will be a further damning of all science, thereby decreasing even more the limited money available for pure research? Or does it merely tell us that most scientists do live such an ivory-tower existence that they are capable of thinking rationally only about pure science, dismissing most practical matters as subjects for the lawyers, students, clergy, and politicians to face up to?

Read the rest of Watson's "Moving Toward the Clonal Man."

Revisit more pieces from The Atlantic's archives with the Technology Channel.

It's really easy to take the infrastructure of television and radio for granted. They're old technologies; most innovation activity has moved to other sectors. But what we forget is that the system has to be maintained. Things break. Replacement parts have to be made and installed. Storms deliver damage. This video is perhaps the most dramatic visualization of the importance of operations and maintenance that you're ever likely to see. In it, a technician climbs to the top of a tower more than 1,700 feet tall. Most of the time he's not using a rope (which is allowed by safety regulations) and he's dragging a huge bag of tools as he goes up, up, up. You should definitely watch the video. Go all the way to the end. If you're even a little bit afraid of heights, you'll get the emotional fear response that we need to stimulate people into maintaining and rebuilding the country's infrastructure. Last time the nation's engineers told us about the hazardous state of our dams, bridges, roads, levees and pipelines, few listened. Maybe in its own weird way, this video can help us remember that we can't take the things we built in the 20th century for granted.

[via Boing Boing]

It has become a cliche of the cell phone age to hate that other people carry on their conversations in public. Something about hearing half of a dialogue seems to irk those with even the slightest tendency to be tech reactionaries. They are a sign of the particular brand of decline associated with new things. "Why did those phones seem like the embodiment of everything I had to escape?" complains Philip Roth's narrator in Exit Ghost. "They were an inevitable technological development, and yet, in their abundance, I saw the measure of how far I had fallen away from the community of contemporary souls."

But lest you think that it was only the mobile telephone that flummoxed and annoyed early observers, we bring you Mark Twain's wonderful 1880 piece, "A Telephonic Conversation."

Sage Stossel, one of our contributing editors and a living memory bank of The Atlantic's archives, described the piece like this: "In 1880, Twain, bemused by this new device that permitted eavesdroppers to hear only one side of a conversation, wrote an amusing description of overhearing his wife talk on the telephone."

There is something fundamentally wrong about a one-sided conversation, "the queerest of all the queer things in this world," as Twain puts it. It's speech detached from its surroundings and social environment, existing fully only on the electrified line connecting two people.

Twain, of course, makes the dislocations of the new communication mode funny. His liberal use of incongruity feels snarky, but not in a bad way. And maybe that's because he wasn't opposed to the telephone, even if he found some of its aftereffects odd. His family was one of the first to install a telephone in the city of Hartford.

Without answering, I handed the telephone to the applicant, and sat down. Then followed that queerest of all the queer things in this world, -- a conversation with only one end to it. You hear questions asked; you don't hear the answer. You hear invitations given; you hear no thanks in return. You have listening pauses of dead silence, followed by apparently irrelevant and unjustifiable exclamations of glad surprise, or sorrow, or dismay. You can't make head or tail of the talk, because you never hear anything that the person at the other end of the wire says. Well, I heard the following remarkable series of observations, all from the one tongue, and all shouted, -- for you can't ever persuade the gentle sex to speak gently into a telephone

Read the rest of Twain's "A Telephonic Conversation."

Revisit more pieces from The Atlantic's archives with the Technology Channel.

How to Think About... is a video series that provides you with quick frames for thinking about the world's blizzard of technologies and services. The idea is simple: imagine we're having a beer and you ask me, "What do you think about X?"

I switch on the camera and respond. These videos are informal, extemporaneous affairs and we hope they feel like the start of a talk. We'd love to hear how you think about these things, too.

So, you think you've honed the Netflix recommendation engine by rating a thousand movies? That's nothing, according to the company's internal statistics.

Several hundred Netflix members have rated more than 50,000 filmed entertainment programs. 50,000! To watch all those at a pace of one movie or TV show per day, it would take 136 years.

But those users are just the extreme end of a broader behavioral pattern. About a tenth of one percent (0.07%) of Netflix users -- more than 10,000 people --  have rated more than 20,000 items. And a full one percent, or nearly 150,000 Netflixers, have rated more than 5,000 movies. By contrast, only 60 percent of Netflix users rate any movies at all, and the typical person only gives out 200 starred grades.

Who are the subset of users who choose to make evaluating movies into an obsession instead of a casual exercise? They are nurturing the Netflix algorithm, training it. But why?

The two biggest raters I was able to track down had each reviewed in the neighborhood of 6,500 programs. Both are long-time users and neither intended to end up putting so much data into the system. But they were aware that there was an algorithm out there awaiting their input to reshape itself to their desires.

Mike Reilly, a producer, has rated more than 6,500 movies. At first, he just rated movies as they showed up, but then he heard about the Netflix Prize, a high-profile competition to improve the accuracy of the service's predictions.

"I became fascinated with the concept, the different approaches people were taking, and the practicality of these applied theories," Reilly told me.

He didn't employ a consistent methodology, rating in spurts and usually while searching for something to watch. What's fascinating is that Reilly noticed changes in the quality of the Netflix predictions as he rated more and more movies.

"The recommendations are better by far [than at the beginning]. I would say that from 0 to about 500 was pretty useless, at 1,000 to 2,000 it got a lot better -- then tailed off to about 5,000. From then on it's been pretty fantastic," he said. "It's really difficult to find something you simply don't know about -- this new system not only finds it, but can really pinpoint why it thinks you'd like it -- there's not just content, but the context as well, and that's really helpful."

That said, even after 6,500 ratings, the system still recommends bad choices occasionally.

"At this point it's just throwing, like, every Star Trek episode at me -- I've never really seen [that program] and am not interested, but it's like 'this is all that's left so we're going to keep asking, oh, and are you sure you still don't want to watch Mystery Science Theater 3000?'" Reilly said. "It's the same with kids movies."

Lorraine Hopping Egan, a book author, has rated 6,471 movies, but feels that the recommendations she gets aren't commensurate with the time she's invested.

"When I first joined, I went into a ratings frenzy because it was fun to say 'I saw that! I loved that! Overrated!' But mostly, I've rated movies as they popped up, in part so that they would stop coming up and I'd see more missed gems," she wrote to me. "But after 10 years, the recommendations are pretty thin and off-track."

Egan has found herself relying on regular old word-of-mouth and professional movie critics more than the algorithmic recommendations.

Some less intense users seem to get better results. Josh West, a developer here at The Atlantic, had a particularly elegant way of training his algorithm. He got to 416 ratings and consciously stopped starring movies.

"I felt like it knew my taste perfectly. It would predict I'd give a movie 3.6 stars -- and that is exactly how I would feel about it," West told me. "It predicted my rating more precisely than I could because you can only give something 3 or 4 stars, so I just stopped doing it."

Other people have adopted more complicated training techniques. Culture writer and co-founder of HiLoBrow.com, Josh Glenn, rated 2,638 movies in a single morning.

"I decided to rate as many as I could, really quickly, because I was sick of having movies suggested to me that I've either seen already or would never want to watch," Glenn wrote to me in an e-mail. "So I rated every movie I don't like or don't want to see with one star -- for some reason, I don't like clicking the NOT INTERESTED button. I try to save four stars for my all-time favorites. I don't have a system for two vs. three stars, and I don't use half stars."

His system may have worked too well. Now, Glenn, who only watches the movies available online from Netflix, gets very few recommendations. But that doesn't bother him too much.

"Maybe I'm an enabler, but I make excuses for Netflix," he said. "I watch a lot more movies than most people (I think) so I understand why they can't keep me satisfied."

The practice of rating Netflix movies can be hypnotic. If you go into the official page for rating movie, it displays your number of reviews in the upper right. As you rate movie after movie, your score goes up and up. When you really think about it, the Netflix rating system works on the world's simplest game mechanic: do something, get a point, move to a slightly more complex situation.

It's not unlike a casual game, perhaps like Zynga's smash hit, Farmville, a Facebook game in which you raise virtual crops. Except in this case, what you're growing isn't a virtual representation of wheat or tomatoes, but your own personal movie-picking servant, a savant twin of yourself that knows nothing but you and movies.

We'd love to hear your algorithmic training methodologies, or about any extreme feats of Netflixing. Get in touch in the comments or send an e-mail to me at amadrigal[at]theatlantic.com.

H/T to Mike McCaffrey for suggesting the idea of algorithmic farming on Twitter one day.