Recordings by Wikipedia

The Ethics of the Free Culture Movement

Volker E. — Sun, 11 Oct 2009 17:07:16 +0000

Speaker(s) Lawrence Lessig Speaker(s) Jonathan Zittrain

Language spoken English

Date of Recording Sat, 2006-04-08

Tags: creative commons Creative Commons en free culture read-write Wikimania

Player-image:

“[T]he critical thing to recognize […] is that the legal code is not free culture – you are free culture. The legal code is just the ‘plumbing’ of free culture, it’s code”.

In the plenary session of Wikimania 2006 Lawrence Lessig explains his notion of the difference between “read-only” and “read-write” cultures. Subsequently he addresses Creative Commons and the above mentioned idea of legal code as foundation – as the “plumbing” of free culture. Most importantly might be the statements of bringing the licences Creative Commons and Wikipedia’s GNU Free Documentation License closer together to provide people broadest possibilities.

License:

http://www.archive.org/details/wikimania_2006_larry_lessig_plenary

http://wikimania2006.wikimedia.org/wiki/Archives#Lawrence_Lessig_-_The_E…

Licensed under http://creativecommons.org/licenses/by/2.5/

00:00 Welcome to this plenary session of Wikimania 2006! [Applause]

I was thinking earlier today this is kinda like Woodstock for the 21st century. [Laughs] It’s raining, several babies were had earlier in the day and Jimmy Hendrix is playing later today. My name is Jonathan Zittrain, I teach cyberlaw here and at Oxford, I can say without exaggeration the most on what I know on the subject comes from Larry [Lawrence, ann.] Lessig, whom I have know as a colleague, a co-counsel and a law clerk. You can guess who was the law clerk to whom? [Laughs] So I’m very pleased to be able to introduce him, there’s a concept in the blogging world that appears to be having enough prominence that it might make it into the real world – of bridge blogging. People who actually take disparate insular circles of bloggers maybe seperated by cultural or language or other differences and actually runs interference between them. I think of Larry as a bridge lawyer, a bridge entity. He is somebody who can go to audience of engineers and speak their language and tell them difficult truths that they need to hear. He can go to an audience of lawyers and scare them – that’s no small feat. [Laughs] And basically place after place he has a message of hope, despair. He makes you laugh, he makes you cry, he makes you laugh some more. And maybe, just maybe you grow a little in the process. So, he is here, having arrived on the red-eye from the West Coast. He is a little sleepy, I think, so if he falls asleep, it’s not us, it’s him. But in the meantime in order to keep them awake we’ve encouraged him to use a few slides and visual aides to give you a presentation today. So without further ado it’s my joy to introduce Lawrence Lessig. [Prolonged applause]

02:11 Thank you.

So, for about the last nine months I’ve been going around talking about the difference between what I refer to as “read-only“ and “read-write” cultures. I was inspired to start talking about cultures like this by something I read by Tim Woo, describing a story that happened exactly a hundred years ago: 1906. This man, composer of awful, awful music John Philip Sousa went to this place - United States Congress, to talk about this technology he referred to as the “talking machines”. Sousa was not a fan of the talking machines, this is what he had to say: “These talking machines are going to ruin the artistic development of music in this country. When I was a boy, in front of every house in the summer evenings, you would find young people together, singing the songs of the day or the old songs. Today you hear these infernal machines going night and day. We will not have a vocal cord left” Sousa said, “the vocal cord will be eliminated by a process of evolution, as was the tail of man when he came from the ape.”

03:28 Now, it’s this picture I want you to focus on: the idea of young people together, singing the songs of the day or the old songs. This is a picture of culture. It’s a picture of culture we could call using modern computer terminology “read-write culture”. It’s a picture of culture where people participate in the creation and the re-creation of their culture - in that sense it’s read-write. Sousa’s fear was that the capacity for this read-write culture would be lost because of these “infernal machines”. These machines would take it away, displace the practice, and in its place we’d have the mere image of the read-write culture: what we call the read-only culture; a culture where creativity is consumed but the consumer is not a creator, a culture which is top-down, where the vocal cords spread among the people have been lost. That was his fear.

04:33 As you look back, the 20^th century, at least in what we call the (quote) “developed world“ (end quote) hard not to conclude that Sousa was right. Never before in the history of human culture has the production of culture been as concentrated, never before as professionalized, never before has the spread creativity been as effectively displaced and displayed by these “infernal machines”. The 20^th century was the century where this image of read-write got displaced by this read-only culture.

05:14 Now recently as I’ve been hearing myself talk about “read-only”/”read-write” again and again and again, I began to notice that in fact this is something much more general than a story about culture. That in fact, if you think historically, there were lots of contexts in which we’ve moved from “read-write” to “read-only”. Think about the context of labor. In the 19^th century there was a party born, a party called The Republican Party – not The Republican Party of Jefferson but The Republican Party of Lincoln. Its first presidential candidate was a man named Fremont. Fremont’s slogan was “Free soil, Free labor, Free speech, Free men, Fremont”. The Free Labor movement is the part I want you to focus on. Now, in the 19^th century people didn’t say “Free labor - what’s that, labor that doesn’t get paid?“ – they understood that free labor meant free not in the sense of free beer but free in the sense of free to engage your capacities as a laborer. This was the fundamental ideology of the movement: autonomy, the capacity for people to use the means of production to create; they were owners and producers both.

06:34 Now, this ideal of Lincoln and the whole movement that won the civil war was displaced by technology, by industrialization of technology, by the 20^th century, and by the 20^th century it has effectively killed this movement. But the 21^st century has begun to see it return. This is the message of the extraordinarily important, indeed I think the most important book written in our field in the last 20 years, a book by Yochai Benkler: “The Wealth of Networks”. This is the whole theme of his book: how we begin to see the return of this kind of free labor throughout society using network; it is the lesson of the free software movement: people who are creating and are creators, not slaves to wage labor but are themselves empowered to produce. It is what Wikipedia is all about, this idea of labor, what we could call read-write labor.

07:36 So not just labor- also in the context of politics, the 19^th century was born with the founding movements (what we would call a party) called the federalists; this was the party that was Washington’s party and all the original founders, we like to romanticize them but they were extraordinarily elitist aristocrats. They hated the idea of democracy. Really. Not the idea that we wouldn’t be responsible to the people but not really responsible to the people by the people voting on what things should be. That elitism was killed by a popular participatory democracy that was born, again, by another republican party – the party of Jefferson. And that movement expanded as Jackson, and then Van Buren, transformed what politics would be about - it would be party politics, which meant getting millions of people around the country to participate in spreading the message. They consumed the message and then they told their neighbors, in small groups, in small meetings, in writings, in letters to the other, in millions of pamphlets by thousands of different newspapers.

08:48 That read-write politics was again killed in the 20^th century by a kind of broadcast politics: the 30-second commercial, where the whole focus of what it is to be political is about broadcasting a message that people consume. And, again, the 21st century has given us a chance to revive this earlier vision of what politics can be. [Joe] Trippi is the key behind this, and the original part of the [Howard] Dean campaign before it dissolved was an idea of political blogs meaning blogs where people engaged others and forced them to talk and explain their reasons. And by forcing them to explain their reasons - transform them from mere couch potatoes to troops, to people willing to fight to defend the ideas they were trying to push. This is what the net enabled, this active, participatory, read-write politics. And when you put these different spheres together, labor culture politics, we get to see how exactly weird the 20^th century was. It was weirdly totalitarian. Because we had, in the 20^th century, read-only labor, read-only culture, read-only politics. Weirdly powerful centralizing institutions, forces that tried to control and direct how culture, labor, politics developed. Massive images like these(…), and leaders like these(…) [Laughs][Applause] – massively powerful controlling centralized read-only society – that was the 20^th century. Profoundly weird, if you look across history. The one century. No other century in the history of man had been like that. But fortunately, that century is over, that century is past. The 21^st century is the revival of a different way to organize and produce within society. Some could say it’s the same as it ever was. Now, again, freer, read-write society, that’s the potential that we’ve began to see realize through the spread of this technology.

11:13 As you know, in this mix - we could imagine talking about read-write labor, read-write culture, read-write politics - I spend most of my time talking about this: read-write culture. Something that I celebrate as an idea under the name “free culture”, following the ideals and the ethics to demand freedom at its core, that Richard Stallman says it’s “Free Software”, not “Open Source“ software. I’m a believer in this way of getting people to focus on the values behind the movement, and it’s the Internet which has enabled the explosion of this new read-write culture.

But the critical thing to recognize is it’s not just one new culture that’s out there, they’re in fact two, two very different new cultures being produced by the Internet, these cultures are very important, and importantly different.

The first is the new kind of read-only culture: culture of massively efficient technologies to facilitate the buying and consumption of culture produced anywhere. Its ideal is to make it possible that at any time, anywhere, you can buy the culture you want, culture created elsewhere. The poster-child for this form of cultural expression is the Apple Corporation, right? Apple iTunes: 99 cents, buy whatever song you want, play it on the iPod, and only on the iPod, right?[Laughs] but if you do that, in America at least you’re guaranteed to be cool.[Laughs] And it’s not just music now - videos too, download to your iPod, and it’s not just Apple, Amazon, experimenting with paper page ways of selling books, eBook Reader is experimenting with paper-read ways of selling books, the point is that all of these contexts were trying to find ways to increasingly perfect the power of the copyright owner to control how people consume culture. That’s the read-only internet.

13:11 But at the same time, there’s another Internet being created, Internet being created by companies like these (…), companies that are interested, of course, in people consuming culture, but not just in consuming – creating and sharing their creativity. You live in this space. I don’t have to show you much of these things but some of them are fun so I’m going to show you some of them: How many people have heard of anime music video? I’m sure there’s a Wikipedia entry about it so these are the people who wrote it, okay? Anime music video? You don’t know what anime is? The Japanese cartoon spreading across American kid culture at least, anime music video is created by people taking these anime and re-editing them and setting them to music. So I’m going to show you two quick clips, all the artwork here is found art, just been re-edited and set to the musical track. (…) 15:22 So this is a remix, right? It’s a remix of culture [Applause] …that is built upon the capacity of this technology and, of course, it’s not just AMVs (anime music video) that are doing this kind of remix, you’ve seen it in the context of music: we all know this album by The Beatles called “The White Album” inspiring this album by Jay-Z called “The Black Album”, inspiring this album by DJ Dangermouth called “The Gray Album”, which synthesized the tracks of “The White Album” and “The Black Album” together to produce something “Gray”.

Or in the context of ordinary life, in 2004 this film “Tarnation” made its debut at Cannes, said by the BBC to “wow” Cannes – this is a film made for 218 dollars. Kid took video that he had shot through all his life, and with an iMac given to him by a friend, he remixed it to produce a film that could wow Cannes and win the 2004 Los Angeles International Film Festival. Or here’s a new favorite of mine, remix in the context of religion (…)

18:36 So this is the read-write culture that the Internet is producing. It is digital creativity, and, of course, what you see here is nothing new in the capacity of film-makers or television studios, this has been around since television or film was born. What’s new here is that these tools are democratizing the capacity for people to take sounds and images from the culture around them and remix them in a way that says something. Now, those of us who spend our life as academics writing texts, like to believe that people understand the world through the texts that we write. But we increasingly need to recognize that writing words is the Latin of our modern time. And the ordinary language of the people, the vulgar language of the people is not words, it’s this: video and sound. And what these technologies have done has spread the capacity for ordinary people to use this way of speaking to speak more powerfully. These are tools of creativity that are now tools of speech, it is a new potential to speak, it is a new potential to learn, it is a new literacy in this age. And it’s reviving the capacity of this read-write culture.

19:55 So there are two radically different cultures here produced by the Internet, and, of course, the point that none of you don’t know, is that the law’s attitude about these two very different cultures is radically different. The law, copyright law. Copyright law doesn’t like the read-write culture, copyright law loves the read-only culture, copyright law supports the read-only culture and weakens the read-write culture in the current way that it is architected. The current way that it’s architected says that to use culture in a digital context, because every single use produces a copy means every single use presumptively requires permission. A free society, a free culture shifts to a permission society merely because the platform through which we get access to our culture happens to make a copy every time we access and use our culture.

Now, that conflict would be bad enough, but it’s exacerbated by this war, the current war to protect the business model of the read-only culture. And that war will have the consequence – if allowed to run its course of killing the potential of the read-write culture. And it’s to resist this potential to destroy what the read-write culture is, that many of us fight to get a different balance in the law. Because the tools – or in the terminology of the time the “weapons“ being deployed to fight the (quote) “piracy” that happens on the network – in order to protect the read-only culture, the weapons of law and technology (law: new laws, and technology: digital rights, management technology, etc.,) will kill the potential here unless they are resisted.

22:00 So how should they be resisted? Well in this very place, when I began to learn something about this, about eight years ago, my first instinct as a lawyer was “Oh there’s a simple solution, let’s litigate. Let’s file a law suit. Let’s go to the Supreme Court and say to the Supreme Court: «You conservative justices, look to your principles, your principles that say the original intent of the constitution should control, because we all know the framers would never have embraced the conception of control the copyright law has become”. So I raced to the Supreme Court, there I am, in the Supreme Court, standing in front of the Supreme Court, arguing case getting the Supreme Court to say “Yes, the framers values will control here” – because of course these conservative justices had said this in context after context… oh well, maybe just conservative context… but context after context before. This is about my last moment as a naive law professor, that’s me, do you see the kind of happy, powerful look I have on my face right there? Because shortly after this argument, on a 7-2 vote, the Supreme Court said “Oops.. Framers-shmamers, who cares, congress gets to do whatever the hell it wants with copyright, gets to interpret the constitution however it wants”. So the idea that we were going to go to the Court so we can get the Court to defend the values of free culture was in my view just simply wrong.

23:25 What we needed was to excite a popular movement enough so that the politicians and the judges begin to recognize what the important values here are. And that’s when a bunch of us shifted our work to private means, to begin to demonstrate the value of free culture, so that the political system can wake up to it.

And there are two critical private steps that I think we need to take: one is that we need to practice free culture, building and demonstrating and showing people its value in a thousand different contexts, and the second is that we need to enable free culture. We need to make it possible everywhere, not just in the “hackers’ den“ – but in schools and in universities and in public places all over.

Now, this practice of free culture is what you do. It is the celebration of the Wikipedia success: this read-write encyclopedia, of course, was impossible to conceive in the 20th century’s mind: too complex, demands high quality, and the percentage of probability here that volunteers, uncontrolled by any central controller, could create something successful through such an experiment, is about the same percentage that volunteers uncontrolled could create something called an operating system. [Laughs][Applause]

And it was just about the time that the world was surprised by the GNU/Linux operating system, that the world began to see that these things that were impossible were impossible in the 20^th century’s mind. And we began to see how it was possible now, in the 21^st century, with this infrastructure of technology, to enable the freedom to create. You showed us how this was possible, building on an infrastructure and architecture that enabled this work, an end-to-end architecture of the internet, which enabled and empowered this read-write creativity.

And to come here, as you do in this conference, to celebrate that freedom, to celebrate that creativity, to celebrate this essential capacity of humans that you are teaching everybody in the world is the most exciting thing that could happen on such a miserably hot August day in this place called Cambridge.

26:02 Now, there are lessons though, that we should learn about how this extraordinary potential is made possible: things that we’ve learnt about the natural dynamic of institutions playing in spheres of creativity. Because if you look over the last fifty years there is a constant pattern repeated, and we should learn lessons from this constant pattern, or we should re-learn lessons – and the one I want to focus you on here is the lesson of interoperability.

There is a constant pattern of instinct in the development of technology: the proprietary instinct; it’s natural, people have this, I’m going to develop it, I’m going to control it, but the lesson is the lesson that freedom is a bigger, more important, value. We’ve learnt this again and again.

The Defense Department opened our thinking here – bizarrely – because of their strong resistance to the idea that computer companies were constantly presenting them with, which was: “You want to buy IBM equipment - everything you do has got to be IBM.”, “You want to buy Data General equipment – everything you do has got to be Data General.” The idea that these computers could talk to each other – not possible, right? This was an era of autistic computing. Very smart machines that just couldn’t talk, and the Defense Department said “Enough. We’re not spending millions of dollars on autistic machines. We’re going to end this” – and that began the movement to develop standards to facilitate interoperability among competing, competitive, platforms. 27:45 But, of course, the instinct doesn’t die with the defense Department’s decree. How many people recognize this little machine? The IBM PS/2? This machine, of course building on the success of the DOS operating system, had this weird trick where, at least a certain number of versions of this, the 3.5-inch diskette would only work on other IBM PS/2 machines, even though it was the same operating system running on Compaq, running on anything, it was coded to make sure you had to be using your disks with IBM PS/2 machines. Why? – Well “if we stop interoperability, we can protect our power”, that was their idea. Understandable, but stupid idea. Because market forces, and in some cases the government, and consumers in particular reject this idea of control. It’s understandable, and, of course, it’s good if you can get that power to control how people develop the platform that you have given them, but the thing we have to focus on is – good for whom? Because it’s much better for a society, for innovation, for what it is, to be human in this platform, that there is interoperability and free standards facilitating the widest range of competition and the opportunity for the widest range of people to build on this platform.

That’s the lesson that we’ve learnt – I think we’ve learnt – from the last 25 years, of how this network has developed. And that lesson is something which we should remember as you practice free culture.

But the second thing I think we need to be doing is to work to enable the platform of free culture. It’s not enough to build the infrastructure, we have to make it possible for this infrastructure to encourage the kind of freedom that we think is essential. And there is a clear and present danger, a threat, to this freedom. It’s a threat waged because of this fight against (quote) “piracy” in the context of the copyright wars, wars which my friend Jack Valenti [Chairman and Chief Executive Officer of the Motion Picture Association] refers to as (quote) “terrorist wars”, where apparently our children are the terrorists in these wars. [Laughs]

30:03 Right now the point that we see, that nobody… well, ten other people outside of this room understand, the point that all of us understand, is that when they build the locks to protect the read-only internet, when they finally layer that into the infrastructure, that will lock out the potential of the read-write internet. And so to enable this freedom is to enable it technically by joining the fight of the Free Software Foundation and many others against DRM [Digital rights management] by supporting free code access to allow content of freely flow across different platforms, to support free culture, free software (meaning free software that is enabling free culture too). These are important steps each of you can take should take to facilitate the spread of free culture here. That’s at the technical layer. But we should also enable it at the legal layer; we need to enable a platform, one that protects the freedom of free culture.

31:10 Now, here at Harvard we, when I was here, came up with this idea of trying to do this through boring legal tools that began the Creative Commons movement. It’s a movement that stole a little bit of the genius of Richard Stallman, using copyright law to carve out a space of freedom. It was a movement that said “Give a simple way to authors for them to mark their content with the freedoms they intend their content to carry.” Shifting the default from All Rights Reserved to Some Rights Reserved lots of important freedoms granted – and that’s what the ground in Creative Commons license is about.

31:50 These licenses, as you know, come in three layers: one designed to teach ordinary humans what the freedoms here are – that’s the human-readable Commons Deed, one designed to teach the lawyers what the freedoms here are – a billion page legal document called a license, and one designed to speak to computers about what the freedoms here are: the machine-readable RDF, which makes it possible to identify content on the basis of the freedoms, a facility which Yahoo! and Google have now used to create special search-portals, where you can search the web filtering on the basis of the freedoms associated with the content.

32:27 This movement began in the United States and then immediately we saw it had to be spread internationally. So we took this three-layer model and we said “Let’s create at the legal layer ports of a license.” Porting the code from one jurisdiction to another (just like you would have to port if you wrote code for the GNU/Linux operating system and then on to something like the Windows operating system) – you port: you’ve got to radically change it but you want it to function in the same way on these different platforms. That was the objective, there are now more than 70 countries in the process of this porting.

This is a picture of the free world as it exists right now: green countries have launched CC licenses, yellow countries will launch in the next 6 months, red countries are yet to be “liberated” in this process.

33:18 And as these licenses have been ported, the growth in the uptake of the licenses has increased. We launched in December 2002, in a year there were about a million linkbacks to our licenses, in a year and a half there were 1.8 million, at two years there was about 4 million, two and a half years there was about 12 million, at three years there’s 45 million linkbacks (three years is last December). In June we announced there are 140 million linkbacks to our licenses out there. This is the idea – to build this, spread this, support this platform of content that explicitly says “We’re free for you to use“ and protects it using the legal code.

34:04 Now, the critical thing to recognize in this story is that the legal code is not free culture – you are free culture. The legal code is just the “plumbing” of free culture, it’s code, like TCP/IP, implementing a free network but not itself the free network. The network is technical. The culture that gets built on top of it takes the real work and deserves the real praise, deserves the celebration of free culture – not the licenses.

34:41 Licenses are nothing more than tools we’ve developed to minimize the harm of out dated legal systems, to minimize the harm and the cost of these evil creatures called lawyers, to make them tiny in the process of building free culture, to make it seamless for people to do what they want to do - express the freedoms and build on freedoms of others – in a simple way. To make it simple for them to practice this free culture. That’s the read-write culture that we’re supporting.

35:15 More than anyone else in the world, you have ignited the imagination around this extraordinary and exciting possibility of free culture again. The best evidence I have of this is, again, this book by Yochai [Benkler], which just “spores” unbelievable amounts of praise on top of you guys as he talks about what the future is going to be. He’s obsessed with the genius of what you guys have done. And he’s not a guy who gets easily obsessed by things, and he doesn’t praise people too easily, but he is symbolizing what the potential here is by teaching the world in this – I think the most important – book how this network will enable a certain kind of creativity. His praise is praise you should feel and be proud of. And it is in part to praise you that I climbed on a plane at midnight last night and didn’t sleep until… maybe in 10 minutes, to come here and talk. But not just to praise.

36:22 For I come here also to make a plea, a plea to you who built free culture to see what you’ve done and to see the power that you now have and to use it to do good, to enable free culture generally.

And there are two ways (there are many others too but there are two ways I want to identify here): one is to help others spread the practice that your extraordinary demonstration exemplifies. And we’re beginning cooperation between Creative Commons and you to do this through a project that we call the PDWiki Project. The idea of the P(ublic)D(omain)Wiki Project will first launch in Canada, where a company that has the full database of all published works in Canada since 1900, has agreed to turn over that database. That database has all published works and all authors obviously of those published works, and this Wiki Project will invite a Canadian community to begin to build Wiki entries around these authors and the works – reviews, criticisms, other works in the public domains maybe links, whatever the community wants to build. But one part of this process will be to add the information about the authors necessary, so that we can begin to determine what work is and is not in the public domain. A determination which no government has yet decided is important enough to make to make it possible for people to build upon the public domain. And eventually, on top of this Wiki an API will be implemented, so anybody in the world can pin and for free get information about what work is in the public domain and what work is not in the public domain.

This is to take the practice you’ve demonstrated in this impossible context, and to now do it in the what most people would think of as an extraordinarily boring context, the boring context of just figuring out tiny little details about what authors of Canada are like, but it excites more people to this idea of free culture that you demonstrate.

38:32 And the second thing, the second practice I come here to make a plea for is that you demand a usable platform for freedom here. Now, my understanding of what this demand should look like came from a conversation I had with Jimbo [Wales][co-founder and promoter of Wikipedia] someplace in Europe) I know it was Europe cause the coffee we were drinking was so awful, but I can’t remember exactly where it was)… [Laughs]

– Budapest!

…No, it wasn’t actually, I can’t remember – anyway, so we’re walking around a park and talking about the problem that all of us recognize – the lack of interoperability among free culture projects.

Islands of freedom: yours, Creative Commons [CC], The Free Art Licenses (which have produced, again, artistic freedom) – content that can’t talk to each other. Great freedom, great cultures, great islands but that can’t interact; can’t take Wikipedia content and mix it with the Creative Commons Share Alike picture of movie without violating the terms of the licenses.

Now, all of us recognize this is a bad design. But I confess my first instinct was the instinct of my profession: control. I identified the limitations in the FDL [Free Documentation License] and I wanted to find a way to convert all users of the FDL to the CC licenses. So we’d have a hundred billion licenses out there that were CC.

40:00 But as I listened to the quiet, repetitive message from the words of Jimbo [Wales], and more importantly, the ethic that he was trying to push in this conversation, I realized that that instinct was a mistake. We don’t need more monopolies here of anything, what we need is a layer (like the TCP/IP-Layer) which facilitates interoperability of content, allows content to move (between quotes) “equivalent – let me quivel about that in a minute – licenses”, where what we mean by equivalent is licenses where people mean the same thing. So, the FDL and the Creative Commons by ShareAlike is saying the same thing: use my content however you want, to copy them, modify, as long as you give me attribution, as long as the modification is distributed under an equivalent license. It’s the same thing as what the FDL is doing, what the GPL is doing, all of them.

So the derivatives from one license under the existing system can’t be used under another, can’t be re-licensed under another.

41:09 And as this conversation evolved, in my mind at least I recognized that a solution here (maybe not THE solution) but a solution here, would be to facilitate an infrastructure where content licensed under one type of license can be re-licensed in a derivative form, when a derivative is made – under another equivalent license.

The virtue of this system is that it invites a diversity in the underlying legal code. Just like we have a diversity in the code implementing the protocols of TCP/IP, an ecology of this underlying legal code, different efforts to facilitate the same functionality in equivalently functional ways. Just like the way the web originally was architected, where no one architecture must control all implementations of the values here, so that there’s no single point of failure if one license gets deemed to be problematic by some legal court for some reason.

And more fundamentally, in a perfectly hierarchian way, that there’s a kind of market signal about which licenses are valued by the users of those licenses. So as we see people re-licensing from the BSA License to the Free Art License, we get a signal that there’s something about the Free Art license that people (at least in this market) value more. And that signal is a discipline to those who would compete to provide this plumbing, to make the plumbing as good as it can be. This legal code then would be signaling the strengths and protecting against the weaknesses of any particular license, through the competition that all commodity layers invites. The legal layer would become in this sense that commodity layer giving people the power to do what they want, without exerting any monopoly power over how they might do that.

43:25 Now, I don’t believe Creative Commons is the entity that should run this structure. What I think the structure should do is enable a process where we can certify licenses as “close enough”, and invite the license authors to add a clause to the license, a clause that says “content licensed under this license can be re-licensed…” – No, sorry: “Derivatives of content licensed under this license, can be re-licensed under any equivalent license“ – where “equivalent” is determined by some public body, so that you permit migration in the process of making derivatives.

We’ve proposed that we (I sit on both boards, so it’s a little bit disingenuous to say that we’re really giving it away, but) we’ve proposed that The Software Freedom Law Center, center of the Eben [Moglen] chairs – be the entity that tries to structure and raise the funds necessary to support this process, a process to facilitate federation of free licenses, in a way that will assure the kind of competition and security that competitive layers produce.

Now, depending on the day I’m optimistic about this process – maybe it’s the flights at the moment I’m not quite optimistic about – whether it will work at least not yet do we have a commitment from all of us involved, to the simplest, most efficient design for this project. Not yet have we rejected what I think it was an ethics that sounds more like the ethics of the IBM PS/2.

Now, it’s here where I think you have some influence, you have some power. You have the power to decide what is the best system here, and to demand it. Might be mine, might not be mine, but you have the power to demand that that be the platform; so it’s not just good for you and your work as you develop the free culture, the Wikipedia project, but all who would like to develop and support the free culture project. A platform that invites the kind of competitive innovation and use of this project.

The good you could do here is extraordinary, because if we don’t solve this problem soon it is an ecology, an environmental problem that we will face in three, five, eight years, as these islands of creativity find no simple way to interact and maintain the objective of the creator – which is not to spend his life tinkering with legal code, not to become plumbers but to become authors. You could do good here, you should do good here.

46:25 Now, Tom Brokaw told us that my parents’ generation was the greatest generation – I think we should also recognize they lived in the weirdest century. [Laughs] Not everyone in that century was weird in this sense, not everyone was a weirdo at least from our perspective there were certain people who “got it”.

Here’s one of my heroes, Dave Clark, this is what he wrote at early stage of the development of the Internet: “We reject kings, presidents, and voting. We believe in rough consensus and running code” – that’s an ethic. It’s an ethic about making the system work not through the exercise of power from one central place, but through the practice of creating. That’s one person who maybe was a weirdo in the 20^th century but is not a weirdo in the 21^st century.

Here is a second person who may have been a weirdo in the 20^th century but is not a weirdo in the 21^st century, he’s a hero of the movement that all of us participate in. In 2002 this is what he [Richard Stallman] said about this movement: “If we don’t want to live in a jungle, we must change our attitudes. We must start sending the message that a good citizen is one who cooperates when appropriate.”

You are good citizens, all of you are practicing this kind of citizenship. And you need to inspire others within this movement to practice the same kind of citizenship. It’s an honor to be able to address you, but I plead, I plead with you, that you use the great capital that you’ve built, to do good far beyond the particular place in which you happen to have demonstrated the success and virtue of freedom.

Thank you very much. [Prolonged Applause]

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Stephen Wolfram on Wolfram Alpha

Volker E. — Wed, 30 Sep 2009 17:02:02 +0000

Speaker(s) Stephen Wolfram

Language spoken English

Date of Recording Wed, 2009-05-27

Tags: Wikipedia Wikipedia en Mathematica NKS NKS Radio Berkman

Player-image:

David Weinberger interviews Stephen Wolfram on his highly praised “computational knowledge engine” Wolfram Alpha shortly before it was launched publicly for Radio Berkman.

“[…]asking if we look at the world, the universe as it is, and you know,what are the kind of underlying primitives, what are the computational,the simple programs that can potentially drive all of this stuff, andWolfram Alpha it’s sort of the realization that all this knowledge thatis out there in this world […]”

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Image URL: http://www.flickr.com/photos/hybernaut/87907765/
License: CC under Attribution-Noncommercial-Share Alike 2.0 Generic

00:00 Hi there. You are listening to a special release of Radio Berkman today. For the first time we’re releasing full audio from our most recent interview. David Weinberger’s interview with Stephen Wolfram, the founder of the computational knowledge engine Wolfram Alpha, will still appear as its own episode next week. But for now you can listen to the full 55 minute interview. Enjoy!

David Weinberger: 00:23 What is Wolfram Alpha?
Stephen Wolfram: So, our kind of best three-word description is “it’s a computational knowledge engine”, so its purpose is to take the knowledge that has been accumulated over the course of years, and those parts of it that can be computed with, to be able to compute with them and to be able to generate answers to specific questions using a sort of corpus of existing knowledge, which is in the data, the methods, the models and so on, that had been accumulated - put them in computational form and be able to answer sort of any specific question, where the answer can be computed.

DW: 01:06 What reported typical question do you have in mind for this?
SW: So, there can be sort of pure formal questions, that are like mathematical questions, like “What’s the intercall of this or that?”. There could be questions about the world, like “What’s the population of such and such a country?”, “What was the weather like on a particular day in a particular place?”. There can be things that sort of combine some pure data with computation, like for example, “Where will a particular… What will the tide be on a particular day at a particular place?”, where you have to know some geographical data, some historical information about tides, and then you have to compute the physics of the tides to work out what will happen. And there are things which are both… where the answers are both numbers and quantitative kinds of things, and where the answers are more kind of textual or symbolic.

DW: 01:59 The answers that the site gives are not simple one-word answers or numbers, they give that too, but you are including a range of types of information that somebody might want…
SW: 02:10 Right, so the idea is: “What would an expert do if you ask them this question, what would they actually tell you, would they just tell you one answer, would they give you kind of some background, would they give you some related answers?” Kind of the idea is to generate a report that sort of gives you both, that gives you sort of all versions of what you might immediately want to know about a particular thing. So that would be typically some graphical presentations, some tables of data, some… maybe some synthesized text, these kinds of things. It’s sort of a big challenge to sort of do this kind of automated presentation, where you try and take out and in some cases where there might be a huge amount of stuff that you could compute, the problem is, to sort of pick out those things, that are most kind of immediately cognitively useful to the person who asked that question.

DW: 03:03 In the demonstration that I’ve seen, not a lot of the answer text, the answers that are given, are links out, or links to more information in fact. Is that on purpose or is that to come?
SW: Well, so, it’s hard enough to get kind of within one kind of coherent system, to sort of build up the things that we think are worth presenting to people so to speak, without having to worry about whether some external link goes to something that’s meaningful or not. Generally what we are trying to do is to try and have this be kind of, this is giving you kind of the report that you want, there is some drill-down that you can have, which might be an infinite amount of drill-down that’s possible from… So, for example, the typical thing would be, there’s some table of results, and then there’s a little button that says “more”, you press “more”, you press it once you get more stuff, you press it again you get even more stuff, you can keep pressing it sometimes forever, and that’s kind of the main way to kind of get more information, is something sort of within this controlled environment that we have set up.

Now, we also have a few links out, but we also have a sidebar where we intend to put sort of related links to things that people might find useful given the particular questions that they are asking. And often those links will be to things that are complementary to the kind of sort of direct factual material that we are giving. Things that, for example, a linking out to, I don’t know, Wikipedia or something, where there is some narrative description of some particular topic that kind of complements, the just the facts kind of results that we’re giving on Wolfram Alpha.

DW: 04:45 There aren’t also a lot of links internally to more of your own results. And so, (again, just seeing the demonstration, I haven’t played with it and besides it’s not alive yet) the visual sense seems to be: you’ve got your answer, it’s a rich answer, there’s lots there, but this is the definitive endpoint of your inquiry. As opposed to having internal links that would say, “here is the answer and please explore more click, and get more… results.
SW: Right, so the “more”-thing, there really is a pretty… sort of a pretty elaborated world underneath all this “more”-buttons and so on. I mean, there is really a lot to explore there. If it’s a question of “go off and ask another related question, or a question about some entity that happened to come up here, then yes there are, there is a pop-up that comes up and there are links that pop up that you can go and make queries about the entities that appear. I would say there’s probably more that could be developed about that, about the way that those pop-ups work and the way that one kind of goes to explore more things. Our big emphasis has been: once we’re in an area where we can actually could compute things, we can often compute a lot of stuff, and the big issue is: what’s actually useful to show somebody, sort of at the first level? And for the people who have been developing all these wonderful computations it’s sometimes a little disappointing when I say “Look, we can only put ten results here on the first thing people see, and the rest has to be based on drill-down” because, you know, it’s not useful to give 50 things on one page, nobody is going to be able to pick out the thing that’s actually useful to them. But so there’s a pretty rich world of sort of additional stuff that you can get through just by pressing buttons and pulling down pull-downs for alternate views of things and so on.

DW: 06:40 So how does it work? It’s pretty impressive.
SW: There are two key kind of foundations that make Wolfram Alpha possible, and then there are a variety of different sort of pieces of technology that have to be assembled, to create the experience that we are setting up. So in terms of the sort of the foundations, one of them is Mathematica and the other is my NKS – New Kind of Science direction. Mathematica is sort of the underlying infrastructure, the language, the platform on which the site is built. I’ve been working on developing Mathematica for 23 years, so it’s a big thing that has had lots of developments and has had a nice sort of accelerating pace of developments in recent years, particularly as sort of all the pieces that we have assembled over the years that really sort of fit together according to the principles that we have laid down, sort of seem to be interacting in a nice way, that lets the system sort of grow very rapidly, so kind of the one important sort of answer to “How does it work?” is “It’s a big mathematical program.” It’s quite big, it’s five or six million lines of mathematical code, and most measurements one can make is perhaps ten, maybe twenty times more succinct as a programming language than a typical kind of, you know, C, Java type programming language. So that’s a lot of code but if one was doing it without the Mathematica platform, I think it is not a manageable kind of project to be doing at this point.

DW: 08:23 So Mathematica enables you to do mathematical computation but also symbolic and logical computation as well?
SW: Perhaps the biggest… Mathematica was such a great name for Mathematica when Mathematica was young, and the thing that one needed to know about what it did was: it does mathematics on a computer, in a sense it’s been… I had sort of hoped that the field of mathematics would have grown up to the point where the kinds of things that Mathematica does are actually considered mathematics. In a sense it’s a misnomer, and has been for a very long time because Mathematica has grown far beyond the doing of mathematics, it’s really a general environment for doing sort of anything that is formal and technical and so on. It has been kind of a place where we can fit together sort of all the algorithms that exist for practically anything. And we have sort of maintained these kind of unifying principles for the system that have been just wonderful in terms of developing new capabilities, because it means, you know, we are adding something that is supposed to be some kind of numerical process for working out such or such a thing, but, because we have this kind of very unified system, we are able in the inerts of that numerical thing, to be calling on all sorts of, you know, computational geometry, or some other, you know, kind of…, I don’t know, algebraic algorithm, or anything like this. So it’s really, it’s… Mathematica itself kind of outgrew the bounds of its name a long time ago.

10:03 What’s happened with Wolfram Alpha, one of the things that… Well, people used to say, they used to say “This Mathematica thing is obviously really powerful…” and lots of people around the world, lots of sort of leaders and RND in lots of places, lots people being educated, people doing educating bill use Mathematica a lot. But people said, “This is really a powerful thing, you really should be able to, you know, why are you even selling it to people, I mean, why don’t you just you keep it for yourself and build something great with it?”. Wolfram Alpha is probably the largest project that’s been done with Mathematica (I don’t necessarily know everything that everybody has done all around the world with Mathematica but ) 10:45 what Mathematica provides for us is kind of a uniform way of representing knowledge in symbolic form, and applying… and we already have in Mathematica a zillion algorithms, for operating on that kind of symbolic representation, whether it is in a purely mathematical way, whether it’s in a sort proving theorems and some logical way, whether it is representing things graphically in some way, these kinds of things. So, that’s kind of the technical platform that makes Wolfram Alpha I think conceivable. From a conceptual point of view, I think, it’s sort of the NKS New Kind of Science thing that makes Wolfram Alpha kind of conceivable. I mean, I’ve kind of wondered, you know, “Wolfram Alpha” is kind of the third big project I’ve tried to do in my life, Mathematica and “NKS” are the other two, I kind of have this meta-theory, that most people have at most one big idea in their lives, 11:46 so if I’ve got three big projects, they are really all the same idea, and they are really all about kind of taking a sort of a rich set of things that one is interesting in dealing with and understanding what are the sort of underlying primitives from which one can build up all those things, whether it is in the computer-language of Mathematica having sort of computational primitives that level with build-up programs, whether it is in the case of NKS, asking if we look at the world, the universe as it is, and you know, what are the kind of underlying primitives, what are the computational, the simple programs that can potentially drive all of this stuff, and Wolfram Alpha it’s sort of the realization that all this knowledge that is out there in this world, so there is a feasibly small sort of set of frameworks, and algorithms and computations from which one can sort of move out to deal with all this different kind of things.

But for me, kind of the paradigm of NKS, which is sort of that idea that they really are for the world at large, these kinds of small programs that are capable to be rich enough in their behavior to sort of capture a lot of what goes on in the world, the idea that there are these small programs is the thing that is sort of from a paradigmatic point of view, makes it to me conceivable that one can do something with Wolfram Alpha, and that it isn’t just too big, and that one just doesn’t have to say (and sort of the traditional kinds of sciences might say) “It’s just too big, the only way to do this is to have a billion people work on it and there just can’t be sort of underlying sort of fairly small programmatic principles that they will build this thing up”. So that for me, at least in my current…, I have noticed that when I do these projects it takes me about a decade to actually realize what the real points of what I did was. Sometimes other people pointed out to me, in much less time than that, but, you know, as I say, my meta-theory is, people have at most one big idea. So, all these things are connected and exactly how kind of NKS, the paradigm of NKS flows into Wolfram Alpha, I know some part of it, there’s probably more to that flow than I yet known.

DW: In fact I am very interested in the relation of NKS, of new kind of science and Wolfram Alpha. 14:03 Can you give an example, maybe, of where NKS-thinking shows up in the development of…?
SW: Right, so I mean, for example, a very straightforward thing is linguistic analysis of inputs. So, we have… It might seem that you…, it’s just too messy, too complicated, there is too many possibilities. The fact is that what we and up with all this fairly small simple rules that we often deduce from looking at some giant corpus of information from the web and giant collection of ways that people say things, we try and abstract these fairly simple rules, and then we have to ask ourselves, you know, “So what consequences do these rules have?” and you know, if you look at the folks who are developing the linguistic analysis parts of Wolfram Alpha, for some strange reason almost all these senior people working on linguistic analysis for Wolfram Alpha have worked on NKS. This is a strange coincidence. Or maybe it isn’t such a coincidence because when you actually look at work that’s being done, at these images, these graphics, the job on the screen that look awfully like kind of evolution of 15:10[?] or some such other thing, there are all these blobs of color and so on, that represent in this case various pieces of potential linguistic analysis for phrases or whatever else it is. But it is very much sort of an NKS approach, if you take this fairly small underlying programs and you say “What did this build up into and does successfully manage to correspond to something that is useful for sort of doing linguistic analysis?” I mean in general in developing Wolfram Alpha and actually also a lot in developing Mathematica these days, the algorithms that we have were not engineered by people. There are things that were essentially found in the computational universe of possible algorithms, I mean it’s kind of like, I view it as being kind of like an algorithmic version of mining, I mean, when we deal with sort of standard engineering technology, you know, we’re used to going out into the world and going and finding the iron ore and, you know, sort of putting it together and making something, you know, making a steel thing out of it or something like that. Now, what NKS sort of suggests is that out in the computational universe of possible programs, there’s lots of interesting quite small programs that exist, and the question is: “Can we go as humans and find programs that are useful to mine from that collection of possible programs, that are useful to mine for the particular technological purposes that we have?”. And that’s something we’ve ended up doing a lot of, I mean a lot of the pieces of code that exist in Mathematica and Wolfram Alpha are things that were sort of just found in the computational universe and turned out to be really useful, in the same way that people found that, you know, magnetite was really useful, or liquid crystals were really useful in the material world.

DW: It’s not clear to me how you are using NKS in the natural language processing 17:00 the NKS in the NLP. It looked like, just on the service of the sorts of priories that you were entering when you were giving your examples, it looked like you were doing…, that what was happening was the normal thing for the sort of query-managing, that there are stock-words that you take out because they are not interesting words and they were sort of human… referred to human fluff-finders, and your discussion is sort of fluff-words that you take out that aren’t necessary, and what you end up with is a set of core-words where you can induce or deduce a relationship, and one of your examples was… a query was MSFT, that… Microsoft’s stock symbol, - SUN, in which case we got back interesting data about both of both companies, and so somewhere Wolfram Alpha decided that with a stock-symbol like MSFT, which it had noticed presumably has some data base of stock-symbols, that, therefore is a pretty good assumption that SUN doesn’t refer to the solar entity but to another company. That’s what I sort of thought was going on, based on nothing, is there an NKS piece of that is doing the parsing or is…
SW: Well I think in that particular case… let’s see what’s going on in that particular case…
DW: Well take a… If that’s not a case where the NKS supplies then take one word…

SW: I’ve got to do more work on this because…, it’s funny: when I talk to the folks who are working on linguistic analysis for Wolfram Alpha, if you ask them, they’ll say “Yes we know lots on NKS, we worked on NKS for a bunch of years right before working on Wolfram Alpha” and I’ll say “How you are using NKS on this”, and they’ll say that, they’ll kind of scratch their heads and they’ll say “Well I’m not completely sure.” And then I’ll ask them “Okay, how does this or that work?” and they’ll start explaining “Well this is a simple algorithm, we run it, it…, you know, produces all this stuff, and then, you know, you look at the pictures that are sitting on their screens and so on, and say “This is awfully like kind of the NKS paradigm, a small program trying to figure out what it does, trying to see in this particular case whether what it does is useful for a particular purpose”

I think we have to come up with much more slam-dunk examples of, you know, this is the case where the way that this particular piece of parsing is done is sort of a pure NKS-play, but what’s kind of going on, I mean in general, you know with parsing of inputs, you know, there are certain rules, sometimes they are as simple as templates, sometimes they are more grammatical kind of things that are more like the structure of mathematical expression, or some, you know, human language expressions, and sort of the point is, there are these sort of small fragments of program that are sort of being applied over and over again, and producing kind of the richness of interpretation that we end up actually seeing and interpreting language, but we need some better slam-dunk examples.

DW: 20:17 So we’ve got Mathematica, we’ve got NKS, and there are other components to Wolfram Alpha. There is data for example…
SW: You know, the way I see the system from a sort of technology point of view, there are really kind of four pillars of the technology that we’ve built: the first of them is kind of curated data, so, you know, we have kind of… we have got a lot of data that we have organized, correlated, cleaned up, made computable - not just static data, but also data that’s kind of flowing in from all sorts of feeds and all this kind of thing. We’ve kind of built this sort of industrial pipeline for curating data, where we put a lot of effort into identifying the right sources, we get the data in, we have a lot of automation and kind of trying to find correlations, anomalisms and so on of the data, then we feed it to actual sort of human data curators, we sort of expose it to domain experts, because one thing I’ve discovered is that if there isn’t a domain expert somewhere in the pipeline you are going to get the wrong answer. And then, you know, the goal is to take sort of raw data from the outside and get it to the point where it is really consistent and computable with. So that’s kind of the first component, it’s kind of this curated data component.

21:34Another component is the, kind of the computational algorithms that go inside. There’s the question of taking the fruits of science and engineering and all sorts of forms of analysis, and encoding them in a computational form so that we can actually use them, when people need to use them, apply them to data that we have and so on. That has been a big effort of kind of just seeing just what is… and just what the sciences and so on achieved, and how do we make it computational and there’s just the big Mathematica program - five or six million lines of mathematical code now, that encodes sort of the things that one can compute about the world based on sciences and so on. That’s kind of the second component of Wolfram Alpha.

22:18 The third component is sort of a linguistic analysis component of how do we take these funny utterances that humans will feed to Wolfram Alpha and trying to understand what we can compute from that. And in a sense that that was a part of…, I mean every part of this project seemed to me like it might be impossible, this is one of ones where I had sort of an argument from history that this part would be impossible, which is that people had been trying to do natural language understanding for a really long time, and its successes are not that wide-spread, but what I realized at some key-moment was: what we are trying to do is kind of the opposite what people have mostly been trying to do. What people mostly have been trying to do is they will throw a thousand, a millions of pages of sort of perfectly formed text, linguistic texts, and they ask their computers “Go and understand all this narratives, go read this books and tell me what they are about” -kind of thing. Computers have found it really hard to do that. Our problem is kind of the opposite. There is a certain sort of things that we know how to compute things with, and that we represent in sort of symbolic form and if we can figure out, if we can get something that is in that symbolic form, then we are often running. 23:34 So really the question is “Can we take this sort of human utterances and map them to this symbolic forms that we happen to know about?” and then we can go off and actually do our thing and compute things with them. So it’s sort of the opposite from the traditional natural language understanding problem. And it’s something where we have been, I mean I was not sure whether we would sort of be squashed by the ambiguities and vicissitudes of language. It has turned out to be surprisingly that all these different notations and pieces of compact representation that people use as specialists in particular areas don’t overlap as much as I thought they did. And kind of a good test now, sometimes we’ll see these things which we can’t deal with, and I’ll show them to a bunch of people and no person will be able to figure out what on Earth this was about. And one of the things we realized is, there may be some perfect kind of almost mathematical theory of how language should be specified but it sort of can’t work, because, as we are putting on all these different kinds of specialist notations, if everyone was arbitrarily extended in perfect way, they would inevitably collide with each-other. And so we only get to where, you know, …if we can just do the part that humans can understand - that’s really what we need to do. Because it’s kind of the humans who are going to be entering the… you know, in this sort of… at least this interface to Wolfram Alpha, it’s humans who are doing the entering of things. So anyway, this sort of third component is this linguistics analysis component and being able to deal with kind of freeform linguistic input.

25:00 Fourth component is really how you present the results. Because sometimes there’s just a zillion things you can compute about something, and there’s the question of “Which actual results do we present so that it is kind of cognitively useful to the human who is reading it?”. And there’s sort of a collection of expert knowledge, heuristics, algorithms, this sort of thing that we call computational aesthetics which is kind of the question of “When you’re going to present some piece of data in graphical form, what’s the kind of way to do that that is most likely to be accessible to a person?”. So this kind of the fourth component is this kind of automated presentation component that is important. And one of the things I realized about the system as we built it is: we couldn’t really shirk on any one of these four things, we can’t… We need a lot of actual curated data, otherwise there’s no kind of raw material to do anything with. We need to be able to compute with it, it’s no good to be able to just say “Look up the data”, because the chance that somebody just wants to just look up the particular thing that you put in is not going to be very great, we need to actually be able to compute from that. And we need to have a way for people to interact with the system, you know, we need to be able to… we’re not going to expect them to read the Mathematica documentation now - even just for Mathematica, it’s 10,000 pages. So for Wolfram Alpha we’re not going to expect people to read the whole encyclopedia in order to be able to interact with us, so the only choice is to have a zero documentation approach, where we’re just dealing with sort of pure sort of language as people will produce it. And then finally this presentation of information: we can generate all sorts of wonderful stuff but if we can’t present it in a form that people can absorb, that’s also not useful, so we kind of need all these four components to actually build a system that can be useful.

DW: 26:57 You can actually take one of the components - the data component, and do you want it to split it in two and there’s a component of that that could conceivably be counted as important, which is the metadata, the decision about which information about the information you’re going to capture. This metadata, which at least much of it is apparently in the form of anthologies, of connected concepts, may have its own value…
SW: -Right, right, well I mean I think…
DW: -And its own limitations, by the way!
SW: -Right, we didn’t think of the assembly of data into Wolfram Alpha as being an exercise in anthology, we really thought of it as just “Let’s figure out how we compute things in all these different domains”. Obviously, we’re educated folks so we know about, kind of the idea of anthologies and the various efforts people have made to sort of do semantic web concept things with anthologies, these kinds of things, right? 27:54 When you’re going to describe a kind of thing, there’s a question of “What are the pieces of that description?”, “What are the…” you know, if you’re going to describe I don’t know, you have some entity…
DW: -Say, a country.
SW: -Right. So there’s a notion of a country there’s a notion of that country has certain properties and certain attributes, like what date were you talking about this country at, there’s questions about you know, are there groups of countries that are kind of, you know, classes, from which countries in NATO or something like that or countries in South America, these are all part of the anthological structure of the knowledge about countries. And for us, we have a pretty definite representation of that kind of knowledge about countries, we need that because otherwise we can’t have it sort of operate with other kinds of information, we need to be able to have, you know… This is part of making Wolfram Alpha a sort of manageable thing, is that there are global frameworks that operate across a bunch of different domains. And for that we need a sort of organized anthology of things. Now there are some tricks of…

29:10One of the big challenges with anthologies is: you have an anthology about countries: how does it relate to the anthology about cities, how does it relate to the anthology about this, that and the other, right? We were approaching this from a very kind of practical engineering point of view: just building anthologies across hundreds of domains, but we realized that there were important ways to have these anthologies communicate with each other, that one of them was actually a very sort of science-oriented way, which is units of measure of things or physical quantities that things represent.

29:49 So, for instance, if you have two things that both have units or volume, there’s a reasonable chance that these things are somehow related to each other. Not always! There can be a thing where… (I’m not sure I can generate it for length cubed in particular, but) it’s quite commonly the case that for something which has sort of the same physical dimensions, there may be three or four different kinds of concepts that that relates to. And so for example, a length could be a depth it could be an altitude it could be a height, these are related concepts and often they can be dealt with in a sort of an interoperable kind of way: you can divide two of these things and it’s meaningful, you can perhaps add them and it’s meaningful. But then there are some other kinds of purposes for which you can’t combine them and for which you might want to give some different-related information and so on. So it gets quite subtle, but we have kind of a reasonable pragmatic way at least to deal with sort of things like properties that are related and that can be combined in particular ways, these types of things. 30:51 I mean I think that… (I would love it to be the case because I’m kind of a theoretical scientist by life pursuit so to speak) it would be a great if there was just a grand theory of all this stuff, if we could just start from scratch as one of the sort of philosophers of all - whether it was you know from the Roget’s to the Mortimer Adler’s to the whatever whoever wants it to organize knowledge in a very sort of structured way. Just sort of say “Top down, let’s just describe the anthology of the world and let’s just then build a system based on the anthology of the world that we’ve thereby described”. I don’t expect…, that isn’t what we did and despite my great predilection for grand theories of things, that seems really hard and then in some sense not useful, because 31:47 what we’re dealing with is sort of human-accessible knowledge (and here I might get a little philosophical on things, but): there’s sort of the question of “What knowledge is possible?” and “What knowledge is human-related?”. So for instance from NKS we’ve learnt that in the computational universe of possible programs there’s lots of stuff out there. We as humans have only explored some tiny corners of this. For example in our mathematics, it is the case that you could invent the particular mathematics that we’ve pursued as growing from some particular axiom systems and so on - but we could just say “Let’s throw it open, let’s look at the space of all possible axiom systems”, gazillion of possible axiom systems of which the mathematics that we’ve pursued is only ten of them for example, but there are gazillions of others and they all have their properties and they all have particular features. This stuff that has humanly been accessed is only this tiny corner of this ten axiom system that we kind of grandfathered through from the Babylonians on.

32:56Now, what happens when we look at all the other ones? What happens with all this knowledge that hasn’t been humanly-accessed knowledge? Well one of the things that I learnt from NKS is a lot of it is knowledge you just can’t get in any kind of quick way. That is, you go right into computational irreducibility and undecidability and so on, there’s lots that you can’t compute about the world. There’s a certain amount that you can compute that is the parts that have formed the science that we’ve developed traditionally as humans, the mathematics we’ve developed, things like that. There’s also a lot out there that is not accessible in that way. Now the other thing that is the case is: one problem is you can’t compute it. If you fall off the parts that are in the language of NKS “computationally reduceable”, the parts for which theoretical science works well, if you fall off those parts - and there’s a lot away from those parts - you just can’t compute that much, and you just have to follow simulations to see what will happen, if things can’t quickly work out what will happen. 34:00 There’s a second problem which is: you can’t linguistically describe what you’re talking about. We as humans we’ve developed a certain language, the concepts that we’ve put into our language are concepts that we have done a lot with, that we have sort of become familiar with, those are the things that we tend to give words to. And so, one of the other limitations in a sense is sort of a philosophical level of limitation, is that Wolfram Alpha - its interface is linguistic, and so the things that once can easily describe to it are things for which there is human language. And that sort of is a…, there’s a lot else out there that is both very hard to compute with and for which there isn’t sort of a human linguistic representation that’s convenient. I mean when you see a picture of a [? 34:51] evolution and somebody says “Quick, describe it!” we don’t have words to describe these things, it’s a long…, you’d be writing…, you’d basically eventually just say “Well just look at the picture, you know, see what it looks like: that!”. 35:08 I think that in some sense one of my sort of my flip, almost flip, rather philosophical comments about Wolfram Alpha is this idea of computational irreducibility, the idea that there’s lots of sophisticated computation that goes on in the world and nature, that makes it sort of the complexity of nature, it’s the flip side of that fact that makes Wolfram Alpha possible, by which I mean that there’s in a sense comparatively little about which we can talk linguistically or about which we can compute. In the space of all possible things there’s only this small corner and that means that when we get to linguistically talk about things, because there’s only this small corner we can talk about, it is feasible to actually understand this broad range of language, because it can only talk about this small corner of things. If we were able to talk about everything, it would be much harder to understand with a short description what we could possibly be talking about. I’m sorry this is getting a very philosophical end of things. 36:11
DW: So I want to ask you about… within that small corner of things that we can talk about, the possible limitation, back on the metadata side: as many have pointed out, metadata is politics: the decision about what counts, what’s interesting to us and ought to be tracked about say, a country or any other entity is itself a reflection of cultural and possibly political interest. So if I were to ask Wolfram Alpha a question that I think it’s not designed for - such as how many people in the U.S. have been tortured, or give me a list of world religions, which is a very straightforward sort of question, that one’s…, the first question honestly is politically loaded, the second one is very straightforward sort of question.
SW: Will scientology show up on it?
DW: It’s a good question. (…) What’s the decision process, what do you do with that?
SW: Right.
DW: Inevitable decision…
SW: 37:06 So what we’re trying to do in this phase is a project. What we’ve done is to take generally reliable public sources of data so to speak, and try and make them computable. We’re not going out and individually surveying people and saying “What is your religion?” or something like that. So this question of, in the case of for example religions, we’ve actually we’ve been agonizing about this particular one, and there was a big push on part of some people on the project that we just shouldn’t put this in because it’s too much of a mess, right? But then other people pointed out “Look, it’s more useful to have something than to have nothing”. I mean it is useful to know roughly how many Christians are there in the world or are there in South America or something like that, that’s useful information. Now, even if it’s slightly wrong because there might be different definitions. “How many animists are there in America?” or something, right? There might be different definitions of that, you know, I sometimes think that after my work on NKS that I should count myself as an animist… but that’s a separate discussion.

DW: There’s a headline for you: “Wolfram becomes animist”

SW: (Laughs) Right. 38:33 Our decision procedure is probably similar to the decision procedure of folks who’ve tried to put together encyclopedias or any other kind of authoritative reference work. We try and do the best we can, we talk to experts, in our case we have a couple of constraints: we want to have something… eventually we’re going to have a definite number for something. Even though we may annotate it with a footnote, at some level we’re going to have to make a decision, there is a definite number for this thing. So in the case of religions you can look the CIA fact book will have some data on that, there are particular organizations that collect data on that kind of thing. I don’t know in particular the case of that example, I know that’s something that we’re actually actively trying to unscramble right now. With luck, those different sources will vaguely, at least close to agree. At least to the first or second decimal place they’ll agree, the first and second significant figure they’ll agree.

DW: Let me give you a different kind of example cause…, 39:37 for example if I ask for a list of broadband penetration bionation, this is hardly contested - what constitutes broadband. The FCC has had one way of counting broadband that is extremely ingenuous, but people will use these numbers from your site to the extent to which your site becomes the place where you go to get the answers that the experts would have given you, the extent in which it succeeds is going to be used in these debates. Does it concern you that some of the answers at least, are in fact based upon good decisions but decisions nonetheless about what constitutes good data and how they’re categorized, that data?

SW: 40:19 We have three escape valves for this issue. The first thing is: you type your input and we’ll typically give what we call the input interpretation. The first part of output will be the input interpretation. So if you say something like, you know, “How much do school teachers get paid?” - it’s a complicated question, right? The input interpretation will probably say “school teachers… including this and that and the other, excluding this…, medium wage”. So we’re saying what question we’re actually answering, that might be, we hope is, close to any useful variant of the question you asked. But it might not be in a question where you might have had something different in mind, you might have had the minimum wage or the maximum wage or something else. Now, typically in our sort of report that we generate we will give what we know about the range of wages or some such other thing but when we have to compute from it if you say “How much do teachers get paid divided by doctors?”, by the time you’re doing that we’ll give a definite answer for the ration of medium wages and then we may give a bunch of additional information about the data that went into it, so the first… in a sense “escape” valve, first place where we get to put things through a more precise filter is we have this input interpretation that says “This is what we’re actually dealing with”.
41:51 Another thing is that when we give answers, we try to give pithy footnotes that give an indication of what it is we’re actually assuming, I mean, a typical example is some health kind of thing, where there’ll be a classification, for your overweight for instance. Now, to many people that’s a value judgement, to some other people it’s a precise medical distinction. What we’re dealing with is more the precise medical distinction than the value judgement, and so we’ll try to make a pithy footnote cause we know if we put a giant essay there, nobody’s going to read it. It sort of says, I don’t remember that particular one but it’ll say, “based on CDC or WHO guidelines” or some such other thing. So that’s how we try to deal with these things where there’s a distinction that’s been made and it sort of, we try to go with whatever the standard’s body has said and then say what the standard’s body is.

42:54 And then the final thing that we get to do is, we sometimes get to put up a thing saying “assuming you mean blah”, then we give a bunch of results, and use “blah” instead. And so that’s another place where we get to if people have that sort of a controversial thing. One that came up recently that was a slightly different issue but there’s questions about “Do you assume this country is part of that country?” and things like that, well we can assume both cases. There were these funny cases where we just had one recently that was sort of horrifying was the pets-versus-food problem. So the issue is, if you type in “rabbit”, what do you get? And for reasons of bad linguistic paralyzation one was getting nutrition content. So what one realizes is that in the U.S. this is very upsetting to people, and in other countries it’s a different story. So that’s a case where we get to use GIP information and we’ll make the decision in the U.S. a rabbit is an animal with these characteristics; I don’t know in the case of rabbits in particular but let’s assume that they’re popular food in some other country and are not usually kept as pets there, then the GIP of that country will cause us to flip around that assumption. 44:33

DW: How open is this site? It’s open to everybody to use, it’s a free site but the data and the metadata and the algorithms are seemingly quite valuable, other people could make use of it. What are the plans for making this…, for opening the site up?

SW: Well so the first thing is that there’s going to be an API for people to make different levels of…, have different sort of kinds of access to the site.

DW: So another computer program can call yours and gets results back?
SW: Right. Or it could be another website that is including some pods of output from Wolfram Alpha on that site providing some input field with some specialized input field that can go and use the Wolfram Alpha engine and get results back or it could be some, you know, desktop application program that is making calls to Wolfram Alpha. The thing one realizes is, it’s a living breathing thing, it’s not very easy to take pieces of it and just say to people “Here’s a big piece, go do something useful with it” because, for example let’s take the data: the data changes all the time the data is changing. Some data’s changing very rapidly on times it goes over seconds some data changes on time goes over months - it’s not the case, we can’t just say sort of “Dump the data out” because the data is really just the seed for, in many many cases, the data is the seed for an algorithm.

DW: 46:02 It’s also frequently data that is proprietary and licensed, another reason why you can’t simply dump it out.

SW: Right. The idea is that we will provide a resource for anybody who wants to use what we have, we will provide a good way for them as humans to use it, or as humans using programs to have their programs use it, and the idea is to provide an efficient well kept-up industrially strong kind of thing that people can go to get what they need from this. We certainly were aware of the fact that there are computations that people will want to do that will run longer than the site could possibly run them for, and so the good news is that we have a great sort of vehicle for running this sort of computations, and that’s Mathematica, and so the issue is…, and Mathematica has for twenty years happily run on millions of actual computers around the world on people’s desktops, and so one of the things that I think we’ll see happening is that there’ll be things where there’s sort of the Wolfram Alpha kind of central store of data, sort of computational elements and so on, and then there’s the sort of the fit client for it which is the Mathematica that’s able to take pieces of what’s coming from Wolfram Alpha and run that stuff locally. So this is sort of a way of again opening out and enabling more people to get access to the capabilities that we’re providing.

DW: 47:52 The anthologies or scheme or whatever you want to call it, those themselves and the fact that you have a way of representing say, a nation and multiple other demands, that’s something that people working on the semantic web are working quite hard doing. Is that…, you’re gonna make that available as well?
SW: Probably. So one thing we hope to do is to have nice organized, almost formalized process for people to contribute data to our data repository. And in order to make that possible we kind of have to say how is this state going to be structured. And so I think we’ll probably have quite an effort for particular domains in sort of exposing some of the structures that we’ve already built and possibly people will propose other pieces of structure that need to get added on to that, and so I think that that will get exposed and if there’s good traffic in data being provided for some access by people in Wolfram Alpha, if there’s good traffic to that, I suspect that the anthology that we have will sort of become kind of a useful standard for other people who are doing things where they try to fit semantic data together.

DW: 49:02 A useful free standard, a useful open standard? Will either of those do?

SW: I’m not sure I thought through all of these, my guess is that we will want it to be an open standard because from our point of view the more data that exists in the world that is structured in particularly a way that is easy to deal with - the better everybody is, and the better we are too. I mean in a sense our objective is to make this sort of good, sort of definitive sort of place to go to get computational knowledge, and there are many components to that, I mean the data is just one component. And if more of the world would help in assembling the data that would be great. I mean so far I have to say that we hire lots of data curators and they do a great job, but we hope that there will be quite a community of sort of volunteer data curators that will provide sort of tools for volunteer data curators. But I’m a little bit less… I’ll be very interested to see what happens. You know, Wikipedia has been a fantastic success story of kind of the things assembled by crowds so to speak, but I have this feeling that writing the great essay that shows up on Wikipedia is a somehow generally more exciting activity than…, I mean I really like lining up things and data but I have a suspicion that that is more of a…, the nerdiness quotient or something has to be higher. I’m proudly in that box so to speak, but I think it’s a more professional judgement intensive kind of activity I think, because you see, here’s the point: when you see an essay on Wikipedia, the very texture of that essay sort of tells you what’s going on, it tells you whether the person knows what they’re talking about, tells you whether… it just gives you some sort of feeling for what’s going on. Data is in a sense very cold, you know, it’s just “7.2” you know, “The number is 7.2”, doesn’t come attached to an essay, it’s just “7.2”. And what you need, the kind of reliability that you need and the kind of people who are going to make that number 7.2 as opposed to 7.8, you have to have a…, at least my current view is that it’s important to have a somewhat more controlled environment because you don’t get to know all of the footnotes so to speak immediately, as you do in kind of the essay-type setting. 51:59 So right now my best mechanism is: you hire good data curators, we have all these tests for data curators, we hire good people, they have to have quite a bit of domain knowledge otherwise they get things wrong, and it’s a somewhat more sort of organized and professionalized thing. Now, I’m certainly hoping that we will be able to provide the tools and the framework to have a very good community of volunteer and so on data curators out there who can potentially help with what we’re doing, but we fully expect that what they’re doing will have to sort of fit in to this whole pipeline we’ve built and that will have to be doing our sort of data ordered in processes and so on, and right now it just doesn’t feel like the kind of thing for which it’s going to work to just say “Ok world, everybody just put the pieces you feel like in”.

DW: 52:05 In the media we’ve already seen Wolfram Alpha referred to as a Google killer. Who’re you trying to kill – anybody, with this?
SW: That’s not what I’m doing… I build something…

DW: Trying to kill Wikipedia, trying to kill reference librarians?
SW: No, no, I’m… The thing I like to do is I like to build stuff. This is a thing that I want to build. I build stuff as a practical person, it has to fit in the ecosystem of the world it has to support itself, things like that. If we look at Google and Wikipedia these are both sort of great achievements, and they’re both very complimentary to what we’re doing. We’re dealing with the facts just the facts, Wikipedia is dealing with the narrative and you might have noticed the side bar to Wolfram Alpha often pulls up Wikipedia links with the rollovers that show sort of some little piece of narrative from Wikipedia. That I think is a terrific kind of synergy there and maybe we’ll get other kinds of encyclopedia-kinds of narrative things that can sort of complement Wolfram Alpha. As far as search is concerned, traditional web search again is a very complementary kind of thing, I mean I use Wolfram Alpha lots, I also use search engines and Google and so on lots. I use them for different kinds of things, I’ll use Wolfram Alpha when I have some specific question that is where I’m trying to compute something, but whereas I’ll use a search engine when I’m looking for some fragmentary piece of information that isn’t systematic at all, where there’s just something about…David Weinberger, that just happens to be out there, that I can kind of pick up and read the narrative about it.

55:01Perhaps I’m a business idealist, ok? I’ve been lucky enough to have a successful business for twenty-something years and that I’ve been able to sort of pursue with Mathematica and so on, something that I consider to be kind of intellectually… that sort of high intellectual integrity that also can be a very pleasantly successful business, and I certainly hope to do the same kind of thing with Wolfram Alpha and I’m hoping for the best possible synergy with folks who have complimentary kinds of things. I mean I hope we won’t be killing anybody I hope nobody will be trying to kill us so to speak, I think that we’re doing something that I believe can lead to a lot of positive progress in the world and I’m just keen to see that happen.

David Weinberger: Thank you very much.
Stephen Wolfram: My pleasure./p>

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