It's long been known that the ranks of Wikipedia editors are mainly male. But now illustrator Santiago Ortiz has created an interactive looking at the proportion of edits on individual Wikipedia articles made by men vs. women, and it turns out that the gender divide on "the free encyclopedia that anyone can edit" is even starker than we thought.
It's long been known that the ranks of Wikipedia editors are mainly male. But now illustrator Santiago Ortiz has created an interactive looking at the proportion of edits on individual Wikipedia articles made by men vs. women, and it turns out that the gender divide on "the free encyclopedia that anyone can edit" is even starker than we thought.
For this chart, the X-axis represents the number of male editors and the Y-axis represents the number of female editors. Each dot is a Wikipedia article, and the line down the middle shows the average 12.9 male editors to 1 female editor per article. The fact that the dots line the bottom means there are far more male editors for each female editor on individual articles.
Ortiz used a dataset culled by Wiki Trip, an independent site that visualizes Wikipedia data from Toolserver, a Wikimedia Foundation supported software platform. Only registered users who said a gender were included in the data, so users who abstained were not included. Theoretically, women could be less willing to volunteer their gender data, which would mean that the 12.9-to-1 ratio could be lower. But by comparing to the activity of those who did specify a gender, it's clear that the female editors who have registered are not contributing nearly as much as their male counterparts.
In Ortiz's interactive version, you can see what the differently colored dots dots mean. Ortiz categorized articles several different ways, including film, books, emotions, sports, most edited, and most visited, and his interactive lets you select the categories.
This is a screenshot of the most edited articles (green) and most visited articles (blue) by gender. (Note: The female editors' Y-axis is scaled differently from the male editor X-axis, so the disparity actually does not look as sharp as it is.) Not a single article is to the left of the 1-to-1 line, meaning no articles have been edited by as many men as women.
In fact, many of the articles that women would theoretically be better authorities on--menstruation, for example--are edited mostly by men, according to this screenshot of articles Ortiz selected. Only "cloth menstrual pads" inches past the 1-to-1 line.
Wikimedia Foundation's Sue Gardner has said that they want to add equalize the gender ratio more because "it’s an issue of quality," she told Forbes. "We want to bring the sum of all human knowledge to everybody and we can’t do that unless everybody’s at the table.” One thing's for sure: They've still got a long way to go.
Want to add to this story? Let us know in comments or send an email to the author at sdai at theatlantic dot com. You can share ideas for stories on the Open Wire.
Serena Dai
video by Sight Systems
A short futuristic film by Eran May-raz and Daniel Lazo. This is our graduation project from Bezaleal academy of arts. Please share if you enjoyed it!
Norvig vs. Chomsky and the Fight for the Future of AI
When the Director of Research for Google compares one of the most highly regarded linguists of all time to Bill O’Reilly, you know it is on. Recently, Peter Norvig, Google’s Director of Research and co-author of the most popular artificial intelligence textbook in the world, wrote a webpage extensively criticizing Noam Chomsky, arguably the most influential linguist in the world. Their disagreement points to a revolution in artificial intelligence that, like many revolutions, threatens to destroy as much as it improves. Chomsky, one of the old guard, wishes for an elegant theory of intelligence and language that looks past human fallibility to try to see simple structure underneath. Norvig, meanwhile, represents the new philosophy: truth by statistics, and simplicity be damned. Disillusioned with simple models, or even Chomsky’s relatively complex models, Norvig has of late been arguing that with enough data, attempting to fit any simple model at all is pointless. The disagreement between the two men points to how the rise of the Internet poses the same challenge to artificial intelligence that it has to human intelligence: why learn anything when you can look it up?
Chomsky started the current argument with some remarks made at a symposium commemorating MIT’s 150th birthday. According to MIT’s Technology Review,
Chomsky derided researchers in machine learning who use purely statistical methods to produce behavior that mimics something in the world, but who don’t try to understand the meaning of that behavior. Chomsky compared such researchers to scientists who might study the dance made by a bee returning to the hive, and who could produce a statistically based simulation of such a dance without attempting to understand why the bee behaved that way. “That’s a notion of [scientific] success that’s very novel. I don’t know of anything like it in the history of science,” said Chomsky.
To frame Chomsky’s position as scientific elegance versus complexity is not quite fair, because Chomsky’s theories have themselves become more and more complex over the years to account for all the variations in human language. Chomsky hypothesized that humans biologically know how to use language, besides just a few parameters that need to be set. But the number of parameters in his theory continued to multiply, never quite catching up to the number of exceptions, until it was no longer clear that Chomsky’s theories were elegant anymore. In fact, one could argue that the state of Chomskyan linguistics is like the state of astronomy circa Copernicus: it wasn’t that the geocentric model didn’t work, but the theory required so many additional orbits-within-orbits that people were finally willing to accept a different way of doing things. AI endeavored for a long time to work with elegant logical representations of language, and it just proved impossible to enumerate all the rules, or pretend that humans consistently followed them. Norvig points out that basically all successful language-related AI programs now use statistical reasoning (including IBM’s Watson, which I wrote about here previously).
But Norvig is now arguing for an extreme pendulum swing in the other direction, one which is in some ways simpler, and in others, ridiculously more complex. Current speech recognition, machine translation, and other modern AI technologies typically use a model of language that would make Chomskyan linguists cry: for any sequence of words, there is some probability that it will occur in the English language, which we can measure by counting how often its parts appear on the internet. Forget nouns and verbs, rules of conjugation, and so on: deep parsing and logic are the failed techs of yesteryear. In their place is the assumption that, with enough data from the internet, you can reason statistically about what the next word in a sentence will be, right down to its conjugation, without necessarily knowing any grammatical rules or word meanings at all. The limited understanding employed in this approach is why machine translation occasionally delivers amusingly bad results. But the Google approach to this problem is not to develop a more sophisticated understanding of language; it is to try to get more data, and build bigger lookup tables. Perhaps somewhere on the internet, somebody has said exactly what you are saying right now, and all we need to do is go find it. AIs attempting to use language in this way are like elementary school children googling the answers to their math homework: they might find the answer, but one can’t help but feel it doesn’t serve them well in the long term.
In his essay, Norvig argues that there are ways of doing statistical reasoning that are more sophisticated than looking at just the previous one or two words, even if they aren’t applied as often in practice. But his fundamental stance, which he calls the “algorithmic modeling culture,” is to believe that “nature’s black box cannot necessarily be described by a simple model.” He likens Chomsky’s quest for a more beautiful model to Platonic mysticism, and he compares Chomsky to Bill O’Reilly in his lack of satisfaction with answers that work. “Tide goes in, tide goes out. Never a miscommunication. You can’t explain that,” O’Reilly once said, apparently unsatisfied with physics as an explanation for anything. But is Chomsky’s dismissal of statistical approaches really as bad as O’Reilly’s dismissal of physics in general?
I’ve been a Peter Norvig fan ever since I saw his talk he gave to the Singularity Institute patiently explaining why the Singularity is bunk, a position that most AI researchers believe but somehow haven’t effectively communicated to the popular media. So I found similar joy in Norvig’s dissection of Chomsky’s famous “colorless green ideas sleep furiously” sentence, providing citations to counter Chomsky’s claim that its parts had never been spoken before. But I can’t help but feel that an indifference to elegance and understanding is a shift in the scientific enterprise, as Chomsky claims.
“Everything should be simple as possible, but no simpler,” Einstein once said, echoing William of Ockham’s centuries-old advice to scientists that entities should not be multiplied beyond necessity. The history of science is full of oversimplifications that turn out to be wrong: Kepler was right on the money with his Laws of Motion, but completely off-base in positing that the planets were nested in Platonic solids. Both models were motivated by Kepler’s desire to find harmony and simplicity hidden in complexity and chaos; in that sense, even his false steps were progress. In an age where petabytes of information can be stored cheaply, is an emphasis on brevity and simplicity an anachronism? If the solar system’s structure were open for debate today, AI algorithms could successfully predict the planets’ motion without ever discovering Kepler’s laws, and Google could just store all the recorded positions of the stars and planets in a giant database. But science seems to be about more than the accumulation of facts and the production of predictions.
What seems to be a debate about linguistics and AI is actually a debate about the future of knowledge and science. Is human understanding necessary for making successful predictions? If the answer is “no,” and the best way to make predictions is by churning mountains of data through powerful algorithms, the role of the scientist may fundamentally change forever. But I suspect that the faith of Kepler and Einstein in the elegance of the universe will be vindicated in language and intelligence as well; and if not, we at least have to try.
Noam Chomsky photo by Duncan Rawlinson and his Online Photography School. Peter Norvig photo by Peter Norvig.
Kevin Gold is an Assistant Professor in the Department of Interactive Games and Media at RIT. He received his Ph.D. in Computer Science from Yale University in 2008, and his B.A. from Harvard in 2001. When he is not thinking up new ideas for his research, he enjoys reading really good novels, playing geeky games, listening to funny, clever music, and reading the webcomics xkcd and Dresden Codak.
A new paper, Iterated Prisoner’s Dilemma contains strategies that dominate any evolutionary opponent (pdf), has just been published in the Proceedings of the National Academy of Sciences. Written by William Press and Freeman Dyson, it represents a substantial breakthrough in strategies that work in the Prisoner’s Dilemma game.
The key point? There does exist a strategy where a player can “enforce a unilateral claim to an unfair share of rewards.”
The implications of this paper are fascinating. For biological evolution, it opens up new thinking about reproductive strategies and life history theory, as well as the direct impact on ideas about the evolution of cooperation.
For cultural evolution, it seems to provide some powerful insights into the evolution of inequality in human society. As the agriculture revolution and population growth led to the ability to monopolize social resources and create differential wealth, what happened with social class? Did human cooperation turn from fairness to enforcing the sort of unfair game that Press and Dyson outline?
Zero Determinant Strategies and Beating Tit-for-Tat
Here is the paper’s abstract for Press and Dyson (2012):
The two-player Iterated Prisoner’s Dilemma game is a model for both sentient and evolutionary behaviors, especially including the emergence of cooperation. It is generally assumed that there exists no simple ultimatum strategy whereby one player can enforce a unilateral claim to an unfair share of rewards. Here, we show that such strategies unexpectedly do exist. In particular, a player X who is witting of these strategies can (i) deterministically set her opponent Y’s score, independently of his strategy or response, or (ii) enforce an extortionate linear relation between her and his scores. Against such a player, an evolutionary player’s best response is to accede to the extortion. Only a player with a theory of mind about his opponent can do better, in which case Iterated Prisoner’s Dilemma is an Ultimatum Game.
PNAS also gives an informative commentary by Alexander Stewart and Joshua Plotkin entitled “Extortion and cooperation in the Prisoner’s Dilemma (pdf).”
One of the key points they highlight is the analysis of Press and Dyson about long-term versus short-term strategies, in this case, framed in terms of memory of previous encounters. (From my side, I think the broader framing – of long versus short strategies – is useful, since it resonates with a great deal of evolutionary thinking, from reproductive strategies to optimality to life history strategies…)
First, they prove that any “long-memory” strategy is equivalent to some “short-memory” strategy, from the perspective of a short-memory player. This means that an opponent who decides his next move by analyzing a long sequence of past encounters might as well play a much simpler strategy that considers only the immediately previous encounter, when playing against a short-memory player. Thus, the possible outcomes of the IPD can be understood by analyzing strategies that remember only the previous round.
I want to focus in on that point – “considers only the immediately previous encounter.” That’s the language of math. But for evolutionary strategies, any situation or context where a player can only take into account (or base his/her decisions on) the previous encounter is playing the short-memory strategy. If a person is structurally forced to do that – say, they have a limited amount of money and need to buy food to support their family, and so have to pay the “market price” – then it is a short-memory or short-term strategy.
What does that mean, according to the game theory math of Press and Dyson? That one player can determine another player’s scores, and make sure the pay-offs benefit him or her over the long-run. Or, to put it another way, to systematically screw the other person.
That is, X can set Y’s score to any value in the range from the mutual noncooperation score to the mutual cooperation score. What is surprising is not that Y can, with X’s connivance, achieve scores in this range, but that X can force any particular score by a fixed strategy p, independent of Y’s strategy q. In other words, there is no need for X to react to Y, except on a timescale of her own choosing. A consequence is that X can simulate or “spoof” any desired fitness landscape for Y that she wants, thereby guiding his evolutionary path. For example, X might condition Y’s score on some arbitrary property of his last 1,000 moves, and thus present him with a simulated fitness landscape that rewards that arbitrary property.
Press and Dyson call these “zero determinant” strategies, because the player can enforce a linear relationship of pay-offs that systematically favor the enforcer. Nothing the other player can do can change that result, so long as the original player chooses a unilinear strategy of their own that sets up this linear relationship.
The outcome? Press and Dyson have discovered strategies that trump Tit-for-Tat, the Prisoner’s Dilemma strategy that has been a consistent winner in the past, beating out strategies that cheat or defect more often. Over the past thirty years, Tit-for-Tat has been a major impetus behind ideas about the evolution of cooperation.
Tit-for-Tat cooperates if you cooperate, and can create a long series of positive outcomes. Except now, Tit-for-Tat, a short-term memory player, loses when it encounters the sort of unlevel playing field that zero-determinant strategists create. As William Poundstone puts it in the illuminating coverage and discussion of this paper over on Edge:
Robert Axelrod’s 1980 tournaments of iterated prisoner’s dilemma strategies have been condensed into the slogan, Don’t be too clever, don’t be unfair. Press and Dyson have shown that cleverness and unfairness triumph after all.
New Strategies: Mischief, Extortion, and Willing Partner in Your Own Defeat
So, what unequal-determinant strategies work? (Yes, I think “unequal-determinant” is a much better name for those specific strategies that force others to play by your set of pay-offs.)
In their commentary, Stewart and Plotkin highlight three unilinear strategies that can all end in zero-determinant games (in other words, where the second player has zero control):
Unequal-Determinant #1: Mischief
If a player X is aware of ZD [zero-determinant] strategies, then she can choose a strategy that determines her opponent Y’s long term score, regardless of how Y plays. There is nothing Y can do to improve his score, although his choices may affect X’s score.
Unequal-Determinant #2: Extortion
Suppose once again that X is aware of ZD strategies, but that Y is an “evolutionary player,” who possesses no theory of mind and instead simply seeks to adjust his strategy to maximize his own score in response to whatever X is doing, without trying to alter X’s behavior. X can now choose to extort Y. Extortion strategies, whose existence Press and Dyson report, grant a disproportionate number of high payoffs to X at Y’s expense (example in Fig. 1). It is in Y’s best interest to cooperate with X, because Y is able to increase his score by doing so. However, in so doing, he ends up increasing X’s score even more than his own. He will never catch up to her, and he will accede to her extortion because it pays him to do so.
Unequal-Determinant #3: Willing Partner in Own Defeat
If both players are sentient and witting of ZD strategies, then each will initially try to extort the other, resulting in a low payoff for both. The rational thing to do, in this situation, is to negotiate a fair cooperation strategy… Knowledge of ZD strategies offers sentient players an even better option [than Tit-for-Tat]: both can agree to unilaterally set the other’s score to an agreed value (presumably the maximum possible). Neither player can then improve his or her score by violating this treaty, and each is punished for any purely malicious violation.
I almost labeled this strategy as “willing partner in victory and defeat,” which is probably a fairer label. After all, one can imagine a mother and fetus in this sort of negotiation, where the mother can often play a zero-determinant strategy for the organism developing inside her. In this case, mothers and offspring likely engage in a cooperation strategy where a mother retains maximum reproductive potential after birth (the baby doesn’t take too much…) and the mother invests enough in the fetus to set its developmental potential to its maximum.
However, that forgets the unequal relationship between the player who can set the conditions for a game and a player who cannot. For those who cannot set the conditions, then the options available can systematically screw them. The options available to one player won’t be the same as with the other player – the maximum possible for each will be different, and even rational cooperation ends in defeat. A z-d strategist, then, will often play to be able to set the matrix of payoffs so that the “maximum” pay-offs still creates a situation like the extortion one, where the other player gets some benefits while still losing out in the end.
Key Questions that Arise with Zero-Determinant Strategies
Whether zero-dimensional strategies are specifically in play in any particular evolutionary case (and whether we can get the data to show that), it remains useful to ask the sorts of questions zero-dimensional approaches raise.
For example, one of the most interesting points that arises from the Press and Dyson (2012) papers is the discrepancy that gets created between players who can execute long-term strategies and those who can only execute short-term or immediate strategies. We can still do this type of analysis, even without being able to ascertain whether a particular case hues to zero-dimensional prisoner dilemma dynamics.
Important questions to ask include:
-Who is playing long versus short strategies?
-Who gets to set the pay-offs?
-What incentives exist for cooperation? And punishments are there for cheating? (In other words, do conditions favor more extortion or more cooperation?)
-And, likely most crucial, are zero-dimensional strategies available to the agents or organisms in question?
The last question goes beyond answering questions of whether or not there are iterative interactions of the type described by the prisoner’s dilemma, as well as whether an animal has a theory of mind or agentive powers of the type capable to execute zero-dimensional strategies, as Press and Dyson seem to indicate as necessary. (I’d actually be open to zero dimensional strategies being found through the brute, random processes of evolution as well; doesn’t have to be a cognitive thing alone.) Rather, are they accessible given differing fitness landscapes, types of reciprocal/dynamic interactions, and/or sets of pay-offs provided to social interactions in specific groups?
Or we might take the reverse engineering route, and find evolutionary scenarios that already exist which we might fruitfully analyze using the lens of skewed but linear pay-offs from iterative interactions between agents pursuing different strategies.
For example, can sexual reproduction possibly be a zero-dimensional strategy, specifically in cases where females can systematically skew pay-offs for males that treat intercourse as a short-term interaction?
Life History Theory and Z-D Strategies
One area where this type of analysis might fruitfully be applied is in life history theory, parental investment, and other evolutionary processes that rely on development.
Take the weanling’s dilemma, where developing infants need to make the trade-off between gaining potential access to greater nutritional intake through food but at the risk of exposure to more pathogens. In one sense, this decision is an iterative one between infant and child – they have to both agree to keep breast feeding for another day. Here the mother is in a position to engage in zero dimensional strategizing that favors not only this child, but also other potential children she might have. For the infant, the trade-offs are largely immediate, about whether to give up access to free calories.
The infant is in no position to engage in the sort of linear strategizing that a mother can impose. Particularly for infants that appear to be weaker or to not offer the same evolutionary pay-offs, mothers might cut off high levels of investment earlier. For infants that appear to offer greater pay-offs, the mother might do the opposite, and set the table to ensure the maximum pay-off from interactions, even if that ends up having a higher overall cost in terms of reproductive effort. Similarly, for species with large amounts of paternal investment, the same dynamics could play into how fathers invest in their offspring.
In other words, zero-dimensional strategies are a way to think about facultative adjustments that organisms can make in reproductive and life history strategies.
As just a thought to throw out there, might zero-dimensional approaches shed new light on the epidemiological transition? Has it made sense, where fitness pay-offs are high for offspring through investment and development, to invest more as a parent and thus set the highest set of pay-offs for a child?
As another thought, are epigenetic mechanisms that last more than a generation a way to try to game the system, to try to create the sort of long-term skewing to ensure better adjustment to unpredictable or short-term environments?
On the Evolution of Social Inequality
If a partner can be forced into taking a “short-memory” strategy, then an agent opens up the field to enact the zero-dimensional strategies. To play at mischief, to extort others, or to set partners up to play and yet fail…
One mystery of human evolution is why systemic inequality appeared with the emergence of complex social structures. Hunter-gatherers have fewer social divisions than agricultural societies, and often have a series of cultural and behavioral mechanisms that help enforce cooperation. With the rise of agriculture, the ability to dominate concentrated resources, and the need for communal defense in war, many human societies developed social classes and structural inequality. Why?
Evolutionary analysis using zero-dimensional strategies helps provide insight – people in positions of power could enforce short-term strategies on other members in society, even as they enacted their own longer term linear tactics to determine the pay-offs that reciprocal interactions provided to everyone. In other words, the rich got richer…
I will be interested to see how these types of analyses develop.
I am also struck by some of the language in the Press and Dyson (2012) paper, which sounds remarkably close to analyses of power within cultural anthropology.
Here are two initial examples, developed to be provocative:
For any strategy of the longer-memory player Y, shorter-memory X’s score is exactly the same as if Y had played a certain shorter-memory strategy (roughly, the marginalization of Y’s long-memory strategy: its average over states remembered by Y but not by X), disregarding any history in excess of that shared with X.
Or, to draw on Eric Wolf’s magnum opus, Europe and the People without History. Discounting shared history – shared accountability – is certainly one way to force short-term interactions, and that will benefit a dominant trading partner.
X can force any particular score by a fixed strategy p, independent of Y’s strategy q. In other words, there is no need for X to react to Y, except on a timescale of her own choosing… For example, X might condition Y’s score on some arbitrary property of his last 1,000 moves, and thus present him with a simulated fitness landscape that rewards that arbitrary property.
Cultural distinctions often draw on exactly those arbitrary but symbolically powerful distinctions that determine status. To draw on Pierre Bourdieu, Distinction: A Social Critique of the Judgment of Taste, here is the Amazon description:
In the course of everyday life people constantly choose between what they find aesthetically pleasing and what they consider tacky, merely trendy, or ugly… The different aesthetic choices people make are all distinctions-that is, choices made in opposition to those made by other classes. Taste is not pure. Bourdieu finds a world of social meaning in the decision to order bouillabaisse, in our contemporary cult of thinness, in the “California sports” such as jogging and cross-country skiing. The social world, he argues, functions simultaneously as a system of power relations and as a symbolic system in which minute distinctions of taste become the basis for social judgement.
Those social judgments – or traditions as people call them – function marvelously as zero-dimensional strategies that enforce greater pay-offs from the people on the “good” side of the judgments.
Coca-Cola and Capitalism
Can we also analyze capitalism in this same way? Is capitalism an example of a zero-determinant strategy?
On the idealized side, the rational cooperative strategy of fair play provides maximal pay-offs to everyone involved. On the critical side, companies that can extract excess profit from workers or from consumers can use that increased pay-off to set conditions that favor themselves.
Workers often play shorter term strategies than companies – they have to eat everyday. Companies can then use zero-determinant (or unequal-determinant) strategies. They can extract the maximum productivity from their workers, all the while setting up strategies that ensure the companies get much greater pay-offs over time than the workers. The workers buy into it, because they get what they need in an immediate sense. But they often are losing in the long run.
Consumers are similar. You want a coke or you don’t. But the Coca Cola company isn’t playing that game. They want to extract maximum profit from a very lucrative commodity. The iterative interaction – you buying coke and coke and Coca-Cola selling can after can – is played at different time-scales, and with very different objectives. They can bring enormous resources – placement of coke in supermarkets and in movie theaters, sugar price supports from the government, and so forth – that set up a long-term memory strategy that provides them great pay-offs.
The consumer, well, the consumer gets choice – an immediate decision between one product or another, or one price or another. Funny how the market works that way…
Conclusion
So this 2012 paper by William Press and Freeman on Iterated Prisoner’s Dilemma contains strategies that dominate any evolutionary opponent brings up lots of interesting angles.
The implications of this work go beyond biological evolution into social and cultural evolution, human development and reproduction, and social analysis of social class, capitalism and inequality. I hope I’ve stirred up a little mischief with it.
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Warning: This episode contains mature subject matter. Since its creation in 2005, Reddit has grown into one of the most influential communities on the internet. More than just a content aggregator, it generates information and new content, and has given birth to intriguing collaborative projects that reflect a particular group character and value system.
In complex environments, weak hierarchies and strong networks are the best organizing principle. One good example of complexity that we can try to fathom is nature itself. Networks thrive in nature. As Howard Bloom stated in a speech at Yale University: One the many lessons bacteria teach with their colonies of trillions is this.
In complex environments, weak hierarchies and strong networks are the best organizing principle. One good example of complexity that we can try to fathom is nature itself. Networks thrive in nature. As Howard Bloom stated in a speech at Yale University:
One the many lessons bacteria teach with their colonies of trillions is this. When it comes to groups, Nature does not favor tribes, she favors size … She favors humongous social groups that network their information so well that they form a high-powered collective intelligence, a group brain.
The Internet has given us a glimpse of the power of networks. We are just beginning to realize how we can use networks as our primary form of living and working. David Ronfeldt has developed the TIMN framework to explain this - Tribal; Institutional; Markets; Networks. The TIMN framework shows how we have evolved as a civilisation. It has not been a clean progression from one organizing mode to the next but rather each new form built upon and changed the previous mode. He sees the network form not as a modifier of previous forms, but a form in itself that can address issues that the three other forms could not address. This point is very important when it comes to things like implementing social business (a network mode) within corporations (institutional + market modes). Real network models (e.g. wirearchy) are new modes, not modifications of the old ones.
Another key point of this framework is that Tribes exist within Institutions, Markets AND Networks. We never lose our affinity for community groups or family, but each mode brings new factors that influence our previous modes. For example, tribalism is alive and well in online social networks. It’s just not the same tribalism of several hundred years ago. Each transition also has its hazards. For instance, while tribal societies may result in nepotism, networked societies can lead to deception.
Ronfeldt states that the initial tribal form informs the other modes and can have a profound influence as they evolve.
Balanced combination is apparently imperative: Each form (and its realm) builds on its predecessor(s). In the progression from T through T+I+M+N, the rise of a new form depends on the successes (and failures) achieved through the earlier forms. For a society to progress optimally through the addition of new forms, no single form should be allowed to dominate any other, and none should be suppressed or eliminated. A society’s potential to function well at a given stage, and to evolve to a higher level of complexity, depends on its ability to integrate these inherently contradictory forms into a well-functioning whole. A society can constrain its prospects for evolutionary growth by elevating a single form to primacy — as appears to be a tendency at times in market-mad America.
Each form also seems to be triggered by major societal changes in communications. The written word enabled institutions, the printed word fostered regional and global markets, and the electric (digital) word is empowering worldwide networks.
Here is David Ronfeldt giving a 20 minute overview of TIMN.
* Content from jarche.com is protected under a Creative Commons Attribution-NonCommercial License
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Chris Hedges lectures at The New School, a university in New York City offering distinguished degree, certificate, and continuing education programs in art and design, liberal arts, management and policy, and the performing arts. THE NEW SCHOOL | http://www.newschool.edu Journalist Chris Hedges discusses his recent book Empire of Illusion: the End of Literacy and the Triumph of Spectacle.
The Edge's Annual Question is a great compilation of brief effusions from science groupies like me. This year the question was What scientific concept would improve everybody's cognitive toolkit? My answer was this: Brilliant people, be they anthropologists, psychologists or economists, assume that brilliance is the key to human achievement.
"Critical Code Studies starts here."
That was the tagline of the Critical Code Studies Working Group (CCSWG), a gathering of over 100 scholars from countries across the globe for an applied experiment in field formation. The Working Group met over the course of six weeks, beginning February 2010, to engage the work of Critical Code Studies.
As we defined it in the early days of the CCS blog, Critical Code Studies is the application of hermeneutics to the interpretation of the extra-functional significance of computer source code. It is a study that follows the developments of Software Studies and Platform Studies into the layer of the code. In their oft-taught text, Structure and Interpretation of Computer Programs, Herald Abelson, Gerald Jay Sussman, and Julie Sussman declare, "Underlying our approach to this subject is our conviction that 'computer science' is not a science and that its significance has little to do with computers. The computer revolution is a revolution in the way we think and in the way we express what we think" (xvi). Computer science in this context is the study of procedural epistemology, as they put it. While books like Noah Wardrip-Fruin's Expressive Processing and Ian Bogost's Unit Operations have begun to outline ways for discussing those processes, Critical Code Studies looks to examine more closely the particular symbols used to express those procedural ideas.
The electronic book review published the initial essay on CCS four years before the 2010 CCSWG. After convening the blog, I began collaborating on methodologies and approaches, and participated in discussions of these at the Society for Literature, Science, and the Arts (SLSA), Digital Humanities, the Modern Language Association, the Electronic Literature Organization, and elsewhere. These productive conversations made clear that it was not enough to simply assert that code can be read. It was time to establish and demonstrate how to interpret code productively. CCS was in need of a talented group of scholars working together in intense collaborative sessions to develop approaches based on their interests and building on their experience. We had found the ocean and now had to fashion our craft, or perhaps even a manner of sailing. What would these new methodologies be? How would they be distinct from or connected to other technocultural interpretive projects? The CCSWG arose out of these questions.
The working group represented the first formalized meeting to develop methodologies specifically for CCS. However, I expect that this working group would have been far less productive without the electronic book review. For in offering to publish the discussion threads of the working group, ebr transformed the heated discussions and creative exchanges from the late night banter of a sequestered research group into a reviewed and edited scholarly publication, bringing the threads (or ripostes) to the eyes of many others. The working group could then proceed in its Ning-island retreat, trusting that the best fruits of its frenzy could be brought back to the mainland. And that has led here.
The CCSWG was a six-week long experiment in alternative academic production. It was originally instigated and organized by founding member Max Feinstein. Under the guidance of David Parry, the initial proposal for an invited discussion group evolved into an open call for an online seminar. To add structure and sweetness, each week featured a different invited guest speakers hosting a week-long discussion thread on a different theme.
As the organizing chair, I led off with the thread that follows. The second week thread was hosted by Jeremy Douglass, who has been involved in CCS from its origins on our collaborative blog Writer Response Theory. In his presentation, Douglass shifted the discussion towards a broader world of critical code studies "in the wild," calling on the working group for a "code scavenger hunt" to expand our bestiary of code criticism. The third week took an innovative form, featuring Dennis Jerz, whose work on William Crowther's ADVENTURE serves as a model for CCS projects. To build on his work, we published the complete code of Crowther's program in a group-editable text document. This project of collaboratively annotating a piece of code for the purpose of explication and exegesis was a first of its kind, and it, too, will serve as a model for the ways in which scholars will read code together. In the fourth week Wendy Hui Kyong Chun presented the first chapter of her forthcoming: Programmed Visions: Software and Memory, a selection which challenged the fetishization of source code, or "sourcery" as she calls it. In the fifth week, Stephen Ramsay displayed virtuoso video editing in a "live" reading of live coding, one that demonstrates the process of developing a CCS reading, in the spirit of his object of study, on the fly. The sixth and final week brought Mez Breeze, creator of the creole language mezangelle. Her text for the week was a codework she had created for the occasion.
In addition to the weekly discussions, members began individual "Code Critiques," threads dedicated to the analysis of a single coding object, amassing thirty-one in all. One of those, "Random Mazes," submitted by Nick Montfort, is now on course to become a collaboratively authored book, written by a twelve person collective, with MIT Press. The collective's other members include Patsy Baudoin, John Bell, Ian Bogost, Jeremy Douglass, Mary Flanagan, Michael Mateas, Casey Reas, Warren Sack, Mark Sample, and Noah Vawter. The book takes as its working title its single-line object of study: 10 PRINT CHR$(205.5+RND(1)); : GOTO 10, a line that when composed would create a pseudo-random maze pattern scrolling down the screen of a Commodore 64. That program was also the subject of my recent essay in the new journal by Loss Pequeño Glazier, Emerging Language Practices. The generativity of that tiny fragment of code points to the vast potential for explication across the archives of source code. No doubt, other code critiques from those sessions will appear in essays and conference presentations, as the ones appearing in the CCS conference at the University of Southern California held in July and the upcoming panel at MLA 2011 entitled "Close Reading the Digital," which features several code-based critiques.
On the First Week
When I developed the presentation on the first week, I truly believed CCS had already arrived. That is to say, I was under the impression that the community of new media scholars had accepted that the application of hermeneutics to the study of code was a given and, thus, we could call a kind of "open season" on code. The success of conference presentations, the number of people joining the working group, and the positive feedback from critics and editors had led me to believe that the working group could begin its work without debate or at least that members would debate competing interpretations rather than the risks, threats, and dangers posed by interpretation itself. I imagined the equivalent of English literary seminars or Communication symposiums, fueled by coffee and Continental critical theory, conjecturing on the meaning of lines of code as we had once debated the meaning of Ulysses, the Labyrinth of Knossos, PacMan, Flight Paths, cellular automata, and the Bonaventure Hotel. A brief examination of the opening remarks of my video should convey as much. There is a bit of a light touch on examples, the tone a bit heady, the vision through glasses tinted Internet-blue. However, within the first few exchanges it becomes clear that the essential methodologies had yet to be negotiated and formalized, and that if another round of science wars was to be averted, anything-goes was a goner.
Contestations
One of the strongest reactions appears in the ambivalence or bipolar reaction of Gabriel Menotti Gonring who writes, "I wasn't expecting to see something so close to a critical reading applied to code, but the results look promising!" But a moment later, he offers to play the "devil's advocate and wonder if this methodology cannot lead to the old problem of mistaking code with text, and treating it as a system of metaphors to be interpreted / discourses to be unveiled." Barbara Hui echoes this critique, writing she is "most ambivalent about [....] the move to read lines of code as text, i.e. computer code as a sort of poetry or metaphor." José Carlos Silvestre picks up her critique, "borrowing from [Claus] Pias," to calling the practice "hermeneutics forgetful-of-technics" or "technik-vergessene Hermeneutik." Finally, Federica Fabretti raises this specter of the potential "violence of CCS" in interpreting code as though it were any other kind of semiotic object.
Returning again was what I call the "programmer's objection," in which those who have more experience or even make a living programming or teaching programming worry about making "too much" of particular lines of code. Is there a violence in interpreting code metaphorically? Hui identifies the source of this anxiety most presciently when she writes, "Perhaps programmers acquire a different/additional kind of literacy that is very difficult to ignore once you become fluent in it." Quickly lines were drawn in the discussion between those who seemed to be abstracting out code from its functional significance and those who wanted to pay closer attention to the material and formal effects the code would have when processed. Critics were cast or self-identified either as the interpreters ready to make merry or the programmers forced to defend their ground. Such a division, real or merely staged, reminded me of my experience studying Ancient Greek and Spanish and how my early lessons lead me to notice cultural difference while I was learning vocabulary while later lessons had me concentrating on getting the declensions and subjuntivo right. "Getting it right" is a mode in which the communicator focuses on functionality, on syntactic accuracy, rather than reflecting on ramifications and implications. In this state, a language learner wants to be processed accurately by the system. I would hardly argue that the person who wants to be fluent will be less capable of finding meaning, but focusing on achieving legibility within the system involves a different use of attention than conjecturing about significance.
Equally, I am reminded of Peter J. Bentley who discusses his own programmer's perspective in "The Meaning of Code." He writes,
When you've been programming for long enough, when you've grown up programming computers, you think in a subtly different way.... You become used to breaking down problems into smaller, easier parts....But code can also dehumanize a person. There is no subtlety, no humor, no scope for emotion in code....Code is so literal, so unambiguous, that it takes a while to train a mind to think in the same way. These limitations of code can produce side-effects in people that write it - a joke is lost...an ambiguity the cause of excessive confusion. (33)
Bentley is perhaps too self-effacing when including this last assessment of a programmer's sense of meaning. Certainly, he is not calling programmers humorless. The co-evolution of the Internet and techy humor sites, such as Penny Arcade, quickly refutes that reading. However, there may be some value to asking whether working so closely with unambiguous code points one away from pursuing potentially ambiguous connotations and meanings. As Alan J. Perlin has written in his forward to Abelson and Sussman, "The computer is a harsh taskmaster. Its programs must be correct and what we wish to say must be said accurately in every detail" (x). Certainly, CCS must acknowledge the unambiguous denotations of computer source code before it can begin to approach the connotations.
According to the discussants, code readings require an investigation of the material context of the code before moving into more symbolic interpretations. That context includes the historical background of the code, the manner in which the code operates, the style and paradigm in which it was written, the history and culture of the language used, and how it interacts with other software including the operating system. Furthermore, the investigation of the operation of the code should pursue the run-time effects of the particular lines of the code and any distinctions in the processing of that code on different hardware. These technical and historical specifications outline perhaps just the most basic elements necessary to build toward interpretation.
Interpretation is a humanistic activity that is easy to attack and even easier to undervalue in a field of study so fully beholden to progress narratives and the tyranny of Moore's Law. Interpretation is non-falsifiable, subjective, and fuzzy. It is not an activity with sex appeal to granting bodies or the Department of Defense. Other scholarly endeavors to celebrate the history of code, to study the sociology of those who coded, to document the process of discovery, are far easier to present as "valuable" across the disciplines. As the conversation unfolded in the CCSWG, the nature of the interventions became clear. Self-identified programmers such as Hui and Silvestre were not arguing that code could not be interpreted symbolically, as they assured us repeatedly. However, in any attempt to distinguish CCS from other areas, such as Software Studies, they wanted to know what could be gained by looking at the code in an analysis more fully contextualized within the functioning of the program, its syntactical and logical conventions, the other programs interacting with it, and the processes produced. In short, within the "programmer's objection," was a call for a much more rigorous approach to code prior to the subsequent interpretive moves.
Alternative Readings
Like any worthwhile seminar-style discussion, Week 1 evoked/provoked/produced many ways of interpreting this code:
- Micha Cárdenas offered a method of reading a line closely by loosely-but-closely translating the source code into a kind of expressive pseudo-code.
- Jonathan Cohn found the annual "dynabyte" ad to be a hook into a nationalistic reading of the worm.
- Evan Buswell argued we should be reading MS Outlook and the Microsoft Operating System, since these programs are the source of the vulnerability, pointing to the way the worm takes advantage of the "SpecialFolder" of the OS.
- Hugh Cayless saw the code as "junk" and proposed reflecting on how such a distinction could be drawn.
- Marisa wondered if the mouse click should be considered part of the code.
- Jennifer Lieberman suggested reading the code by contrasting the syntagmatic with the paradigmatic ("the other possibilities or ideas represented by what is not on the page/screen").
Resolutions
This week demonstrated the desire for scholars to maintain a rigorous attention to material specifications of the code, to read the code fully informed by how it might resonate within cultures of coding. It also led me to clarify my statements on reading "code as text," as framed in the ebr article. Intended as a direct response to John Cayley's claim in "The Code is Not the Text - unless it is the Text", my phrasing - reading "code as text" - actually confused the issue. Code for CCS is not text in the sense of a poem, a collection of signs, standing alone. Code is the text in the sense of Cultural Studies, the object of study within its material, historical context. For a thoughtful reflection of text and context in Cultural Studies, see: Kovala, Urpo. "Cultural Studies and Cultural Text Analysis." CLCWeb: Comparative Literature and Culture 4.4 (2002): http://docs.lib.purdue.edu/clcweb/vol4/iss4/2 While at times such analysis leads to interpretive moves often applied to text, code should not be reduced to its symbolic representation. As the week's conversation clarified, code is a collection of un-processed signs that are one component of much larger systems that include the operating system, compiler, hardware, et cetera. Wendy Chun's week pursues this topic further as she interrogates the perils of fetishizing code. As Federica writes, while she shares "Mark's emphasis on always going back to code" and "his puzzlement at/impatience for 'general' talk on code which just uses code as illustrative/decorative," she feels "so utterly limited by reading 'just' code (and by posting de-contextualized pieces of code)." In other words, Critical Code Studies should not be confined to the study of merely the set of symbols in the code. They are the trail markers of a much more extensive analysis.
Secondly, another word from the original ebr article became a sticking point: extra-functional. On the one hand, Barbara Hui and others expressed concerns about moving away from the functional, of leaving functionality behind. On the other hand, when readings seemed to focus too much on the overall processes and too little on the effects of the code on the machine in run-time, that seemed more like Software Studies. I would like to clarify that extra-functional does not mean detached from function but instead growing-out of and beyond the functional effects. As many stated here, the closest attention must be to the effects of the code, how the symbols are interpreted by the machine; however, that functionality is one part of the larger ways in which code becomes meaningful to its readers.
This tension continued to play out through the following weeks of the working group, and I would argue that focusing on the code is necessary while we are developing specific approaches that prove most productive when discussing it. In any given work of new media criticism, code critiques are likely to be just one facet, but for now CCS needs to attend to the work of formalizing some procedures for analyzing code. This first week establishes several of the key sites on which those processes will be developed.
Works Cited
Abelson, Harold and Gerald J. Sussman with Julie Sussman. Structure and Interpretation of Computer Programs. Cambridge, Massachusetts: MIT, 1985.
Bentley, Peter J. "The Meaning of Code." Ars Electronica 2003: Code:The Language of our Time. Hatje Cantz Publishers, 2003. http://90.146.8.18/en/archiv_files/20031/FE_2003_bentley_en.pdf
Bogost, Ian. Unit Operations: An Approach to Videogame Criticism. The MIT Press, 2008. Print.
Marino, Mark C. "Critical Code Studies - Mark C. Marino." electronic book review electropoetics (2006): n. pag. 25 Aug. 2010.
Perlis, Alan J. Foreward. Structure and Interpretation of Computer Programs.
Wardrip-Fruin, Noah. Expressive Processing: Digital Fictions, Computer Games, and Software Studies. The MIT Press, 2009.
At its peak, this kind of “farming” was a multi-million dollars business – and a bothersome source of scamming, spamming, and potentially game-ruining distortions of virtual market forces. In its latest title, then, Blizzard has taken the logical next step – and brought the entire business in-house.
Cashing out
Enter one of Diablo 3’s boldest gambles: a “real money auction house”, where players can sell to each other almost every kind of virtual goodie it’s possible to earn through hours and days of dungeon-crawling and baddie-bashing. At the time of writing, a week after the game’s release, the auction house’s doors had yet to open – but what’s promised is a service allowing players freely to sell the spoils of war to each other at any price they like, with Blizzard taking a 15% cut (or flat fee on some items), plus a similar percentage again for those wishing to “cash out” their profits via PayPal.
The service won’t be available everywhere in the world, and using it is entirely optional – but it’s likely to have at least hundreds of thousands of users. In Blizzard’s words, it’s designed to provide “the foundation for a player-driven economy that’s safe, fun, and accessible to everyone” – an aspiration that will be music to many ears after years of gold-purchase spamming in World of Warcraft and its ilk. What Diablo 3’s auction house also represents, though, is a fascinating fiscal experiment in its own right – not least because it explicitly acknowledges something that has been implicitly true within online gaming for a long time: economics is an awful lot of fun.
Indeed, economics isn’t just “fun” like football or kiss-chase. It’s good, hard, complex, peer-to-peer fun, in the way that work ought to be but most often isn’t. As anyone who’s made more than a few purchase on eBay will know, electronic auction houses offer labyrinthine delight once you’ve learned their language of bids, deadlines, quests for rare purchases and sudden discovery. Match this with the option of earning your assets by teaming up with other players and slaughtering picturesque hordes, and you have a seriously entertaining business proposition. Not to mention the pleasures of making a sale and learning to read the market. Does it look like demand for high-end barbarian swords is rising? Buy now, hoard your product, then sell when prices hit their peak.
So far as Blizzard Entertainment themselves are concerned, potential problems abound that will make compulsive viewing for anyone interested in the future of digital assets: from attempts at exploitation and monopoly to the security of users’ accounts and the black market’s ability to undercut official sales. Then, of course, there’s the nebulous realm of addiction, work-life balance, and the question of just how much time it’s healthy to spend waving around unreal swords.
To me, though, what’s most interesting is the near-seamless alignment of play and labour a system like Diablo 3’s represents – and the willingness of all involved to pour time, money and belief into assets that have no existence beyond the magic circle of the game.
In this respect, the fantastical prizes of a game like Diablo 3 are very much like money itself – which is only as real as collective belief, and just as meaningless if this fails (ask a Greek if you don’t believe me). If you need a lesson in 21st Century social realism, in fact, there’s a lot to be learned from video games. There are few things we humans like more than the serious business of play. And of all the games we play, there’s none more absurdly serious than money.
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In most online communities, 90% of users are lurkers who never contribute, 9% of users contribute a little, and 1% of users account for almost all the action.
back to topJakob Nielsen's Alertbox, October 9, 2006
Participation Inequality: Encouraging More Users to ContributeSummary:
In most online communities, 90% of users are lurkers who never contribute, 9% of users contribute a little, and 1% of users account for almost all the action.All large-scale, multi-user communities and online social networks that rely on users to contribute content or build services share one property: most users don't participate very much. Often, they simply lurk in the background.
In contrast, a tiny minority of users usually accounts for a disproportionately large amount of the content and other system activity. This phenomenon of participation inequality was first studied in depth by Will Hill in the early '90s, when he worked down the hall from me at Bell Communications Research (see references below).
When you plot the amount of activity for each user, the result is a Zipf curve, which shows as a straight line in a log-log diagram.
User participation often more or less follows a 90-9-1 rule:
- 90% of users are lurkers (i.e., read or observe, but don't contribute).
- 9% of users contribute from time to time, but other priorities dominate their time.
- 1% of users participate a lot and account for most contributions: it can seem as if they don't have lives because they often post just minutes after whatever event they're commenting on occurs.
Early Inequality Research
Before the Web, researchers documented participation inequality in media such as Usenet newsgroups, CompuServe bulletin boards, Internet mailing lists, and internal discussion boards in big companies. A study of more than 2 million messages on Usenet found that 27% of the postings were from people who posted only a single message. Conversely, the most active 3% of posters contributed 25% of the messages.In Whittaker et al.'s Usenet study, a randomly selected posting was equally likely to come from one of the 580,000 low-frequency contributors or one of the 19,000 high-frequency contributors. Obviously, if you want to assess the "feelings of the community" it's highly unfair if one subgroup's 19,000 members have the same representation as another subgroup's 580,000 members. More importantly, such inequities would give you a biased understanding of the community, because many differences almost certainly exist between people who post a lot and those who post a little. And you would never hear from the silent majority of lurkers.
Inequality on the Web
There are about 1.1 billion Internet users, yet only 55 million users (5%) have weblogs according to Technorati. Worse, there are only 1.6 million postings per day; because some people post multiple times per day, only 0.1% of users post daily.Blogs have even worse participation inequality than is evident in the 90-9-1 rule that characterizes most online communities. With blogs, the rule is more like 95-5-0.1.
I established a Facebook account in 2008. My motivation was ignoble: I wanted to distribute my journalism more widely. I have acquired since then just over four thousand “friends”—in Afghanistan, Pakistan, India, the Middle East, and of course, closer to home. I have discovered the appeal of Facebook’s community—for example, the extraordinary emotional support that swells in virtual space when people come together online around a friend’s illness or life celebrations.
Through its bedrock appeals to friendship, community, public identity, and activism—and its commercial exploitation of these values—Facebook is an unprecedented synthesis of corporate and public spaces. The corporation’s social contract with users is ambitious, yet neither its governance system nor its young ruler seem trustworthy. Then came this month’s initial public offering of stock—a chaotic and revealing event—which promises to put the whole enterprise under even greater pressure.
There are many reasons to be skeptical about Facebook’s I.P.O., which raised $16 billion for the company. For investors, as my colleague John Cassidy has pointed out, the company’s founders and early investors are likely to do better with this much-hyped event than individual investors. The offering itself was as visible a disaster as a lead underwriting bank (in this case, Morgan Stanley) has turned in for some time: Facebook shares have fallen by more than ten per cent; there were trading screwups by Nasdaq; and lawsuits and regulatory investigations into whether Morgan and Facebook properly shared information with investors have already started.
This launch-pad explosion is also one more reason to be wary of what my colleague James Surowiecki has analyzed: Facebook’s two-tiered corporate-governance system, which ensures that founder Mark Zuckerberg retains firm control, and can’t be easily challenged by dissident shareholders, even if he steers badly off course, as highly self-confident men in their late twenties sometimes do.
Those are reasons for investors to be doubtful; at least as worrying is what the I.P.O.-palooza signals about Facebook’s sovereignty over citizens, here and abroad. Facebook has become a public square of global importance. By the end of the summer, it may have more than a billion users, or about fifteen per cent of the world’s population. Some of these people are restive and see Facebook as a substitute public space for speech and dissent that their own authoritarian regimes don’t provide. Facebook users have already helped to foment revolution in some places (Egypt and Tunisia) and are still trying, at great cost, to overthrow one of the Middle East’s most brutal regimes.
Within the United States, Facebook is a venue for all sorts of issue and political campaigns. And yet, on the site, as a practical matter, what speech is permitted or banned is determined largely by Facebook’s terms of service. The terms function as a corporate constitution binding users to the provider’s conception of what speech is acceptable. My colleague at the New America Foundation, Rebecca MacKinnnon, in her recent book “Consent of the Networked,” calls this realm “Facebookistan.” Once Facebook users sign on and accept the terms of service, their postings are subordinate to the corporation’s rules, for as long as they choose to stay. In a place like Syria, the Facebook rules users encounter are much more permissive than local laws; in the United States, that is not so clear.
You might expect dense legalese, but the terms’ language is clear and soaring, echoing the tones of constitutional documents. Some of the declaratory sentences lay out the commitments by Facebook’s royal “We.” Others describe the obligations of the subject “You.” The terms are organized into sections, like articles. One entitled “Safety” seems to self-consciously echo the Ten Commandments: “You will not bully, intimidate, or harass any user…. You will not post content that: is hateful, threatening or pornographic; incites violence; or contains nudity or graphic or gratuitous violence.” And there is this hint of Facebook’s expansive authority: “You will not encourage or facilitate any violations of this Statement.”
The terms obfuscate Facebook’s business strategies in such simple language that the deception—the sense of what is being left out—is almost poetic: “Sometimes we get data from our advertising partners, customers, and other third parties that helps us (or them) deliver ads, understand online activity, and generally make Facebook better.”
Facebook has made jarring mistakes as its leaders have learned what it means to run a profit-motivated political and public forum. In 2009, for example, the corporation exposed Iranian dissidents to danger by unilaterally changing privacy rules that allowed the Iranian authorities to see the identities of activists’ online friends. The error was corrected quickly, but in general, Facebook has encouraged its users to accept greater and greater losses of privacy. Zuckerberg believes the world will be better off if it adopts “radical transparency,” as the journalist David Kirkpatrick put it in his book, “The Facebook Effect.”
Zuckerberg’s business model requires the trust and loyalty of his users so that he can make money from their participation, yet he must simultaneously stretch that trust by driving the site to maximize profits, including by selling users’ personal information. The I.P.O. last week will exacerbate this tension: Facebook’s huge valuation now puts pressure on the company’s strategists to increase its revenue-per-user. That means more ads, more data mining, and more creative thinking about new ways to commercialize the personal, cultural, political, and even revolutionary activity of users.
There is something vaguely dystopian about oppressed peoples in Syria or Iran seeking dignity and liberation inside a corporate sovereign that is, for its part, creating great wealth for its founders and asserting control over its users.
Facebook is hardly the only corporation managing these sorts of dilemmas—Google is a target of investigations seeking greater information about how it manages customer information it collects, about which it has sometimes been opaque, and it too has broken trust with users. Facebook points out that it has been responsive to revolts and protests from within. Zuckerberg proudly told Kirkpatrick that he revelled in the ways Facebook’s users had forced him to become more democratic: “History tells us that systems are most fairly governed when there is an open and transparent dialogue between the people who make decisions and those who are affected by them. We believe history will one day show that this principle holds true for companies as well.”
That is a laudable conception. Yet for now, at least, Facebook concedes to its users only when it judges that it is in the corporation’s interest to do so; what user votes and consultations there may be are purely advisory. As MacKinnon observes, this system suggests the political control strategies of the Chinese Communist Party: periodic campaigns of state-managed openness and managed local democracy.
While talking to varied audiences recently about my new book (warning: marketing ahead), “Private Empire: ExxonMobil and American Power,” I have been reminded how uneasy Americans from of all ideological orientations are about corporate power and sovereignty these days. They believe in capitalism and market efficiencies, to be sure, but they fear heavily concentrated private power, especially where it encroaches on their economic and personal choices. They ask, “What should we do?”
Perhaps it starts with exercising citizenship. I have decided to exercise mine—in Facebookistan, that is. This seems the right time to leave such a crowded and volatile public square.
It takes a while to find it, but if you are a Facebook user, there is a small settings button entitled “deactivate account.” If you click, Facebook displays the faces of people “who will miss you.” If you are determined nonetheless to depart, and scroll further down, you are required to choose a “reason for leaving” before you are permitted to go. Unfortunately, “inadequate citizen rule” or “doubts about corporate governance” are not among the choices. From the available list, I went with “I don’t feel safe on Facebook.”
Farewell, Facebook friends. May you enjoy everywhere the full rights of free citizens.
Illustration by Kate Prior.
A counterpoint to Jaron Lanier’s dystopian visions of runaway technological cataclysm in “One Half of a Manifesto.”
Originally published July 31, 2001. Published on KurzweilAI.net July 31, 2001.
Postscript regarding Ray Kurzweil can be read here. “One Half of a Manifesto” can be read here.
In Jaron Lanier’s Postscript, which he wrote after he and I spoke in succession at a Vanguard event, Lanier points out that we agree on many things, which indeed we do. So I’ll start in that vein as well. First of all, I share the world’s esteem for Jaron’s pioneering work in virtual reality, including his innovative contemporary work on the “Teleimmersion” initiative, and, of course, in coining the term “virtual reality.” I probably have higher regard for virtual reality than Jaron does, but that comes back to our distinct views of the future.
As an aside I’m not entirely happy with the phrase “virtual reality” as it implies that it’s not a real place to be. I consider a telephone conversation as being together in auditory virtual reality, yet we regard these to be real conversations. I have a similar problem with the term “artificial intelligence.”
And as a pioneer in what I believe will become a transforming concept in human communication, I know that Jaron shares with me an underlying enthusiasm for the contributions that computer and related communications technologies can have on the quality of life. That is the other half of his manifesto. I appreciate Jaron pointing this out. It’s not entirely clear sometimes, for example, that Bill Joy has another half to his manifesto.
And I agree with at least one of Jaron’s six objections to what he calls “Cybernetic Totalism.” In objection #3, he takes issues with those who maintain “that subjective experience either doesn’t exist, or is unimportant because it is some sort of ambient or peripheral effect.” The reason that some people feel this way is precisely because subjective experience cannot be scientifically measured. Although we can measure certain correlates of subjective experience (e.g., correlating certain patterns of objectively measurable neurological activity with objectively verifiable reports of certain subjective experiences), we cannot penetrate to the core of subjective experience through objective measurement. It’s the difference between the concept of “objectivity,” which is the basis of science, and “subjectivity,” which is essentially a synonym for consciousness. There is no device or system we can postulate that could definitively detect subjectivity associated with an entity, at least no such device that does not have philosophical assumptions built into it.
So I accept that Jaron Lanier has subjective experiences, and I can even imagine (and empathize with!) his feelings of frustration at the dictums of “cybernetic totalists” such as myself (not that I accept this characterization) as he wrote his half manifesto. Like Jaron, I even accept the subjective experience of those who maintain that there is no such thing as subjective experience. Of course, most people do accept that other people are conscious, but this shared human assumption breaks down as we go outside of human experience, e.g., the debates regarding animal rights (which have everything to do with whether animals are conscious or just quasi-machines that operate by “instinct”), as well as the debates regarding the notion that a nonbiological entity could conceivably be conscious.
Consider that we are unable to truly experience the subjective experiences of others. We hear their reports about their experiences, and we may even feel empathy in response to the behavior that results from their internal states. We are, however, only exposed to the behavior of others and, therefore, can only imagine their subjective experience. So one can construct a perfectly consistent, and scientific, worldview that omits the existence of consciousness. And because there is fundamentally no scientific way to measure the consciousness or subjective experience of another entity, some observers come to the conclusion that it’s just an illusion.
My own view is that precisely because we cannot resolve issues of consciousness entirely through objective measurement and analysis, i.e., science, there is a critical role for philosophy, which we sometimes call religion. I would agree with Jaron that consciousness is the most important ontological question. After all, if we truly imagine a world in which there is no subjective experience, i.e., a world in which there is swirling stuff but no conscious entity to experience it, then that world may as well not exist. In some philosophical traditions (i.e., some interpretations of quantum mechanics, some schools of Buddhist thought), that is exactly how such a world is regarded.
I like Jaron’s term “circle of empathy,” which makes it clear that the circle of reality that I consider to be “me” is not clear-cut. One’s circle of empathy is certainly not simply our body, as we have limited identification with, say, our toes, and even less with the contents of our large intestines. Even with regard to our brains, we are aware of only a small portion of what goes on in our brains, and often consider thoughts and dreams that suddenly intrude on our awareness to have come from some foreign place. We do often include loved ones who may be physically disparate within our circle of empathy. Thus the aspect of the Universe that I consider to be “myself” is not at all clear cut, and some philosophies do emphasize the extent to which there is inherently no such boundary.
Having stated a few ways in which Jaron and I agree with each other’s perspective, I will say that his “Half of a Manifesto” mischaracterizes many of the views he objects to. Certainly that’s true with regard to his characterization of my own thesis. In particular, he appears to have only picked up on half of what I said in Atlanta, because the other half addresses at least some of the issues he raises. Moreover, many of Jaron’s arguments aren’t really arguments at all, but a amalgamation of mentally filed anecdotes and engineering frustrations. The fact that Time magazine got a prediction wrong in 1966, as Jaron reports, is not a compelling argument that all discussions of trends are misguided. Nor is the fact that dinosaurs did not continue to increase in size indefinitely a demonstration that every trend quickly dies out. The size of dinosaurs is irrelevant; a larger size may or may not impart an advantage, whereas an increase in the price-performance and/or bandwidth of a technology clearly does impart an advantage. It would be hard to make the case that a technology with a lower price-performance had inherent advantages, whereas it is certainly possible that a smaller and therefore more agile animal may have advantages.
Jaron Lanier has what my colleague Lucas Hendrich calls the “engineer’s pessimism.” Often an engineer or scientist who is so immersed in the difficulties of a contemporary challenge fails to appreciate the ultimate long-term implications of their own work, and, in particular, the larger field of work that they operate in. Consider the biochemists in 1985 who were skeptical of the announcement of the goal of transcribing the entire genome in a mere 15 years. These scientists had just spent an entire year transcribing a mere one ten-thousandth of the genome, so even with reasonable anticipated advances, it seemed to them like it would be hundreds of years, if not longer, before the entire genome could be sequenced. Or consider the skepticism expressed in the mid 1980s that the Internet would ever be a significant phenomenon, given that it included only tens of thousands of nodes. The fact that the number of nodes was doubling every year and there were, therefore, likely to be tens of millions of nodes ten years later was not appreciated by those who struggled with “state of the art” technology in 1985, which permitted adding only a few thousand nodes throughout the world in a year.
In his “Postscript regarding Ray Kurzweil,” Jaron asks the rhetorical question “about Ray’s exponential theory of history…[is he] stacking the deck by choosing points that fit the curves he wants to find?” I can assure Jaron that the more points we add to the dozens of exponential graphs I presented to him and the rest of the audience in Atlanta, the clearer the exponential trends become. Does he really imagine that there is some circa 1901 calculating device that has better price-performance than our circa 2001 devices? Or even a 1995 device that is competitive with a 2001 device? In fact what we do see as more points (representing specific devices) are collected is a cascade of “S-curves,” in which each S-curve represents some specific technological paradigm. Each S-curve (which looks like an “S” in which the top portion is stretched out to the right) starts out with gradual and then extreme exponential growth, subsequently leveling off as the potential of that paradigm is exhausted. But what turns each S-curve into an ongoing exponential is the shift to another paradigm, and thus to another S-curve, i.e., innovation. The pressure to explore and discover a new paradigm increases as the limits of each current paradigm becomes apparent.
When it became impossible to shrink vacuum tubes any further and maintain the requisite vacuum, transistors came along, which are not merely small vacuum tubes. We’ve been through five paradigms in computing in this past century (electromechanical calculators, relay-based computers, vacuum-tube-based computing, discrete transistors, and then integrated circuits, on which Moore’s law is based). As the limits of flat integrated circuits are now within sight (one to one and a half decades away), there are already dozens of projects underway to pioneer the sixth paradigm of computing, which is computing in three dimensions, several of which have demonstrated small-scale working systems.
It is specifically the processing and movement of information that is growing exponentially. So one reason that an area such as transportation is resting at the top of an S-curve is that many if not most of the purposes of transportation have been satisfied by exponentially growing communication technologies. My own organization has colleagues in different parts of the country, and most of our needs that in times past would have required a person or a package to be transported can be met through the increasingly viable virtual meetings made possible by a panoply of communication technologies, some of which Jaron is himself working to advance. Having said that, I do believe we will see new paradigms in transportation. However, with increasingly realistic, high-resolution full-immersion forms of virtual reality continuing to emerge, our needs to be together will increasingly be met through computation and communication.
Jaron’s concept of “lock-in” is not the primary obstacle to advancing transportation. If the existence of a complex support system necessarily caused lock-in, then why don’t we see lock-in preventing ongoing expansion of every aspect of the Internet? After all, the Internet certainly requires an enormous and complex infrastructure. The primary reason that transportation is under little pressure for a paradigm-shift is that the underlying need for transportation has been increasingly met through communication technologies that are expanding exponentially.
One of Jaron’s primary themes is to distinguish between quantitative and qualitative trends, saying in essence that perhaps certain brute force capabilities such as memory capacity, processor speed, and communications bandwidths are expanding exponentially, but the qualitative aspects are not. And toward this end, Jaron complains of a multiplicity of software frustrations (many, incidentally, having to do with Windows) that plague both users and, in particular, software developers like himself.
This is the hardware versus software challenge, and it is an important one. Jaron does not mention at all my primary thesis having to do with the software of intelligence. Jaron characterizes my position and that of other so-called “cybernetic totalists” to be that we’ll just figure it out in some unspecified way, what he refers to as a software “Deus ex Machina.” I have a specific and detailed scenario to achieve the software of intelligence, which concerns the reverse engineering of the human brain, an undertaking that is much further along than most people realize. I’ll return to this in a moment, but first I would like to address some other basic misconceptions about the so-called lack of progress in software.
Jaron calls software inherently “unwieldy” and “brittle” and writes at great length on a variety of frustrations that he encounters in the world of software. He writes that “getting computers to perform specific tasks of significant complexity in a reliable but modifiable way, without crashes or security breaches, is essentially impossible.” I certainly don’t want to put myself in the position of defending all software (any more than I would care to characterize all people as wonderful). But it’s not the case that complex software is necessarily brittle and prone to catastrophic breakdown. There are many examples of complex mission critical software that operates with very little if any breakdowns, for example the sophisticated software that controls an increasing fraction of airplane landings, or software that monitors patients in critical care facilities. I am not aware of any airplane crashes that have been caused by automated landing software; the same, however, cannot be said for human reliability.
Jaron says that “Computer user interfaces tend to respond more slowly to user interface events, such as a keypress, than they did fifteen years ago…What’s gone wrong?” To this I would invite Jaron to try using an old computer today. Even we put aside the difficulty of setting one up today (which is a different issue), Jaron has forgotten just how unresponsive, unwieldy, and limited they were. Try getting some real work done to today’s standards with a fifteen year-old personal computer. It’s simply not true to say that the old software was better in any qualitative or quantitative sense. If you believe that, then go use them.
Although it’s always possible to find poor quality design, the primary reason for user interface response delays is user demand for more sophisticated functionality. If users were willing to freeze the functionality of their software, then the ongoing exponential growth of computing speed and memory would quickly eliminate software response delays. But they’re not. So functionality always stays on the edge of what’s feasible (personally, I’m waiting for my Teleimmersion upgrade to my videoconferencing software).
This romancing of software from years or decades ago is comparable to people’s idyllic view of life hundreds of years ago, when we were unencumbered with the frustrations of machines. Life was unencumbered, perhaps, but it was also short (e.g., life expectancy less than half of today’s), labor-intensive (e.g., just preparing the evening meal took many hours of hard labor), poverty-filled, disease and disaster prone.
With regard to the price-performance of software, the comparisons in virtually every area are dramatic. For example, in 1985 $5,000 bought you a speech recognition software package that provided a 1,000 word vocabulary, no continuous speech capability, required three hours of training, and had relatively poor accuracy. Today, for only $50, you can purchase a speech recognition software package with a 100,000 word vocabulary, continuous speech, that requires only five minutes of training, has dramatically improved accuracy, natural language understanding ability (for editing commands and other purposes), and many other features.
How about software development itself? I’ve been developing software myself for forty years, so I have some perspective on this. It’s clear that the growth in productivity of software development has a lower exponent, but it is nonetheless growing exponentially. The development tools, class libraries, and support systems available today are dramatically more effective than those of decades ago. I have today small teams of just three or four people who achieve objectives in a few months that are comparable to what a team of a dozen or more people could accomplish in a year or more 25 years ago. I estimate the doubling time of software productivity to be approximately six years, which is slower than the doubling time for processor price-performance, which is approximately one year today. However, software productivity is nonetheless growing exponentially.
The most important point to be made here is that there is a specific game plan for achieving human-level intelligence in a machine. I agree that achieving the requisite hardware capacity is a necessary but not sufficient condition. As I mentioned above, we have a resource for understanding how to program the methods of human intelligence given hardware that is up to the task, and that resource is the human brain itself.
Here again, if you speak to some of the neurobiologists who are diligently creating detailed mathematical models of the hundreds of types of neurons found in the brain, or who are modeling the patterns of connections found in different regions, you will in at least a few cases encounter the same sort of engineer’s / scientist’s myopia that results from being immersed in the specifics of one aspect of a large challenge. However, having tracked the progress being made in accumulating all of the (yes, exponentially increasing) knowledge about the human brain and its algorithms, I believe that it is a conservative scenario to expect that within thirty years we will have detailed models of the several hundred information processing organs we collectively call the human brain.
For example, Lloyd Watts has successfully synthesized (that is, assembled and integrated) the detailed models of neurons and interconnections in more than a dozen regions of the brain having to do with auditory processing. He has a detailed model of the information transformations that take place in these regions, and how this information is encoded, and has implemented these models in software. The performance of Watt’s software matches the intricacies that have been revealed in subtle experiments on human hearing and auditory discrimination. Most interestingly, using Watts’ models as the front-end in speech recognition has demonstrated the ability to pick out one speaker against a backdrop of background sounds, an impressive feat that humans are capable of, and that up until Watts’ work, had not been feasible in automated speech recognition systems.
The brain is not one big neural net. It consists of hundreds of regions, each of which is organized differently, with different types of neurons, different types of signaling, and different patterns of interconnections. By and large, the algorithms are not the sequential, logical methods that are commonly used in digital computing. The brain tends to use self-organizing, chaotic, holographic (i.e., information not in one place but distributed throughout a region), massively parallel, and digital-controlled-analog methods. However, we have demonstrated in a wide range of projects the ability to understand these methods, and to extract them from the rapidly escalating knowledge of the brain and its organization.
The speed, cost effectiveness, and bandwidth of human brain scanning is also growing exponentially, doubling every year. Our knowledge of human neuron models is also rapidly growing. The size of neuron clusters that we have successfully recreated in terms of functional equivalence is also scaling up exponentially.
I am not saying that this process of reverse engineering the human brain is the only route to “strong” AI. It is, however, a critical source of knowledge that is feeding into our overall research activities where these methods are integrated with other approaches.
Also, it is not the case that the complexity of software, and therefore its “brittleness” needs to scale up dramatically in order to emulate the human brain, even when we get to emulating its full functionality. My own area of technical interest is pattern recognition, and the methods that we typically use are self-organizing methods such as neural nets, Markov models, and genetic algorithms. When set up in the right way, these methods can often display subtle and complex behaviors that are not predictable by the designer putting them into practice. I’m not saying that such self-organizing methods are an easy short cut to creating complex and intelligent behavior, but they do represent one important way in which the complexity of a system can be increased without the brittleness of explicitly programmed logical systems.
Consider that the brain itself is created from a genome with only 23 million bytes of useful information (that’s what’s left of the 800 million byte genome when you eliminate all the redundancies, e.g., the sequence “ALU” which is repeated hundreds of thousands of times). 23 million bytes is not that much information (it’s less than Microsoft Word). How is it, then, that the human brain with its 100 trillion connections can result from a genome that is so small? I have estimated that just the interconnection data alone to characterize the human brain is a million times greater than the information in the genome.
The answer is that the genome specifies a set of processes, each of which utilizes chaotic methods (i.e., initial randomness, then self-organization) to increase the amount of information represented. It is known, for example, that the wiring of the interconnections follows a plan that includes a great deal of randomness. As the individual person encounters her environment, the connections and the neurotransmitter level patterns self-organize to better represent the world, but the initial design is specified by a program that is not extreme in its complexity.
It is not my position that we will program human intelligence link by link as in some huge CYC-like expert system. Nor is it the case that we will simply set up a huge genetic (i.e., evolutionary) algorithm and have intelligence at human levels automatically evolve itself. Jaron worries correctly that any such approach would inevitably get stuck in some local minima. He also interestingly points out how biological evolution “missed the wheel.” Actually, that’s not entirely accurate. There are small wheel-like structures at the protein level, although it’s true that their primary function is not for vehicle transportation. Wheels are not very useful, of course, without roads. However, biological evolution did create a species that created wheels (and roads), so it did succeed in creating a lot of wheels, albeit indirectly (but there’s nothing wrong with indirect methods, we use them in engineering all the time).
With regard to creating human levels of intelligence in our machines, we will integrate the insights and models gained from reverse engineering the human brain, which will involve hundreds of regions, each with different methods, many of which do involve self-organizing paradigms at different levels. The feasibility of this reverse engineering project and of implementing the revealed methods has already been clearly demonstrated. I don’t have room in this response to describe the methodology and status of brain reverse engineering in detail, but I will point out that the concept is not necessarily limited to neuromorphic modeling of each neuron. We can model substantial neural clusters by implementing parallel algorithms that are functionally equivalent. This often results in substantially reduced computational requirements, which has been shown by Lloyd Watts and Carver Mead.
Jaron writes that “if there ever was a complex, chaotic phenomenon, we are it.” I agree with that, but don’t see this as an obstacle. My own area of interest is chaotic computing, which is how we do pattern recognition, which in turn is the heart of human intelligence. Chaos is part of the process of pattern recognition, it drives the process, and there is no reason that we cannot harness these methods in our machines just as they are utilized in our brains.
Jaron writes that “evolution has evolved, introducing sex, for instance, but evolution has never found a way to be any speed but very slow.” But he is ignoring the essential nature of an evolutionary process, which is that it accelerates because each stage introduces more powerful methods for creating the next stage. Biological evolution started out extremely slow, and the first halting steps took billions of years. The design of the principal body plans was faster, requiring only tens of millions of years. The process of biological evolution has accelerated, with each stage faster than the stage before it. Later key steps, such as the emergence of homo sapiens, took only hundreds of thousands of years. Human technology, which is evolution continued indirectly (created by a species created by evolution), continued this acceleration. The first steps took tens of thousands of years, outpacing biological evolution, and has accelerated from there. The World Wide Web emerged in only a few years, distinctly faster than, say, the Cambrian explosion.
Jaron complains that “surprisingly few of the most essential algorithms have overheads that scale at a merely linear rate.” Without taking up several pages to analyze this statement in detail, I will point out that the brain does what it does in its own real-time, using interneuronal connections (where most of our thinking takes place) that operate at least ten million times slower than contemporary electronic circuits. We can observe the brain’s massively parallel methods in detail, ultimately scan and understand all of its tens of trillions of connections, and replicate its methods. As I’ve mentioned, we’re well down that path.
To correct a few of Jaron’s statements regarding (my) time frames, it’s not my position that the “singularity” will “arrive a quarter of the way into the new century” or that a “new criticality” will be “achieved in the about the year 2020.” Just so that the record is straight, my view is that we will have the requisite hardware capability to emulate the human brain in a $1,000 of a computation (which won’t be organized in the rectangular forms we see today such as notebooks and palmtops, but rather embedded in our environment) by 2020. The software will take longer, to around 2030. The “singularity” has divergent definitions, but for our purposes here we can consider this to be a time when nonbiological forms of intelligence dominate purely biological forms, albeit being derivative of them. This takes us beyond 2030, to perhaps 2040 or 2050.
Jaron calls this an “immanent doom” and “an eschatological cataclysm,” as if it were clear on its face that such a development were undesirable. I view these developments as simply the continuation of the evolutionary process and neither utopian nor dystopian. It’s true, on the one hand, that nanotechnology and strong AI, and particularly the two together, have the potential to solve age-old problems of poverty and human suffering, not to mention clean up the messes we’re creating today with some of our more primitive technologies. On the other hand, there will be profound new problems and dangers that will emerge as well. I have always considered technology to be a double-edged sword. It amplifies both our creative and destructive natures, and we don’t have to look further than today to see that.
However, on balance, I view the progression of evolution as a good thing, indeed as a spiritual direction. What we see in evolution is a progression toward greater intelligence, greater creativity, greater beauty, greater subtlety (i.e., the emergence of entities with emotion such as the ability to love, therefore greater love). And “God” has been described as an ideal of an infinite level of these same attributes. Evolution, even in its exponential growth, never reaches infinite levels, but it’s moving rapidly in that direction. So we could say that this evolutionary process is moving in a spiritual direction.
However, the story of the twenty-first century has not yet been written. So it’s not my view that any particular story is inevitable, only that evolution, which has been inherently accelerating since the dawn of biological evolution, will continue its exponential pace.
Jaron writes that “the whole enterprise of Artificial Intelligence is based on an intellectual mistake.” Until such time that computers at least match human intelligence in every dimension, it will always remain possible for skeptics to say the glass is half empty. Every new achievement of AI can be dismissed by pointing out yet other goals have not yet been accomplished. Indeed, this is the frustration of the AI practitioner, that once an AI goal is achieved, it is no longer considered AI and becomes just a useful technique. AI is inherently the set of problems we have not yet solved.
Yet machines are indeed growing in intelligence, and the range of tasks that machines can accomplish that previously required intelligent human attention is rapidly growing. There are hundreds of examples of narrow AI today (e.g., computers evaluating electrocardiograms and blood cell images, making medical diagnoses, guiding cruise missiles, making financial investment decisions, not to mention intelligently routing emails and cell phone connections), and the domains are becoming broader. Until such time that the entire range of human intellectual capability is emulated, it will always be possible to minimize what machines are capable of doing.
I will point out that once we have achieved complete models of human intelligence, machines will be capable of combining the flexible, subtle, human levels of pattern recognition with the natural advantages of machine intelligence. For example, machines can instantly share knowledge, whereas we don’t have quick downloading ports on our interconnection and neurotransmitter concentration level patterns. Machines are much faster (as I mentioned contemporary electronics is already ten million times faster than the electrochemical information processing used in our brains) and have much more prodigious and accurate memories.
Jaron refers to the annual “Turing test” that Loebner runs, and maintains that “we have caused the Turing test to be passed.” These are misconceptions. I used to be on the prize committee of this contest until a political conflict caused most of the prize committee members to quit. Be that as it may, this contest is not really a Turing test, as we’re not yet at that stage. It’s a “narrow Turing test” which deals with domain-specific dialogues, not unrestricted dialog as Turing envisioned it. With regard to the Turing test as Turing described it, it is generally accepted that this has not yet happened.
Returning to Jaron’s nice phrase “circle of empathy,” he writes that his “personal choice is to not place computers inside the circle.” But would he put neurons inside that circle? We’ve already shown that a neuron or even a substantial cluster of neurons can be emulated in great detail and accuracy by computers. So where on that slippery slope does Jaron find a stable footing? As Rodney Brooks says in his September 25, 2000 commentary on Jaron’s Half of a Manifesto, Jaron “turns out to be a closet Searlean.” He just assumes that a computer cannot be as subtle — or as conscious — as the hundreds of neural regions we call the human brain. Like Searle, Jaron just assumes his conclusion. (For a more complete discussion of Searle and his theories, see my essay “Locked in his Chinese Room, Response to John Searle” in the forthcoming book Are We Spiritual Machines?: Ray Kurzweil vs. the Critics of Strong AI, Discovery Institute Press, 2001. This entire book will be posted on http://www.KurzweilAI.net).
Near the end of Jaron’s essay, he worries about the “terrifying” possibility that through these technologies the rich may obtain certain opportunities that the rest of humankind does not have access to. This, of course, would be nothing new, but I would point out that because of the ongoing exponential growth of price-performance, all of these technologies quickly become so inexpensive as to become almost free. Look at the extraordinary amount of high-quality information available at no cost on the web today which did not exist at all just a few years ago. And if one wants to point out that only a small fraction of the world today has Web access, keep in mind that the explosion of the Web is still in its infancy.
At the end of his Half of a Manifesto, Jaron writes that “the ideology of cybernetic totalist intellectuals [may] be amplified from novelty into a force that could cause suffering for millions of people.” I don’t believe this fearful conclusion follows from Jaron’s half of an argument. The bottom line is that technology is power and this power is rapidly increasing. Technology may result in suffering or liberation, and we’ve certainly seen both in the twentieth century. I would argue that we’ve seen more of the latter, but nevertheless neither Jaron nor I wish to see the amplification of destructiveness that we have witnessed in the past one hundred years. As I mentioned above, the story of the twenty first century has not yet been written. I think Jaron would agree with me that our destiny is in our hands. However, I regard “our hands” to include our technology, which is properly part of the human-machine civilization.
#Polytopia-(the emergence of) - is a work/proposal in progress#We are called to be architects of the future, not its victims.
R. Buckminster Fuller
Establishing a common ground
It is commonly agreed/argued that ‘The common good’ is a utilitarian ideal, thus representing "the greatest possible good for the greatest possible number of individuals". In the best-case scenario, the "greatest possible number of individuals" would mean all sentient beings. This definition of the common good presents it as a quality, which is convertible, or reducible, to the sum total of all the private interests of the individual members of a society and interchangeable with them. Wiki
The goal of a Polytopia is simply NOT to define the common good and by that escaping the trap of statistical interest. In stead of :” the sum total of all the private interests of the individual members of a society” let us propose here a different approach, the approach of the co-emergent interest.
A co-emergent interest let us define as:” the interest emerging out of a collaborative mind mutuality, emphasizing the unique perspective of each and every individual member, voluntarily desiring to cooperate/share/connect/interact with other minds, for the purpose of allowing : “A ‘positive – unified’ vision of transcultural diversity, as applied to sentient beings”, a Polytopia, to emerge.
Po-ly-to-pi-a
nounA ‘positive – unified’ vision of transcultural diversity, as applied to sentient beings;
Po-ly-to-pi-a
nounAn emergent collaboration marketplace of ideas, thoughts, dreams, sensations, ambitions, views and agendas, apparent and evolving on the net
“In the beginning there was information. The word came later.”
Fred Dretske, Knowledge and the Flow of Information
So now is later, and here comes the word, and the word is:
Polytopia
What is a Polytopia?
Polytopia (1) an endlessly open (trans) culture of unique and diverse states of mind
1.1- ‘endlessly open’ reflects the proposition that there are no end goals, no end results, in short, no end but infinity.
1.2- culture and not society, culture has no borders, no nationality, and is an inherently dynamic process, society is defined by characteristics of its past.
1.3- unique as in ‘unique and independent perspective’.
1.4- diverse as in “the variation of life forms within a given ecosystem, reflects its diversity”Polytopia (2) an open source, collaboration between consciously aware entities towards an increase in combined interactive intelligence
2.1- ‘open source’ as in ‘allows concurrent input of different agendas, approaches and priorities’, also as in ‘ publicly available strategic resources for critical decision making’
2.2- collaboration reflects cooperation as in ‘sharing knowledge, learning and building consensus without the requirement of leadership’.
2.3- ‘consciously aware entities’ and not necessarily human (see Ai, robots, cybrids, hybrids and/or any life-form exhibiting sentiency).
2.4- ‘combined interactive intelligence’ reflects mutuality and cross-fertilization, emergence of higher empathy modes, fecundity.Polytopia (3) an alliance of effort, both abstract and concrete
3.1- as in ‘collectively mustering mind and material resources’
Polytopia (4) an emergent transcultural entity composed of real critical beings, intelligently discernible and primarily manifested via the Grid
4.1- ‘emergent entity’ as in ‘not predicated by its original components’ also as in ‘not guided but naturally occurring’ also as in ‘the way complex systems and patterns arise out of a multiplicity of relatively simple interactions’.
4.2- ‘real critical beings’ as in ‘fiercely independent’, also ‘critical’ as in ‘essential’, also ‘being’ as ‘dynamically present’
4.3-‘intelligently discernible’ as in ‘evidently there’Polytopia (5) A multidimensional co-enhancing, mind mutuality of evolving beings; A Polytopia is a transcultural endeavor with vast implications on the personification of the unique individual
5.1- ‘Multidimensional’ as in ‘objectives are pursued simultaneously through multiple dimensions’, also as in ‘multiplicity of sets of preferences’
5.2- ‘co-enhancing’ as in ‘ consenting and cooperating in reciprocal enhancement’, also as in ‘ enriching each and every being’Polytopia (6) A transcultural occurrence that has evolved beyond the obsolete infancy notions of gender, race, ethnicity, nationality, financial status, institutional religion and or any other related concepts of cultural and or genetic nature.
6.1-it’s about freedom, liberty, and free expression, unshackling the future from the past and becoming the architects of our own destiny.
There is nothing in a caterpillar that tells you it's going to be a butterfly.
R. Buckminster FullerPolytopia (7)
Poly – many (or diverse)
+
Topia- (from the Greek Topos) – place (or state, as in ‘state of affairs’ or ‘state of mind’)Po-ly-to-pian
nounA sentient being engaged in the creation, maintenance and evolution of a Polytopia
Polytopianism
The developing ‘scientific-art’ of intelligent and peaceful application of voluntary, consent based, decentralized transcultural cooperation amongst independently evolving free sentient beings.
Polyethics - A distributed value system for sentient interaction and co-evolution (following spaceweaver suggestion)
Polytopianism Associated concepts: autonomy, liberty, sustainability, self-sustainability, individuality, self-authority, virtuality, consensual organization, sharing, mutuality, creativity, infinity, abundance, love, empathy, acceptance, distribution, open source, resources, provision, relationships, humanism, self-reliance, pleasure, hedonism, longevity, freedom of expression, play, game, independence, infinity, myth, emergence, futurism, transhumanism, extropian, ubiquitous infoverse technologies, metaverse,
Polytopianism Discontinued (demanding discussion and elaboration) Concepts: authority, governments, violence, institutional religion, borders, nations, nationality, oppression, regime, dandyism, tribalism, revenge, regionalism, guilt, regret, heresy, repression, elitism, greed, hunger, consumerism, finiteness, lack, confines, gender, money, race, ethnicity, age, anthropomorphism, power, dominion, marriage, prejudice, selfishness, racism, chauvinism, pettiness, money,
Polytopianism remains ambiguous regarding the concepts of: ego, moderation, identity, ambition, hierarchy, rating, education,
—
note1: ( My aim in this proposition is to emphasize that the concepts of Utopia and Dystopia are anachronistic, outdated and outright obsolete. In their stead I shall try and propose a fresh perspective on the notion of the future of humanity, a natural humanity, perpetually evolving. The future of humanity I propose is one of Polytopia, a term designating an open ended and emergent process of co-evolution and cross-fertilization considering all and any conscious aware entities.)
note2: at present it is my view that a polytopia can emerge under the correct circumstances
note3: at present i am uncertain if Meganmay Internation proposal is a polytopia or if polytopia can replace internation
DIGITAL MAOISM
(JARON LANIER:) My Wikipedia entry identifies me (at least this week) as a film director. It is true I made one experimental short film about a decade and a half ago. The concept was awful: I tried to imagine what Maya Deren would have done with morphing. It was shown once at a film festival and was never distributed and I would be most comfortable if no one ever sees it again.
In the real world it is easy to not direct films. I have attempted to retire from directing films in the alternative universe that is the Wikipedia a number of times, but somebody always overrules me. Every time my Wikipedia entry is corrected, within a day I'm turned into a film director again. I can think of no more suitable punishment than making these determined Wikipedia goblins actually watch my one small old movie.Twice in the past several weeks, reporters have asked me about my filmmaking career. The fantasies of the goblins have entered that portion of the world that is attempting to remain real. I know I've gotten off easy. The errors in my Wikipedia bio have been (at least prior to the publication of this article) charming and even flattering.
Reading a Wikipedia entry is like reading the bible closely. There are faint traces of the voices of various anonymous authors and editors, though it is impossible to be sure. In my particular case, it appears that the goblins are probably members or descendants of the rather sweet old Mondo 2000culture linking psychedelic experimentation with computers. They seem to place great importance on relating my ideas to those of the psychedelic luminaries of old (and in ways that I happen to find sloppy and incorrect.) Edits deviating from this set of odd ideas that are important to this one particular small subculture are immediately removed. This makes sense. Who else would volunteer to pay that much attention and do all that work?
***
The problem I am concerned with here is not the Wikipedia in itself. It's been criticized quite a lot, especially in the last year, but the Wikipedia is just one experiment that still has room to change and grow. At the very least it's a success at revealing what the online people with the most determination and time on their hands are thinking, and that's actually interesting information.
No, the problem is in the way the Wikipedia has come to be regarded and used; how it's been elevated to such importance so quickly. And that is part of the larger pattern of the appeal of a new online collectivism that is nothing less than a resurgence of the idea that the collective is all-wise, that it is desirable to have influence concentrated in a bottleneck that can channel the collective with the most verity and force. This is different from representative democracy, or meritocracy. This idea has had dreadful consequences when thrust upon us from the extreme Right or the extreme Left in various historical periods. The fact that it's now being re-introduced today by prominent technologists and futurists, people who in many cases I know and like, doesn't make it any less dangerous.
There was a well-publicized study in Nature last year comparing the accuracy of the Wikipedia to Encyclopedia Britannica. The results were a toss up. While there is a lingering debate about the validity of the study. The items selected for the comparison were just the sort that Wikipedia would do well on: Science topics that the collective at large doesn't care much about. "Kinetic isotope effect" or "Vesalius, Andreas" are examples of topics that make the Britannica hard to maintain, because it takes work to find the right authors to research and review a multitude of diverse topics. But they are perfect for the Wikipedia. There is little controversy around these items, plus the Net provides ready access to a reasonably small number of competent specialist graduate student types possessing the manic motivation of youth.
A core belief of the wiki world is that whatever problems exist in the wiki will be incrementally corrected as the process unfolds. This is analogous to the claims of Hyper-Libertarians who put infinite faith in a free market, or the Hyper-Lefties who are somehow able to sit through consensus decision-making processes. In all these cases, it seems to me that empirical evidence has yielded mixed results. Sometimes loosely structured collective activities yield continuous improvements and sometimes they don't. Often we don't live long enough to find out. Later in this essay I'll point out what constraints make a collective smart. But first, it's important to not lose sight of values just because the question of whether a collective can be smart is so fascinating. Accuracy in a text is not enough. A desirable text is more than a collection of accurate references. It is also an expression of personality.
For instance, most of the technical or scientific information that is in the Wikipedia was already on the Web before the Wikipedia was started. You could always use Google or other search services to find information about items that are now wikified. In some cases I have noticed specific texts get cloned from original sites at universities or labs onto wiki pages. And when that happens, each text loses part of its value. Since search engines are now more likely to point you to the wikified versions, the Web has lost some of its flavor in casual use.
When you see the context in which something was written and you know who the author was beyond just a name, you learn so much more than when you find the same text placed in the anonymous, faux-authoritative, anti-contextual brew of the Wikipedia. The question isn't just one of authentication and accountability, though those are important, but something more subtle. A voice should be sensed as a whole. You have to have a chance to sense personality in order for language to have its full meaning. Personal Web pages do that, as do journals and books. EvenBritannica has an editorial voice, which some people have criticized as being vaguely too "Dead White Men."
If an ironic Web site devoted to destroying cinema claimed that I was a filmmaker, it would suddenly make sense. That would be an authentic piece of text. But placed out of context in the Wikipedia, it becomes drivel.
Myspace is another recent experiment that has become even more influential than the Wikipedia. Like the Wikipedia, it adds just a little to the powers already present on the Web in order to inspire a dramatic shift in use. Myspace is all about authorship, but it doesn't pretend to be all-wise. You can always tell at least a little about the character of the person who made a Myspace page. But it is very rare indeed that a Myspace page inspires even the slightest confidence that the author is a trustworthy authority. Hurray for Myspace on that count!
Myspace is a richer, multi-layered, source of information than the Wikipedia, although the topics the two services cover barely overlap. If you want to research a TV show in terms of what people think of it, Myspace will reveal more to you than the analogous and enormous entries in the Wikipedia.
***
The Wikipedia is far from being the only online fetish site for foolish collectivism. There's a frantic race taking place online to become the most "Meta" site, to be the highest level aggregator, subsuming the identity of all other sites.
The race began innocently enough with the notion of creating directories of online destinations, such as the early incarnations of Yahoo. Then came AltaVista, where one could search using an inverted database of the content of the whole Web. Then came Google, which added page rank algorithms. Then came the blogs, which varied greatly in terms of quality and importance. This lead to Meta-blogs such as Boing Boing, run by identified humans, which served to aggregate blogs. In all of these formulations, real people were still in charge. An individual or individuals were presenting a personality and taking responsibility.
These Web-based designs assumed that value would flow from people. It was still clear, in all such designs, that the Web was made of people, and that ultimately value always came from connecting with real humans.
Even Google by itself (as it stands today) isn't Meta enough to be a problem. One layer of page ranking is hardly a threat to authorship, but an accumulation of many layers can create a meaningless murk, and that is another matter.
In the last year or two the trend has been to remove the scent of people, so as to come as close as possible to simulating the appearance of content emerging out of the Web as if it were speaking to us as a supernatural oracle. This is where the use of the Internet crosses the line into delusion.
Kevin Kelly, the former editor of Whole Earth Review and the founding Executive Editor of Wired, is a friend and someone who has been thinking about what he and others call the "Hive Mind." He runs a Website calledCool Tools that's a cross between a blog and the old Whole Earth Catalog. On Cool Tools, the contributors, including me, are not a hive because we are identified.
In March, Kelly reviewed a variety of "Consensus Web filters" such as "Digg" and "Reddit" that assemble material every day from all the myriad of other aggregating sites. Such sites intend to be more Meta than the sites they aggregate. There is no person taking responsibility for what appears on them, only an algorithm. The hope seems to be that the most Meta site will become the mother of all bottlenecks and receive infinite funding.
That new magnitude of Meta-ness lasted only a month. In April, Kelly reviewed a site called "popurls" that aggregates consensus Web filtering sites...and there was a new "most Meta". We now are reading what a collectivity algorithm derives from what other collectivity algorithms derived from what collectives chose from what a population of mostly amateur writers wrote anonymously.
Is "popurls" any good? I am writing this on May 27, 2006. In the last few days an experimental approach to diabetes management has been announced that might prevent nerve damage. That's huge news for tens of millions of Americans. It is not mentioned on popurls. Popurls does clue us in to this news: "Student sets simultaneous world ice cream-eating record, worst ever ice cream headache." Mainstream news sources all lead today with a serious earthquake in Java. Popurls includes a few mentions of the event, but they are buried within the aggregation of aggregate news sites like Google News. The reason the quake appears on popurls at all can be discovered only if you dig through all the aggregating layers to find the original sources, which are those rare entries actually created by professional writers and editors who sign their names. But at the layer of popurls, the ice cream story and the Javanese earthquake are at best equals, without context or authorship.
Kevin Kelly says of the "popurls" site, "There's no better way to watch the hive mind." But the hive mind is for the most part stupid and boring. Why pay attention to it?
***
Readers of my previous rants will notice a parallel between my discomfort with so-called "Artificial Intelligence" and the race to erase personality and be most Meta. In each case, there's a presumption that something like a distinct kin to individual human intelligence is either about to appear any minute, or has already appeared. The problem with that presumption is that people are all too willing to lower standards in order to make the purported newcomer appear smart. Just as people are willing to bend over backwards and make themselves stupid in order to make an AI interface appear smart (as happens when someone can interact with the notorious Microsoft paper clip,) so are they willing to become uncritical and dim in order to make Meta-aggregator sites appear to be coherent.
There is a pedagogical connection between the culture of Artificial Intelligence and the strange allure of anonymous collectivism online. Google's vast servers and the Wikipedia are both mentioned frequently as being the startup memory for Artificial Intelligences to come. Larry Page is quoted via a link presented to me by popurls this morning (who knows if it's accurate) as speculating that an AI might appear within Google within a few years. George Dyson has wondered if such an entity already exists on the Net, perhaps perched within Google. My point here is not to argue about the existence of Metaphysical entities, but just to emphasize how premature and dangerous it is to lower the expectations we hold for individual human intellects.
The beauty of the Internet is that it connects people. The value is in the other people. If we start to believe the Internet itself is an entity that has something to say, we're devaluing those people and making ourselves into idiots.
***
Compounding the problem is that new business models for people who think and write have not appeared as quickly as we all hoped. Newspapers, for instance, are on the whole facing a grim decline as the Internet takes over the feeding of the curious eyes that hover over morning coffee and, even worse, classified ads. In the new environment, Google News is for the moment better funded and enjoys a more secure future than most of the rather small number of fine reporters around the world who ultimately create most of its content. The aggregator is richer than the aggregated.
The question of new business models for content creators on the Internet is a profound and difficult topic in itself, but it must at least be pointed out that writing professionally and well takes time and that most authors need to be paid to take that time. In this regard, blogging is not writing. For example, it's easy to be loved as a blogger. All you have to do is play to the crowd. Or you can flame the crowd to get attention. Nothing is wrong with either of those activities. What I think of as real writing, however, writing meant to last, is something else. It involves articulating a perspective that is not just reactive to yesterday's moves in a conversation.
The artificial elevation of all things Meta is not confined to online culture. It is having a profound influence on how decisions are made in America.
What we are witnessing today is the alarming rise of the fallacy of the infallible collective. Numerous elite organizations have been swept off their feet by the idea. They are inspired by the rise of the Wikipedia, by the wealth of Google, and by the rush of entrepreneurs to be the most Meta. Government agencies, top corporate planning departments, and major universities have all gotten the bug.
As a consultant, I used to be asked to test an idea or propose a new one to solve a problem. In the last couple of years I've often been asked to work quite differently. You might find me and the other consultants filling out survey forms or tweaking edits to a collective essay. I'm saying and doing much less than I used to, even though I'm still being paid the same amount. Maybe I shouldn't complain, but the actions of big institutions do matter, and it's time to speak out against the collectivity fad that is upon us.
It's not hard to see why the fallacy of collectivism has become so popular in big organizations: If the principle is correct, then individuals should not be required to take on risks or responsibilities. We live in times of tremendous uncertainties coupled with infinite liability phobia, and we must function within institutions that are loyal to no executive, much less to any lower level member. Every individual who is afraid to say the wrong thing within his or her organization is safer when hiding behind a wiki or some other Meta aggregation ritual.
I've participated in a number of elite, well-paid wikis and Meta-surveys lately and have had a chance to observe the results. I have even been part of a wiki about wikis. What I've seen is a loss of insight and subtlety, a disregard for the nuances of considered opinions, and an increased tendency to enshrine the official or normative beliefs of an organization. Why isn't everyone screaming about the recent epidemic of inappropriate uses of the collective? It seems to me the reason is that bad old ideas look confusingly fresh when they are packaged as technology.
***
The collective rises around us in multifarious ways. What afflicts big institutions also afflicts pop culture. For instance, it has become notoriously difficult to introduce a new pop star in the music business. Even the most successful entrants have hardly ever made it past the first album in the last decade or so. The exception is American Idol. As with the Wikipedia, there's nothing wrong with it. The problem is its centrality.
More people appear to vote in this pop competition than in presidential elections, and one reason why is the instant convenience of information technology. The collective can vote by phone or by texting, and some vote more than once. The collective is flattered and it responds. The winners are likable, almost by definition.
But John Lennon wouldn't have won. He wouldn't have made it to the finals. Or if he had, he would have ended up a different sort of person and artist. The same could be said about Jimi Hendrix, Elvis, Joni Mitchell, Duke Ellington, David Byrne, Grandmaster Flash, Bob Dylan (please!), and almost anyone else who has been vastly influential in creating pop music.
As below, so above. The New York Times, of all places, has recently published op-ed pieces supporting the pseudo-idea of intelligent design. This is astonishing. The Times has become the paper of averaging opinions. Something is lost when American Idol becomes a leader instead of a follower of pop music. But when intelligent design shares the stage with real science in the paper of record, everything is lost.
How could the Times have fallen so far? I don't know, but I would imagine the process was similar to what I've seen in the consulting world of late. It's safer to be the aggregator of the collective. You get to include all sorts of material without committing to anything. You can be superficially interesting without having to worry about the possibility of being wrong.
Except when intelligent thought really matters. In that case the average idea can be quite wrong, and only the best ideas have lasting value. Science is like that.
***
The collective isn't always stupid. In some special cases the collective can be brilliant. For instance, there's a demonstrative ritual often presented to incoming students at business schools. In one version of the ritual, a large jar of jellybeans is placed in the front of a classroom. Each student guesses how many beans there are. While the guesses vary widely, the average is usually accurate to an uncanny degree.
This is an example of the special kind of intelligence offered by a collective. It is that peculiar trait that has been celebrated as the "Wisdom of Crowds," though I think the word "wisdom" is misleading. It is part of what makes Adam Smith's Invisible Hand clever, and is connected to the reasons Google's page rank algorithms work. It was long ago adapted to futurism, where it was known as the Delphi technique. The phenomenon is real, and immensely useful.
But it is not infinitely useful. The collective can be stupid, too. Witness tulip crazes and stock bubbles. Hysteria over fictitious satanic cult child abductions. Y2K mania.
The reason the collective can be valuable is precisely that its peaks of intelligence and stupidity are not the same as the ones usually displayed by individuals. Both kinds of intelligence are essential.
What makes a market work, for instance, is the marriage of collective and individual intelligence. A marketplace can't exist only on the basis of having prices determined by competition. It also needs entrepreneurs to come up with the products that are competing in the first place.
In other words, clever individuals, the heroes of the marketplace, ask the questions which are answered by collective behavior. They put the jellybeans in the jar.
There are certain types of answers that ought not be provided by an individual. When a government bureaucrat sets a price, for instance, the result is often inferior to the answer that would come from a reasonably informed collective that is reasonably free of manipulation or runaway internal resonances. But when a collective designs a product, you get design by committee, which is a derogatory expression for a reason.
Here I must take a moment to comment on Linux and similar efforts. The various formulations of "open" or "free" software are different from the Wikipedia and the race to be most Meta in important ways. Linux programmers are not anonymous and in fact personal glory is part of the motivational engine that keeps such enterprises in motion. But there are similarities, and the lack of a coherent voice or design sensibility in an esthetic sense is one negative quality of both open source software and the Wikipedia.
These movements are at their most efficient while building hidden information plumbing layers, such as Web servers. They are hopeless when it comes to producing fine user interfaces or user experiences. If the code that ran the Wikipedia user interface were as open as the contents of the entries, it would churn itself into impenetrable muck almost immediately. The collective is good at solving problems which demand results that can be evaluated by uncontroversial performance parameters, but bad when taste and judgment matter.
***
Collectives can be just as stupid as any individual, and in important cases, stupider. The interesting question is whether it's possible to map out where the one is smarter than the many.
There is a lot of history to this topic, and varied disciplines have lots to say. Here is a quick pass at where I think the boundary between effective collective thought and nonsense lies: The collective is more likely to be smart when it isn't defining its own questions, when the goodness of an answer can be evaluated by a simple result (such as a single numeric value,) and when the information system which informs the collective is filtered by a quality control mechanism that relies on individuals to a high degree. Under those circumstances, a collective can be smarter than a person. Break any one of those conditions and the collective becomes unreliable or worse.
Meanwhile, an individual best achieves optimal stupidity on those rare occasions when one is both given substantial powers and insulated from the results of his or her actions.
If the above criteria have any merit, then there is an unfortunate convergence. The setup for the most stupid collective is also the setup for the most stupid individuals.
***
Every authentic example of collective intelligence that I am aware of also shows how that collective was guided or inspired by well-meaning individuals. These people focused the collective and in some cases also corrected for some of the common hive mind failure modes. The balancing of influence between people and collectives is the heart of the design of democracies, scientific communities, and many other long-standing projects. There's a lot of experience out there to work with. A few of these old ideas provide interesting new ways to approach the question of how to best use the hive mind.
The pre-Internet world provides some great examples of how personality-based quality control can improve collective intelligence. For instance, an independent press provides tasty news about politicians by reporters with strong voices and reputations, like the Watergate reporting of Woodward and Bernstein. Other writers provide product reviews, such as Walt Mossberg in The Wall Street Journal and David Pogue in The New York Times. Such journalists inform the collective's determination of election results and pricing. Without an independent press, composed of heroic voices, the collective becomes stupid and unreliable, as has been demonstrated in many historical instances. (Recent events in America have reflected the weakening of the press, in my opinion.)
Scientific communities likewise achieve quality through a cooperative process that includes checks and balances, and ultimately rests on a foundation of goodwill and "blind" elitism — blind in the sense that ideally anyone can gain entry, but only on the basis of a meritocracy. The tenure system and many other aspects of the academy are designed to support the idea that individual scholars matter, not just the process or the collective.
Another example: Entrepreneurs aren't the only "heroes" of a marketplace. The role of a central bank in an economy is not the same as that of a communist party official in a centrally planned economy. Even though setting an interest rate sounds like the answering of a question, it is really more like the asking of a question. The Fed asks the market to answer the question of how to best optimize for lowering inflation, for instance. While that might not be the question everyone would want to have asked, it is at least coherent.
Yes, there have been plenty of scandals in government, the academy and in the press. No mechanism is perfect, but still here we are, having benefited from all of these institutions. There certainly have been plenty of bad reporters, self-deluded academic scientists, incompetent bureaucrats, and so on. Can the hive mind help keep them in check? The answer provided by experiments in the pre-Internet world is "yes," but only provided some signal processing is placed in the loop.
***
Some of the regulating mechanisms for collectives that have been most successful in the pre-Internet world can be understood in part as modulating the time domain. For instance, what if a collective moves too readily and quickly, jittering instead of settling down to provide a single answer? This happens on the most active Wikipedia entries, for example, and has also been seen in some speculation frenzies in open markets.
One service performed by representative democracy is low-pass filtering. Imagine the jittery shifts that would take place if a wiki were put in charge of writing laws. It's a terrifying thing to consider. Super-energized people would be struggling to shift the wording of the tax-code on a frantic, never-ending basis. The Internet would be swamped.
Such chaos can be avoided in the same way it already is, albeit imperfectly, by the slower processes of elections and court proceedings. The calming effect of orderly democracy achieves more than just the smoothing out of peripatetic struggles for consensus. It also reduces the potential for the collective to suddenly jump into an over-excited state when too many rapid changes to answers coincide in such a way that they don't cancel each other out. (Technical readers will recognize familiar principles in signal processing.)
The Wikipedia has recently slapped a crude low pass filter on the jitteriest entries, such as "President George W. Bush." There's now a limit to how often a particular person can remove someone else's text fragments. I suspect that this will eventually have to evolve into an approximate mirror of democracy as it was before the Internet arrived.
The reverse problem can also appear. The hive mind can be on the right track, but moving too slowly. Sometimes collectives would yield brilliant results given enough time but there isn't enough time. A problem like global warming would automatically be addressed eventually if the market had enough time to respond to it, for instance. Insurance rates would climb, and so on. Alas, in this case there isn't enough time, because the market conversation is slowed down by the legacy effect of existing investments. Therefore some other process has to intervene, such as politics invoked by individuals.
Another example of the slow hive problem: There was a lot of technology developed slowly in the millennia before there was a clear idea of how to be empirical, how to have a peer reviewed technical literature and an education based on it, and before there was an efficient market to determine the value of inventions. What is crucial to notice about modernity is that structure and constraints were part of what sped up the process of technological development, not just pure openness and concessions to the collective.
Let's suppose that the Wikipedia will indeed become better in some ways, as is claimed by the faithful, over a period of time. We might still need something better sooner.
Some wikitopians explicitly hope to see education subsumed by wikis. It is at least possible that in the fairly near future enough communication and education will take place through anonymous Internet aggregation that we could become vulnerable to a sudden dangerous empowering of the hive mind. History has shown us again and again that a hive mind is a cruel idiot when it runs on autopilot. Nasty hive mind outbursts have been flavored Maoist, Fascist, and religious, and these are only a small sampling. I don't see why there couldn't be future social disasters that appear suddenly under the cover of technological utopianism. If wikis are to gain any more influence they ought to be improved by mechanisms like the ones that have worked tolerably well in the pre-Internet world.
The hive mind should be thought of as a tool. Empowering the collective does not empower individuals — just the reverse is true. There can be useful feedback loops set up between individuals and the hive mind, but the hive mind is too chaotic to be fed back into itself.
***
These are just a few ideas about how to train a potentially dangerous collective and not let it get out of the yard. When there's a problem, you want it to bark but not bite you.
The illusion that what we already have is close to good enough, or that it is alive and will fix itself, is the most dangerous illusion of all. By avoiding that nonsense, it ought to be possible to find a humanistic and practical way to maximize value of the collective on the Web without turning ourselves into idiots. The best guiding principle is to always cherish individuals first.
Responses to Lanier's essay from Douglas Rushkoff, Quentin Hardy, Yochai Benkler, Clay Shirky, Cory Doctorow, Kevin Kelly, Esther Dyson, Larry Sanger, Fernanda Viegas & Martin Wattenberg, Jimmy Wales, George Dyson, Dan Gillmor, Howard Rheingold
Now, another big idea is taking hold, but this time it's more painful for some people to embrace, even to contemplate. It's nothing less than the migration from individual mind to collective intelligence. I call it "here comes everybody", and it represents, for good or for bad, a fundamental change in our notion of who we are. In other words, we are witnessing the emergence of a new kind of person.
Lately, there's been a lot of news concerning the Wikipedia and other user-generated websites such as Myspace, Flickr, and others.
For example, in today's Wall Street Journal "portals" column, Lee Gomes ("Why Getting the User To Create Web Content Isn't Always Progress", June 7, 2006, p B1) writes:
"At first, it seemed like the sort of silly, self-serving thing that many companies are wont to say about their products. Only later did I realize it represented the opening of another front in the battle against traditional culture being waged by certain parts of the technology industry."
"Mash-ups", which allow active (vs. "passive") participation, is another term for "'user-generated content', referred to by the smart set as "UGC:"
...for a big part of the tech world, these sorts of mash-ups are becoming the highest form of cultural production.
This is most clearly occurring in books. Most of us were taught that reading books is synonymous with being civilized. But in certain tech circles, books have come to be regarded as akin to radios with vacuum tubes, a technology soon to make an unlamented journey into history's dustbin.
The New York Times Magazine recently had a long essay on the future of books that gleefully predicted that bookshelves and libraries will cease to exist, to be supplanted by snippets of text linked to other snippets of text on computer hard drives. Comments from friends and others would be just as important as the original material being commented on; Keats, say.
Yesterday, at a panel discussion at a Newsweek Conference on Science, Technology and Education, the moderator, Brian Williams, Anchor and Managing Editor, NBC Nightly News, spent a great deal of his time at the hour-long panel disparaging the Wikipedia.
Williams noted that NBC Nightly News was the largest news provider in America, reaching 9 to 12 million Americans, vastly more than any of the discrete digital audiences for websites; when he goes to his office and walks in the door, people are there and they are gathering the news. They are professionals, you know their names, and this is very different than anonymous contributors to the Wikipedia or other user-generated websites.
On Monday of this week, in "Digital Publishing Is Scrambling the Industry's Rules" (June 5, 2006,) Motoko Rich writes:
"Yochai Benkler, a Yale University law professor and author of the new book "The Wealth of Networks: How Social Production Transforms Markets and Freedom" (Yale University Press), has gone even farther: his entire book is available — free — as a download from his Web site. Between 15,000 and 20,000 people have accessed the book electronically, with some of them adding comments and links to the online version.
"Mr. Benkler said he saw the project as "simply an experiment of how books might be in the future." That is one of the hottest debates in the book world right now, as publishers, editors and writers grapple with the Web's ability to connect readers and writers more quickly and intimately, new technologies that make it easier to search books electronically and the advent of digital devices that promise to do for books what the iPod has done for music: making them easily downloadable and completely portable.
"Not surprisingly, writers have greeted these measures with a mixture of enthusiasm and dread. The dread was perhaps most eloquently crystallized last month in Washington at BookExpo, the publishing industry's annual convention, when the novelist John Updike forcefully decried a digital future composed of free downloads of books and the mixing and matching of 'snippets' of text, calling it a 'grisly scenario.' "
John Updike's comments were also reported by Bob Thompson in The Washingon Post ("Explosive Words", May 22, 2006, p C01):
"Unlike the commingled, unedited, frequently inaccurate mass of "information" on the Web, he said, "books traditionally have edges." But "the book revolution, which from the Renaissance on taught men and women to cherish and cultivate their individuality, threatens to end in a sparkling pod of snippets".
"So, booksellers," he concluded, "defend your lonely forts. Keep your edges dry. Your edges are our edges. For some of us, books are intrinsic to our human identity."
***
About ten years ago, the big realization (as expounded by Wired, Nicholas Negroponte, among others) was a perceptual migration from atoms to bits, from the world of the physical to the world of information.
Now, another big idea is taking hold, but this time it's more painful for some people to embrace, even to contemplate. It's nothing less than the migration from individual mind to collective intelligence. I call it "here comes everybody", and it represents, for good or for bad, a fundamental change in our notion of who we are. In other words, we are witnessing the emergence of a new kind of person.
I've been tracking this development since 1969 when I wrote in By The Late John Brockman:
"The mass. The human mass. The impossible agglomerate mass. The incommunicable human mass. The people." From their places masses move, stark as laws. Masses of what? One does not ask. There somewhere man is too, vast conglomerate of all of nature’s kingdoms, as lonely and as bound."* The impossible people.
*Beckett, Molloy, p. 110
This isn't going away. Rather than demonize, we need to think through what's going on.
In this regard, no one is deeper, more thoughtful, on the social and economic effects of Internet technologies than Clay Shirky, a consultant and NYU professor. His writings, mostly web-based, are focused on the rise of decentralized technologies such as peer-to-peer, web services, and wireless networks that are leading us into a new world of user-generated content. As adjunct professor in NYU's graduate Interactive Telecommunications Program (ITP), he teaches courses on the interrelated effects of social and technological network topology — how our networks shape culture and vice-versa.
Shirky commands wide respect within the user-generated web community, both for his authoritative writings as well as his leadership role as a speaker. I have reached out to him for help in organizing a serious response to Jaron Lanier's essay, and he graciously accepted. The people he assembled, a "who's who" of the movers, shakers, and pundits of this new universe of collective intelligence, of the "hive mind", have written essays that are at once unfailingly interesting, maddening, thought-provoking, depressing, and a window not to the future but to where we are today.
I am now pleased to turn the proceedings over to Clay Shirky with warm thanks from Edge for his help in organizing this project. But before I get off the stage, one final note.
Shakespeare's snippets pound in my head, as I ask myself Banquo's question...
"MACBETH
...Say from whence
You owe this strange intelligence? or why
Upon this blasted heath you stop our way
With such prophetic greeting? Speak, I charge you.
Witches vanish"BANQUO
The earth hath bubbles, as the water has,
And these are of them. Whither are they vanish'd?"MACBETH
Into the air; and what seem'd corporal melted
As breath into the wind. Would they had stay'd!"BANQUO
Were such things here as we do speak about?
Or have we eaten on the insane root
That takes the reason prisoner?"— JB
On "Digital Maoism: The Hazards of the New Online Collectivism" By Jaron Lanier
Introduction by Clay Shirky
When Jaron Lanier's piece on "Digital Maoism" first went out on Edge, I knew he'd be generating hundreds of responses all over the net. After talking to John Brockman, we decided to try to capture some of the best responses here.
Lanier's piece hits a nerve because human life always exists in tension between our individual and group identities, inseparable and incommensurable. For ten years now, it's been apparent that the rise of the digital was providing enormous new powers for the individual. It's now apparent that the world's networks are providing enormous new opportunities for group action.
Understanding how these cohabiting and competing revolutions connect to deep patterns of intellectual and social work is one of the great challenges of our age. The breadth and depth of the responses collected here, ranging from the broad philosophical questions to reckonings of the ground truth of particular technologies, is a testament to the complexity and subtlety of that challenge.
— Clay Shirky
Projects like Wikipedia do not overthrow any elite at all, but merely replace one elite — in this case an academic one — with another: the interactive media elite.
— Douglas RushkoffOur new tool for communication and computation may take us away from distinct individualism, and towards something closer to the tender nuance of folk art or the animal energy of millenarianism.
— Quentin HardyNetworked-based, distributed, social production, both individual and cooperative, offers a new system, alongside markets, firms, governments, and traditional non-profits, within which individuals can engage in information, knowledge, and cultural production. This new modality of production offers new challenges, and new opportunities. It is the polar opposite of Maoism.
— Yochai BenklerThe personal computer produced an incredible increase in the creative autonomy of the individual. The internet has made group forming ridiculously easy. Since social life involves a tension between individual freedom and group participation, the changes wrought by computers and networks are therefore in tension. To have a discussion about the plusses and minuses of various forms of group action, though, is going to require discussing the current tools and services as they exist, rather than discussing their caricatures or simply wishing that they would disappear.
— Clay ShirkyWikipedia isn't great because it's like the Britannica. The Britannica is great at being authoritative, edited, expensive, and monolithic. Wikipedia is great at being free, brawling, universal, and instantaneous.
— Cory DoctorowThe bottom-up hive mind will always take us much further that seems possible. It keeps surprising us. In this regard, the Wikipedia truly is exhibit A, impure as it is, because it is something that is impossible in theory, and only possible in practice. It proves the dumb thing is smarter than we think. At that same time, the bottom-up hive mind will never take us to our end goal. We are too impatient. So we add design and top down control to get where we want to go.
— Kevin KellySo, to get the best results, we have people sharpening their ideas against one another rather than simply editing someone's contribution and replacing it with another. We also have a world where the contributors have identities (real or fake, but consistent and persistent) and are accountable for their words. Much like Edge, in fact.
— Esther DysonHow can both I reject epistemic collectivism and yet say that Wikipedia is a great project, which I do? Well, the problem is that epistemic collectivists like Wikipedia but for the wrong reasons. What's great about it is not that it produces an averaged view, an averaged view that is somehow better than an authoritative statement by people who actually know the subject. That's just not it at all. What's great about Wikipedia is the fact that it is a way to organize enormous amounts of labor for a single intellectual purpose.
— Larry SangerThis rich context, attached to many Wikipedia articles, is known as a "talk page." The talk page is where the writers for an article hash out their differences, plan future edits, and come to agreement about tricky rhetorical points. This kind of debate doubtless happens in the New York Times and Britannica as well, but behind the scenes. Wikipedia readers can see it all, and understand how choices were made.
— Fernanda Viegas & Matthew WattenbergMy response is quite simple: this alleged "core belief" is not one which is held by me, nor as far as I know, by any important or prominent Wikipedians. Nor do we have any particular faith in collectives or collectivism as a mode of writing. Authoring at Wikipedia, as everywhere, is done by individuals exercising the judgment of their own minds.
— Jimmy WalesLanier does not want to debate the existence or non-existence of metaphysical entities. But his argument that online collectivism produces artificial stupidity offers no reassurance to me. Real artificial intelligence (if and when) will be unfathomable to us. At our level, it may appear as dumb as American Idol, or as pointless as a nervous twitch that corrects and uncorrects Jaron Lanier's Wikipedia entry in an endless loop.
— George DysonThe debate does demonstrate how much we need to update our media literacy in a digital, distributed era. Our internal BS meters already work, but they've fallen into a low and sad level of use in the Big Media world. Many people tend to believe what they read. Others tend to disbelieve everything. Too few apply appropriate skepticism and do the additional work that true media literacy requires.
— Dan GillmorCollective action involves freely chosen self-election (which is almost always coincident with self-interest) and distributed coordination; collectivism involves coercion and centralized control; treating the Internet as a commons doesn't mean it is communist (tell that to Bezos, Yang, Filo, Brin or Page, to name just a few billionaires who managed to scrape together private property from the Internet commons).
— Howard Rheingold
Don't worry, we won't post anything.
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