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Kurt Laitner

Inequality: Why egalitarian societies died out - opinion - 30 July 2012 - New Scientist - 0 views

www.newscientist.com/...tarian-societies-died-out.html

inequality egalitarianism vs hierarchical

shared by Kurt Laitner on 07 Aug 12 - No Cached
  • FOR 5000 years, humans have grown accustomed to living in societies dominated by the privileged few. But it wasn't always this way. For tens of thousands of years, egalitarian hunter-gatherer societies were widespread. And as a large body of anthropological research shows, long before we organised ourselves into hierarchies of wealth, social status and power, these groups rigorously enforced norms that prevented any individual or group from acquiring more status, authority or resources than others.*
  • How, then, did we arrive in the age of institutionalised inequality? That has been debated for centuries. Philosopher Jean-Jacques Rousseau reasoned in 1754 that inequality was rooted in the introduction of private property. In the mid-19th century, Karl Marx and Friedrich Engels focused on capitalism and its relation to class struggle. By the late 19th century, social Darwinists claimed that a society split along class lines reflected the natural order of things - as British philosopher Herbert Spencer put it, "the survival of the fittest". (Even into the 1980s there were some anthropologists who held this to be true - arguing that dictators' success was purely Darwinian, providing estimates of the large numbers of offspring sired by the rulers of various despotic societies as support.)
  • But by the mid-20th century a new theory began to dominate. Anthropologists including Julian Steward, Leslie White and Robert Carneiro offered slightly different versions of the following story: population growth meant we needed more food, so we turned to agriculture, which led to surplus and the need for managers and specialised roles, which in turn led to corresponding social classes.
  • ...8 more annotations...
  • One line of reasoning suggests that self-aggrandising individuals who lived in lands of plenty ascended the social ranks by exploiting their surplus - first through feasts or gift-giving, and later by outright dominance
  • At the group level, argue anthropologists Peter Richerson and Robert Boyd, improved coordination and division of labour allowed more complex societies to outcompete the simpler, more equal societies
  • From a mechanistic perspective, others argued that once inequality took hold - as when uneven resource-distribution benefited one family more than others - it simply became ever more entrenched. The advent of agriculture and trade resulted in private property, inheritance, and larger trade networks, which perpetuated and compounded economic advantages.
  • Many theories about the spread of stratified society begin with the idea that inequality is somehow a beneficial cultural trait that imparts efficiencies, motivates innovation and increases the likelihood of survival. But what if the opposite were true?
  • In a demographic simulation that Omkar Deshpande, Marcus Feldman and I conducted at Stanford University, California, we found that, rather than imparting advantages to the group, unequal access to resources is inherently destabilising and greatly raises the chance of group extinction in stable environments.
  • Counterintuitively, the fact that inequality was so destabilising caused these societies to spread by creating an incentive to migrate in search of further resources. The rules in our simulation did not allow for migration to already-occupied locations, but it was clear that this would have happened in the real world, leading to conquests of the more stable egalitarian societies - exactly what we see as we look back in history.
  • In other words, inequality did not spread from group to group because it is an inherently better system for survival, but because it creates demographic instability, which drives migration and conflict and leads to the cultural - or physical - extinction of egalitarian societies.
  • Egalitarian societies may have fostered selection on a group level for cooperation, altruism and low fertility (which leads to a more stable population), while inequality might exacerbate selection on an individual level for high fertility, competition, aggression, social climbing and other selfish traits.
Tiberius Brastaviceanu

Decision Quality - 0 views

decision-quality.com/index.php

decision making decision-making

shared by Tiberius Brastaviceanu on 17 Jul 12 - No Cached
Tiberius Brastaviceanu

McMaster-Carr - 1 views

www.mcmaster.com

mechanical mechanics supplier supplies product company guides components

shared by Tiberius Brastaviceanu on 19 Jul 12 - Cached
  • Tiberius Brastaviceanu on 19 Jul 12
     
    Feedback Diigo Web Highlighter (v1.7.0)  Highlight     Book
  • Philippe Comtois on 20 Jul 12
     
    Fast shipping ... We are making purchase with them for years !
Kurt Laitner

Buddhist Economics: How to Stop Prioritizing Goods Over People and Consumption Over Cre... - 0 views

www.brainpickings.org/...buddhist-economics-schumacher

buddhist economics wealth enough small is beautiful

shared by Kurt Laitner on 08 Jul 14 - No Cached
  • Kurt Laitner on 08 Jul 14
     
    Review of EF Schumacher's book "Small is Beautiful: Economics as if People Mattered" published in 1973, very relevant today
Kurt Laitner

Digital Reality | Edge.org - 0 views

edge.org/...il_gershenfeld-digital-reality

*neilgershenfeld fab labs IoT molecular computing nano star trek replicator

shared by Kurt Laitner on 17 Feb 15 - No Cached
  • When you snap the bricks together, you don't need a ruler to play Lego; the geometry comes from the parts
  • first attribute is metrology that comes from the parts
  • digitizing composites into little linked loops of carbon fiber instead of making giant pieces
  • ...75 more annotations...
  • In a 3D printer today, what you can make is limited by the size of the machine. The geometry is external
  • is the Lego tower is more accurate than the child because the constraint of assembling the bricks lets you detect and correct errors
  • That's the exponential scaling for working reliably with unreliable parts
  • Because the parts have a discrete state, it means in joining them you can detect and correct errors
  • detect and correct state to correct errors to get an exponential reduction in error, which gives you an exponential increase in complexity
  • The next one is you can join Lego bricks made out of dissimilar materials.
  • The last one is when you're done with Lego you don't put it in the trash; you take it apart and reuse it because there's state in the materials. In a forest there's no trash; you die and your parts get disassembled and you're made into new stuff. When you make a 3D print or laser cut, when you're done there's recycling attempts but there's no real notion of reusing the parts
  • The metrology coming from the parts, detecting and correcting errors, joining dissimilar materials, disconnecting, reusing the components
  • On the very smallest scale, the most exciting work on digital fabrication is the creation of life from scratch. The cell does everything we're talking about. We've had a great collaboration with the Venter Institute on microfluidic machinery to load designer genomes into cells. One step up from that we're developing tabletop chip fab instead of a billion dollar fab, using discrete assembly of blocks of electronic materials to build things like integrated circuits in a tabletop process
  • a child can make a Lego structure bigger than themself
  • There's a series of books by David Gingery on how to make a machine shop starting with charcoal and iron ore.
  • There are twenty amino acids. With those twenty amino acids you make the motors in the molecular muscles in my arm, you make the light sensors in my eye, you make my neural synapses. The way that works is the twenty amino acids don't encode light sensors, or motors. They’re very basic properties like hydrophobic or hydrophilic. With those twenty properties you can make you. In the same sense, digitizing fabrication in the deep sense means that with about twenty building blocks—conducting, insulating, semiconducting, magnetic, dielectric—you can assemble them to create modern technology
  • By discretizing those three parts we can make all those 500,000 resistors, and with a few more parts everything else.
  • Now, there's a casual sense, which means a computer controls something to make something, and then there's the deep sense, which is coding the materials. Intellectually, that difference is everything but now I'm going to explain why it doesn't matter.
  • Then in turn, the next surprise was they weren't there for research, they weren't there for theses, they wanted to make stuff. I taught additive, subtractive, 2D, 3D, form, function, circuits, programming, all of these skills, not to do the research but just using the existing machines today
  • What they were answering was the killer app for digital fabrication is personal fabrication, meaning, not making what you can buy at Walmart, it’s making what you can't buy in Walmart, making things for a market of one person
  • The minicomputer industry completely misread PCs
  • the Altair was life changing for people like me. It was the first computer you could own as an individual. But it was almost useless
  • It was hard to use but it brought the cost from a million dollars to 100,000 and the size from a warehouse down to a room. What that meant is a workgroup could have one. When a workgroup can have one it meant Ken Thompson and Dennis Ritchie at Bell Labs could invent UNIX—which all modern operating systems descend from—because they didn't have to get permission from a whole corporation to do it
  • At the PC stage what happened is graphics, storage, processing, IO, all of the subsystems got put in a box
  • To line that up with fabrication, MIT's 1952 NC Mill is similar to the million-dollar machines in my lab today. These are the mainframes of fab. You need a big organization to have them. The fab labs I'll tell you about are exactly analogous to the cost and complexity of minicomputers. The machines that make machines I'll tell you about are exactly analogous to the cost and complexity of the hobbyist computers. The research we're doing, which is leading up to the Star Trek Replicator, is what leads to the personal fabricator, which is the integrated unit that makes everything
  • conducting, resistive, insulating.
  • The fab lab is 2 tons, a $100,000 investment. It fills a few thousand square feet, 3D scanning and printing, precision machining, you can make circuit boards, molding and casting tooling, computer controlled cutting with a knife, with a laser, large format machining, composite layup, surface mount rework, sensors, actuators, embedded programming— technology to make technology.
  • Ten years you can just plot this doubling. Today, you can send a design to a fab lab and you need ten different machines to turn the data into something. Twenty years from now, all of that will be in one machine that fits in your pocket.
  • We've been living with this notion that making stuff is an illiberal art for commercial gain and it's not part of the means of expression. But, in fact, today, 3D printing, micromachining, and microcontroller programming are as expressive as painting paintings or writing sonnets but they're not means of expression from the Renaissance. We can finally fix that boundary between art and artisans
  • You don't go to a fab lab to get access to the machine; you go to the fab lab to make the machine.
  • Over the next maybe five years we'll be transitioning from buying machines to using machines to make machines. Self-reproducing machines
  • But they still have consumables like the motors, and they still cut or squirt. Then the interesting transition comes when we go from cutting or printing to assembling and disassembling, to moving to discretely assembled materials
  • because if anybody can make anything anywhere, it challenges everything
    • Kurt Laitner
      Kurt Laitner on 17 Feb 15
       
      great quote (replace challenges with changes for effect)
  • Now, the biggest surprise for me in this is I thought the research was hard. It's leading to how to make the Star Trek Replicator. The insight now is that's an exercise in embodied computation—computation in materials, programming their construction. Lots of work to come, but we know what to do
  • And that's when you do tabletop chip fab or make airplanes. That's when technical trash goes away because you can disassemble. 
  • irritated by the maker movement for the failure in mentoring
  • At something like a Maker Faire, there's hall after hall of repeated reinventions of bad 3D printers and there isn't an easy process to take people from easy to hard
  • We started a project out of desperation because we kept failing to succeed in working with existing schools, called the Fab Academy. Now, to understand how that works, MIT is based on scarcity. You assume books are scarce, so you have to go there for the library; you assume tools are scarce, so you have to go there for the machines; you assume people are scarce, so you have to go there to see them; and geography is scarce. It adds up to we can fit a few thousand people at a time. For those few thousand people it works really well. But the planet is a few billion people. We're off by six orders of magnitude. 
  • Next year we're starting a new class with George Church that we've called "How to Grow Almost Anything", which is using fab labs to make bio labs and then teach biotech in it. What we're doing is we're making a new global kind of university
  • Amusingly, I went to my friends at Educause about accrediting the Fab Academy and they said, "We love it. Where are you located?" And I said, "Yes" and they said, "No." Meaning, "We're all over the earth." And they said, "We have no mechanism. We're not allowed to do that. There's no notion of global accreditation."
  • Then they said something really helpful: "Pretend."
  • Once you have a basic set of tools, you can make all the rest of the tools
  • The way the Fab Academy works, in computing terms, it's like the Internet. Students have peers in workgroups, with mentors, surrounded by machines in labs locally. Then we connect them globally by video and content sharing and all of that. It's an educational network. There are these critical masses of groups locally and then we connect them globally
  • You still have Microsoft or IBM now but, with all respect to colleagues there, arguably that's the least interesting part of software
  • To understand the economic and social implications, look at software and look at music to understand what's happening now for fabrication
  • There's a core set of skills a place like MIT can do but it alone doesn't scale to a billion people. This is taking the social engineering—the character of MIT—but now doing it on this global scale.
  • Mainframes didn't go away but what opened up is all these tiers of software development that weren't economically viable
  • If you look at music development, the most interesting stuff in music isn't the big labels, it's all the tiers of music that weren't viable before
  • You can make music for yourself, for one, ten, 100, 1,000, a million. If you look at the tracks on your device, music is now in tiers that weren't economically viable before. In that example it's a string of data and it becomes a sound. Now in digital fab, it's a string of data and it becomes a thing.
  • What is work? For the average person—not the people who write for Edge, but just an average person working—you leave home to go to a place you'd rather not be, doing a repetitive operation you'd rather not do, making something designed by somebody you don't know for somebody you'll never see, to get money to then go home and buy something. But what if you could skip that and just make the thing?
    • Kurt Laitner
      Kurt Laitner on 17 Feb 15
       
      !!!
  • It took about ten years for the dot com industry to realize pretty much across the board you don't directly sell the thing. You sell the benefits of the thing
  • 2016 it's in Shenzhen because they're pivoting from mass manufacturing to enabling personal fabrication. We've set Shenzhen as the goal in 2016 for Fab Lab 2.0, which is fab labs making fab labs
  • To rewind now, you can send something to Shenzhen and mass manufacture it. There's a more interesting thing you can do, which is you go to market by shipping data and you produce it on demand locally, and so you produce it all around the world.
  • But their point was a lot of printers producing beautiful pages slowly scales if all the pages are different
  • In the same sense it scales to fabricate globally by doing it locally, not by shipping the products but shipping the data.
  • It doesn't replace mass manufacturing but mass manufacturing becomes the least interesting stuff where everybody needs the same thing. Instead, what you open up is all these tiers that weren't viable before
  • There, they consider IKEA the enemy because IKEA defines your taste. Far away they make furniture and flat pack it and send it to a big box store. Great design sense in Barcelona, but 50 percent youth unemployment. A whole generation can't work. Limited jobs. But ships come in from the harbor, you buy stuff in a big box store. And then after a while, trucks go off to a trash dump. They describe it as products in, trash out. Ships come in with products, trash goes out
    • Kurt Laitner
      Kurt Laitner on 17 Feb 15
       
      worse actually.. the trash stays
  • The bits come and go, globally connected for knowledge, but the atoms stay in the city.
  • instead of working to get money to buy products made somewhere else, you can make them locally
    • Kurt Laitner
      Kurt Laitner on 17 Feb 15
       
      this may solve greece's problem, walk away from debt, you can't buy other people's (country's) stuff anymore, so make it all yourself
  • The biggest tool is a ShotBot 4'x8'x1' NC mill, and you can make beautiful furniture with it. That's what furniture shops use
  • Anything IKEA makes you can make in a fab lab
  • it means you can make many of the things you consume directly rather than this very odd remote economic loop
  • the most interesting part of the DIY phone projects is if you're making a do-it-yourself phone, you can also start to make the things that the phones talk to. You can start to build your own telco providers where the users provide the network rather than spending lots of money on AT&T or whoever
  • Traditional manufacturing is exactly replaying the script of the computer companies saying, "That's a toy," and it's shining a light to say this creates entirely new economic activity. The new jobs don't come back to the old factories. The ability to make stuff on demand is creating entirely new jobs
  • To keep playing that forward, when I was in Barcelona for the meeting of all these labs hosted by the city architect and the city, the mayor, Xavier Trias, pushed a button that started a forty-year countdown to self-sufficiency. Not protectionism
  • I need high-torque efficient motors with integrated lead screws at low cost, custom-produced on demand. All sorts of the building blocks that let us do what I'm doing currently rest on a global supply chain including China's manufacturing agility
  • The short-term answer is you can't get rid of them because we need them in the supply chain. But the long-term answer is Shenzhen sees the future isn't mass producing for everybody. That's a transitional stage to producing locally
  • My description of MIT's core competence is it's a safe place for strange people
  • The real thing ultimately that's driving the fab labs ... the vacuum we filled is a technical one. The means to make stuff. Nobody was providing that. But in turn, the spaces become magnets. Everybody talks about innovation or knowledge economy, but then most things that label that strangle it. The labs become vehicles for bright inventive people who don't fit locally. You can think about the culture of MIT but on this global scale
  • My allegiance isn't to any one border, it's to the brainpower of the planet and this is building the infrastructure to scale to that brainpower
  • If you zoom from transistors to microcode to object code to a program, they don't look like each other. But if we take this room and go from city, state, country, it's hierarchical but you preserve geometry
  • Computation violates geometry unlike most anything else we do
  • The reason that's so important for the digital fabrication piece is once we build molecular assemblers to build arbitrary systems, you don't want to then paste a few lines of code in it. You need to overlay computation with geometry. It's leading to this complete do-over of computer science
  • If you take digital fab, plus the real sense of Internet of Things—not the garbled sense—plus the real future of computing aligning hardware and software, it all adds up to this ability to program reality
  • I run a giant video infrastructure and I have collaborators all over the world that I see more than many of my colleagues at MIT because we're all too busy on campus. The next Silicon Valley is a network, it's not a place. Invention happens in these networks.
  • When Edwin Land was kicked out of Polaroid, he made the Rowland Institute, which was making an ideal research institute with the best facilities and the best people and they could do whatever they want. But almost nothing came from it because there was no turnover of the gene pool, there was no evolutionary pressure.  
  • the wrong way to do research, which is to believe there's a privileged set of people that know more than anybody else and to create a barrier that inhibits communication from the inside to the outside
  • you need evolutionary pressure, you need traffic, you need to be forced to deal with people you don't think you need to encounter, and you need to recognize that to be disruptive it helps to know what people know
  • For me the hardest thing isn't the research. That's humming along nicely. It's that we're finding we have to build a completely new kind of social order and that social entrepreneurship—figuring out how you live, learn, work, play—is hard and there's a very small set of people who can do that kind of organizational creation.
    • Kurt Laitner
      Kurt Laitner on 17 Feb 15
       
      our challenge in the OVN space
  • Kurt Laitner on 17 Feb 15
     
    what is heavy is local, what is light is global, and increasingly manufacturing is being recreated along this principle
Tiberius Brastaviceanu

The Baffler - 0 views

thebaffler.com/...the_meme_hustler

O'Reilly paper

shared by Tiberius Brastaviceanu on 06 Apr 13 - No Cached
  • This tendency to view questions of freedom primarily through the lens of economic competition, to focus on the producer and the entrepreneur at the expense of everyone else, shaped O’Reilly’s thinking about technology.
  • the O’Reilly brand essence is ultimately a story about the hacker as hero, the kid who is playing with technology because he loves it, but one day falls into a situation where he or she is called on to go forth and change the world,
  • His true hero is the hacker-cum-entrepreneur, someone who overcomes the insurmountable obstacles erected by giant corporations and lazy bureaucrats in order to fulfill the American Dream 2.0: start a company, disrupt an industry, coin a buzzword.
  • ...139 more annotations...
  • gospel of individualism, small government, and market fundamentalism
  • innovation is the new selfishness
  • mastery of public relations
  • making it seem as if the language of economics was, in fact, the only reasonable way to talk about the subject
  • memes are for losers; the real money is in epistemes.
  • “Open source software” was also the first major rebranding exercise overseen by Team O’Reill
  • It’s easy to forget this today, but there was no such idea as open source software before 1998; the concept’s seeming contemporary coherence is the result of clever manipulation and marketing.
  • ideological cleavage between two groups
  • Richard Stallman
  • Free Software Foundation, preoccupied with ensuring that users had rights with respect to their computer programs. Those rights weren’t many—users should be able to run the program for any purpose, to study how it works, to redistribute copies of it, and to release their improved version (if there was one) to the public
  • “free software.”
  • association with “freedom” rather than “free beer”
  • copyleft
  • profound critique of the role that patent law had come to play in stifling innovation and creativity.
  • Plenty of developers contributed to “free software” projects for reasons that had nothing to do with politics. Some, like Linus Torvalds, the Finnish creator of the much-celebrated Linux operating system, did so for fun; some because they wanted to build more convenient software; some because they wanted to learn new and much-demanded skills.
  • Stallman’s rights-talk, however, risked alienating the corporate types
  • he was trying to launch a radical social movement, not a complacent business association
  • By early 1998 several business-minded members of the free software community were ready to split from Stallman, so they masterminded a coup, formed their own advocacy outlet—the Open Source Initiative—and brought in O’Reilly to help them rebrand.
  • “open source”
  • The label “open source” may have been new, but the ideas behind it had been in the air for some time.
  • In those early days, the messaging around open source occasionally bordered on propaganda
  • This budding movement prided itself on not wanting to talk about the ends it was pursuing; except for improving efficiency and decreasing costs, those were left very much undefined.
  • extremely decentralized manner, using Internet platforms, with little central coordination.
  • In contrast to free software, then, open source had no obvious moral component.
  • “open source is not particularly a moral or a legal issue. It’s an engineering issue. I advocate open source, because . . . it leads to better engineering results and better economic results
  • While free software was meant to force developers to lose sleep over ethical dilemmas, open source software was meant to end their insomnia.
  • Stallman the social reformer could wait for decades until his ethical argument for free software prevailed in the public debate
  • O’Reilly the savvy businessman had a much shorter timeline: a quick embrace of open source software by the business community guaranteed steady demand for O’Reilly books and events
  • The coup succeeded. Stallman’s project was marginalized. But O’Reilly and his acolytes didn’t win with better arguments; they won with better PR.
  • A decade after producing a singular vision of the Internet to justify his ideas about the supremacy of the open source paradigm, O’Reilly is close to pulling a similar trick on how we talk about government reform.
  • much of Stallman’s efforts centered on software licenses
  • O’Reilly’s bet wa
  • the “cloud”
  • licenses would cease to matter
  • Since no code changed hands
  • So what did matter about open source? Not “freedom”
  • O’Reilly cared for only one type of freedom: the freedom of developers to distribute software on whatever terms they fancied.
  • the freedom of the producer
  • who must be left to innovate, undisturbed by laws and ethics.
  • The most important freedom,
  • is that which protects “my choice as a creator to give, or not to give, the fruits of my work to you, as a ‘user’ of that work, and for you, as a user, to accept or reject the terms I place on that gift.”
  • O’Reilly opposed this agenda: “I completely support the right of Richard [Stallman] or any individual author to make his or her work available under the terms of the GPL; I balk when they say that others who do not do so are doing something wrong.”
  • The right thing to do, according to O’Reilly, was to leave developers alone.
  • According to this Randian interpretation of open source, the goal of regulation and public advocacy should be to ensure that absolutely nothing—no laws or petty moral considerations—stood in the way of the open source revolution
  • Any move to subject the fruits of developers’ labor to public regulation
  • must be opposed, since it would taint the reputation of open source as technologically and economically superior to proprietary software
  • the advent of the Internet made Stallman’s obsession with licenses obsolete
  • Many developers did stop thinking about licenses, and, having stopped thinking about licenses, they also stopped thinking about broader moral issues that would have remained central to the debates had “open source” not displaced “free software” as the paradigm du jour.
  • Profiting from the term’s ambiguity, O’Reilly and his collaborators likened the “openness” of open source software to the “openness” of the academic enterprise, markets, and free speech.
  • “open to intellectual exchange”
  • “open to competition”
  • “For me, ‘open source’ in the broader sense means any system in which open access to code lowers the barriers to entry into the market”).
  • “Open” allowed O’Reilly to build the largest possible tent for the movement.
  • The language of economics was less alienating than Stallman’s language of ethics; “openness” was the kind of multipurpose term that allowed one to look political while advancing an agenda that had very little to do with politics
  • highlight the competitive advantages of openness.
  • the availability of source code for universal examination soon became the one and only benchmark of openness
  • What the code did was of little importance—the market knows best!—as long as anyone could check it for bugs.
  • The new paradigm was presented as something that went beyond ideology and could attract corporate executives without losing its appeal to the hacker crowd.
  • What Raymond and O’Reilly failed to grasp, or decided to overlook, is that their effort to present open source as non-ideological was underpinned by a powerful ideology of its own—an ideology that worshiped innovation and efficiency at the expense of everything else.
  • What they had in common was disdain for Stallman’s moralizing—barely enough to justify their revolutionary agenda, especially among the hacker crowds who were traditionally suspicious of anyone eager to suck up to the big corporations that aspired to dominate the open source scene.
  • linking this new movement to both the history of the Internet and its future
  • As long as everyone believed that “open source” implied “the Internet” and that “the Internet” implied “open source,” it would be very hard to resist the new paradigm
  • Telling a coherent story about open source required finding some inner logic to the history of the Internet
  • “If you believe me that open source is about Internet-enabled collaboration, rather than just about a particular style of software license,”
  • everything on the Internet was connected to everything else—via open source.
  • The way O’Reilly saw it, many of the key developments of Internet culture were already driven by what he called “open source behavior,” even if such behavior was not codified in licenses.
  • No moralizing (let alone legislation) was needed; the Internet already lived and breathed open source
  • apps might be displacing the browser
  • the openness once taken for granted is no more
  • Openness as a happenstance of market conditions is a very different beast from openness as a guaranteed product of laws.
  • One of the key consequences of linking the Internet to the world of open source was to establish the primacy of the Internet as the new, reinvented desktop
  • This is where the now-forgotten language of “freedom” made a comeback, since it was important to ensure that O’Reilly’s heroic Randian hacker-entrepreneurs were allowed to roam freely.
  • Soon this “freedom to innovate” morphed into “Internet freedom,” so that what we are trying to preserve is the innovative potential of the platform, regardless of the effects on individual users.
  • Lumping everything under the label of “Internet freedom” did have some advantages for those genuinely interested in promoting rights such as freedom of expression
  • Forced to choose between preserving the freedom of the Internet or that of its users, we were supposed to choose the former—because “the Internet” stood for progress and enlightenment.
  • infoware
  • Yahoo
  • their value proposition lay in the information they delivered, not in the software function they executed.
  • The “infoware” buzzword didn’t catch on, so O’Reilly turned to the work of Douglas Engelbart
  • to argue that the Internet could help humanity augment its “collective intelligence” and that, once again, open source software was crucial to this endeavor.
  • Now it was all about Amazon learning from its customers and Google learning from the sites in its index.
  • The idea of the Internet as both a repository and incubator of “collective intelligence”
  • in 2004, O’Reilly and his business partner Dale Dougherty hit on the idea of “Web 2.0.” What did “2.0” mean, exactly?
  • he primary goal was to show that the 2001 market crash did not mean the end of the web and that it was time to put the crash behind us and start learning from those who survived.
  • Tactically, “Web 2.0” could also be much bigger than “open source”; it was the kind of sexy umbrella term that could allow O’Reilly to branch out from boring and highly technical subjects to pulse-quickening futurology
  • O’Reilly couldn’t improve on a concept as sexy as “collective intelligence,” so he kept it as the defining feature of this new phenomenon.
  • What set Web 2.0 apart from Web 1.0, O’Reilly claimed, was the simple fact that those firms that didn’t embrace it went bust
  • find a way to harness collective intelligence and make it part of their business model.
  • By 2007, O’Reilly readily admitted that “Web 2.0 was a pretty crappy name for what’s happening.”
  • O’Reilly eventually stuck a 2.0 label on anything that suited his business plan, running events with titles like “Gov 2.0” and “Where 2.0.” Today, as everyone buys into the 2.0 paradigm, O’Reilly is quietly dropping it
  • assumption that, thanks to the coming of Web 2.0, we are living through unique historical circumstances
  • Take O’Reilly’s musings on “Enterprise 2.0.” What is it, exactly? Well, it’s the same old enterprise—for all we know, it might be making widgets—but now it has learned something from Google and Amazon and found a way to harness “collective intelligence.”
  • tendency to redescribe reality in terms of Internet culture, regardless of how spurious and tenuous the connection might be, is a fine example of what I call “Internet-centrism.”
  • “Open source” gave us the “the Internet,” “the Internet” gave us “Web 2.0,” “Web 2.0” gave us “Enterprise 2.0”: in this version of history, Tim O’Reilly is more important than the European Union
  • For Postman, each human activity—religion, law, marriage, commerce—represents a distinct “semantic environment” with its own tone, purpose, and structure. Stupid talk is relatively harmless; it presents no threat to its semantic environment and doesn’t cross into other ones.
  • Since it mostly consists of falsehoods and opinions
  • it can be easily corrected with facts
  • to say that Tehran is the capital of Iraq is stupid talk
  • Crazy talk, in contrast, challenges a semantic environment, as it “establishes different purposes and assumptions from those we normally accept.” To argue, as some Nazis did, that the German soldiers ended up far more traumatized than their victims is crazy talk.
  • For Postman, one of the main tasks of language is to codify and preserve distinctions among different semantic environments.
  • As he put it, “When language becomes undifferentiated, human situations disintegrate: Science becomes indistinguishable from religion, which becomes indistinguishable from commerce, which becomes indistinguishable from law, and so on.
  • pollution
  • Some words—like “law”—are particularly susceptible to crazy talk, as they mean so many different things: from scientific “laws” to moral “laws” to “laws” of the market to administrative “laws,” the same word captures many different social relations. “Open,” “networks,” and “information” function much like “law” in our own Internet discourse today.
  • For Korzybski, the world has a relational structure that is always in flux; like Heraclitus, who argued that everything flows, Korzybski believed that an object A at time x1 is not the same object as object A at time x2
  • Our language could never properly account for the highly fluid and relational structure of our reality—or as he put it in his most famous aphorism, “the map is not the territory.”
  • Korzybski argued that we relate to our environments through the process of “abstracting,” whereby our neurological limitations always produce an incomplete and very selective summary of the world around us.
  • nothing harmful in this per se—Korzybski simply wanted to make people aware of the highly selective nature of abstracting and give us the tools to detect it in our everyday conversations.
  • Korzybski developed a number of mental tools meant to reveal all the abstracting around us
  • He also encouraged his followers to start using “etc.” at the end of their statements as a way of making them aware of their inherent inability to say everything about a given subject and to promote what he called the “consciousness of abstraction.”
  • There was way too much craziness and bad science in Korzybski’s theories
  • but his basic question
  • “What are the characteristics of language which lead people into making false evaluations of the world around them?”
  • Tim O’Reilly is, perhaps, the most high-profile follower of Korzybski’s theories today.
  • O’Reilly openly acknowledges his debt to Korzybski, listing Science and Sanity among his favorite books
  • It would be a mistake to think that O’Reilly’s linguistic interventions—from “open source” to “Web 2.0”—are random or spontaneous.
  • There is a philosophy to them: a philosophy of knowledge and language inspired by Korzybski. However, O’Reilly deploys Korzybski in much the same way that the advertising industry deploys the latest findings in neuroscience: the goal is not to increase awareness, but to manipulate.
  • O’Reilly, of course, sees his role differently, claiming that all he wants is to make us aware of what earlier commentators may have overlooked. “A metaphor is just that: a way of framing the issues such that people can see something they might otherwise miss,
  • But Korzybski’s point, if fully absorbed, is that a metaphor is primarily a way of framing issues such that we don’t see something we might otherwise see.
  • In public, O’Reilly modestly presents himself as someone who just happens to excel at detecting the “faint signals” of emerging trends. He does so by monitoring a group of überinnovators that he dubs the “alpha geeks.” “The ‘alpha geeks’ show us where technology wants to go. Smart companies follow and support their ingenuity rather than trying to suppress it,
  • His own function is that of an intermediary—someone who ensures that the alpha geeks are heard by the right executives: “The alpha geeks are often a few years ahead of their time. . . . What we do at O’Reilly is watch these folks, learn from them, and try to spread the word by writing down (
  • The name of his company’s blog—O’Reilly Radar—is meant to position him as an independent intellectual who is simply ahead of his peers in grasping the obvious.
  • “the skill of writing is to create a context in which other people can think”
  • As Web 2.0 becomes central to everything, O’Reilly—the world’s biggest exporter of crazy talk—is on a mission to provide the appropriate “context” to every field.
  • In a fascinating essay published in 2000, O’Reilly sheds some light on his modus operandi.
  • The thinker who emerges there is very much at odds with the spirit of objectivity that O’Reilly seeks to cultivate in public
  • meme-engineering lets us organize and shape ideas so that they can be transmitted more effectively, and have the desired effect once they are transmitted
  • O’Reilly meme-engineers a nice euphemism—“meme-engineering”—to describe what has previously been known as “propaganda.”
  • how one can meme-engineer a new meaning for “peer-to-peer” technologies—traditionally associated with piracy—and make them appear friendly and not at all threatening to the entertainment industry.
  • O’Reilly and his acolytes “changed the canonical list of projects that we wanted to hold up as exemplars of the movement,” while also articulating what broader goals the projects on the new list served. He then proceeds to rehash the already familiar narrative: O’Reilly put the Internet at the center of everything, linking some “free software” projects like Apache or Perl to successful Internet start-ups and services. As a result, the movement’s goal was no longer to produce a completely free, independent, and fully functional operating system but to worship at the altar of the Internet gods.
  • Could it be that O’Reilly is right in claiming that “open source” has a history that predates 1998?
  • Seen through the prism of meme-engineering, O’Reilly’s activities look far more sinister.
  • His “correspondents” at O’Reilly Radar don’t work beats; they work memes and epistemes, constantly reframing important public issues in accordance with the templates prophesied by O’Reilly.
  • Or take O’Reilly’s meme-engineering efforts around cyberwarfare.
  • Now, who stands to benefit from “cyberwarfare” being defined more broadly? Could it be those who, like O’Reilly, can’t currently grab a share of the giant pie that is cybersecurity funding?
  • Frank Luntz lists ten rules of effective communication: simplicity, brevity, credibility, consistency, novelty, sound, aspiration, visualization, questioning, and context.
  • Thus, O’Reilly’s meme-engineering efforts usually result in “meme maps,” where the meme to be defined—whether it’s “open source” or “Web 2.0”—is put at the center, while other blob-like terms are drawn as connected to it.
  • The exact nature of these connections is rarely explained in full, but this is all for the better, as the reader might eventually interpret connections with their own agendas in mind. This is why the name of the meme must be as inclusive as possible: you never know who your eventual allies might be. “A big part of meme engineering is giving a name that creates a big tent that a lot of people want to be under, a train that takes a lot of people where they want to go,”
  • News April 4 mail date March 29, 2013 Baffler party March 6, 2013 Žižek on seduction February 13, 2013 More Recent Press I’ve Seen the Worst Memes of My Generation Destroyed by Madness io9, April 02, 2013 The Baffler’s New Colors Imprint, March 21, 2013
  • There is considerable continuity across O’Reilly’s memes—over time, they tend to morph into one another.
mayssamd

P2P Accounting for Planetary Survival - P2P Foundation - 0 views

wiki.p2pfoundation.net/ounting_for_Planetary_Survival

IoPA Book

shared by mayssamd on 20 Dec 22 - No Cached
  • mayssamd on 22 Dec 22
     
    Book containing relevant information relating to P2P accounting systems in additions to several case studies
Kurt Laitner

How Particle Physics Is Improving Recommendation Engines | MIT Technology Review - 0 views

www.technologyreview.com/...proving-recommendation-engines

rival vs non-rival goods recommendation particle physics

shared by Kurt Laitner on 17 Jan 13 - No Cached
  • how to deal with recommendations for objects whose value diminishes with the number of people who use it.
  • Clearly the resulting distribution of these different types of particles is entirely different.
  • explore the space between these extremes
  • ...4 more annotations...
  • The analogy here is with goods that any number of people can share or that only one person can have.
  • a single object/state can be shared with a relatively small number of users/particles
  •  Preventing oversubscription ensures that the population of users sample a wider range of DVDs, which in turn provides a broader range of recommendations.
  • Retailers are not just interested in renting DVDs or selling books or whatever. They want to maximise profits.
  • Kurt Laitner on 17 Jan 13
     
    Interesting discussion of rival and nov-rival goods recommendations engine. Goes to scarcity vs abundance, how to manage deamnd for scarce goods.
Francois Bergeron

Thinking Space: We are in a new transition, part 2 - 3 views

yihongs-research.blogspot.ca/...-in-new-transition-part-2.html

thinking space transition web of minds

shared by Francois Bergeron on 28 Oct 12 - No Cached
  • Modern education is a typical effort that people are trying to make a product line of high-quality mind asset.
  • Without explicit, formal presentation of mind asset, we cannot efficiently connect and compose varied mind asset and we cannot well measure the value of mind asset. The issue of mind aggregation is particularly critical because individual mind is often too shallow to be high quality.
  • There is a natural gap between the presented value of the mind asset in the book and the real value of the mind asset in real world. This gap of knowledge understanding is a typical difficulty of mind asset measurement.
  • ...1 more annotation...
  • Because of the Web, the first time in history human mind becomes a critical circulating asset in society that ordinary people can buy, sell, produce, and share.
  • Francois Bergeron on 28 Oct 12
     
    proposed by Kurt
Kurt Laitner

Goodbye, Dilbert: 'The Rise of the Naked Economy' » Knowledge@Wharton - 2 views

knowledge.wharton.upenn.edu/...bye-dilbert-rise-naked-economy

sensorica the rise of the naked economy book review

shared by Kurt Laitner on 09 Jan 14 - No Cached
  • “teaming”: bringing together a team of professionals for a specific task
  • The old cubicle-based, static company is increasingly being replaced by a more fluid and mobile model: “the constant assembly, disassembly, and reassembly of people, talent, and ideas around a range of challenges and opportunities.”
  • Therefore, the new economy and its “seminomadic workforce” will require “new places to gather, work, live, and interact.”
  • ...17 more annotations...
  • The consumer electronics company Plantronics, for example, knowing that on any given day 40% of its workforce will be working elsewhere, designed its corporate campus to only 60% capacity
  • Their joint enterprise, NextSpace, became their first venture into what they call “coworking,” or the creation of “shared collaborative workspaces.”
  • also nurtures what the authors call “managed serendipity” — ad hoc collaboration between people with diverging but complementary skills
  • the number of coworking spaces worldwide has shot up from 30 in 2006 to 1,130 in 2011
  • someone needs to keep an eye on the big picture, to “connect the dots.”
  • workspaces are designed on a flexible, on-demand and as-needed basis
  • Coonerty and Neuner found that the most productive collaborations tended to pair highly specialized experts with big-picture thinkers
  • they were struck by the number of entrepreneurs and freelancers working at coffee shops in the area
  • Business Talent Group
  • Clients get the specialized help they need at a cost below that of a full-time employee or traditional consulting firm, and specialists are well compensated and rewarded with flexible schedules and a greater degree of choice about which projects to take.
  • This has produced a new market dynamic in which the headhunter of yesteryear has been replaced by “talent brokers” who connect highly specialized talent with companies on a project-by-project basis
  • Matthew Mullenweg, doesn’t have much faith in traditional office buildings or corporate campuses: “I would argue that most offices are full of people not working.”
  • On the other hand, Mullenweg is a big believer in face-to-face collaboration and brainstorming, and flies his teams all over the globe to do so.
  • He also set up an informal workspace in San Francisco called the Lounge
  • Additionally, a 2010 Kauffman-Rand study worried that employer-based health insurance, by discouraging risk-taking, will be an ongoing drag on entrepreneurship
  • the problem of payroll taxes for freelancers
  • up to 44% of independent workers encounter difficulty getting paid fully for their work
Tiberius Brastaviceanu

POWER-CURVE SOCIETY: The Future of Innovation, Opportunity and Social Equity in the Eme... - 1 views

www.aspeninstitute.org/...tion-opportunity-social-equity

book paper value networks

shared by Tiberius Brastaviceanu on 17 Oct 13 - No Cached
  • how technological innovation is restructuring productivity and the social and economic impact resulting from these changes
  • concern about the technological displacement of jobs, stagnant middle class income, and wealth disparities in an emerging "winner-take-all" economy
  • personal data ecosystems that could potentially unlock a revolutionary wave of individual economic empowerment
  • ...70 more annotations...
  • the bell curve described the wealth and income distribution of American society
  • As the technology boom of the 1990s increased productivity, many assumed that the rising water level of the economy was raising all those middle class boats. But a different phenomenon has also occurred. The wealthy have gained substantially over the past two decades while the middle class has remained stagnant in real income, and the poor are simply poorer.
  • America is turning into a power-curve society: one where there are a relative few at the top and a gradually declining curve with a long tail of relatively poorer people.
  • For the first time since the end of World War II, the middle class is apparently doing worse, not better, than previous generations.
  • an alarming trend
  • What is the role of technology in these developments?
  • a sweeping look at the relationship between innovation and productivity
  • New Economy of Personal Information
  • Power-Curve Society
  • the future of jobs
  • the report covers the social, policy and leadership implications of the “Power-Curve Society,”
  • World Wide Web
  • as businesses struggle to come to terms with this revolution, a new set of structural innovations is washing over businesses, organizations and government, forcing near-constant adaptation and change. It is no exaggeration to say that the explosion of innovative technologies and their dense interconnections is inventing a new kind of economy.
  • the new technologies are clearly driving economic growth and higher productivity, the distribution of these benefits is skewed in worrisome ways.
  • the networked economy seems to be producing a “power-curve” distribution, sometimes known as a “winner-take-all” economy
  • Economic and social insecurity is widespread.
  • major component of this new economy, Big Data, and the coming personal data revolution fomenting beneath it that seeks to put individuals, and not companies or governments, at the forefront. Companies in the power-curve economy rely heavily on big databases of personal information to improve their marketing, product design, and corporate strategies. The unanswered question is whether the multiplying reservoirs of personal data will be used to benefit individuals as consumers and citizens, or whether large Internet companies will control and monetize Big Data for their private gain.
  • Why are winner-take-all dynamics so powerful?
  • appear to be eroding the economic security of the middle class
  • A special concern is whether information and communications technologies are actually eliminating more jobs than they are creating—and in what countries and occupations.
  • How is the power-curve economy opening up opportunities or shutting them down?
  • Is it polarizing income and wealth distributions? How is it changing the nature of work and traditional organizations and altering family and personal life?
  • many observers fear a wave of social and political disruption if a society’s basic commitments to fairness, individual opportunity and democratic values cannot be honored
  • what role government should play in balancing these sometimes-conflicting priorities. How might educational policies, research and development, and immigration policies need to be altered?
  • The Innovation Economy
  • Conventional economics says that progress comes from new infusions of capital, whether financial, physical or human. But those are not necessarily the things that drive innovation
  • What drives innovation are new tools and then the use of those new tools in new ways.”
  • at least 50 percent of the acceleration of productivity over these years has been due to ICT
  • economists have developed a number of proxy metrics for innovation, such as research and development expenditures.
  • Atkinson believes that economists both underestimate and overestimate the scale and scope of innovation.
  • Calculating the magnitude of innovation is also difficult because many innovations now require less capital than they did previously.
  • Others scholars
  • see innovation as going in cycles, not steady trajectories.
  • A conventional approach is to see innovation as a linear, exponential phenomenon
  • leads to gross errors
  • Atkinson
  • believes that technological innovation follows the path of an “S-curve,” with a gradual increase accelerating to a rapid, steep increase, before it levels out at a higher level. One implication of this pattern, he said, is that “you maximize the ability to improve technology as it becomes more diffused.” This helps explain why it can take several decades to unlock the full productive potential of an innovation.
  • innovation keeps getting harder. It was pretty easy to invent stuff in your garage back in 1895. But the technical and scientific challenges today are huge.”
  • costs of innovation have plummeted, making it far easier and cheaper for more people to launch their own startup businesses and pursue their unconventional ideas
  • innovation costs are plummeting
  • Atkinson conceded such cost-efficiencies, but wonders if “the real question is that problems are getting more complicated more quickly than the solutions that might enable them.
  • we may need to parse the different stages of innovation: “The cost of innovation generally hasn’t dropped,” he argued. “What has become less expensive is the replication and diffusion of innovation.”
  • what is meant by “innovation,”
  • “invention plus implementation.”
  • A lot of barriers to innovation can be found in the lack of financing, organizational support systems, regulation and public policies.
  • 90 percent of innovation costs involve organizational capital,”
  • there is a serious mismatch between the pace of innovation unleashed by Moore’s Law and our institutional and social capacity to adapt.
  • This raises the question of whether old institutions can adapt—or whether innovation will therefore arise through other channels entirely. “Existing institutions are often run by followers of conventional wisdom,”
  • The best way to identify new sources of innovation, as Arizona State University President Michael Crow has advised, is to “go to the edge and ignore the center.”
  • Paradoxically, one of the most potent barriers to innovation is the accelerating pace of innovation itself.
  • Institutions and social practice cannot keep up with the constant waves of new technologies
  • “We are moving into an era of constant instability,”
  • “and the half-life of a skill today is about five years.”
  • Part of the problem, he continued, is that our economy is based on “push-based models” in which we try to build systems for scalable efficiencies, which in turn demands predictability.
  • The real challenge is how to achieve radical institutional innovations that prepare us to live in periods of constant two- or three-year cycles of change. We have to be able to pick up new ideas all the time.”
  • pace of innovation is a major story in our economy today.
  • The App Economy consists of a core company that creates and maintains a platform (such as Blackberry, Facebook or the iPhone), which in turn spawns an ecosystem of big and small companies that produce apps and/or mobile devices for that platform
  • tied this success back to the open, innovative infrastructure and competition in the U.S. for mobile devices
  • standard
  • The App Economy illustrates the rapid, fluid speed of innovation in a networked environment
  • crowdsourcing model
  • winning submissions are
  • globally distributed in an absolute sense
  • problem-solving is a global, Long Tail phenomenon
  • As a technical matter, then, many of the legacy barriers to innovation are falling.
  • small businesses are becoming more comfortable using such systems to improve their marketing and lower their costs; and, vast new pools of personal data are becoming extremely useful in sharpening business strategies and marketing.
  • Another great boost to innovation in some business sectors is the ability to forge ahead without advance permission or regulation,
  • “In bio-fabs, for example, it’s not the cost of innovation that is high, it’s the cost of regulation,”
  • This notion of “permissionless innovation” is crucial,
  • “In Europe and China, the law holds that unless something is explicitly permitted, it is prohibited. But in the U.S., where common law rather than Continental law prevails, it’s the opposite
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