Archive for category memetics
Alex Peake, founder of Primer Labs and creator of the Code Hero game has a mission to foster a new generation of meme hackers who understand their intimate entanglement with technology.
In this lively and engaging talk given at the 2011 Humanity+ conference (held at Caltech), Peake argues that we have a choice ahead of us: to embrace our far more cybernetically enhanced children as they participate in inventing and shaping True AI, or to recoil in horror and fight back in futility. He hopes we choose the former, and founded Primer Labs to play his part in creating the conditions necessary to bring about the autocatalysis of intelligence symbiosis — the intentional marriage of the minds of machine and man.
When queried for comment, Peake remarked that he’d had to cut what he considered to be an important point from the Red Queen section of the talk. Here’s his annotation to this talk which fills in that gap.
We are no longer sufficient input for our children.
They need us to add the temetic ai to the mix to form essentially a second species much like the red queen explains the value of dual gender sources: increased adaptiveness to survive the predation of microorganisms just as we must handle our rising tide of memetic influences. Generations are epochs compared to the iteratively improved law of accelerating return changes.
The cycles of marketers to control people and scientists to advance the state of the art require us to up our game and raise our kids with the machine mentors to augment their development apace with the attempts to hijack it by influencers and the need to recreate solutions faster than problems.
More autocatalyzing coders means more rapid progress towards the singularity and more people with the perspective to be masters of tech rather than slaves to it. combining our generational parenting upbringings with learning to self-improve an autocatalytic iterative intelligence amplification a.i. is a way to immanentize and humanize the eschaton.
Alex Peake can be found at PrimerLabs.com
On February 28th, 2010, the social media landscape rippled in delight when Charlie Sheen unleashed a torrent of unprecedented idiosyncracy on Good Morning America (“I’m bi-winning!”), which will no doubt leave lasting effects on the world of discrete internet memes.
Over at the Atlantic, Alexis Madrigal reported Corporations Try to Ride the Sheen Machine on Twitter that big names like McDonald’s and the Red Cross have figured out how to drink from the goblet of #tigersblood.
@Cruisedeals got in on the #planbetter action not only by purchasing a Featured Tweet but by seizing the bit.ly suffix “planbetter”.
McDonald’s success briefly trumped even that of ground zero himself, @charliesheen, as their “McWinning” tweet seemed to launch their own #mclobster meme into the Trending Topics that evening.
[pic missing due to server error -- will fix ASAP. -Ed]
In the following days, many more corporations bought access to the Sheen memes, as Madrigal reported, with more than a little vitriol for the practice (More Corporations Getting High on a Drug Called Charlie Sheen and Sheen Profiteering Not Just a Twitter Thing).
Well, kosher or not, the Charlie Sheen meme feeding frenzy illustrates what can be considered the rudiments of a type of social media marketing that may actually be effective.
Crucial to this collection of memes is the fact that Sheen himself is Twitter-savvy. “On Wednesday,” reported Anna Holmes at the New York Times, “his just-created Twitter account hit a million followers, setting a Guinness World Record.” For better or for worse, “he has in essence achieved a sort of folk-hero status.”
But as Madrigal points out, corporations are seizing on the memes Sheen is releasing without aligning themselves with #teamsheen at all.
All these companies are trying to figure out how close they can get to the Charlie Sheen flame (err, jet fighter) without getting burned. Notice that this Spirit Airlines ad doesn’t even mention the name Charlie Sheen but merely drops his memetic gifts into its language.
It’s another version of buying the promoted tweet for the hashtag #Winning on Twitter and then running an ad on it that doesn’t reference Sheen at all. All the brand association for a self-selecting audience, none of the risk that you look awful when Sheen is committed or does something heinous. In corporate speak, that’s a win-win! In Madrigal speak, it’s cowardly and parasitic.
An excerpt from the video clip that started it all is transcribed below.
GMA: “Your passion is coming off as erratic to people.”
Sheen: “You borrow my brain for 5 seconds, and [you'd] just be like, DUDE can’t handle it, unplug this bastard!”
GMA: “Some are saying that you’re bipolar.”
Sheen: “Wow, what does that mean?”
GMA: “I guess that, you know, you’re on two ends of the spectrum.”
Sheen: “Wow. And then what? What’s the cure? Medicine? Make me like `them`? Not gonna happen. I’m bi-WINNING. I win here, and I win there. Now what?”
3/7/2011 update: For completeness’ sake, we simply must report also the fact that Charlie Sheen is looking to hire a “social media intern” to work with #teamsheen.
At PivotCon 2010, Douglas Rushkoff made some extremely cogent arguments about why brands cannot go viral on social networks — even when there’s plenty of activity on companies’ websites and Facebook pages — and why it’s pointless to try to push brand concepts (such as mascots) around as memes in the expectation of driving actual product sales.
This talk is exceptionally amusing both for its venue — he’s at a branding conference talking about social media — and for the fact that he opens the talk by saying, essentially, “you think you’re talking about what’s happening, but you’re not.” He managed to rankle more than a few career marketeers who oversimplified his message to mean “marketing is evil”; the mild antagonism to this particular audience inherent in his message did not go unnoticed by PivotCon organizer Chris Shipley who made no bones about the reason they decided to schedule his talk dead last.
But beyond its entertainment value, Rushkoff’s message provides historical perspective on the concept of a conversation and what is entailed — that is, the transmission of ideas — and his ontology draws increasingly, as the talk progresses, on memetics.
Memes, he says, are traded and spread for many reasons, but largely because they embody information that is useful. (One must put aside entirely the question of what is True, since, for example, many superstitions can be considered useful by many people for many years.) Rushkoff explains the social world as a non-fictional playground for the interchange of “facts” that may or may not replicate, depending on many fitness factors.
Companies often inflate their brands into elements of storytelling in an effort to engage the consumer in a dialogue about that story, rather than about the content of their product. “The Keebler elves were invented to stop people from thinking about where Keebler cookies were made and how… to protect the consumer from the reality of what it really is,” said Rushkoff.
But “on a social network, people want to share information that someone is going to actually value.” NOT, he argues, a brand image.
The only way to work in the non-fiction space is to understand that facts are memetic components. And these are memes, that certain kinds of facts will replicate, certain kinds don’t. All a meme is, is an idea that says MAKE ME. Make me. The Keebler Elf is not one of those ideas. I see the Keebler Elf and I don’t go, “Oh I’ve got to share this with EVERYBODY! There’s an ELF… making a cookie!”
Rushkoff finishes off by recommending that companies who want to make a positive impression on social networks start adopting a marketing strategy rooted in respect for the kinds of memes people actually want to share, rather than trying to impose a brand-centric conversation. “[You need to] understand what kinds of ideas replicate, what kinds of ideas AMPLIFY your good, your service, and what kinds of ideas work against it.”
This is effectively an invitation for marketers to explore the concept of memes in an ecosystem of information and other memes.
What are the… memetic neighborhoods that support what you do, even if they seem unrelated? And what memetic camps hurt what you do, even if they seem unrelated? If you’re in the stock broker business, you don’t want to be promoting yacht club memes right now.
If marketers can bring themselves to fully accept the implications of Rushkoff’s memetic understanding of branding vis-a-vis social networks, rather than knee-jerking back into a cocoon of monolithic brand identity, perhaps the often quixotic quest for an accurate evaluation of return on marketing investment could lead the way towards a quantitative understanding of memetics.
Douglas Rushkoff, meanwhile, has created a group called The Institute for Applied Memetics, about which little has been made public. We are eagerly awaiting more information on the shape and function of this organization.
A recently published paper by Lupyan and Dale did a statistical analysis of some 2000 languages that offers pretty strong evidence in favor of memetic selection in language evolution. Here’s an excerpt from the abstract:
The analyses suggest that languages spoken by large groups have simpler inflectional morphology than languages spoken by smaller groups as measured on a variety of factors such as case systems and complexity of conjugations. Additionally, languages spoken by large groups are much more likely to use lexical strategies in place of inflectional morphology to encode evidentiality, negation, aspect, and possession. Our findings indicate that just as biological organisms are shaped by ecological niches, language structures appear to adapt to the environment (niche) in which they are being learned and used.
Check out the whole paper — it’s awesome fodder for linguistics nerds, and the methods employed (statistical analysis of lots of data points) seem to form a good basis for the testing of other memetic hypotheses.
A recent post at EclipseSource points us at three disparate software systems that share a piece of common architecture. The author notes that git, Clojure, and CouchDB all use persistent trees, and all have similar-looking infographics demonstrating these in their documentation. So what’s up here? Did Linus crib from Apache? How did these extremely disparate systems wind up with such astonishingly similar solutions to widely different problems?
The answer, of course, is memes. When we learn to program — or to perform any other skilled trade — we proceed by imitation. A programming language tutorial amounts to a set of prompts for imitation, spreading the programming meme. We learn a lot, become proficient, and then write our own tutorials — and so on.
The point I’m trying to make is more subtle, though. In my last post, I talked about meme mutation in scientific explanations. When we relate scientific examples, they become subject to laws of folklore mutation — they essentially become urban legends. The same thing is at work in learning skilled tasks: when we pick up a new programming technique, for instance, we recall it as it was explained to us.
The result is that code written by people (and for this read: buildings designed by people, systems built by people, products made by people…) demonstrates and adheres to a sort of emergent grammar. This is why obfuscators work: though they retain the programmatic meaning of the code to the machine, the semantic value to the programmer is lost, thus rendering the code nearly unusable.
Another result is that the state of the art in programming advances much, much faster than we expect it to. No person working in isolation could possibly generate the advances we’ve seen in programming practice in our lifetimes. The whole memesphere is running forward full speed, and it is merely taking us along for the ride.
The most observable result of all of this is that we see synchronization across fields. Practices like persistent trees come into favor all at once, and suddenly make sense in a lot of different places. Our tools advance faster than we could advance them, and so we all wind up adopting what makes the most sense.
New Scientist published an article yesterday about Google’s research foray into machine learning on quantum computers. This is great stuff, but the article’s introduction to quantum mechanics irked me:
Quantum computers, however, promise much faster processing, by exploiting the principle of quantum superposition: that a particle such as an ion, electron or photon can be in two different states at the same time. While each basic “bit” of data in a conventional computer can be either a 1 or a 0 at any one time, a qubit can be both at once.
This sounds really cool, but it’s completely opaque — it provides no intuition into the workings of quantum computers or the actual science behind what makes qubits special. Now, I’m told by those in the know that it takes a few years of undergraduate physics coursework to understand the universe well enough to comprehend the finer details of quantum computation, but for a one-paragraph layman’s introduction, I’m confident we can do better. For instance, check out this NYTimes article from 2001:
This is possible because a quantum computer’s counters consist of single atoms, which can point up or down, indicating 1 or 0, the two symbols of binary arithmetic. More important, quantum mechanics allows an atom to point both up and down simultaneously, indicating 1 and 0 at the same time.
Thus two atoms can simultaneously register four quantities: 00, 01, 10 and 11 (the numbers 0 through 3 in the binary tongue). Three atoms can hold eight numbers, four can hold 16. By the time you reach seven atoms — the number harnessed for the I.B.M. experiment — you have a tiny device with the capability of simultaneously registering 128 different numbers. Flipping the atoms up and down in various patterns causes a calculation to be performed — on all the numbers at once.
This is a little better! We can start to get a sense of what’s happening here. Obviously we’re nowhere near a working understanding of the principles involved, but we at least understand a little more about the mechanism of computation — we might even be encouraged to go and pick up a book or paper on the subject to learn more. So why do we see that terrible opaque summary in the New Scientist article?
The problem is that we understand things in terms of examples, and examples mutate. The “0, 1, or superposition” example appears elsewhere, and seems to have more or less mutated away from anything resembling a glimmer of light into the workings of quantum computers. When we explain quantum computing to a friend, it’s much easier to just repeat a meme we heard elsewhere than it is to construct a novel one.
This generalizes. When you go and introduce meme mutation in scientific explanations to your friends, you’re likely to bring up quantum computing explanations as an example. When I explain alleles, I’m likely to talk about purple- and white-flowered pea plants — that’s the example I was given when I learned about them.
These explanations permute as they’re re-given — we lose details, get things wrong, and otherwise jumble things up. If our goal is to keep our explanations comprehensible, then we must be cognizant of this — we must engineer our explanations to be robust in the face of transmission errors.
Dennett begins by focusing on the apparent resistance in society to applying evolutionary thinking to thinking. As he puts it:
What do they think it is that prevents the products of human ingenuity from being themselves fruits of the tree of life, and hence in some sense obeying evolutionary rules?
Or in other words, why is it that we so readily attribute the productions of animals to evolution, but not the productions of humans? “Beaver dams – yes; Hoover Dam – no.”
Leaving the question largely rhetorically unanswered, he progresses to demonstrate through an amusing and fascinating series of biological examples the ways in which we can expand our perspective of the stuff of thought to encompass Darwinian principles.
Share This Course, an experiment in collaborative content-production lead by Mark Pesce and aided by the Maybe Logic Academy, has begun as of this weekend. Its three-month mission: to explore meta-literary territory; to seek out new definitions of “intelligence” and “author”; to boldly flow where no course has flown before.
It is not yet cliche to say that we are living in “remix culture”. What is creativity? What constitutes a cultural artifact?
The derivativeness of everything novel calls into question the definition of original; copyright has outlived its usefulness as profit-protection, as enforcement costs much more than the lost profit on increasingly worthless bits; and in the Age of the Informavore, information is fed by attention and there is extreme asymmetry between the amount of information being produced, and the comparatively paltry amount of brainpower available in the world to feed it. As Cory Doctorow puts it, in explaining why he gives all his books away for free in electronic form, “If you’re not making art with the intention of having it copied, you’re not really making art for the twenty-first century.”
Therefore, it is not information that has value, but the attention given to information. And by extension, information passed along because it has been marked as valuable becomes more valuable for having been shared.
Everything is changing, most of all the institutions which have acted both as providers and gatekeepers to culture, knowledge and power. The tables have been turned; now power sits squarely in our own hands. All of this is so sudden and so new that we have no sense of how to put this new-found power to work. Yet all of it hinges on a single point: we have learned how to share with one another, and every day we share ever-more-effective ways of sharing with one another. This, by itself, is enough to change the course of history.
I (Naomi / @nthmost) am participating in this course, which has already proven valuable in terms of novel thoughts I would enjoy sharing. If you can spare a few extra minutes in your day, go sign up for the course and participate in the redefinition of creation and artifact.
Artificial Intelligence and Molecular Biology, freely available as a series of PDFs online, opens with an introduction to molecular biology for computer scientists. In addition to being a terrifically well-written and accessible introduction to molecular biology, it introduces some ideas that are pertinent to memetics and the study of emergent phenomena in general.
Particularly relevant is the chapter’s treatment of the definition of life:
No simple definition [of life] captures our intuitions about what is alive and what is not. Reproductive ability alone is not enough; computer programs create endless copies of themselves — that does not make them alive. Crystals influence the matter around them to create structures similar to themselves but they’re not alive, either. Most living things take in materials from their environment and capture forms of energy they can use to transform those materials into components of themselves or their offspring. Viruses, however, do not do that; they are nearly pure genetic material, wrapped in a protective coating. The cell that a virus infects does all the synthetic work involved in creating new viruses. Are viruses a form of life? Many people would say so.
Viruses, on their own, are just lumps of RNA with a few other molecular structures glued on. In isolation from host cells, they are surprisingly static and uninteresting. But when they interact with their host cells, viruses become some of the most fascinating structures in biology. They reproduce, mutate, evolve — that is to say, they live — entirely at the mercy of more complex organisms, but they do this with astonishing efficacy. We are still regularly surprised by the ingenuity they show in their interactions with and subversion of host cells, organisms, and even whole populations.
Likewise, information is merely longitudinal air waves or magnetic polarity on hard disk platters — but when it interacts with people, it comes alive. And if influenza or HIV, without any direct knowledge of the existence of humans, can infect the entire globe and cause massive-scale changes in human behavior, then imagine what self-optimizing information is capable of. We’re not just looking at perturbations in the air — we’re looking at structure that is every bit as rich and diverse as that which we see in biology.
The implications of this are perhaps best articulated by the subsequent paragraph in AIMB.
Another approach to defining “life” is to recognize its fundamental interrelatedness. All living things are related to each other. … Organisms came to differ from each other, and to reach modern levels of complexity through evolution. Evolution has three components: inheritance, the passing of characteristics from parents to offspring; variation, the processes that make offspring other than exact copies of their parents; and selection, the process that differentially favors the reproduction of some organisms, and hence their characteristics, over others. These three factors define an evolutionary process. Perhaps the best definition of life is that it is the result of the evolutionary process taking place on Earth. Evolution is the key not only to defining what counts as life but also to understanding how living systems function.
An advertisement by American Express demonstrates patternicity
Patternicity: The tendency to find meaningful patterns in meaningless noise
Social psychologist John Barge had this to say in the latest Edge conversation in reaction to The Age of the Informavore, a talk with Frank Schirrmacher:
The discovery of the pervasiveness of situational priming influences for all of the higher mental processes in humans does say something fundamentally new about human nature (for example, how tightly tied and responsive is our functioning to our particular physical and social surroundings). It removes consciousness or free will as the bottleneck that exclusively generates choices and behavioral impulses, replacing it with the physical and social world itself as the source of these impulses.
In other words, the context in which an idea or decision tree exists has a great deal of influence over the uptake of that idea by the actor and/or likelihood of a particular decision.
But the discovery that people are actually rather easy to influence and predict (once we know the triggering environmental cues or prompts) is fact is today being exploited as a research tool because we know now that we can activate and study complex human psychological systems with very easy priming manipulations. … It is because priming studies are so relatively easy to perform that this method has opened up research on the prediction and control of human judgment and behavior… This has indeed produced (and is still producing) an explosion of knowledge of the IF-THEN contingencies of human responses to the physical and social environment.
Can a prior experience of patternicity constitute a proportion of situational priming necessary to trigger a greater likelihood of a patternicity event within some [yet to be explored] timeframe?
To put it another way, and make use of the American Express ad: are some of those faces pretty unlikely to be recognized as faces unless the observer has recently been encouraged to experience a similar pattern-matching event?
If so, then the marriage of patternicity and situational priming as a set of probabilities that a meme will be understood and assimilated could be a potent pathway for experimentation. One could be designed around this ad, in fact. Is there is a range of likelihoods of recognition of an image as a “face” given no prior “face” images to look at, and a different range of likelihoods given the lead-up of situational priming implicit in prior application of patternicity?
Bargh’s statements taken at face value, it appears social psychology and analysis will be providing some keen predictive potential for memeticists in the very near future.
Related: Michael Shermer writes about Patternicity: Seeing Patterns in Meaningless Noise on Wired Magazine.