Category Archives: Data Science

There Must Be A Pony Somewhere: Digging in Data to Find a Story

CartoonicornQuote investigator wrote a cute quip about the origins of this blog’s title quote (“…there must be a pony somewhere…”), and lately, it has me thinking about a job I share with many techy-journalists: digging through data (evidence) for a story (pony). I’ve commented on that a bit exhaustively in this blog, but the metaphor carries through to building a data journalism team, composed of a ragtag herd of unicorns, racehorses, and predominantly, ponies. Online Journalism Blog did a short piece about the taxonomy of journo-developers too, bulleting a few typical types (racehorses, unicorns, mules),  to which I’d like to add ponies before diving a little deeper into what this means in terms of characterizing a professional population by its equine analog.

At this week’s MIT Civic Media Conference, Joi Ito kicked off an introductory talk with a nod to his coder fellow, a “unicorn” journalism-coder-analyst that had just joined the team, so the metaphor has stuck with some steady citation and I think it’s worth discussing here. In the next few sections, I’ll cover a few adventures in geo-journalism, talks and projects I’ve done around mapping in the past months. Moreover, this will be a blog about our equine habits and heros in data journalism, and some musings on what media hackery earns in terms of recognition and reward.

Dev-Journo Taxonomies

zebracornThere’s an understandable spectrum of personality types and professional competencies in Data Journalism. There are the fantastic anomalies: unicorns; the hardy worker hybrids: mules; the strange and rare portmanteaux whose skills define along a folksonomic schema: looking at you zorse, zebroids, donkras. I gave a talk on Data Journalism a few months ago (check vimeo below), and the thesis of my presentation echoed the essentially hybrid aspects of the job.

Those born under the sign of the Horse are a flexible group of people. They tend to be stubborn when it comes their ideas, but they are also incredibly patient when it comes to hearing out what other people have to say. They favor straight-forward conversation, but avoid trouble where possible; a paradoxical combo, but one that makes the horse persistently fascinating as a sub-population in the animal kingdom.

Data Journalism in DR

So in the data space, why fixate on ponies as representative of some substantial sample population in the greater software engineering venn? Because ponies are slightly different than horses; capable of the same intelligence and empathy but perpetually twee-er and often assumed to be less mature. Some of the brilliance I’ve witness from millenials in the data journalism space has made me think that another branch from the taxonomic tree should recognize those whose aptitude is impressive in code but whose journalism background, and experience in general perhaps seems premature.

Pony Projects

muybridge-2When social media steps down from the free speech party, and while governments and institutions of modern social exchange continue to use networks as a way of monitoring and managing society, it’s often the critics and the activists who have to pick up the slack to produce objective publications and in this space the post-modern (and often, outsider/premature) workhorses of the data journalism space have something to contribute.

As a class, proto-journalists and data mungershave developed some tools to analyze trends and provide objective and dissected-unicornuncensored criticism of the information they represent. Zeynep Tufekci’s talk at this year’s MIT Civic Conference on citizen investigative journalism in Turkey gave a nod to the use of social media (and twitter feeds in particular) as infrastructure for collecting public opinion and fact-checking specious claims. Many tools for crowdsourcing, Ushahidi included, can be deployed to provide for citizen journos-ponies, smaller breeds of self-taught but domain-proficient reporters, with tools for reporting. And while much of this citizen-driven practice is perhaps under-promoted in the contemporary news space, some of the most renegade journalism efforts are sustained by citizens running depolarization operations on social media platforms in their home countries, as Zeynep’s talk suggested.

Pony Hierarchies


Part of the persistent argument in discussions that blend net neutrality, privacy and surveillance censorship revolves around how important crowdsourced and social content has become for developing honest and unbiased alternative reporting models globally. Though not to be confused with incident data directly, social media reports like CrisisNET’s Syrian Youtube Map and Conflict Map’s tweet and social media tracking plan provide these kind of windows into the world of social streaming to study crises. In analysing, contributing, and disecting social media content, pony-journalism has become a more dominant approach to assessing conflict and geo-journalism at a global scale.

Muybridge Motion Studies

In fact, arguments around how to classify the oft-hyphenated and obscure titles applied to data-journalists are more about the hybridity of their job descriptions and the range of skills they deploy than about the elegance of the metaphor. As an equine-hybrid class, we’re often trying to find new ways of developing and pushing content, a nod to the aggressivness and tirelessness of the horse behavioral type. But part of that race, maybe the most important part, is about designing content and news to appeal to people, to visualize data in new and yet intuitive ways. Our objective is to find ways to relate to populations, and in a sea of bar charts and statistical models, sometimes maps are the more affective way of relating complex digital data to a simple physical topography. That’s where the map making (mentioned above) comes in.

fancyTwo of the most relatable and persistently referenced data types in post-modern visualization are geo-data and time-series. Why? Because we relate to them, we can consider our perpective relative to time and space; they have become our touchstones for syncing digital and physical worlds. Overwhelmingly, the projects at this year’s Civic Media Conference demo sessions fell into some kind of mapping context, and I think that trend is telling for the direction of visualization schema and citizen journalism: What We Watch, a map of youtube trends; Terra Incognita, a Chrome extension for mapping exploration; Media Cloud, a collection of tools for monitoring and mapping media globally; or Cliff, a project to automate media geo-parsing, being a few among many featured projects. Tools like We Feel and CrisisNET are aimed at facilitating this kind of study, enabling study of social media and reporting strategies. In each case, it will be interesting to watch how they compete in the investigative reporting space; the race seems primed to recognize their utility.

Pony Prizes

BookAnimalsTo address another interesting aspect of the data-journo ecosystem, I’ll now pivot to another curious theme in the MIT Civic Conference and others like it: the concept of work- “family.” In keeping with the metaphor of this post, and I would argue that family in the case of a company or sponsor, is more analogous to genus hierarchies than to social kinship models. People who share a company share a type and a goal, they’re a team but one built on affinity, not consanguinity.

This is a family:

IRL family reference

This is a team:



A company/funder/sponsor/laboratory/media-outlet/workplace is a herd of ponies. As individual members, we are unique in our methods and backgrounds and generally attracted to the same trajectory, but probably more powerful in that dispassionate diversity which a team or herd-mentality affords, less complicated by emotional entanglements internally and therefore more competent at empathy externally (that is, with our users/subjects/sources for stories). In a recent HBR article, “Your Company is Not Your Family,” the author uses the analogy of sports teams and the mentions of the spurs made me think that the pony metaphor might be as ridiculously apt.

The Spurs stand out for the stability and longevity of their player relationships, yet even their current 13-man roster only includes one player from their first championship in 1999: power forward Tim Duncan.

The PrinciplesTo consider your company analogous to your family, is to cripple it by a lack of adventure. Families, while wonderful, are a default, they usher you to growth, but if all goes well, you flourish on your own. You want to build a company of people who are flourishing, and will continue to do so under guidance and not parentage.

Joi Ito concluded the MIT Civic Conf with a series of “guiding principles” at the media lab, and those statements reinforce all-the-more why a lab/company isn’t a family. A team can be built on shared principles, but they’re not the same as those on which a family is founded.

Follow your unicornnon-believerYour family pushes you, educates you, and prefers (often) your safety over risk taking, whereas your work, and your class (genus/type/subgroups) often push you to independent and outlier achievements unsanctioned by precedent and rarely “safe” in practice. A total aside in this blogpost, to be sure, but I think often data journalism professionals (and by extension, other political/social-professionals who put position before the public they serve) seems to allow confused allegiance to cleave them from simple human and social empathies.

This is a point I treated in a recent interview with Danish news about the relationship between developers and journalists. Nothing revolutionary, but at the time I compared the ideal scenario to one of mutual respect in difference, and not to a familial metaphor. My collaborators aren’t my siblings, they’re my colleagues, and the relationship is pretty different in my mind.

We sometimes risk an allegiance to an editor or organization over an allegiance to the public, and it’s important to remember that the protection and privacy of your subjects and sources is just as precious as that of your employer-parents, regardless of who is paying our salaries. Too often, I’ve seen people at conferences too proprietarily motivated to share ideas, too proud to admit that many share the same ones and have started similar projects. There was a lot of overlap at this year’s Knight News Challenge award announcement, and I think it’s fair to ask overlapping orgs to collaborate and share their plans and programs of research as the year progresses, though I doubt they’ll be held to this. Sometimes, considering your company like your family can confuse your objective to do good in the world and supplant it with one to do good for your own.cartoonicorn1

This brings up another aspect of social good work, and journalism worth mentioning here. Often, the competition in the data journalism space is built on a capitolistic motivation to secure funding and support and resist the superior publication of another outfit that prematurely scoops your content. In this fear, we privilege our company over our vocation, which is to spread solid news, to share it with the world. There’s no shortage of conflict and controversy worth commenting on, so the competition seems sad and contrived especially in the social good and open source space. But recently, I’ve been reading economic coverage of the pay-gap issue and have come to appreciate that this competition has deep roots, founded in our cultural resistance to recognizing social-good as grant-worthy.

unicorn shower

some related items found on the Pinterest “unicorn” keyword search


The most prize-worthy ponies deserve reward, and I think it’s interesting to consider how we approach compensation when the goal of your work is social good. The resounding answer seems to be: we don’t.

Econ-Theorist David Graeber’s recent interview on the trends in our financial sector indicates that we rarely value work performed with altruistic motives, and that we waste most of our workforce on “bullshit jobs.” While our intentions might be genuine, study of our current workforce specialization schema indicates that we dole out few directly productive (as in “product-building”) positions, and most work is “administrative” or “managerial”: “…[l]ots of people [in Graeber’s interview pool] said their basic function was to create tasks for other people.” One quote that struck me as particularly insightful:

Geoff Shullenberger recently that pointed out that in many companies, there’s now an assumption that if there’s work that anyone might want to do for any reason other than the money, any work that is seen as having intrinsic merit in itself, they assume they shouldn’t have to pay for it… ~David Graeber

You can read more about his provocative, and well-argued perspective, here, and while he applies his study to translations jobs, I think the scope can widen to anyone doing fulfilling, socially-conscience, and context-driven journalism, globally; we’re all in the information translation/transformation/communication business at root.

You know, you’re describing what’s happened to journalism. Because people want to do it, it now pays very little. Same with college teaching. ~ Thomas Frank

Upshot: not compensating people doing good, critical, and socially beneficial things in the world is crippling our perspective on geopolitics and progress.

Problems with Ponies Abroad

Other than economic obstacles to pursuing social good, there’s other hiccups to the hierarchies of investigative journalism that relate to how we privilege unicorns over the content they cover, and here we return to our discussion of mapping. When I was at a hackathon last month in Aarhus, Denmark, my team won the Guardian API award at the event not for building something incredibly revolutionary, but something quick that simplified news content into a digest for mobile journos.


Our app was called GeoNewsies, and its objective was to allow travelers to search by country and pull down a digest of the news in that nation prior to, or during travel. A two-paneled webpage and android app, it pulled in the top 10 articles from the Guardian relative to a particular place (panel left), next to the top trending tweet topics in that place (panel right); a bit like or other rss aggregate sites.


The interface was unstellar, simple, and arguably flattened the geo-political happenings in a place to a top 10 trends list, but our objective illustrated something tragic and important about how we process news media today, and maybe it’s not what you would expect. Our point wasn’t that people only can afford to read short blurbs and dramatic reductions of the richness available in pre-travel research, but moreso: often, travelers fail to self-educate about the context they are about to enter, and this unfortunately extends to even traveling journalists working investigative beats abroad.


Sometimes, the best witness to activity in a particular place is someone on the ground an local; this is why so much social media analysis and source relations with citizen journalists remain important to our global understanding of news. Displacing a data journo-“unicorn” to code in a foreign environment is rarely as productive as sourcing information and accounts from the local population, and then enlisting the unicorns or racehorses to usher an idea to production; or better, training the local ponies and mules to race.

Scotland Tourism’s Sweater-Pony Campaign

Burak Arikan’s MonoVacation tourism visualizations speak to this touristic approach to documentation of place that has become our practice in journalism. Arikan built a projected mashup of the tourism video/commercials of many nation, exploring typical symbols and their geo-contextual meanings relative to the nation of video production. Horses were a trend, repeatedly used in travel commercials to express freedom and tourist wimsy, perhaps. Abstracted a bit further from the original project focus, and deftones - because obviyou might consider the horse comparison to data journalism as a sometimes apt description of investigative practice: short sprint production and reporting with often unfortunately abbreviated context: a tourists’ view of geo-politics. Often a foreign media outlet’s assessment of the on-the-ground occurrence in one place lacks the depth of historical and hyperlocal understanding that social media reporting/analysis can provide if controlled, curated, and harnessed to meaningful ends. Oualwaysr attention span for international news is something that perhaps can’t be corrected but our approach to economizing a broader range of opinion and local perspective is something that might be best achieved with social analysis and local data journalism training.

As someone who came rather late to code; I’m pretty comfortable advocating the premise that code can be trained, and not limited to the hierarchies of mythical creatures. I’d argue that researching for a story involves a healthy amount of logic that is more intuition and contextual/location knowledge than technical skill. Compelling news applications about a particular time and space are ones that root in a thorough knowledge of the geo-politics of a place, and often those come through most clearly from content generated by local mules, rather than unicorns.

Post-HorseRace: Project Persistance

equus-evolutionIt’s safe to say, however, that team assembly and the logic of our production pipeline aren’t the only concerns in developing sustainable news applications. With news apps, we deal in a particularly friable media; one whose impact often limits to the extent that it’s API/library/dependency components have yet to deprecate. When we think about endurance and the persistence of applications, we sometimes think about the ephemerality of our work.

What happens when the horserace is over; how will we remember our efforts?

This worry is not new of course, and its one that’s been persistently suffered by media producers and providers globally. Born digital projects are so vulnerable to almost immediate atrophy, and while you may make history with a web-based piece; the probability of it outlasting  even newsprint articles from 30 years ago is pretty pathetically weak.

We’re tackling that next month (July 23rd) at the 2014 Digital Preservation Conference in DC, if your’e interested, so check it out. Our objective in presenting is both to survey the state of media production today and discuss preservation options, but also acknowledge some technological trends we should avoid. Contemporary product development is replete with light-bulb conspiracies of ‘planned obsolescence’ and at the opposite spectral pole, stories of technology built for eternity. Somewhere in the middle, there’s a place for news apps in our geo-political history; a few pony programmers might just figure-out how. 🙂


ilovethisTo sum up this (rather-too-longform) piece about pony personalities in the geo-newsroom, I’d say that a lot of our professional expectations as journalists and developers presume a few narrow ideas: firstly, that a simple taxonomy can define competence in global news coverage, secondly that companies can operate like parents, and thirdly that the integrity and sustainability of your work are secondary considerations to the general scheme and scope of a path defined by paternity.

I’ll close with a link to my MIT Civic Media Ignite slides (presentation, references); it’s a talk about teleportation and mapping, but no less fantastical than the expectations of data journos globally (that we tell the future, that we perform our pony tricks on demand, that we manage to t[rans/ele]port). An area of growing interest in the data journo world is how we manage to create compelling narratives about remote happenings, and often these are through our modern tools of teleportation (things like Ushahidi’s BRCK or OpenNews’ Keyblur for deploying networks without Internet, or applications like Crowdmap, CrisisNET, and Media Cloud Focus, helping us to understand global coverage and crowdsourcing context from operatives on the ground. These applications are among the suite of devices at our current disposal for feats of science fiction fantasy, bringing our ambitions of teleporting and unicorn reporting all the more close to our realities of remote monitoring and pony-journo practice.


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Fortune Telling with Data: Modeling Threat with Feeble Predictors

Anna Pavlova - foxy chiromancerWhile in college, and unbeknownst to most people, I dabbled in some performance artistry and predictive analysis. Part of the performative nature of college is developing the capacity to claim competence in topics and credentials one has yet to earn, to educate formally while informally fumbling through social mechanics for which no adequate prerequisite is ever published, and finally to make elaborate promises to potential employers you can’t yet keep. Corroborating this farce with some documentation is usually expected, a cover letter here, a résumé there.

On professional applications, it seemed impressive to have a range of acronym memberships to organizations with undefined but assumed-legitimate titles. Interviewers, however, seemed inattentive to merit or documentation fluff, so in small print among some legit scholarships and volunteer positions, I wedged in a nod to my extra-curricular involvement in the “ESP Volunteer Aid Org.”


I am not a performance artist or a seer and this was an experiment I dropped shortly after, but I think there’s some irony in that, while attempting to assemble my prospective professional qualifications, I spent some time considering a career in heightened sensory perception. Or rather, I tried to jest-test the merit system with an obviously bogus acronym.  Funny in retrospect, but previous experience in ESP isn’t far from the tacit prerequisite many would assign to data mungers these days, and particularly anyone who does even mild statistical analysis on crisis datasets. With all these data, surely someone would be able to claim clairvoyance, solve international crises with a affinity for computational analysis of historic precedent, surely the answers are there?


fortunetellerAs someone who works with data, and more importantly as someone currently living in the future (compared to those currently living in my home country…whoohoo Nairobi time), I thought it might be appropriate to exercise my clairvoyance and provide clarity on life in Nairobi, assessing some of the current intensities, and the probabilities they might aggravate. In any case, the explosion of state department travel warnings in my inbox this week has made my reticence on the subject a bit obnoxious so I’m going to diverge from my typical open source software soap-boxing to write a bit about the statistics of terrorism and the particulars of my current condition.

I write from the position of a math hobbyist,  and an amateur clairvoyant, and Electric Powerso I’ve peppered this post with some specious but thoughtful observations about the news I’ve been following, what I’m currently experiencing, and the links, images, and resources my limited bandwidth allows me to explore.

In brief, Nairobi has been intense of late. The state department issued 4 official warnings this week, encouraging US residents and visitors to avoid Eastleigh, travel to Mombasa, proximity to Burundi, and most recently, travel in Kenya, period. These precipitate from the bombing earlier this week in Nairobi (6 fatalities and 20+ injuries), the recent church attack in Mombasa (4 fatalities) and general insecurity about al-Shabaab and armed operatives threatening attacks in any country conducting peacekeeping and/or military efforts in Somalia. :(

For it be morrow.. Speculation about the probability of a “large scale attack to come” made me start thinking about the meaning of “large scale” and projected “imminence” when it comes to statistically predicting events of high variability. How large is a “large scale” event? Anything where multiple deaths result seems “large” to me, though my definition has adjusted to accommodate recent conditions. If authorities are projecting a large-scale event to come, what about the unsettling events of now? How soon is imminence, not to be too much of a Morrissey fan-girl, but how soon is now?

Which to choose?So with all of these questions and my own preoccupation with quantifying self, I thought it might be time to read up on a few predictive models of the likelihood that something might happen.

Periodically people people post comparatives online like “you’re __times likely to die of x than get involved in a terrorist attack.” These were net popularized post-9/11 it seems, though, the scale and impact of that attack in the domestic US was fairly singular and data collected prior to it would do little to predict its occurrence and re-occurrence without admission of several limiting factors and uncontrolled variables.  That said, in the Annals of Applied Science (vol. 7, no. 4 2013) last year, Clauset and Woodward wrote a paper called “Estimating the Historical and Future Probabilities of Large Terrorist Events” in which they hoped to define a generic statistical algorithm for estimating the likelihood of terror events in complex social systems. These kind of predictive stats depend on so many variables outside precedent empirical data but the authors present multiple tail models and disclaimers about the limitations of their predictions to control for this (Matlab code and sample data available here if you want to play).

Kreskin's ESP board game

Of particular interest is their summary forecast are estimates for 3 potential scenario probabilities based on possibly forecast from past data:

“Rather than make potentially overly specific predictions, we instead consider three rough scenarios (the future’s trajectory will presumably lay somewhere between): (i) an optimistic scenario, in which the average number of terrorist attacks worldwide per year returns to its 1998–2002 level, at about ⟨nyear⟩ = 400 annual events; (ii) a status quo scenario, where it remains at the 2007 level, at about 2000 annual events; and finally (iii) a pessimistic scenario, in which it increases to about 10,000 annual events.”

That then, looks something like this:

Crystalball looking cuteThe Clauset and Woodard analysis further predicts a range forecast of 19-46% chance that at least one catastrophic global event will take place in the next decade. But to localize this a bit, and for the sake of argument, let’s take their “status quo” (a modest median) probability calculation trained from the RAND-MIPT Terrorism Knowledge Base, and set up the conditional probability that there will be a terrorist happening of catastrophic proportion (p=0.461) while I am in Nairobi (approx. 30/(365*10yr) possible days; p=0.008) . The condition is fairly unlikely, but unfortunately increases when you factor in covariates like my general foreignness (> victim likelihood…bummer, p=0.475), and the  logic that the violence occurring with agglutinative regularity will likely foster additional conflict and escalated tension:
“For instance, international terrorist events, in which the attacker and target are from different countries, comprise 12% of the RAND-MIPT database and exhibit a much heavier-tailed distribution, with αˆ = 1.93 ± 0.04 and xˆmin = 1.”
EAC MapTrying to control for multiple variables is complicated, so even in a problem which can be structured as conditional (probability of x given n state) struggles in this scenario. Does the probability of one state affect the other and yet still require factoring in both? And if so, perhaps it’s a joint probability issue between independent events. When the prediction derives from historical information, perhaps a Bayesian use of prior probabilities could be trained for future forecasting but even then…complicated. And regardless, perhaps the historical data is limiting in applicability due to scope; my definition of “catastrophic” scales down to the mere injury of a family member/friend, decidedly distant from the catastrophic proportion of 9/11 or any event with Chiromancerupwards of 1000 fatalities used to make these kind of probabilistic predictions.
Most of the math here is beyond my own research level but one factor that strikes me as strange, given my work with crises in the context of maps, is the absence of particularly specific geo-data analysis. The East African Community (EAC) hasn’t been spared much violence in the past few years, and sadly, in the last few months in Kenya. so I’m interested in reading about statistical modeling done on conflict probabilities with geo-specificity. Maybe this a usecase for the Wolfram Language when I’m brought out of my beta-in-waiting status; something to counter the pop-y around the world travel time estimates and polar auto-opposite calculations that have been so fun but maybe not particularly applicable to my current situation. What might be applicable is a computational knowledge engine that would assess my IP address, map it to a lat/long and then calculate how far I should move in the city to avoid conflict on a daily basis (*winks* at Wolfram friends).
To be fair, the Clauset and Woodward research  honestly nods to the variables not-completely considered in their analysis:
“Technology, population, culture and geopolitics are believed to exhibit nonstationary dynamics and these likely play some role in event severities…our approach is nonspatial and says little about where the event might occurrefinements will likely require strong assumptions about many context-specific factors (Clauset + Woodward, 15).”
Evangeline Adams (American Astrologer) explores a mapBut, I’m still wondering about alternatives to these estimations, what is the best research body to design these kinds of models and who has the best open test data on the topic? I’m generally skeptical of predictions based on historic data without geo-reference these days, since so much of what happens depends on a cultural/historical/social context that is impossible to divorce from a particular place;  the general forecast of 19-46% chance of something happening in the next decade at a global scale is hard to conceptualize when you consider the umpteen geopolitical factors that might cluster likelihood around certain high-tension locales (Clauset + Woodward 14). Perhaps there will one day be a service to prioritize these factors and co-variates based on personalized social and surveilled data as Seth’s Worry App concept suggests:
“Worry is the very first technological solution that maximizes the benefit of mankind’s oldest task: anxiety.
Using this flow of data, the Worry app computes the things you ought to be worried about. For example, instead of needlessly wasting time worrying about a random event like being bitten by a brown recluse spider, the Worry GPS system can point out that based on where you are, you’d be better off worrying about a different, unpreventable event like being killed by a fire hydrant flying through the air or perhaps by an angry rooster wielding a knife. The Worry app will alert you to that, which dramatically increases the effectiveness of your worrying.”
Destiny awaitsI’m into anxiety optimization and maximized thought efficiencies, perhaps a maturation of my adolescent ESP :). 
In all seriousness, there is probably little statistical value in projecting these possibilities where I am currently. Fortune telling with so many variables can be complex, though the projections remain pretty unsettling.
But apart from all the speculative quant, there are some simple qualitative observations that I can make:
  • things are heating up every day here
  • any situation where “safety in numbers” is a paradox because avoiding congregation points (malls, churches, etcet) has become a way of avoiding conflictESPad is probs bad
  • at the end of the day, statistical randomness is a really unfortunate jerk who even despite your best precautions can allow for some pretty horrific happenings (a carjacking happened in my inner circle this week, for example)
  • there’s something broken about the fact that the entire reported anti-terror budget of Nairobi is less than my current apartment’s rent outside the city (both cases supposedly sustaining a month’s worth of expenses). If we drill down on that statement semantically, and not quite statistically, we can conclude that the collective safety of a city in a time of “imminent” crisis is roughly worth a one bedroom apartment.

Mathematical calculated? Feebly. Statistically significant? Probably. Totally unfortunate? Predictably.

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New Economies of Innovation: Value the Tacit, Trash the Tangible

This is a blog post about economies of technology, it’s long, so let’s start out with 3 concept anec-quotes, and works it’s way to a series of bracketed themes: innovation + enterprise.

# Innovation

In a February 2013 interview with Wired, Larry Page  (Google founder) commented on Google X and paths to innovation:

When I was growing up , I wanted to be an inventor. Then I realized that there’s a lot of sad stories about inventors like Nikola Tesla, amazing people who didn’t have much impact because they never turned their inventions into businesses.

Feb. 2013, Stephen Levy, 7 Massive Ideas that Could Change the World

Let’s ignore that Tesla was in any way slighted as yet another “inventor” who lacked “impact” (WTF) and proceed. This comment led me to question whether we need to monetize to achieve, and how do we create healthy economies for qualities as ill-defined as “innovation” or “integrity.” Maybe innovation alone is an opal not a diamond: beautiful and valuable to be sure, but unless someone contrives rarity or economy (ahem, debeers) around it won’t be nearly as rad. So, can we build a business on intangibles and “values” that as yet have no monetary equivalent?

# Enterprise

Suketu Gandhi comments on this in The Wall Street Journal’s Deloitte Insight , loandefining the “postdigital enterprise”  as one where innovators can either “take your existing processes and apply these new technologies to them,” or rethink the process that technology enables you to enact. In contemporary (apparently “postdigital”) enterprise, maybe the application of technologies to process gives innovation economic weight. Do we need business process to innovate and what do we value in a digital world where lots of interactions and transactions lack the physicality of “real” life? Gandhi also cited “ the big five disruptive technologies,” 3 of which struck me as strangely nebulous, not so much ‘technologies’ as vague ‘values’ of interaction: “social,” “mobility,” “cyber security.” The ability to be social, mobile, and secure seemed to bleed outside the bounds of “technology” as I would typically define it, and venture into the fuzzy region of human interactions and freedoms in the physical world. How do we monetize these, and should we?

# Monetization

To that end, Ecologies of Knowing blogger Pavel asserted that “much of the ubiquity bitcoinbillionaireof computing today is of course driven by opportunities to monetize social interactions and shifts in cultural perception.” As a software architect, I get paid to build things that have no physical product, my work is as intangible as the concepts whose value I’m now interrogating. While part of me is proud that so much of my life is “priceless,” part of me is a bit distressed that that I haven’t founded a business on the obscure intangibles and important aspects of my life. How can we re:define an economy to appropriately capture what we value? Can we bank on innovation, social mobility and security without building an enterprise? Or do ideas lack value when they lack an emphasis on economy?

Taken together, all of these anec-quotes coalesce in the topics at hand for this blogpost: bitcoins, cultural [in]security currency, innovation ecologies/economies, and basically banking on intangibles over bills. Let’s treat each in turn.

## Bitcoin to Begin

A few weeks ago, I hosted a Stereo Semantics radio show about new forms of banking. I’m interested in the development of independent economies, new currencies of exchange appropriate for our internet and IRL environments. Part in parcel to this obsession is my newfound interest in Bitcoins. As per the consistent popularity of Bitcoin in contemporary media, I’ve built a short URList (my new favorite OSStartup) on the topic.

18 Links from: Bitcoins

moonjelly, via Urlist

To take it further, and more topically, a recent NY Times article treated Bitcoin forays into governmental policy and Bitcoin progress toward legitimacy in exchange-traded funding.

The Times tempered this topic judiciously with an explanation of Bitcoin, and my URList includes a series of past and real-time updated publications/interactives focused on the topic. IRL, I’ve attended a few meetups on Bitcoin Startup philosophy and can submit from my cursory exploration that the Bitcoin ecosystem is pretty nascent, warbly in the real world, even now, long-after it’s debut. It’s hard to codify what conditions and cooperation merit my financial “trust” but I find that most startups built on Bitcoin fall in a category of specious, less-traveled by other landscapes of internet innovation.

## [In]security Currency

So, In prefacing with this artificial currency of contemporary fascination, I started privacyIsDeadthinking about other domains where potential economies could be crafted, and I found that defining values like “trustworthiness,” “integrity,” and “security,” also meandered in a nebulous and ill-articulated part of my consciousness. A recent MoMA PS1 panel discussion on Privacy and [National} Security, further forked this thought to consider a slurry of “rights” billed to US citizens but now in question in a post-PRISM world. What do we value? What are our intangible freedoms that form the substrate of our cultural currency? Services like and Sitegeist would suggest that we value proximous information over privacy. In promotional material, the former markets itself as a “sixth sense for the world around you, showing your hidden connections, and making your day more fun.” The latter bills (ha) as an “the app present[ing] solid data in a simple at-a-glance format to help you tap into the pulse of your location.” Sounds exciting, discovering a secret garden of semiotics and site-specific information? How exhilarating! Until a third party starts tracking it, and determines your habits, patterns, behaviors, your prospective memories, your potential to commit thoughtcrime… so how do we balance an interest in information with a right to resist being polled? Right now, we don’t.

A recent app built by Open Data City in Germany for a local conference tracks bitcoinminerpopulation movements in a timeseries visualization hosted here  and blogged about here. ODC’s sensors detected passive interactions with mobile devices on the conference floor via each devices’ unique mac address. The visualized animation of conference traffic from sensor perception point to point is stellar and stunning but also scary. What’s disturbing about this isn’t just the tracking of these data points, more incriminating and valuable metadata is captured daily by our social applications and email clients, later mined by 3rd party services that sell us products and promotions. What’s disturbing is that unlike those social apps that we opt into voluntarily, if idiotically, on the daily, these sensors were tracking participants without explicit consent; if you had a device (phone, laptop, tablet) you were traceable, part of someone else’s time series art project. Potentially innocuous since mac addresses were probably anonymized by some hash, probably difficult to relate to your identity, but what about the other traffic patterns evident on your device? Could tweets, correspondence, conversations be layered over mac address traffic to trace aspects of your “private” interactions? :/ The project authors allude to this in their blog post:

One thing is clear: The application displays the duality of such records. On the one hand it is clear what data traces you leave, often unconsciously. Therefore, we hope that the application will help to raise awareness for the protection of their own privacy. And is perhaps only once thought about why someone “Free Wifi” offers before you log.

Zur re:log-Website. Realisiert von OpenDataCity. Unterstützt durch picocell und newthinking. Anwendung steht unter CC-BY 3.0.
But is awareness of this enough? And are we more jazzed by the  “Open Data [City]” potential of these apps than by the one-valued privacy we enjoyed in comparative anonymity? Further, how does “freedom” articulate in our ecology of networked intelligence? Is newfound “freedom” afforded by the “open” arrangement of the internet equivalent to the right to hide or the right to expose what’s been hidden? Is it the right to keep secrets or the right to reveal them? Are these even of value? And further how do we re:define value to suit a digital landscape?

## Innovation Economies

In defense of “open data,” my fascination with Bitcoin follows from persistent interest in open source and internet innovations toward replication of analog concepts. Not going to a lie, I’m totally an open data/knowledge/info fangirl. I’ve enjoyed the transition of Encyclopedias to Wikipedias, of gift economies founded in the likes of Burning Man to online exchange platforms like TimeBanks; I can dig it. There’s an intangible quality to trade and barter of “time” or “security” over monetary payment, and perhaps those tacit economies best express in the bit and byte-built world of the internet. Maybe we need to start thinking about cultural economies, the tacit luxuries that we value for their rarity and not necessarily their potential to facilitate purchase. Intangibles like “freedom,” “privacy,” and “security” are governed by their own economies based on contemporary scarcity. If scarcity and control are the determinants of value and weight, then privacy is the gem in our the rough of our current monetary systems.  


So what’s new about this? Are bitcoins really that different from current economies? Maybe not, but they’re a provocative start to thinking about tacit economies and the value-making of intangibles. To return to the article that inaugurated this blogpost, I’ll revisit the Larry Page interview, if only to root this endless econ-odyssey in a more agreeable symmetry. In response to what he envisions as successful ideas and company concepts, Page asserted that “[y]ou just need to have the conviction to make a long-term investment and to believe that things could be a lot better.” Will the world be better with investment in a more artificial econ? Will I be more content when currency codifies not as a physical bill but as an ephemeral bit? Will that make me appreciate that money really bears little of the emotional weight that I’ve applied to it,  and that intangible and ill-defined values and virtues warrant a more miserly defense than I’ve ever invested in them? Maybe, a bit[coin]…

## Banking on Intangibles

To conclude, I’m not alone in recognizing the impact of bitcoin currency on our potential economic future, nor am I particularly brilliant at applying economic social science to even more subjective qualities of “innovation,” “privacy,” “safety” and “security,” but it’s comforting to read how new systems of value are developing in tandem with technological innovation. Their access points are becoming increasingly available to a pedestrian public, but new post-digital economies demand an understanding of what we value and how we define the ephemeral.  Do we view privacy and innovation as valuable independent of a price point applied post-facto? And as we’re building these economies, I’m not sure how we’ll incorporate those ethics and morals into the “monetizable” and “business-driven” soup of innovation.

Throughout Who Owns the Future?, Jared Lanier comments on this relationship between economy and digital society, and the cost of “free” information to social and cultural constructs.  As citizens of a digitally-driven society, how do we resist violations of our intangible values via capitalization on our social, mobile, and [in]secure interactions? Should we embrace a new economy that appreciates exchanges of ideas and information, that values innovation without insisting on its monetization? Come check out Lanier’s talk at NYPL in October to find out, and in the meantime, let me close with the indubitable paraphrased prescience of one of my favorite poets:

I like to think

(it has to be!)

of a cybernetic ec[onom]y

where we are free of our labors

and joined back to nature,

returned to our mammal brothers and sisters,

and all watched over

by machines of loving grace.

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DIY Data Science + De:bugging Biometrics: balancing bioart, sensor intel, and responsive cityscapes

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A few recent articles about neuro- and cognitive science and last month’s GenSpace Talk have sparked my curiosity about the dual capacity of sensor networks to empower a sentient cityscape and to enable biometric surveillance. The forming being a rather rad consequence of a more digitally developed infrastructure, the latter being the horror storythat hangs on our most distopic scifi futures. So what is the balance when dealing with art and code? How do we manage the development of new technologies which allow us hyper-personal transactions at the expense of anonymity?


According to an article in Science Daily, researchers at Cornell have started to used fMRI scans to predict not just how a person is processing information and in what neurological buckets the activity is dominant, but even who a person is thinking about. Not to be outdone, MIT recently went public with some MatLab code that uses and Eulerian algo to amplify pixels and detect pulse and subcutaneous activity from video files. Meanwhile, what about the prophesied Google Glass and it’s potential to kickstart ‘surveillance’ as a cinema sub-genre? In all cases, we have new windows to our own biology viasecond-hand technological captures. While primarily scientific, these developments have implications for imaging outside of the scientific realm; what new visual art projects might also be augmented by these processing scripts? How will bioart pick up the scientific slack and use open sourced code to develop critical artscience?

When challenged to hack away and build something in the theme of GodMode for 319 Scholes’ Art Hack Day in Brooklyn this weekend, a few of us decided to tackle thanks MM Moser for the logo aidbiometrics andsurveillance with a spoof film, garnering a bit of nerdfamery and some cool coverage along the way (Creator’s Project | Our project, DIY Spoofing for DNA Counter-surveillance, was shot, edited and exhibited in a slurried 36 hour sprint, adapted some Gattaca-like insecurities about the trajectory of genetic surveillance. Check out the project here, and browse the vimeo links to research participant hackers and our other press pages. The whole experience of hacker/artist immersion was infectiously inspiring and full of smart kids in fancy kicks #godmode. In the open source spirit, we submitted the video as a set of DIY protips on how to blend your DNA with that of a friend, then shed both samples in simultaneity, to scramble surveillance readings. However fun and simple our execution, the themes of human tracking

via biometric analysis and the role of the post-modern bioartist in critically questioning this tracking were clear. We were all amateurs in many ways, but the ubiquity of sensingtechnologies and send-away DNA analysis services in our modern cities points to the validity of our concept. How might a project likethis scale beyond a weekend hackathon and a posting on Instructables? How might these themes persist as they propagate in our cities?

Case in point, this week’s submissions to the NYC Reinvent Payphones project solicited several proposals for more “aware” telephone technologies. My company was asked to develop ways to augment underutilized street furniture and part of this process involved an impressive network of sensing technologies to permit data collection and a more personalized and locally sensitive experience. The implication was the soon these ‘augmented’ booths might permit not only private phone calls but intimate and hyper-personalized transactions, automating and diffusing the pressure of city services such as  polling and election activities, postal services, and the DMV. Oh my.

Check out press: Engadget | the Verge | NYDaily News | the GothamistFastCo !

Please vote for our video here so that we can transform the NYC payphones!

ah_ 2013-03-05 at 4.39.41 PM

But what if authentication becomes biometric? Is that fair? Do we want all of our identification to be linked to our biology? If someone spoofs our biological identity rather than spoofing surveillance, are we comfortable with allowing them access to our civic, political, and personal lives? Probably not, but we probably will be soon enough. Doubtless that many people will opt to log in with their default bio-credentials when possible, forgetting that these features, once hacked, cannot be scrambled or reissued, md5 hashed and emailed again to our ‘private’ accounts  in the physical world as they can in the digital.  Moral of the story? Keep tabs on your preference settings, keep your friends swap/spoof close, and your privacy radar closer.

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