heur-e-ka

vuvuzeliacs? color choice? by gender? age? culture? hmmmm?

What would you wager that there are detectable semantic main-effects within the” vuvuzelectic” at the 2010 FIFA World Cup taking place in South Africa right now? Does anyone believe that what is likely to be tallied as millions of “vuvzeliacs” comprise essentially random noise, devoid of cross-culturally derived contextual signals and any collectively individual intent? All on its own those notions have more than enough controversial stuff to fuel international bar-arguments until FIFA 2014 in Brazil arrives in another four years.   Even one thought of the noise-making techno-madness the soccer-fanatic nation of Brazil has in store for us by then makes my head hurt.

Until then, considering “random monotonic noise” versus “hidden lingustic content” might well be an intellectual impossibility for some.  But the notion of “vuvuzelas as instruments of communication despite their immediate mass-appeal as ‘noise‘” is compelling to semioticians.

Now if we could only prove there were such a things.  Ah, small matter.  The idea that “music is a language” and “language is a map” precede me in both time and intellect, I’m afraid.

But there’s a pattern in there for certain, although it’s game-time meaning would be be quite likely to be statistically impossible to strip out from all the differing “vuvuzelica-intents” within the maddening-to-listen-to-signal itself.

A vuvuzela-crawler would be totally on-point here!

If anyone has one to lend me I’d be appreciative.  Mood Logic incorporated hoping their “digitized-song-fingerprint” idea might pan out, and did when you condider AMG bought and intgrated some of that techonology into their product line.

For starters, I’d give you a co-authorship in the paper that is currently “dormant due to data-lessness” inside this interesting notion of human lingual-similarity, intra-species-racial-depth, and a likley collective conscious-cluenessness to the phenomenon while participating so joyfully, albeit segmentedly within it.

Who has enough humanity-insightlessness to bet that the vuvuzela-blowing crowds down at South Africa’s FIFA 2010 World Cup Soccer Finals aren’t hawking a hidden message in an unconscious language?

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The End of Theory: The Data Deluge Makes the Scientific Method Obsolete
Chris Anderson, Editor in Chief of Wired Magazine

from http://www.wired.com/science/discoveries/magazine/16-07/pb_theory

Let’s think about Wired’s notion of “science” looking to Google for “advice” before being replaced altogether by their “compute-baby-compute” approach to inference generation!

Consider, first, that Dr Jessica Tracy’s research about the nature and mechanisms of emotions were featured in Wired Magazine in August, 2008.

Her decision to build an elegant experiment utilizing sighted, born sighted but blinded later in life, and blind-by-birth athletes not only proved an old model of emotions wrong, but put forth a wholly new model from which new and woven more fully informed theories of emotion as measurable units of cognition were possible instead of merely as their long held “learned behaviors“.

Propose for us, Chris Anderson, just how you’d have “data mined” your way into proving that the emotions expressed in victory celebrations were innate as opposed to learned? You’d still be counting all the possible inputs to your vaunted Google-as-an-Enigma-Machine-to-Replace-Science before you’d be able to explain why number crunching actually would be able to accurately predict that every time an athlete puffs out their chest with arms upraised that they’ve just won an event, while the athlete with the slumped shoulders and crestfallen face had lost.

We at Readware and heur-eka, LLC are applying new models of human cognitive processing to help build even more cogent models of content similarity that this vaunted approach has long eschewed, only to see “Google Science“; display Iomega’s “burn-baby-burn” ads with Washington Post articles headlined “DC family burned to death by a Christmas Tree fire on New Year’s Eve“.

Meanwhile, the veritable litany of getting-it-wrong-while-getting-rich events we’ve all witnessed time and again while they were practicing Google-science in the field is the crux of why this argument about “science learning from Google” is so preposterous. The hotbed of science, NASA, has had some accidents, to be sure, but had they been wring as often as Google we’d still be looking at the moon as little more than possible green cheese.

Precision and recall are oppositional forces, and the fact that Google has made billions getting people to click on what turned out to be not at all what they wanted allows you, and people with this apparent ignorance of hence contempt for science to mistake economic success for scientific success.

Add up all the times Google has ever been “right“, as measured by the consumer telling you “that was just what I was looking for” in their AdWords program relative to the number of times we all laughed out loud at the ludicrous and obvious lack of lexical sensibility the AdWords/AdSense “models” promulgated and you might well revise your rather pedestrian view of “science“, “inference“, and the “empirical method“. The truth is that Google’s real-world success came from their role as irrelevance-brokers, never earning a dime and burning through Angel investments and Venture Capital at unprecedented rates when “search” paid the bills (where the citation-index “model“ was hard at work proving how well it worked for search, just not as a proxy for high-tech’s apparent Holy Grail of inference, revenue generation).

It wasn’t until a “financial scientist” with economics models coming out of his ears, CEO Eric Schmidt, came along with those AdWords/AdSense programs that Google was able to rifle through their index to find the most highly valued keywords those game-theory models could make money from regardless of absolute “modeled” relevance. It was this decidedly modeled happenstance that allowed people like Anderson to jump on this bandwagon, leading to this stunning proclamation that science can and ought to take lessons on inference from Google

In reality, i supect that the scientific field best positioned to learn from Google is Anthropology, or perhaps Economics, in order to find out how and why advertisers still pay orders of magnitude more for online keywords that don’t work nearly was well as space and TV advertising.

Another amusing fact is that Google is utterly fraught with economic models, a key domain of mathematical modeling ensuring that the keywords bid upon by online advertisers (both high and low) meet stringent “modeled” conditions that lead to optimal, long-term profits.  To realize this reality of “Google-Economics” is to begin to see this pronouncement of Google as “model free” as similar to a view of a 6,000 year old Earth populated from the Garden of Eden as opposed to the nearly 4 billion years old we know it to be from many a mathematical model originating within both Geology and Cosmology.

It is hubristic, naive, and self-satisfied pronouncements like this that make real scientists laugh out loud at the term “computer science“. Computer Engineering? I’ll buy in to that moniker, but name me a computationally intensive process that ever applied real science instead of technology dressed up as science in the invention and enhancement of computer technology, always seeming to accept the first right answer as the “best answer” available and rarely setting up panels of testable alternatives known to and valued by the world of real inference as “models“.

In a fit of perfect irony imagine the look on Chris Anderson’s face when he realizes his own AdSense advertising revenues are down when this very article proves unable to attract “scientific nonsense” as a keyword for generating online-advertising revenue. Well, not until his SEO/SEM staff reconfigure their own models to include all the comments to his article instead of just “his keywords“!

As it turns out, the “big three” of science, inference, and the empirical method are still the only viable conduits to understanding the world in which we all live. Number Crunch all you want, and then to wake up one day to realize that you’ve been spending all your time chasing spurious and random associations instead of the logical and predicted downstream effects of modeled physical processes!

Not only would you wake up and go to bed as ignorant as you were when you started that cycle in the first place, were your innate human modeling skills to have evolved as Wired Magazine’s Editor-In-Chief Chris Anderson claims is the best way to go in this article that you’d likely never break free from your mistaken initial premise, a self-imposed redux of “Groundhog Day” without Bill Murray realizing such was the case!

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A Proposal for Analytical Pathways

Determining when our choices are actually the end-product of an orchestrated collision between externalized hyperbole and internalized wishful-thinking.

How direct opposition to our human “Common-Sensical” foundations always begins to fall apart when we are presented with tools designed to discover it.

In his article “Semiotics of Investment: The Wall Street Fashion System and Wall Street Investment Bubbles” noted entrepreneur and economist, James Pruett, clearly demonstrated and explained the mechanics behind the time-lag haunting situations where the “facts” as represented by “agents of change” and “vaunted thought leaders” simply cannot be sustained against the reality of the day.  Whether such discrepancies were apparent to those who promulgated such “fact vs. fiction” in the securities, housing, or energy sectors “pre-bubble-bust“, Pruett made an unassailable case for how hidden meaning exists right in front of us every day regardless of our ability or willingness to utilize, hence extend such information to our collective best interests.

His well-posed arguments called for;

In summary: It is meaning in circulation that, in large part, determines the value of exchange.  Meaning is propagated by memes and determined by connotations of the sign.

Origin of the problem: tied the conception of man as a maker and for growth in profits (psychology of increase based on ancient social and environmental conditions and magnified by modern capital markets) which translates into the need to provide for full employment and growth (even if it means generating waste).  The system has reached a critical phase.

Solutions: wealth creation should be tied better aligned with productive labor and organic growth.  What is needed are alternative forms of exchange that lie primarily outside conventional transactions.   Alternative forms of exchange are needed that emphasize reciprocity and collaboration.

Semiotics is the study of symbols inherently anchored within the field of Semantics, the study of meaning.  We at heur-e-ka are applying these premises to find and assign higher-level meaning to what has historically been seen as “unstructured data“, long held to be nothing more than an incomprehensible morass of bland, conflicting, and fuzzy inputs with no discernible path to predictable and automated outputs of any kind.

Personally, I’ve run into many situations in my life where purpose and its expression are clearly oppositional when exposed to some critical thinking;

Wall-Streeters gauging bull v. bear market using the ratio of limo-to-taxi lined up at the NYSE’s corner of Wall and New Streets independently of, often diametrically opposed to, the standing wisdom based on classic open-close-low-high analytical methodology.

Realizing that not one Software Engineer working on the WebSphere family of products used WebSphere’s Development Studio to design and test WebSphere software, choosing a host of their competitors’ IDE’s instead while building an enterprise product-line the general public was being strong-armed by IBM’s massive market penetration to adopt.

Witnessing LinkedIn.com’s email blitzkrieg hawking their $39.99/month Premium Service as the “best way to find a job“, a tactic that essentially acknowledges that their entire business model of “professional connectivity via relevant skillsets and experience” needs to step aside and make room for the longstanding job-hunt truisms known as preferred-access and blatant-nepotism.

Roman Catholic Priests admitting their intellectual departure from RC dogma while continuing to foster the notion among their congregations that their moral code as the only moral code that counts; “Responsible religious leaders must find a balance between helping their congregants to wrestle with tough questions and offering them secure answers. In other professions that is known as spin doctoring.”

The birth of the “Tea Party” only after the very embodiment of their stated platform’s arch-nemesis, big government and socialism, left office with their near unanimous support firmly in-hand, who continue to chant such ludicrously oppositional mantras like “keep the government out of health care” and “don’t mess with my Medicare“.

David Li’s “new analytics“, The Credit Default Swap, and his role in precipitating the Housing Bubble of 2008 despite the underlying and widely acknowledged insider position that no one understood the mechanisms or possible outcomes of these newly formed financial instruments on the age-old brokerage-fee schedule.

Unsustainable fishing practicioners’ constant protests against any regulation while watching their livelihood’s complex resource base steadily erode through the application of their own destructive methods without such oversight.

Here is a decidedly incomplete list of people whose scholarly works in “semioisis” are grounded deeply in real-world examples, backing such an analytical approach.  We like to note that this list is growing in both length and stature every day.

Semiotics of Investment: The Wall Street Fashion System and Wall Street Investment Bubbles; A Wall Street insider describes how the semiotic approach discovered three large economic bubbles with readily available information before they transpired.

Semiotics vs. Semiology: What The Difference Means To Brands and Semiotics and Strategic Brand Management; If there was ever a perfect venue for leveraging “counter-semiotics” it would be the advertising and marketing industries, whose obvious charter is to convince you to do something you’ve never really given serious thought to on your own, or likely ever would without some concerted external goading.

Decoding competitive propositions: a semiotic alternative to traditional advertising research

Guinness Worldwide applies semiotics to classic advertising, addressing the recent characterizations by many experts of online as being advertising revenue limited.

“Analysis of Semiotics: Propaganda and Psychological Warfare“; how media works as the medium through which the governments spread their propaganda. The government manipulate masses using pamphlets, speeches, morality and priorities.

Working in the Future Business…the semiotics of leadership and elections; “The study of semiotics, which is analysis and understanding of signs, symbolism, codes and cues is vital to help de-code and understand why and how brands or communications either ‘connect’ or ‘disconnect’ with people. For it is in our subconscious that these ‘codes’ and ‘cues’ help shape our views and inform our decision-making.”

The future of retail engagement - a semiotic speculation.  A semiotic engagement from CANVAS8, November, 2009.

A Semiotic Study on War Posters; “…the focus of this study is to understand and analyze…the US war propaganda and how the super power used it to their advantage (during WWII) to turn tables amongst the global audience.

Soviet Semiotics and Literary Criticism; “…the reemergence of literary theory in the early sixties came about in a quite unexpected and uniquely Soviet way.  If certain areas are sensitive to investigation, or even taboo, the energy of speculative thought will be deflected into areas which are neutral, perhaps “scientific“, or even technologically useful.  It was by a such a deflection into science and technology that a new and original school of literary semiotics came into being in the Soviet Union”

Semiotics of Human Body and Character: Aristotle’s Logical Foundation of Physiognomics; “…semiotics is the doctrine of the nature of semiosis…for Peirce ‘an action, or influence, which is, or involves, a cooperation of three subjects, such as a sign, its object, and its interpretant, this tri-relative influence not being in any way resolvable into actions between pairs.’ (Peirce, Collected Papers 5.484).”

Symbolism is a pure product of our innate, intellectual ability to deal with “things” as multidimensional mappings.  The notion that an automobile is “transportation only” literally begs the question of why my stuffed-shirt of a neighbor is driving a Ferrari and his trophy-wife is driving a Hummer.

The evidence is mounting for a mechanism whereby we can analytically exploit the notion of “multiple simultaneous states” within our collectively-shared decision-making paradigm.  The manner in which we assign, manifest, then contain “thing-ness” in our world is part-and-parcel of the higher level notion of cognition.  Each of us ultimately determines the nature of this world we live in and our within place it by applying the cognitive tools we share as humans.  But the apparently widening differential gap in learning to hone this very human skill, call it the confluence of experience and intellectual ability, is not so coincidentally the very basis for the Adi Theory of Cognition and Critical Thought that Dr. Tom Adi, Ken Ewell, and I seek to expose to the world though heur-e-ka.com’s Platform-as-a-Service!

Dr Tom Adi recently taught us that “semiotics, in a nutshell, says that the sounds of the names of things point to the cognitive realization of models of those things“.

Our analytical approach at heur-e-ka uses the recodification of content by applying this semiotic model to realize functional models of prediction made far more robust by the invigorating injection of natural variation to transactional signals (browsing, topical momentum, current events, consumer trends, international standards, etc.).  The new and robust signals that result from this approach consistently out-predict and out-perform the admittedly more convenient “binning” technologies known as “NLP” and “keywords”, both of which are never mentioned far from the “low-hanging fruit” mantra that accompanies every effort to “expose and exploit” as opposed to “understand and explain“.

In keeping with that charter, we also see stronger evidence that the only ones who insist on forcing one-dimensional conceptual mappings on to the world inevitably have “some fast-moving low-tide property“, “a few reasonably priced bottles of snake-oil“, or “A Bridge in Manhattan” they’d like to sell you!

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If there’s ever been a puzzling truism to humanity it’s how well we fail to learn from history. In an age when every VC-seeker in Silicon Valley (and his brother) has decided that “the next big thing” is competing for scraps off Google’s table, we are fast approaching another historical threshold. While insider management can rightfully claim that the busted-bubble of dotcom1.0 began in May of 2000, the hammer didn’t really fall until September of that year.

And despite the fact that the travel crash after 911 forced employers to lay people off earlier than the  Silicon Valley “just-in-time-for-Christmas” layoffs of early-December they’ve historically favored again in 2001, never underestimate the “summer slump” just before autumn of any fiscal-year making startup-management quake in their boots.

So, before spring slumps into summer I’d like to predict the next uber-predictable dotcom bubble-bust for September, 2010.

Conveniently conjoint with another human truism, a 10-year anniversary, true Silicon Valley insight will anticipate investors’ penchant for superstition, mysticism, and crossing as many fingers-and-toes possible while “managing” their own “swirling-down-the-bowl” portfolios.  Since doubling down on their current investments in the serial duplication known to most relevance-scientists as “cloned online advertising business-plans dressed up in flashily differing outfits“, this bursting-bubble will be yet another ironic reminder of why semiotics still hasn’t caught on for self-important people who refuse to see the world as anything but “simple” while telling the rest of us how complicated their jobs are.

Semiotics - the sounds in the names of things point to a cognitive realization of models of those things.

TechCrunch cranks out a daily register of newly announced VC-backing, and for the past 5 years it’s been little more than a monotonous drone of keyword-centric online advertising as the monetization scheme of every social.Net, web-service, and PaaS or SaaS proposal made by dotcom1.0 “success-leaders“. That VC’s give one-time “entrepreneurial-winners” a free-pass on their second-round is part of this phenomenon, that final combination of lack of imagination and stupidity having led to over 40 new companies within 40 miles of my San Mateo, CA home all collectively squatting in front of Google’s trough with nearly $1,000,000,000 VC-dollars invested in them.

So, buckle-up, enjoy your summer, and wait along with me for the “dotcom carnage” of September, 2010. Exactly one decade after the doctom1.0 bubble-burst ushered in dot-comatose1.0 leading to the dotcompost1.0 years of 2002-2005 (where new-hires holding their $5,000 hiring bonuses hadn’t even relocated before getting their pink slips) such an “adjustment” is already circling us in preparation for its splashy landing.

Hardly a bad thing, those of us with ideas and technology the “get-rich-quick/stay-rich-for-a-longtime” irrelevance brokerage known as Google simply abhors for their promise in actually promoting true content-and-user-connectivity instead of merely pretending to, we might actually get a shot come January, 2011 when the doubling-down VC’s who took a bath a few months earlier are realizing that the only thing worse for a VC than bad investments is no investments!

Hold me to this. If I’m wrong I’ll eat a bug. I promise!

That will be a cinch for me, since in the preceding 6-months before getting a handle on some practiced messaging the dreaded “did-he-just-eat-a-bug” look I routinely got from them rendered that popular Discovery-Channel meme for “nuclear-winter” a bit less skin-crawling for me.  Only recently have I managed to wrangle sit-downs and face-to-face meetings ending in “No Thanks” from Silicon Valley’s top VC’s.

The worst thing about promulgating the predictive portent of symbols over “data” is how hard it will be to say “I told you so.” Oh well, I’d gladly forego that pleasure for a signed-check from an Angel with the capacity for vision and the balls to believe in something other than that whose time has already come-and-gone.

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” …automated Cognitive-Discovery is within our collective grasp…”

In his book “Hidden Order”, the first in a series to commemorate the Santa Fe Institute’s “Stanislaus M. Ulam Memorial Lectures”, The University of Michigan/Santa Fe Institute’s John Henry Holland teaches us about how adaptation in systems naturally makes them more complex.

Some of his examples;

Non-centralized supply-chains like Grocery Stores, who seem always to have what we want or need (and if they don’t, the sub-net of specialty stores just down the street always seems to have them in stock).

The human body’s Immune and Central Nervous Systems.

Ecosystems as varied as Petri dishes, forests, and coral reefs.

Some current, dire manifestations of not understanding complex, adaptive systems;

inner-city decay

land reclamation projects

the fisheries industry

ecological sustainability

AIDS

mental disease and deterioration

I have another to add to his list. The Job Market.

The recent economic downturn has witnessed a conjectured, albeit wholly believable shift in the power-balance within the social.Net for employment. Many of us are seeking new business to compensate for lost revenue after the Fall of 2008 downturn, seeking jobs due to layoffs, or merely having to scrap our own small-businesses to go back to work for corporate America.

Meanwhile, some of us who like to analyze while we participate have noticed a “sea-change” in the interviewing process that seems indicative of Ulam’s vaunted prophetic ability using this same sort of insight into complex systems. What, I ask you, could be a more complex system than collective human behavior?

Interviewing has always lived within a power differential. dot.Com1.0 in the late 1990’s saw a real advantage move over to the job-seekers as more and more companies got more and more financing from more and more desperate Venture Capital firms as the inevitable downturn approached.

Then dot.Compost(1.0) came and went, power shifted back to the hiring companies and many of my friends, colleagues, and former employees actually left the NorCal/San Francisco/Silicon Valley eCommerce-incubator to find gainful high-tech employment elsewhere. A blip occurred at that time when another power struggle was going on with Google’s skyrocketing success after Eric Schmidt figured out their monetization scheme. Other high-tech companies began to realize that Google’s high-flying revenue and IPO forced them to match their offer sheets despite most of them still struggling in the post-dot.Comatose(1.0) days.

All the top high-tech candidates headed to Mountainview while Silicon Valley stalwarts like Yahoo, Intel, Adobe, Hewlett-Packard, and Oracle had to settle for the 2^nd tier talent, or offer more salary and perquisites than they could afford to pay at the time.

Anti-competitive hiring practices investigation leaders in DC have had the same idea recently, although for different reasons apparently. The DOJ is concerned with crimes, not trends, so the trigger they seek here is of the explicit kind; management directly telling employees to do that which they know is illegal.

But, today, a brazen if not wholly brave new world has emerged. Collecting stories from my own social.Net, I have unearthed some new interviewing stories the like of which none of us, many 20-30 year corporate veterans, have ever witnessed. We understand how groups of people come to the same realization as others; that there are far more applicants than jobs in almost every current employment marketplace has been made plain to them all in the last year.

What we don’t understand, and look to the likes of Ulam and Holland to help us understand, is how it is that they all came to the same conclusion at essentially the same time; we can do whatever we want to our applicants in the interview process regardless of how unprofessional, uncivilized, and downright counter-productive to our current mission it is.

Is this evidence of the “collective wisdom” chimera cognitive scientists have been seeking, rather a Unified Field Theory of their own?

Beyond the academic reassurances inherent to balanced theories, what interests us the most is the “shared yet independently distributed” trigger that allowed all these interviewing entities to come to the same conclusions at just about the same time despite little synergy other than their membership in corporate America. While organizations shape their employees’ daily lives at work, the fact remains that despite any level of perceived “corporate permissiveness” it is individual human beings taking these “interviewing liberties”, and most assuredly without that organization’s blessing.

Imagine any corporate counsel being unconcerned by internal memos telling staff members that “…they can feel free to abuse our prospective future employees as they see fit…”. Not bloody likely, eh?

Let’s take an inventory of some of the more heinous interview stories we’ve heard.

• Being forced to derive complex mathematical formulas in front of a room full of interviewers, then actually performing a differentiation of a complex, 2^nd order polynomial while peppering the applicant with questions and admonitions to make it more clear.
• Six month interview processes for mid-level management positions numbering over 20 actual interviews with over 40 staff members across that time.
• Telephone screening interviews with 7 interviewers on one applicant.
• Technical questions , poorly phrased, and not at all well thought out scrawled hastily on the back of your own resume only to be told that you had “missed the point” of the exercise.

• Junior staff being allowed 1:1 interviews with senior applicants regardless of their lack of preparation or experience with no one more senior in the room to balance that new staffer’s opinion, not to mention verifying their reports back from that session.
• Group interviews of final applicants, ostensibly in order to see which one can best the others in a “final showdown” of the short-list of candidates.
• Sub-specialties within the organization literally testing applicants on their knowledge of domains not even remotely a part of that position’s eventual responsibilities.
• Multiple, all-day interview gauntlets; no break, no lunch, no acknowledgement of the applicant’s discomfort, and a rather clear indifference to it.
• Webcam interviews, a la Ted Koppel’s original Nightline where the applicant is sitting in front of their webcam yet can only hear the interviewers who can see them at the same time.

Yikes! I really hate that last one.  In all fairness, it was only reported once, while the rest all seem to be common practice among most hiring companies today.  And this is being reported to me “nationwide”, not just here in NorCal where high-tech is its own religion.

Who could possibly agree to such power-imbalance induced indignities unless they felt they simply had no choice? I wouldn’t, for one. In a long career of jobs, responsibilities, and increased interview intensity, as I climbed through the ranks my own hiring bottom-line has settled on the realization that companies treating applicants with disrespect will ultimately begin to treat their own employees the same way.

Whatever Ulam might have made of this, what seems most clear to me (after reading about his uncanny ability to make accurate “guesses” about the outcomes of complex systems of every ilk) is that he would have seen it as a network phenomenon. He would have tried to hunt down data indicating information flow within the network that could account for the “everyone else is doing it” mentality, one that seems part-and-parcel of every historical post-mortem on poor human behavior.

Whether seated within the docket of the Nuremburg Trials or standing in front of you in a dreary conference room at the 5-hour mark of what was represented to you as a 2-hour interview gauntlet, don’t mistake the huge difference in “human-on-human catastrophe” separating these two situations for meaning the same dynamic is not at work in both. However it is given, through explicit orders or implicitly via that good old “nudge-nudge-wink-wink” social blind-eye, the permission we all seem inclined to seek before we behave in ways that one might not have dared to contemplate in less “power-polarized” times is the “social trigger” we seek here.

It doesn’t bode well for humanity that even newly encountered precursors of power-differentials within the network can be so quickly assimilated by that system’s units.  Not just “nameless, faceless strangers” are exercising this dynamic these days;  they are our neighbors, former classmates, future colleagues, perhaps even one-day competitors.

We’re compiling as much anecdotal evidence as we can in hopes of dreaming up a data-centric approach we can apply to figuring out this phenomenon. It’s a bit like “social.Net Dark Energy” in its own right. Physicists can see its effect all over the universe, but cannot for the life of them put their hands on the mechanisms at work forging these huge cosmological effects.

So, what do Cosmology, social.Nets, and job interviews have in common? For now, let’s just call all this “the search for mechanisms of influence”.  We’ll report back if we ever manage to crack this nasty little human egg!

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If you thought consumers would never allow an email provider to read their emails in order to get that service for free, you were wrong.   Not everyone uses Gmail, few who do seem at all concerned that Google is reading their emails while trolling for their own commercial benefit, so use it they do.

Now here comes GoogleVoice.

Some fear a similar tactic, Google listening to your phone calls so they troll for more commercial opportunities at your expense.  It is an interesting notion.  Who would have thought people would have given permission for email-snooping, so why be surprised when the day comes that all too few GoogleVoice users even care about such an intrusion?

I suspect that even Google has FCC hurdles to clear for this to become reality, not to mention the technical ones surrounding the woeful state of voice-recognition.  But until that happens let’s not forget how desperately the likes of IBM, Google, and Microsoft have pursued the notion of embedding computing capability in our moments away from the computer.  In fact, the historical thinking has usually been toward making every device a computer; phones, cameras, microwaves, refrigerators, cars.  You name it, if it has a microprocessor it is a computing platform to these companies.

But Google has changed that; an actual paradigm shift we get to watch as it occurs. This would be fun to watch if it weren’t so likely that we, the consumers, will have to pay several unknown prices in the future for allowing such computing scale into our lives.  Count on one thing here; Google will use GoogleVoice as yet another social-network data-collector to augment what they know about “you” (and the aggregate “us”) from…

…Gmail, Google-search, Google-Books, Google-Maps, AdSense, Google-Blogs, Google-Groups, Google-Finance, Google-Scholar, Google-Toolbar, YouTube, …

Imagine the sales-power that comes from knowing exactly where someone is standing when they text their Mother about where to find that “…laundry detergent I like”.  A timely network-text back advising them “…to move to Aisle 5 and look for the sale on Tide…” would be a real coup in terms of healing the “timeliness issue” that has haunted recommendation-engines since their inception.  The consumer would finally be getting what they want if you couldn’t be certain that even if Mom used “Wisk” that the proffered offers will include everything but “Wisk”, since the consumables industry has always valued new customers more than current customers.

T’was always thus!

Now, combine that conduit of real-time information with indexes of what you emailed about lately, to whom and when, and whom they talked to and when, and who and when you called, and who and when they called, and what they searched on and when, and what they subsequently did after that search…

…I suppose we all can imagine the value of knowing such things.

As for me, I simply can’t wait to learn that the eCommerce industry is still complaining about how they can’t serve me content I want when I want it, despite knowing all that they know about me and how I live.  And why not?

Because Google gets paid more by allowing you to serve as a brand-battleground than they ever could by serving you content you want when you want it.

Make no mistake about this, Google’s revenue streams simply do not allow them to care about relevance any further than creating scenarios of access to “classes of consumers” for those advertisers that pay them.  And those advertisers pay them millions.  Which explains why everyone wants to be Google.  And to do so they build technology and pursue statistical inference, at great expense to the VC’s.

Of course, they’ll all continue to fail until they figure out what Google has known since they became a money-making business and not just a hotbed for Stanford dropouts, computing innovation, and Mt. Dew addicts;

As long as there are more people willing to pay for access to consumers than can be made actually selling them something, why do anything to limit the number and degree of “consumer class” they will pay you to access?  Knowing what a consumer wants and when they want it is what all the losers in the search industry assume is the truism that will pay off.

Build it and they will come.  We’ll see, won’t we?

Consumers want the internet to allow them to find what they want whether they know they want it or not.  Making it easier for the consumer to find what they want has always been the unassailed notion “…build it and they will come…”.  This could well be the biggest fallacy of eCommerce.  There is far too much content out there that vendors are trying to pay for anyway they can despite the mistake of having invested in originally.

Simply put, Google helps these content-providers to offload all their long-tail crapola and thus helps them minimize their upstream business-foolishness of investing in, hence trying to push off onto us…

• toasters that play music.
• essays written by illiterate cretins with access to Google-Blogs.
• music recorded in garages by the genetically tone-deaf.
• movies made by talentless hacks with too many high-tech toys.
• social networks that advertise to appear “free” to consumers.
• HumVees.

“Build it and they will come”.  Nope.  Uh-uh.  No way.  “Build ‘it’ into things they use all the time so they only have to come to you once” is more like it!

TV

Genius is truly a rare thing to find in the real world.  The word gets bandied about quite a bit, but when you ask people if they’ve ever met a true genius the answer usually gets down to “really smart” and “genius” being worlds apart.

One thing you can count on, when that rare designation is used in marketing efforts the ensuing experience will almost certainly not bring “genius” to mind in any consumer.

Yet BusinessWeek is already doing so in its assessment of iTunes Genius;

Commentary: Apple’s Blueprint for Genius

Can we really blame mainstream users for thinking iTunes Genius is even remotely smart, not mention functioning at the “genius” level?

With Apple, its assault on the word “genius” began with the Genius Bar where we Mac/iPhone/iPod users could go with our questions in the local MacStore.  My first encounter with Apple “genius” involved my iPhone, which foiled every “genius” at the Genius Bar that day.  Imagine; it turns out that not one of them is actually a “genius“.  Go figure!

The person who ultimately solved the problem for me during a late-night Tech Support telephone session admitted that the MacOS programming, on this score at least, was rather feeble.  Running “fsck -fx” on my MacBook’s root directory took care of the inability to sync my calendars between my MacBook Entourage and my iPhone.  Apparently, the collective “genius” at the Genius Bar missed their classes on Unix.

Enter the “Genius Sidebar” in iTunes, so far clearly able to recommend “Jackson Five” songs when you are playing “Beat It“.  But don’t expect anything much more imaginative than that.

Recommendation of this simplicity was surpassed in the Web1.0 days and even earlier, the likes of MovieCritic.com (powered by LikeMinds) being among the best of the latter day content recommendation engines that asked for nothing in return but increased breadth on content appraisal so it could extend that ability to even more segments of rather markedly fickle content taste.

AMG recently purchased MoodLogic, a company that had an extensive collection of user data regarding their music, including some with lexical anchors for mood and ambience, among other interestingly unique musical dimensions like tonality and valence.  So, going back to even 2005 it was already possible to generate playlists with mood inputs from the user, like “…give me an 8 song playlist starting out mellow, followed by jazzy country rock, then ending with 2 different torch vocal songs…”

And even that wasn’t “genius“.  But it was clever as all get out.  Ultimately, we were constrained by the inability to extend the limited lexical anchors to their logical synonyms, finding that something was lost going to that level of what we thought of as having fairly close lexical dissimilarity.

Extending the reach of similarity modeling was the real champion here.  Looking past the classic data inputs (explicit via ratings and implicit via eCommerce transactions) wasn’t “genius” either, but it was utterly faithful to the successful mathematical modeler’s mantra, “…using data you do have to model data you don’t have…”

Where are such applications today?

LikeMinds is buried deep inside IBM’s WebSphere Portal Server, MoodLogic had its demise announced in 2008 by AMG in favor of its own “digital music recommender“, and NetPerceptions had its lunch eaten by Amazon by 2001.  The rest seem to have either died or were subsumed into one platform or another to work “under the covers“.

So, where are the architects that accomplished this?  Largely pushed aside by platform work, allowing applications as lame as Apple’s Genius to take center stage as Web2.0’s true “genius“.

I, for one, must protest.

Apple has changed the computing world in many wonderful ways over the years.  Hardware, software, and even platform work with the likes of  iTunes and iPhone prove their “genius” in this arena.  But it is a mistake for any media outlet to assign them the de facto role as “knowledge management” leaders simply because they own the world’s most popular and successful music sales platform.  That is just one more stake in the heart of those who seek to serve the consumer content they want, not sell them something they can be convinced to buy.

Perhaps the most compelling downstream benefit of such an effort is the wholesale lopping off of the long tail instead of the current craze to “promote” it via association!  James Surowieck, of The New Yorker and “The Wisdom of Crowds” fame, in his article “In defence of Holywood“, posed the possibility of one day understanding how to know what it is that separates what people want from what is generally thought to be what they want.

In defence of Hollywood…

Enter the optimistic hope of making content production more efficient without crushing any creative spirit behind the entire process, a heady proposition to be sure. Yet why not?  In few creative ventures will the honest artist be able to convince us that their artistic satisfaction isn’t irrevocably conjoined with its positive reception by some segment of humanity. Considering the production of art as purely altruistic strikes me as naive as seeing popularity as synonyous with excellence.

Why wouldn’t being in touch with what people want help an artist hone the edge of their own creative presentations?  Where does it say that an author, screenwriter, or sculptor can’t realize how to help the rest of us see the world in new ways by being in better touch with what any of us want/like/enjoy?  These blades always cut at least two ways, but it seems that the prime mission of generating revenue dulls an application’s “headspace” for any components with less than short-term (nee; instant) payoffs.

In many ways, if what the world sees of the state of recommendation engines today comes from Apple and its iTunes Genius Sidebar, they either weren’t awake to see how much better we were at this game back in 1999 or they just weren’t online yet. However, you can rest assured that the world is still very much in touch with the fact that most of the content-producers of the world haven’t got a clue what kind of content any of us will want next.

When iTunes Genius can recommend songs to you that reflect your mood, not merely mimicking a past playlist that “…seemed to have what I wanted at the time…“, you’ll know you are getting close to being good at recommending content.  Not great, mind you, nor deserving of the “genius” moniker yet, but useful to the consumer to be sure.

Most recommendation engines are great at recreating the past without having learned a thing about what will go together for essentially the same sorts of taste-based reasons in the future.  As usual, the ease of interpolation has won out over the predictive efficacy of the far more difficult extrapolation.  Even worse is that the promise of data mining loses more ground to the ostensible and curiously “never-to-be-questioned“  efficiencies inherent to scaled computational fences.

So, recommendation engines of the world, now that you can scale does that mean your Boolean inference is going to meet Web2.0 consumers’ expectations for dynamic new-content aggregation and presentation?

Don’t feel compelled to answer, since the consumers are already registering their opinion with a big “yawn”.

TV

This month’s “Journal of Predictive Markets” details a study looking at the post idea-market behavior of idea founders, noting that they tend to overvalue agreements to their own ideas relative to the market. Not to mention idea-founders having demonstrated “followings” reminiscent of the relationships mapped within the classic social graph.

A Social Network

from “Introduction to the Formal Analysis of Social Networks Using Mathematica”, Izquierdo & Hanneman, 2006.

The authors are clearly wondering why a system that ostensibly traffics in wisdom can demonstrate such clear aspects of emotional behavior classic thinking about decision making considers to be clearly opposed to wisdom as defined.

So, isn’t this exactly the dynamic that precludes the stock market from being considered a true predictive market; emoted information specific to the bidder working against the sub-current of that predictive market’s actual “collective wisdom”?

When was the last time a graph of an ongoing predictive market vacillated between polar opposites of a strict binomial choice as the market progressed

Never. Graph the progress of any predictive market worth running and you’ll witness a slow build to an equilibrium (”a wisdom ceiling“) instead of wild shifts in market positions based on new wisdom entering the fray as one would expect from set of accumulated opinons from a large, pseudorandomly self-selected population.

The current tendency of predictive market aficionados to avoid looking at that process’ stochastic parade toward its ultimate prediction is quantitatively feeble.

The impetus for this approach, built on the purported success of predictive markets, seems to originate first and foremost from a platform that records (and makes accessible) a crowd’s perception of the future for modeling discrete future events into prior probabilities that can then be applied to ascertaining measures of “collective predictive confidence” in that event’s collectively perceived likelihood.

Ralph Elliott, functional father of technical analysis of the stock market (the original predictive market) espoused;

…the ebb and flow of human emotions and activities follow a natural progression governed by laws of nature, hence the stock market as is an instantaneous estimate of society’s collective confidence in the world’s “state”

If Elliott was wrong, as the “efficient market hypothesis” wonks love to point out, then predictive markets as a conduit to accurate predictions are too. That predictive markets have been demonstrated to generate “accurate results” is undebatable. However, this accelerated belief in an actual “collective wisdom” within human populations is a bit like believing in the graviton because of gravity, not the reverse.

Anyone care to discuss the manner in which we as researchers might be able to discern at what point a wise-crowd parts company with wisdom, any “collective wisdom” garnered reverting to adoption/rejection of that view as opposed to the ongoing generation of newly formed individual “units of wisdom”, and reverts to a classic exercise in tracking the strength of the social graph at work within that crowd?

I love the notion of “collective intelligence” and am working like the devil to tease out aspects of that shared human knowledge-management system to help us understand it, not merely exploit them when predicitve markets work but giving them a “free-pass” when they don’t.

However, I am not ready to declare “collective wisdom” a reality merely because “predictive markets” seem to work in certain social situations.  Frankly, and parroting the esteemed Elliot a bit, one could use the same logic to consider predictive markets as little more than subsets of The Classic Social Graph;

…so, why couldn’t online Discovery transactions be an instantaneous estimate of society’s collective awareness, its ever changing acceptance, of the self-similarity that courses through us all?

TV

If Search gives you lemons you’re going to make Key Lime Pie!

The brokered irrelevance we meet online every day, all session every session, is both staggering and mounting.  Despite all promises to the contrary by those who so imperiously defend their need to know every move we make online in order to improve their offers to us while we are online, none can run away from how firmly rooted online advertising is in the promulgation of mass irrelevance.

It’s getting hard to admit that I’ve been a part of such an unmitigated failure, once hoping to help supply an analytical assist to my home planet.  Maybe I still can help.  Maybe not.  You be the judge.  Meet a recent search of mine, which I’ve littered with my own commentary to supply some contextual anchors. These are biased by me, the disgruntled user, but accurate to a fault in my opinion as an Internet arbitrage mathematician.

QUERY

1. “Joe’s stone crab house” recipe “key lime pie”

While at “Joe’s” I clicked the javascript-print icon, then I clicked a javascript-email icon as well.

2. “Are limes really just young lemons the marketers have picked early to yield them premium pricing on a cheaper resource?”

CONTEXT

1. I am baking a key lime pie soon.

2. I don’t have any limes to zest in the key lime custard, already flavored with key lime juice (previously purchased online last month I might add). Can I go out and pick some of the young green lemons from the tree in my yard and end up with essentially the same thing as lime zest (which the recipe I just successfully searched for, printed in 3×5 index-card format, and emailed to myself calls for)?

Context is what I was essentially telling the search engine I used to accomplish this information quest during the quest.  Why?  Well, mostly in case they are reading this and have been curious about what a consumer might tell them were they ever able to learn how to ask.  Or, better yet, apply what they’d already know had they managed to find a way to contextualize my trips to the Internet using the veritable line of bread-crumbs I’ve left behind me while I’ve surfed online the past 12 years.

FYI, I do search like (2), above sometimes. If your question has ever been posed to the net, not even exactly as you stated, this query tactic will score for you every time.

While search result relevance climbs, and the search engines have a vested interest in continuing that trend, the economics of the online advertising system financing those search results routinely proves just how decidedly opposite its world treats “relevance” relative to the way pure search has.  With its continual sponsored linking of the inexhaustible supply of barely-related content items lurking out there in the eternal optimism of cyberspace, search ultimately traffics mostly in the murky underworld of that which has never been clicked on purposely by any web-user exercising actual content taste.

As a professional Internet-behavior-predictor I like to point out that this type of insight into user-history is the Holy Grail of any contextual parser I could ever hope to develop to do this for me. If you can’t classify the contextual text supplied in such form, then you are simply beyond help (in this field, at any rate).

Anyone want to hazard guesses about the irrelevant crapola being hawked at the edges-of-irrelevance comprising those browsing sessions’ online advertising? Does anyone want to wager their weekly paycheck that while searching that morning I saw an advertisement for a special pie pan that cools quickly when taken out of the oven’s heat or a handy new zesting tool?

Try it yourself and post any examples of even the remotest targeted marketing savvy inherent to it. I’d acknowledge any success in this regard but don’t expect to see much more than examples of an occasional, obviously well-known and rules-based applied marketing truism.

Better yet, go hire one of the many lemon-lawyers you’ll likely see featured in the highly-measured-and-precisely-predicted “high-relevance-content” section of your screen (you know, that part of the screen you can’t really see anymore).  Other good alternatives might be;

to start a virtual insurrection against that particular search engine at some equally albeit differently ineffectual social networking site.

repeatedly send them nasty emails with long distro’s filled with people whom you are pretty sure don’t know one another.

click on ”unrelated” online ads and sponsored content with no intent of subsequent fulfillment of either, not to mention anything that rears its head in your online presence with even a breath of having been borne of that last relevance-bereft transaction.

click aimlessly for a while, especially in content areas you might have liked once, like when you were twelve.  I really favor this one.  It reminds me of the Mike Royko exit-poll strategy he asked of his daily column readers come election time; lie!  Plus, along with a nasty little heuristics nightmare given its demographic-truth-in-a-strangely-knowable-time-warp nature, it requires careful attention to detail, since the worst outcome to this gambit would be to unwittingly connect two things that are actually sensible marketing partners.

Or maybe, just maybe, you could find a way to stop paying for advertising space on pages known to be worthless in generating actual online transactions for the real customers of advertising; the companies selling us stuff in across every medium and distribution channel they can cook up.

All this content can be related in myriad ways except, of course, in the only way that can ever truly matter to eCommerce no matter what Semantic_Web #.# we are a part of at the moment; transactions where content is exchanged for money. This means cash-generating transactional events, not just in-and-out-web-traffic of duped web-users now hopping mad about such search-sponsored, serially irrelevant detours.

In the long run, were advertising expenditures to comprise anything but a fraction of the total cost of sales it simply wouldn’t be worth in it any public venue other than that which they can pick up on the utterly dead-cheap.  So, who is on top when it comes to trying to sell stuff nobody wanted when it was on shelves in real stores yet which has never actually sold on this medium before to people who have shown as little real proclivity to do so as nearly everyone else who surfs the web?

Who can tell?  Through it all somehow you and I got served that same ad when we were online for likely vastly different reasons.  Why?  Who can tell that, either?  Such insight will likely forever defy the ones who continue to spend their money trying to get our attention with that utterly-irrelevant-to-both-of-us-online-ad in the first place.  I guess hope actually does spring eternal.  Well, among the online purveyors of “stuff” at any rate.

Please, we can do better than this, people!

Do I sound like the principal in “Kindergarten Cop”? Well, so be it. We can do better. Some actually are. I know I am. But far too many aren’t. They truly aren’t even trying to pretend to try. C’mon, people!

Rewarding brokered irrelevance mongers with more of your business at a “better price” isn’t really working out for much of anyone in the online advertising world, is it?  You tell me.

TV

ps; limes and lemons are completely different species, and despite being assured of that fact by the corroboration of several pages gleaned from several different search engine results, I zested those young lemons anyway.  My Key Lime Pie rocked despite the entire irrelevance-besmirched substitution process.

Malcolm Gladwell

Staff Writer, The New Yorker Magazine; author of Outliers, Blink, and The Tipping Point

http://www.squidoo.com/malcolm-gladwell

http://en.wikipedia.org/wiki/Malcolm_Gladwell

Duncan Watts

Professor of Sociology, Columbia University; Principal Research Scientist at Yahoo! Research

http://en.wikipedia.org/wiki/Duncan_J._Watts

http://www.boingboing.net/2008/01/28/tippingpoint-skeptic.html

In the recent back-and-forth between the Gladwell and Watts schools regarding the role of user influence in social networks, it occurs to me that at least one type of “human influencer” in consumer decision-making was verified by the in-house Database Marketing Group at MCI in the early nineties.

In an effort to bolster their Friends & Family program within their highly successful “high-long-distance-biller-market-share” push, discovering who was at the center of the highest revenue calling circles was determined to be important. Far from the highest billers influencing others to join their circle, it was found that the lynchpins to the most lucrative calling circles at MCI most often came in the form of the lowest billers within a social circle of close-knit high long-distance billers. So, the fixed income grandmother of increasingly mobile family units and “destitute” college students from close-knit social units, both former members of the “never call if you can possibly avoid it” telemarketing segment due to their own low-billing patterns, became the prime influencers in a thus newly segmented marketplace.

While their influence was mitigated wholly by the context of a shared economic advantage in keeping in touch with others, as opposed to some mystical hold those two profiles have over their friends and family, influence was definitely at work. Selling to these lynchpins had quantifiable success, ultimately credited with increasing downstream revenue from calling circles “built” this way (as opposed to the former strategy of engaging high-long-distance billers from the outset).

The notion of influence is clearly at work here, as with many social situations where person-to-person “weights” can be seen to related to online choices. Without the context of a typical family seeking to save money while keeping in touch with both their growing kids and aging parents, the influence of these very same people to affect such a consumer decision would be quite low. Here, the influence takes the form of a social trigger, of sorts, wherein the conditions for shared advantage are what precede the current Gladwell supposition about influence and explains Watts’ inability to find such people in the real world.

It might well be true that the vaunted influence at work in the “hush-puppy tipping point” were hidden within unsuspected aspects of pricing and seasonal introduction;

…a familiar (albeit dated) brand made available via cheap-lot-supply-chains enabled by sagging, corporate-sponsored, nearly brandname phase-out sales in a pre-recession, back-to-school-sale Northeast might well have set the tipping point table here, with the natural influence inherent to a demographic segment notorious for its peer-group obsession the only real “human” influencer at work the whole time.

TV