echovar

There’s a case that could be made that John Poindexter is the godfather of the real-time Network. I came to this conclusion after reading Shane Harris’s excellent book, The Watchers, The Rise of the Surveillance State. When you think about real-time systems, you might start with the question: who has the most at stake? Who perceives a fully-functional toolset working within a real-time electronic network as critical to survival?

To some, Poindexter will primarily be remembered for his role in the Iran-Contra Affair. Others may know something about his role in coordinating intelligence across organizational silos in the Achille Lauro Incident. It was Poindexter who looked at the increasing number of surprise terrorist attacks, including the 1983 Beruit Marine Barracks Bombing, and decided that we should know enough about these kinds of attacks before they happen to be able to prevent them. In essence, we should not be vulnerable to surprise attack from non-state terrorist actors.

After the fact, it’s fairly easy to look at all the intelligence across multiple sources, and at our leisure, connect the dots. We then turn to those in charge and ask why they couldn’t have done the same thing in real time. We slap our heads and say, ‘this could have been prevented.’ We collected all the dots we needed, what stopped us from connecting them?

The easy answer would be to say it can’t be done. Currently, we don’t have the technology and there is no legal framework, or precedent, that would support this kind of data collection and correlation. You can’t predict what will happen next, if you don’t know what’s happening right now in real time. And in the case of non-state actors, you may not even know who you’re looking for. Poindexter believed it could be done, and he began work on a program that was eventually called Total Information Awareness to make it happen.

TIA System Diagram

In his book, Shane Harris posits a central metaphor for understanding Poindexter’s pursuit. Admiral Poindexter served on submarines and spent time using sonar to gather intelligible patterns from the general background of noise filling the depths of the ocean. Poindexter believed that if he could pull in electronic credit card transactions, travel records, phone records, email, web site activity, etc., he could find the patterns of behavior that were necessary precursors to a terrorist attack.

In order to use real-time track for pattern recognition, TIA (Total Information Awareness) had to pull in everything about everyone. That meant good guys, bad guys and bystanders would all be scooped up in the same net. To connect the dots in real time your need all the dots in real time. Poindexter realized that this presented a personal privacy issue.

As a central part of TIA’s architecture, Poindexter proposed that the TIA system encrypt the personal identities of all the dots it gathered. TIA was looking for patterns of behavior. Only when the patterns and scenarios that the system was tracking emerged from the background, and been reviewed by human analysts, would a request be made to decrypt the personal identities. In addition, every human user of the TIA system would be subject to a granular-level audit trail. The TIA system itself would be watching the watchers.

The fundamental divide in the analysis and interpretation of real-time dot connecting was raised when Jeff Jonas entered the picture. Jonas had made a name for himself by developing real-time systems to identify fraudsters and hackers in Las Vegas casinos. Jonas and Poindexter met at a small conference and hit it off. Eventually Jonas parted ways with Poindexter on the issue of whether a real-time system could reliably pinpoint the identity of individual terrorists and their social networks through analysis of emergent patterns. Jonas believed you had to work from a list of suspected bad actors. Using this approach, Jonas had been very successful in the world of casinos in correlating data across multiple silos in real time to determine when a bad actor was about to commit a bad act.

Jonas thought that Poindexter’s approach with TIA would result in too many false positives and too many bad leads for law enforcement to follow up. Poindexter countered that the system was meant to identify smaller data sets of possible bad actors through emergent patterns. These smaller sets would then be run through the additional filter of human analysts. The final output would be a high-value list of potential investigations.

Of course, once Total Information Awareness was exposed to the harsh light of the daily newspaper and congressional committees, its goose was cooked. No one wanted the government spying on them without a warrant and strong oversight. Eventually Congress voted to dismantle the program. This didn’t change the emerging network-connected information environment, nor did it change the expectation that we should be able to coordinate and correlate data across multiple data silos to stop terrorist attacks in real time. Along side the shutting down of TIA, and other similar government efforts, was the rise of Google, social networks, and other systems that used network-based personal data to predict consumer purchases; guess which web site a user might be looking for; and even the bet on the direction of stocks trading on exchanges.

Poindexter had developed the ideas and systems for TIA in the open. Once it was shut down, the system was disassembled and portions of it ported over to the black ops part of the budget. The system simply became opaque, because the people and agencies charged with catching bad actors in real time still needed a toolset. The tragedy of this, as Shane Harris points out, is that Poindexter’s vision around protecting individual privacy through identity encryption was left behind. It was deemed too expensive and too difficult. But the use of real-time data correlation techniques, social graph analysis, in-memory data stores and real-time pattern recognition are all still at work.

It’s likely that the NSA, and other agencies, are using a combination of Poindexter’s and Jonas’s approaches right now: real-time data correlation around suspected bad actors, and their social graphs— combined with a general sonar-like scanning of the ocean of real-time information to pick up emergent patterns that match the precursors of terrorist acts. What’s missing is a dialogue about our expectations, our rights to privacy and the reality of the real-time networked information environment that we inhabit. We understood the idea of wiretapping a telephone, but what does that mean in the age of the iPhone?

Looking at the structure of these real-time data correlation systems, it’s easy to see their migration pattern. They’ve moved from the intelligence community to wall street to the technology community to daily commerce. Social CRM is the buzz word that describes the corporate implementation; some form of real-time VRM will be the consumer’s version of the system. The economics of the ecosystem of the Network has begun to move these techniques and tools to the center of our lives. We’ve always wanted to alter our relationship to time, we want to know with a very high probability what is going to happen next. We start with the highest-value targets, and move all the way down to a prediction of which television show we’ll want to watch and which laundry detergent we’ll end up telling our friend about.

Shane Harris begins his book The Watchers with the story of Able Danger, an effort to use data mining, social graph and correlation techniques on the public Network to understand Al Qaeda. This was before much was known about the group or its structure. One of the individuals working on Able Danger was Erik Kleinsmith, he was one of the first to use these techniques to uncover and visualize a terrorist network. And while he may not have been able to predict the 9/11 attacks, his analysis seemed to connect more dots than any other approach. But without a legal context for this kind of analysis of the public Network, the data and the intelligence was deleted and unused.

Working under the code name Able Danger, Kleinsmith compiled an enormous digital dossier on the terrorist outfit (Al Qaeda). The volume was extraordinary for its size— 2.5 terabytes, equal to about one-tenth of all printed pages held by the Library of Congress— but more so for its intelligence significance. Kleinsmith had mapped Al Qaeda’s global footprint. He had diagrammed how its members were related, how they moved money, and where they had placed operatives. Kleinsmith show military commanders and intelligence chiefs where to hit the network, how to dismantle it, how to annihilate it. This was priceless information but also an alarm bell– the intelligence showed that Al Qaeda had established a presence inside the United States, and signs pointed to an imminent attack.

That’s when he ran into his present troubles. Rather than relying on classified intelligence databases, which were often scant on details and hopelessly fragmentary, Kleinsmith had created his Al Qaeda map with data drawn from the Internet, home to a bounty of chatter and observations about terrorists and holy war. He cast a digital net over thousands of Web sites, chat rooms, and bulletin boards. Then he used graphing and modeling programs to turn the raw data into three-dimensional topographic maps. These tools displayed seemingly random data as a series of peaks and valleys that showed how people, places, and events were connected. Peaks near each other signaled  connection in the data underlying them. A series of peaks signaled that Kleinsmith should take a closer look.

…Army lawyers had put him on notice: Under military regulations Kleinsmith could only store his intelligence for ninety days if it contained references to U.S. persons. At the end of that brief period, everything had to go. Even the inadvertent capture of such information amounted to domestic spying. Kleinsmith could go to jail.

As he stared at his computer terminal, Kleinsmith ached at the thought of what he was about to do. This is terrible.

He pulled up some relevant files on his hard drive, hovered over them with his cursor, and selected the whole lot. Then he pushed the delete key. Kleinsmith did this for all the files on his computer, until he’d eradicated everything related to Able Danger. It took less than half an hour to destroy what he’d spent three months building. The blueprint for global terrorism vanished into the electronic ether.

The philosopher’s stone of the Network’s age of social media is crowd control. The algorithmic businesses popping up on the sides of the information superhighway require a reliable input if their algorithms are to reliably output saleable goods. And it’s crowdsourcing that has been given the task of providing the dependable processed input. We assign a piece of the process to no one in particular and everyone in general via software on the Network. The idea is that everyone in general will do the job quicker, better and faster than someone in particular. And the management cost of organizing everyone in general is close to zero, which makes the economics of this rig particularly tantalizing.

In thinking about this dependable crowd, I began to wonder if the crowd was always the same crowd. Does the crowd know it’s a crowd? Do each of the individuals in the crowd know that when they act in a certain context, they contribute a dependable input to an algorithm that will produce a dependable output? Does the crowd begin to experience a feedback loop? Does the crowd take the algorithm’s dependable output as an input to its own behavior? And once the crowd has its own feedback loop, does the power center move from the algorithm to the crowd? Or perhaps when this occurs there are two centers of power that must negotiate a way forward.

We speak of social media, but rarely of social machines. On the Network, the machine is virtualized and hidden in the cloud. For a machine to operate at full efficiency, each of its cogs must do its part reliably and be able to repeat its performance exactly each time it is called upon. Generally some form of operant conditioning (game mechanics) will need to be employed as a form of crowd control. Through a combination of choice architecture and tightly-defined metadata (link, click, like, share, retweet, comment, rate, follow, check-in), the behavior of individuals interacting within networked media channels can be scrapped for input into the machine. This kind of metadata is often confused with the idea of making human behavior intelligible to machines (machine readable). In reality, it is a replacement of human behavior with machine behavior— the algorithm requires an unambiguous signal (obsessive compulsive behavior).

The constraints of a media are vastly different than those of a machine. Social media doesn’t require a pre-defined set of actions. Twitter, like email or the telephone, doesn’t demand that you use it in a particular way. As a media, its only requirement is that it carry messages between endpoints— and a message can be anything the medium can hold. Its success as a media doesn’t rely on how it is used, but that it is used.

There’s a thought running round that says that social media is engendering a new age of transparency. When we use the word ‘transparency’ we speak of a material through which light passes with clarity. If conditions aren’t completely clear, we might call the material translucent, which would allow light to pass through it diffusely. And if we can’t see anything at all, we’ll call it opaque, a material with a surface that doesn’t allow even a speck of light through it.

If it is we who are ‘transparent,’ it’s as though our skin has turned to glass and the social, psychological and biological systems operating within us are available for public inspection. It’s thought that by virtue of their pure visibility these systems can be understood, influenced and predicted. Although for most of us, when we lift the hood of our car and inspect the engine it’s strictly a matter of form. We know whether the engine is running or not, but that’s about the limit for a non-specialist.

Much like “open” and “closed,” the word transparency is associated with the forces of good, while opacity is delegated to play for the evil team. We should like to know that a thing is transparent, that we could have a look at it if we chose to, even if we don’t understand it at the moment. Certainly there must an e-book somewhere that we could page through for an hour or so to get a handle on the fundamentals. On the other hand, if a thing is opaque, we’re left with a mystery without the possibility of a solution. After all, we don’t have x-ray vision. How else can we possibly get beneath the surface to find out what’s going on?

Ralph Ellison wrote about social invisibility in his book The Invisible Man. The narrator of the story is invisible because everyone sees him as a stereotype rather than as a real person. In a stereotype, a surface image is substituted for the whole entity. Although Ellison’s narrator acknowledges that sometimes invisibility has its advantages. Surface and depth each have their time and place.

Jeff Jonas, in a recent post called “Transparency as a Mask,” talks about the chilling effect of transparency. If we exist in a social media environment of pure visibility, a sort of panopticon of the Network, how will this change the incentives around our behavior? Jonas wonders whether we might see a mass migration toward the average, toward the center of the standard deviation chart, the normal part of the normal distribution. Here’s Jonas on the current realities of data correlation.

Unlike two decades ago, humans are now creating huge volumes of extraordinarily useful data as they self-annotate their relationships and yours, their photographs and yours, their thoughts and their thoughts about you … and more.

With more data, comes better understanding and prediction.  The convergence of data might reveal your “discreet” rendezvous or the fact you are no longer on speaking terms your best friend.  No longer secret is your visit to the porn store and the subsequent change in your home’s late night energy profile, another telling story about who you are … again out of the bag, and little you can do about it.  Pity … you thought that all of this information was secret.

Initially the Network provided a kind of equal footing for those four or five standard deviations off center, in a sense this is the basis of the long tail. The margins and the center all play in the same frictionless hypermedia environment. When these deviations become visible and are correlated with other private and public data, the compilation of these surface views create an actionable picture with multiple dimensions. Suddenly there’s a valuable information asymmetry produced with an affordable amount of compute time.

Only the mediocre are always at their best.
-Jean Giraudoux

However, once we know someone is scanning and correlating the facets of our presence on the Network, what’s to stop us from signaling normal, creating a mask of transparency?

The secret of success is sincerity. Once you can fake that you’ve got it made.

-Jean Giraudoux

We may evolve, adapt to the new environment. The chameleon and many other creatures change their appearance to avoid detection. We may also become shape shifters, changing the colors of our digital skin to sculpt an impression for key databases.

In ecology, crypsis is the ability of an organism to avoid observation or detection by other organisms. A form of antipredator adaptation, methods range from camouflage, nocturnality, subterranean lifestyle, transparency, or mimicry

Of course, there’s a chance we won’t be fooling anyone. A false signal here or there will be filtered out and the picture will be assembled despite our best efforts at camouflage.

There’s another path through these woods. Historically, a much less travelled path. That’s the path of tolerance, of embracing and celebrating difference, and acknowledging our own strangeness. While it’s possible that a human can empathize with the strangeness of another human, the question we have to ask in this new era of digital transparency is: how can an algorithm be made to process strangeness without automatically equating it with error?

A film is described as cinematic when the story is told primarily through the visuals. The dialogue only fills in where it needs to, where the visuals can’t convey the message. It was watching Jean-Pierre Melville’s Le Samourai that brought these thoughts into the foreground. Much of the film unfolds in silence. All of the important narrative information is disclosed outside of the dialogue.

While there’s some controversy about what percentage of human-to-human communication is non-verbal, there is general agreement that it’s more than half. The numbers are as low as 60% and as high as 93%. What happens to our non-verbal communication when a human-to-human communication is routed through a medium? A written communique, a telephone call, the internet: each of these media have a different capacity to carry the non-verbal from one end to the other.

The study of human-computer interaction examines the relationship between humans and systems. More and more, our human-computer interaction is an example of computer-mediated communications between humans; or human-computer network-human interaction. When we design human-computer interactions we try to specify everything to the nth degree. We want the interaction to be clear and simple. The user should understand what’s happening and what’s not happening. The interaction is a contract purged of ambiguity and overtones. A change in the contract is generally disconcerting to users because it introduces ambiguity into the interaction. It’s not the same anymore; it’s different now.

In human-computer network-human interactions, it’s not the clarity that matters, it’s the fullness. If we chart the direction of network technologies, we can see a rapid movement toward capturing and transmitting the non-verbal. Real-time provides the context to transmit tone of voice, facial expression, hand gestures and body language. Even the most common forms of text on the Network are forms of speech— the letters describe sounds rather than words.

While the non-verbal can be as easily misinterpreted as the verbal, the more pieces of the picture that are transmitted, the more likely the communication will be understood. But not in the narrow sense of a contract, or machine understanding. But rather in the full sense of human understanding. While some think the deeper levels of human thought can only be accessed through long strings of text assembled into the form of a codex, humans will always gravitate toward communications media that broadcast on all channels.

Within the bounds of our brief transit on this earth, we attempt to make our mark. Leaving a permanent trace of one’s life, in some quarters, is a large part of the purpose of our lives. In our digital lives, we leave traces wherever we go. We generate clouds of data as we surf along the surfaces of the Network. In the name of data portability, we claim the data we generate and assert personal ownership over it. We even leave instructions for how the data should be handled in the event of our death. What were footprints in the sand are now captured in digital amber.

While our most everyday communications have migrated to the Network, some of our most secret communications take a different path. It’s believed that governments have been sending secret messages using Numbers Stations since World War I. Here’s Wikipedia’s definition:

Numbers stations (or number stations) are shortwave radio stations of uncertain origin. They generally broadcast artificially generated voices reading streams of numbers, words, letters (sometimes using a spelling alphabet), tunes or Morse code. They are in a wide variety of languages and the voices are usually female, though sometimes male or children’s voices are used.

In an interview with NPR, Mark Stout, the official historian of the International Spy Museum, explains why Numbers Stations are still in use:

“Because [a message] can be broadcast over such an enormous area, you can be transmitting to an agent who may be thousands of miles away,” he says. And, he adds, computer communications almost always leave traces.

“It’s really hard to erase data out of your hard drive or off a memory stick,” he says. “But all you need here is a shortwave radio and pencil and paper.”

By using what’s called a one-time pad, these messages can’t be cracked. Again, here’s Mark Stout:

…because the transmissions use an unbreakable encryption system called a one-time pad: encryption key is completely random and changes with every message.

“You really truly cryptanalytically have no traction getting into a one-time pad system,” Stout says. “None at all.”

The use of short wave radio combines the capacity to send messages over great distances with the ability to obscure the origin of the broadcast. By taking down the message using a pencil and paper, the coded message stays off the information grid of the digital Network. Tools that pre-date the digital Network route around the media that makes permanent copies as a part of the process of transmission. While these messages are out there for anyone to listen to, and even record, the endpoints of the communication and the content of the messages remain opaque.

Historically, we’ve always had a medium that would allow us to communicate without leaving a trace. Now a whisper in the ear becomes an SMS message for your eyes only. While there’s much to be gained from our new modes of permanent public social messaging, I wonder if there’s a case to be made for the message without a paper trail, without a digital imprint, without any trace at all. Can we ever embrace the impermanence of a moment that can only be imperfectly replayed in human memory? The Numbers Station is reminder of another mode of speaking in a temporary medium.

As we consider machines that may think, we turn toward our own desires. We’d like a machine that understands what we mean, even what we intend, rather than what we strictly say. We don’t want to have to spell everything out. We’d like the machine to take a vague suggestion, figure out how to carry on, and then return to us with the best set of options to choose from. Or even better, the machine should carry out our orders and not bother us with little ambiguities or inconsistencies along the way. It should work all those things out by itself.

We might look to Shakespeare and The Tempest for a model of this type of relationship. Prospero commands the spirit Ariel to fulfill his wishes; and the sprite cheerfully complies:

ARIEL
Before you can say ‘come’ and ‘go,’
And breathe twice and cry ‘so, so,’
Each one, tripping on his toe,
Will be here with mop and mow.
Do you love me, master? no?

But The Tempest also supplies us with a counter-example in the character Caliban, who curses his servitude and his very existence:

CALIBAN
You taught me language; and my profit on’t
Is, I know how to curse. The red plague rid you
For learning me your language!

Harold Bloom, in his essay on The Tempest in Shakespeare: Invention of the Human, connects the character of Prospero with Christopher Marlowe’s Dr. Faustus. Faustus also had a spirit who would do his bidding, but the cost to the good doctor, was significant.

For the most part we no longer look to the spirit world for entities to do our bidding. We now place our hopes for a perfect servant in the realm of the machine. Of course, machines already do a lot for us. But frankly, for a long time now, we’ve thought that they could be a little more intelligent. Artificial intelligence, machines that think, the global brain: we’re clearly under the impression that our lot could be improved by such an advancement in technology. Here we aren’t merely thinking of an augmentation of human capability in the mode of Doug Engelbart, but rather something that stands on its own two feet.

In 2002, David Gelernter wrote a book called The Muse in the Machine: Computerizing the Poetry of Human Thought. Gelernter explored the spectrum of human thought from tightly-focused task-driven thought to poetic and dream thoughts. He makes the case that we need both modes, the whole spectrum, to think like a human does. Recently, Gelernter updated his theme in an essay for Edge.org called Dream-Logic, The Internet and Artificial Thought. He returns to the theme that most of the advocates for artificial intelligence have a defective understanding of what makes up human thought:

Many people believe that the thinker and the thought are separate.  For many people, “thinking” means (in effect) viewing a stream of thoughts as if it were a PowerPoint presentation: the thinker watches the stream of his thoughts.  This idea is important to artificial intelligence and the computationalist view of the mind.  If the thinker and his thought-stream are separate, we can replace the human thinker by a computer thinker without stopping the show. The man tiptoes out of the theater. The computer slips into the empty seat.  The PowerPoint presentation continues.

But when a person is dreaming, hallucinating — when he is inside a mind-made fantasy landscape — the thinker and his thought-stream
are not separate.  They are blended together. The thinker inhabits his thoughts.  No computer will be able to think like a man unless it, too, can inhabit its thoughts; can disappear into its own mind.

Gelernter makes the case that thinking must include the whole spectrum of the thought. He extends this idea of the thinker inhabiting his thoughts by saying that when we make memories, we create alternate realities:

Each remembered experience is, potentially, an alternate reality. Remembering such experiences in the ordinary sense — remembering “the beach last summer” — means, in effect, to inspect the memory from outside.   But there is another kind of remembering too: sometimes remembering “the beach last summer” means re-entering the experience, re-experiencing the beach last summer: seeing the water, hearing the waves, feeling the sunlight and sand; making real the potential reality trapped in the memory.

(An analogy: we store potential energy in an object by moving it upwards against gravity.  We store potential reality in our minds by creating a memory.)

Just as thinking works differently at the top and bottom of the cognitive spectrum, remembering works differently too.  At the high-focus end, remembering means ordinary remembering; “recalling” the beach.  At the low-focus end, remembering means re-experiencing the beach.  (We can re-experience a memory on purpose, in a limited way: you can imagine the look and fragrance of a red rose.  But when focus is low, you have no choice.  When you remember something, you must re-experience it.)

On the other side of the ledger, you have the arguments for a technological singularity via recursive self-improvement. One day, a machine is created that is more adept at creating machines than we are. And more importantly, it’s a machine who’s children will exceed the capabilities of the parent. Press fast forward and there’s an exponential growth in machine capability that eventually far outstrips a human’s ability to evolve.

In 2007, Gelernter and Kurzweil debated the point:

When Gelernter brings up the issue of emotions, poetic thought and the re-experiencing of memory as fundamental constituents of human thought, I can’t help but think of the body of the machine. Experience needs a location, a there for its being. Artificial intelligence needs an artificial body. To advance even a step in the direction of artificial intelligence, you have to endorse the mind/body split and think of these elements as replaceable, extensible, and to some extent, arbitrary components. This move begs a number of questions. Would a single artificial intelligence be created or would many versions emerge? Would natural selection cull the herd? Would an artificial intelligence be contained by the body of the machine in which it existed? Would each machine body contain a unique artificial intelligence with memories and emotions that were solely its own? The robot and the android are the machines we think of as having bodies. In Forbidden Planet, the science fiction update of Shakespeare’s The Tempest, we see the sprite Ariel replaced with Robby the Robot.

In Stanley Kubrick’s film 2001: A Space Odyssey, the HAL 9000 was an artificial intelligence who’s body was an entire space ship. HAL was programmed to put the mission above all else, which violated Asimov’s three laws of robotics. HAL is a classic example of an artificial intelligence that we believe has gone a step too far. A machine who has crossed a line.

When we desire to create machines that think; we want to create humans who are not fully human. Thoughts that don’t entirely think. Intelligence that isn’t fully intelligent. We want to use certain words to describe our desires, but the words express so much more than we intend. We need to hold some meaning back, the spark that makes humans, thought and intelligence what they are.

Philosophy is a battle against the bewitchment of our intelligence by means of language.
- Ludwig Wittgenstein

Clearly some filters, algorithms and agents will be better than others, but none of them will think, none will have intelligence. If part of thinking is the ability to make new analogies, then we need to think about what we do when we create and use these software machines. It becomes an easier task when we start our thinking with augmentation rather than a separate individual intelligence.

It’s interesting to think of banks as walled gardens. For example, on the Network, we might call Facebook, or aspects of Apple or Microsoft, a walled garden. The original America Online was the classic example. While most of us prefer to have walls, of some sort, around our gardens; the term is generally used to criticize a company for denying users open access, a lack of data portability and for censorship (pulling weeds). However when we consider our finances, we prefer there be a secure wall and a strong hand in the cultivation and tending of the garden. Context is everything.

More generally, a walled garden refers to a closed or exclusive set of information services provided for users. This is in contrast to providing consumers open access to the applications and content.

The recent financial crisis has presented what appears to be an opportunity to attack the market share of the big banks. Trust in these institutions is lower than normal and the very thing that made them appealing, their size, is now a questionable asset. The bigness of a bank in some ways describes the size of their private Network. On the consumer side, it’s their physical footprint with branches, or stores as some like to call them, and the extension of that footprint through their proprietary ATM network plus affiliated ATM networks. On the institutional side, there’s a matching infrastructure that represents the arteries, veins and capillaries that circulate money and abstractions of money around the country. Network is the medium of distribution. Once the platform of a big bank’s private network is in place, they endeavor to deliver the widest possible variety of product and services through these pipes. Citibank led the way in the financial supermarket space, now all the major players describe themselves as diversified financial services firms.

Every so often, in the life of the Network, the question of centralized versus distributed financial services comes up. Rather than buying a bundle of services from a single financial services supermarket, we wonder whether it’s possible to assemble best of breed services through a single online front-end. This envisions financial services firms providing complete APIs to aggregators so they can provide more friendly user interfaces and better analytics. Intuit/Mint has been the most successful with this model. It’s interesting to note that since the financial supermarkets are generally built through acquisition, under the covers, their infrastructures and systems of record are completely incompatible. So while the sales materials tout synergy, the funds to actually integrate systems go begging. The financial services supermarket in practice is aggregated, not integrated.

We’re starting to see the community banks and credit unions get more aggressive in their advertising— using a variation on the “small is beautiful” theme. For consumers, the difference in products, services and reach has started to narrow. By leveraging the Network, the small financial institution can  be both small and big at the same time. In pre-Network history, being simultaneously small and big violated the laws of physics. In the era of the Network, any two points on the planet can be connected in near real time as long as Network infrastructure is present. An individual can have an international footprint. Of course, being both big and big allows a financial institution to take larger risks because, theoretically at least, it can absorb larger loses. We may see legislation from Congress that collars risk and puts limitations on the unlimited relationship between size and risk.

The Network seems to continually present opportunities for disintermediation of the dominant players in the financial services industry. Ten years ago, account aggregation via the Network seemed to be on the verge. But the model was never able to overcome its usability problems, which at bottom are really internet identity problems. We’re beginning to see a new wave of companies sprouting up to test whether a virtual distribution network through the internet can supplant the private physical networks of the established players. SmartyPig, Square and BankSimple present different takes on disintermediating the standard way we route and hold the bits that represent our money.

Once any Network endpoint can be transformed into a secure transaction environment, the advantage of the private network will have been largely neutralized. And while it hasn’t solved account aggregation’s internet identity problem yet, the mobile network device (some call it a telephone) has significantly changed the identity and network landscape. The walls around the garden represent security and engender trust. The traditional architecture of bank buildings reflect this concept. But the walled garden metaphor is built on top of the idea of carving out a private enclave from physical space. The latest round of disintermediation posits the idea that there’s a business in creating ad hoc secure transaction connections between any two Network endpoints. In this model, security and trust are earned by guaranteeing the transaction wherever it occurs.

There have always been alternative economies, transactions that occur outside of the walled gardens. In the world of leading-edge technology, we tend to look for disruption to break out in the rarefied enclaves of the early adopter. But when the margins of the urban environment grow larger than the traditional center, there’s a good chance that it’s in the improvisational economies of the favelas, shanty towns and slums that these new disruptive financial services will take root.

“Just checking in.” It’s such a neutral phrase. It doesn’t imply any engagement or transaction— the connection has been opened and tested, but no activity is required or expected. From a Unix command line, the ping serves a similar function. The social geo-location services have brought the “check in” into common parlance on the Network. The FourSquare check in can be a neutral communication— no message attached, merely a statement that I’m at such-and-such a location.

The neutrality of the “check in” gesture began to interest me as I started thinking about the explicit gesture of giving a star rating to a restaurant. While I was recently visiting New York City, I decided to try and make use of the Siri and FourSquare apps on my iPhone. I could be observed sitting on a park bench saying ‘good pizza place near here’ into my iPhone and eagerly waiting for Siri to populate a list of restaurant options. I also checked in using FourSquare from several locations around Manhattan. When Siri returned its list of ‘good pizza places’ near me, it used the services of partner web sites that let users rate restaurants and other businesses on a one to five star system. When I asked for good pizza places that translated into the restaurants with the most stars.

The interesting thing about user ratings of businesses by way of the Network is that it’s completely unnecessary for the user to actually visit, or be a customer of, the business. The rating can be entirely fictional. Unless you personally know the reviewer and the context in which the review is proffered— a good, bad or ugly review may be the result of some alternate agenda. There’s no way to determine the authenticity of an unknown, or anonymous, reviewer. Systems like eBay have tried to solve this problem using reputation systems. Newspapers have tried to solve this problem by hiring food critics who have earned the respect of the restaurant ecosystem.

So, while Siri did end up recommending a good Italian restaurant, the Chinese restaurant it recommended was below par. Both restaurants had the same star ratings and number of positive reviews. This got me thinking about the securitization of the networked social gesture. Once a gesture has even a vaguely defined monetary value there’s a motivation to game the system. If more stars equals a higher ranking on Siri’s good pizza place list, then how can a business get more stars? What’s the cost?

I ran across a tweet that summed up the dilemma of wanting a list of ‘good pizza places’ rather than simply ‘pizza places.’ I use FriendFeed as a Twitter client, and while watching the real-time stream I saw an interesting item float by. Tara Hunt retweeted a micro-message from Deanna Zandt referring to a presentation by Randy Farmer at the Web 2.0 conference on Building Web Reputation systems. Deanna’s message read: “If you show ppl their karma, they abuse it.” When reputation is assigned a tradable value, it will be traded. In this case, ‘abuse’ means traded in an unintended market.

Another example of this dilemma cropped up in a story Clay Shirky told at the Gov 2.0 summit about a day care center. The day care center had a problem with parents who arrived late to pick up their children. Wanting to nip the problem in the bud, they instituted a fine for late pick up. What had been a social contract around respecting the value of another person’s time was transformed into a new service with a set price tag. “Late pick up” became a new feature of the day care center, and those parents who could afford it welcomed the flexibility it offered them. Late pick ups tripled, the new feature was selling like hot cakes. Assigning a dollar value to the bad behavior of late pick ups changed the context from one of mutual respect to a payment for service. Interestingly, even when the fines were eliminated, the higher rate of bad behavior continued.

Now let’s tie this back to the neutral gesture of the check in. While in some respect the reporting of geolocation coordinates is a mere statement of fact— there’s also the fact that you’ve chosen to go to the place from which you’ve checked in. There’s a sense in which a neutral check in from a restaurant is a better indicator of its quality than a star rating accompanied by explicit user reviews. If a person in my geo-social network checks in from a restaurant every two weeks, or so, I’d have to assume that they liked the restaurant. The fact that they chose to go there more than once is a valuable piece of information to me. However when game mechanics are assigned to the neutral check in gesture, a separate economics is overlaid. If the game play, rather than the food, provides the motivation for selecting a restaurant, then the signal has been diluted by another agenda.

By binding the check in to the place via the geolocation technology of the device, a dependable, authentic piece of information is produced. Social purchase publishing services, like Blippy, take this to the next level. Members of this network agree to publish a audit trail of their actual purchases. By linking their credit card transaction report in real time to a publishing tool, followers know what a person is actually deciding to purchase. A pattern of purchases would indicate some positive level of satisfaction with a product or service.

The pattern revealed in these examples is that the speech of the agent cannot be trusted. So instead we look to the evidence of the transactions initiated by the agent, and we examine the chain of custody across the wire. A check in, a credit card purchase— these are the authentic raw data from which an algorithm amalgamates some probability of the good. We try to structure the interaction data such that it has the form of a falsifiable proposition. The degree to which a statement of quality can be expressed as an on or off bit defines a machine’s ability to compute with it. A statement that is overdetermined, radiating multiple meanings across multiple contexts doesn’t compute well and results in ambiguous output. The pizza place seems to occupy multiple locations simultaneously across the spectrum of good to bad.

Can speech be rehabilitated as a review gesture? I had a short conversation with Randy Farmer at the recent Internet Identity Workshop (IIW 10) about what he calls the “to: field” in networked communications. The basic idea is that all speech should be directed to some individual or group. A review transmitted to a particular social group acquires the context of the social relations within the group. Outside of that context its value is ambiguous while purporting to be clear. Farmer combines restricted social networks and falsifiable propositions in his post ‘The Cake is a Lie” to get closer to an authentic review gesture and therefore a more trustworthy reputation for a social object.

Moving through this thought experiment one can see the attempt to reduce human behavior and social relations to falsifiable, and therefore computable, statements. Just as a highly complex digital world has been built up out of ones and zeros, the search for a similar fundamental element of The Good is unfolding in laboratories, research centers and start ups across the globe. Capturing the authentic review gesture in a bottle is the new alchemy of the Network.

What’s So Funny About Peace, Love and Understanding?
Nick Lowe

As I walk through
This wicked world
Searching for light in the darkness of insanity.

I ask myself
Is all hope lost?
Is there only pain and hatred, and misery?

And each time I feel like this inside,
There’s one thing I wanna know:
What’s so funny about peace love & understanding? ohhhh
What’s so funny about peace love & understanding?

And as I walked on
Through troubled times
My spirit gets so downhearted sometimes
So where are the strong
And who are the trusted?
And where is the harmony?
Sweet harmony.

Cause each time I feel it slipping away, just makes me wanna cry.
What’s so funny bout peace love & understanding? ohhhh
What’s so funny bout peace love & understanding?

So where are the strong?
And who are the trusted?
And where is the harmony?
Sweet harmony.

Cause each time I feel it slippin away, just makes me wanna cry.
What’s so funny bout peace love & understanding? ohhhh
What’s so funny bout peace love & understanding? ohhhh
What’s so funny bout peace love & understanding?

Thinking about what it might mean to stand at the intersection of technology and the humanities has resulted in an exploration with a very circuitous route.

The Network has been infused with humanity, with every aspect of human character— the bright possibilities and the tragic flaws.

On May 29, 1919, Arthur Stanley Eddington took some photographs of a total eclipse of the sun. Eddington had gone to Africa to conduct an experiment that might determine whether Newton’s or Einstein’s model was closer to physical reality.

During the eclipse, he took pictures of the stars in the region around the Sun. According to the theory of general relativity, stars with light rays that passed near the Sun would appear to have been slightly shifted because their light had been curved by its gravitational field. This effect is noticeable only during eclipses, since otherwise the Sun’s brightness obscures the affected stars. Eddington showed that Newtonian gravitation could be interpreted to predict half the shift predicted by Einstein.

My understanding of the physics is rather shallow, my interest is more in the metaphorics— in how the word-pictures we use to describe and think about the universe changed based on a photograph. Where the universe lined up nicely on a grid before the photograph, afterwards, space became curvaceous. Mass and gravity bent the space that light passed through. Assumed constants moved into the category of relativity.

The Network also appears to be composed of a neutral grid, its name space, through which passes what we generically call payloads of “content.” Each location has a unique identifier; the only requirement for adding a location is that its name not already be in use. You can’t stand where someone is already standing unless you displace them. No central authority examines the suitability of the node’s payload prior to its addition to the Network.

The universe of these location names is expanding at an accelerating rate. The number of addresses on the Network quickly outstripped our ability to both put them into a curated index and use, or even understand, that index. Search engines put as much of the Network as they can spider into the index and then use software algorithms to a determine a priority order of the contents of the index based on keyword queries. The search engine itself attempts to be a neutral medium through with the nodes of the Network are prioritized based on user query input.

Regardless of the query asked, the method of deriving the list of prioritized results is the same. The method and production cost for each query is identical. This kind of equal handling of Network nodes with regard to user queries is the search engine equivalent of freedom, opportunity and meritocracy for those adding and updating nodes on the Network. The algorithms operate without prejudice.

The differential value of the queries and prioritized link lists is derived through an auction process. The cost of producing each query/result set is the same—it is a commodity—but the price of buying advertising is determined by the intensity of the advertiser’s desire. The economics of the Network requires that we develop strategies for versioning digital commodities and enable pricing systems linked to desire rather than cost of production. Our discussions about “Free” have to do with cost-based pricing for digital information goods. However, it’s by overlaying a map of our desires on to the digital commodity that we start to see the contours, the curvaceousness of this space, the segments where versioning can occur.

We’ve posited that the search algorithm treats all nodes on the Network equally. And more and more, we take the Network to be a medium that can fully represent human life. In fact, through various augmented reality applications, human reality and the Network are sometimes combined into a synthetic blend (medium and message). Implicitly we also seem to be asserting a kind of isomorphism between human life and the Network. For instance, sometimes we’ll say that on the Network, we “publish everything, and filter later.” The gist of this aphorism is that where there are economics of low-or-no-cost production, there’s no need to filter for quality in advance of production and transfer to the Network. Everything can be re-produced on the Network and then sorted out later. But when we use the word “everything,” do we really mean everything?

The neutral medium of the Network allows us to disregard the payload of contents. Everything is equivalent. A comparison could be made to the medium of language— anything can be expressed. But as the Network becomes more social, we begin to see the shape of our society emerge within the graph of nodes. Sigmund Freud, in his 1913 book entitled Totem and Taboo, looks at the markers that we place on the border of what is considered socially acceptable behavior. Ostensibly, the book examines the resemblances between the mental life of savages and neurotics. (You’ll need to disregard the archaic attitudes regarding non-European cultures)

We should certainly not expect that the sexual life of these poor, naked cannibals would be moral in our sense or that their sexual instincts would be subjected to any great degree of restriction. Yet we find that they set before themselves with the most scrupulous care and the most painful severity the aim of avoiding incestuous sexual relations. Indeed, their whole social organization seems to serve that purpose or to have been brought into relation with its attainment.

Freud is pointing to the idea that social organization, while certainly containing positive gestures, reserves its use of laws, restrictions and mores for the negative gesture. The structure of societal organization to a large extent rests on what is excluded, what is not allowed. He finds this common characteristic in otherwise very diverse socio-cultural groups. Totems and taboos bend and structure the space that our culture passes through.

In the safesearch filters employed by search engines we can see the ego, id and superego play out their roles. When we search for transgressive content, we remove all filtering. But presumably, we do, as a member of a society, filter everything before we re-produce it on the Network. Our “unfiltered” content payloads are pre-filtered through our social contract. Part of the uncomfortableness we have with the Network is that once transgressive material is embodied in the Network, the algorithms disregard any difference between the social and the anti-social. A boundary that is plainly visible to the human— and is in fact a structural component of its identity and society, is invisible to the machine. Every node on the Network is processed identically through the algorithm.

This issue has also been raised in discussions about the possibility of artificial intelligence. In his book Mirror Worlds, David Gelernter discusses a key difference between human memory and machine memory:

Well for one thing, certain memories make you feel good. The original experience included a “feeling good” sensation, and so the tape has “feel good” recorded on it, and when you recall the memory— you feel good. And likewise, one reason you choose (or unconsciously decide) not to recall certain memories is that they have “feel bad” recorded on them, and so remembering them makes you feel bad.

But obviously, the software version of remembering has no emotional compass. To some extent, that’s good: Software won’t suppress, repress or forget some illuminating case because (say) it made a complete fool of itself when the case was first presented. Objectivity is powerful.

Objectivity is very powerful. Part of that power lies in not being subject to personal foibles and follies with regard to the handling, sorting, connecting and prioritizing of data. The dark side of that power is that the objectivity of the algorithm is not subject to social prohibitions either. They simply don’t register. To some extent technology views society and culture as a form of exception processing, a hack grafted on to the system. As the Network is enculturated, we are faced with the stark visibility of terrorism, perversity, criminality, and prejudice. On the Network, everything is just one click away. Transgression isn’t hidden in the darkness. On the Network, the light has not yet been divided from the darkness. In its neutrality there is a sort of flatness, a lack of dimensionality and perspective. There’s no chiaroscuro to provide a sense of volume, emotion, limit and mystery.

And finally here’s the link back to the starting point of this exploration. A kind of libertarian connection has been made between the neutral quality of the medium of the Network and our experience of freedom in a democratic republic. The machine-like disregard for human mores and cultural practices is held up as virtue and example for human behavior. No limits can be imposed on the payloads attached to any node of the Network. The libertarian view might be stated that the fewest number of limitations should be applied to payloads while still maintaining some semblance of society. Freud is instructive here: our society is fundamentally defined by what we exclude, by what we leave out, and by what we push out. While our society is more and more inclusive, everything is not included. Mass and gravity bend the space that light passes through.

The major debates on the Network seem to line up with the contours of this pattern. China excludes Google and Google excludes China. Pornographic applications are banished from Apple’s AppStore. Android excludes nothing. Closed is excluded by Open, Open is included by Closed. Spam wants to be included, users want to exclude spam. Anonymous commenters and trolls should be excluded. Facebook must decide what the limits of speech are within the confines of its domain. The open internet excludes nothing. Facebook has excluded the wrong thing. The open internet has a right to make your trade secrets visible. As any node on the Network becomes a potential node in Facebook’s social/semantic graph, are there nodes that should be taboo? How do we build a civil society within the neutral medium of the Network? Can a society exist in which nothing is excluded?

In the early days of the Network, it was owned and occupied by technologists and scientists. The rest of humanity was excluded. As the Network absorbs new tribes and a broader array of participants, its character and its social contract has changed. It’s a signal of a power shift, a dramatic change in the landscape. And if you happen to be standing at the crossroads of technology and the humanities, you might have a pretty good view of where we’re going.

After struggling through the live blogging of today’s iPhone 4.0 announcement from Apple, I couldn’t help but think about baseball. It’s Spring, the season has just started and I’ve already listened to most of a game on the radio. The first radio broadcast of a baseball game was in 1921:

In those days many radio stations often did not have the budgets or technology to broadcast games live from the park. Instead, stations would recreate the games in studio.  A telegraph operator would transmit the information back to the studio from the ball park where broadcasters and engineers would recreate game action from the ticker tape. Crowd noise, the crack of the bat, the umpire on the field and other sounds of the game were all manufactured in the studio as the game was being played live elsewhere.

Live blogging seems like public telegraph messages plus photography. The latency is still there— as is the re-creation of the event. Somehow I think those radio listeners in 1921 had a better sense of what was happening in the ball game than we do today watching our web browsers auto-refresh with the latest tidbit. While we grow closer in time, the fidelity of the broadcast is much lower.

In 1994, John Perry Barlow wrote about The Economy of Ideas, and made the observation that time replaces space:

In the virtual world, proximity in time is a value determinant. An informational product is generally more valuable the closer purchaser can place themselves to the moment of its expression, a limitation in time. Many kinds of information degrade rapidly with either time or reproduction. Relevance fades as the territory they map changes. Noise is introduced and bandwidth lost with passage away from the point where the information is first produced.

Thus, listening to a Grateful Dead tape is hardly the same experience as attending a Grateful Dead concert. The closer one can get to the headwaters of an informational stream, the better one’s chances of finding an accurate picture of reality in it. In an era of easy reproduction, the informational abstractions of popular experiences will propagate out from their source moments to reach anyone who’s interested. But it’s easy enough to restrict the real experience of the desirable event, whether knock-out punch or guitar lick, to those willing to pay for being there.

If you can’t be there, I guess a live blog is a reasonable kind of substitute. But the use of text and still photography as a medium to capture and broadcast a live event in real time has the feel of something you’d read about in a history book. The past is here, it’s just not evenly distributed yet.