echovar

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.

For book and record stores, there was a moment when the largest inventory and the lowest prices won out. Large physical stores with endless rows of inventory overwhelmed the small retailer. Eventually the inventory moved into a series of warehouses/databases with query-based web front ends attached to a product delivery system. Inventory expanded to match the number of sellable books in existence, and the customer experience was abstracted to a computer screen, a keyboard and a mouse. Touch, smell, sound, weight, the look of the spine, the creaking of the wooden floor— all of these modes of interaction were eliminated from the equation. Of course, no one is interested in all books, but if a vendor has all books in their inventory, it’s likely the subset you’re interested in can be carved out of the whole stack.

Two of my favorite bookstores don’t have an infinite inventory. I always enjoy browsing and rarely walk out without having purchased something. The trick is that if you don’t have everything, you need to have what’s good. And in order to have what’s good, you need to have a point of view on what’s good. In New York, the tiny Three Lives bookstore always manages to show me something I can’t live without. Last time I was there it was Tom Rachman’s sparkling first novel, The Imperfectionists. In San Francisco, one of my favorites is Lawrence Ferlinghetti’s City Lights Bookstore. City Lights often makes the improbable connection. After a reading (in New York) by Richard Foreman from his book, No-Body, A Novel in Parts, I asked him what he was reading. Foreman said that he’d become very interested in an Austrian writer named Heimito Von Doderer. Subsequently, I looked for books by Von Doderer, but came up empty until a visit to City Lights. City Lights was the perfect connection between Foreman and Von Doderer.

More than just a place to purchase books, both of those bookstores communicate a way of life, a way of thinking, an idea about taste and a larger picture about what’s good and important in our culture. While their inventory of books isn’t infinite, one has a sense of infinite possibility browsing through the stacks.

While at first we luxuriated in the ocean of choice, now we find ourselves thwarted by the process of sorting and prioritizing an infinite set of possibilities. One way to gauge the number of choices to offer is to look at the relative amount of time a person spends evaluating possible choices versus the amount of time spent enjoying the choice. If the selection set is infinite, but only one item will eventually be chosen, the customer may find herself living out one of Zeno’s paradoxes.

There’s a sense in which an infinite inventory is amoral. It avoids the choices forced on a small bookstore with a limited amount of shelf space. And perhaps this gets at something central to human experience— something about time, mortality and the choices we make about what matters. Neil Postman relays a quote from Philip Roth about Writers From The Other Europe.

In commenting on the difference between being a novelist in the West and being a novelist behind the iron curtain (this was before the collapse of Communism in Eastern Europe), Roth said that in Eastern Europe nothing is permitted but everything matters; with us, everything is permitted but nothing matters.

We’re flooded, drowning in information. We’re better than ever at collecting the dots, worse than ever at connecting the dots. This is true at the level of national security, business intelligence, customer and vendor relationship management and the personal daily real-time streams we audit. We cast our nets wider than ever and catch everything. Nothing escapes our grasp.

This high-velocity firehose of messages seems to imply that something is going on. But the items that pass through this pipe require additional filtering to find out whether anything important is really going on. The violence and the sheer volume of messages are an indicator, a message about the medium itself, but the individual messages pass by so quickly that one only gets a sense of their general direction and whether they carry a positive or negative charge. Oddly, there’s a disconnect between message traffic and whether something important is going on. There’s always a high volume of message traffic, there’s rarely anything going on.

Tomorrow, and tomorrow, and tomorrow,

Creeps in this petty pace from day to day

To the last syllable of recorded time,

And all our yesterdays have lighted fools

The way to dusty death. Out, out, brief candle!

Life’s but a walking shadow, a poor player

That struts and frets his hour upon the stage

And then is heard no more: it is a tale

Told by an idiot, full of sound and fury,

Signifying nothing.

The Atlantic Wire’s series ‘What I Read‘ asks various notable personalities about their media diet. When confronted with an all-you-can-eat smorgasbord, how does one choose what to eat? A recent column contained thoughts by Wired editor Chris Anderson on his foraging techniques. One of the best filters that Anderson uses is free of charge, and given to him by a friend:

Nassim Taleb once advised people to ignore any news you don’t hear in a social context. From people you know and, ideally, face to face. You have two combinatorial filters in social communication. First, you’ve chosen to talk with these people, and second, they’ve chosen to bring it up. Those two filters–a social and an importance filter–are really good ways of identifying what really matters to people. If I hear about news through social means, and if I hear about it three times, then I pay attention.

The interesting thing about this technique is that it doesn’t require Network scale technical capability. Spidering the entire Network and running a query through a relevance algorithm isn’t part of the picture. Trading your personal information for access to large scale cloud-based computational capabilities isn’t required either. Authentically connecting with people to learn what really matters to people is the key.

It turns out that not much matters to a mechanical or computational process. While it can sort items into a prioritized list based on this algorithm, or that one, the question of which algorithm should be used is a matter of indifference. And when we crank up the social context to extreme levels, we create some confusion around Taleb’s filters. Not every social media interaction provides the kind of social context Taleb is referring to. Only in their simplest and least technical incarnation, do these combinatorial filters provide high quality output. And despite all the sound and fury, the manifestations of speed whirling around us, “things happen fairly slowly, you know. They do.“

Religion serves as a connecting point for two recent essays. One, by Jaron Lanier, is called The First Church of Robotics; and the other is called The Rabbis and The Thinking Machines by David Gelernter. Ostensibly each of the authors is writing about robots, or androids, and the moral questions that surround the quest to create this type of machine and what our obligations might be should we actually succeed.

Lanier has taken up the cause of deflating the bubble of artificial intelligence that’s growing up around technology and software. We’ve encased the outputs of algorithms with the glow of “intelligence.” We strive for “smarter” algorithms to relieve us from the burdens of our daily drudgery. To counter this, Lanier points out that if we talk about what is called A.I., when we leave out the vocabulary of A.I., we see what’s happening in front of us much more clearly. From Lanier’s essay:

I myself have worked on projects like machine vision algorithms that can detect human facial expressions in order to animate avatars or recognize individuals. Some would say these too are examples of A.I., but I would say it is research on a specific software problem that shouldn’t be confused with the deeper issues of intelligence or the nature of personhood. Equally important, my philosophical position has not prevented me from making progress in my work. (This is not an insignificant distinction: someone who refused to believe in, say, general relativity would not be able to make a GPS navigation system.)

In fact, the nuts and bolts of A.I. research can often be more usefully interpreted without the concept of A.I. at all. For example, I.B.M. scientists recently unveiled a “question answering” machine that is designed to play the TV quiz show “Jeopardy.” Suppose I.B.M. had dispensed with the theatrics, declared it had done Google one better and come up with a new phrase-based search engine. This framing of exactly the same technology would have gained I.B.M.’s team as much (deserved) recognition as the claim of an artificial intelligence, but would also have educated the public about how such a technology might actually be used most effectively.

The same is true of efforts like the “semantic” web. By leaving out semantics and ontology, simply removing that language entirely, you get a much better picture of what the technology is trying to accomplish. Lanier, in his essay, equates the growing drumbeat on behalf of “artificial intelligence” to the transfer of humanity from the human being to the machine. All that confounds us as earthbound mortals is soothed by the patent medicine of the thinking machine. Our lives are extended to infinity, our troubling decisions are painlessly computed for us, and we are all joyously joined in a singular global mind. It’s no wonder Lanier sees the irrational exuberance for artificial intelligence congealing into an “ultra-modern religion.” A religion that wears the disguise of science.

David Gelernter, in his essay, stipulates that we will see a “thinking machine roll out of the lab.” To be clear, he doesn’t believe that machines will attain consciousness. Gelernter simply thinks that something good enough to pass the Turing Test will eventually be built. In this case, it would be a machine that can imitate thinking. And from that we move from the Turing Test to the Duck Test. If it looks like a duck, walks like a duck and quacks like a duck— we call it a duck. However, in this case, we’ll call it a duck even though we know that it’s not a duck. Gelernter elaborates:

Still: it is only a machine. It acts the part of an intelligent agent perfectly, yet it is unconscious (as far as we know, there is no way to create consciousness using software and digital computers). Being unconscious, it has no mind. Software will make it possible for a computer to imitate human behavior in detail and in depth. But machine intelligence is a mere façade. If we kick our human-like robot in the shin, it will act as if it is in pain but will feel no pain. It is not even fair to say that it will be acting. A human actor takes on a false persona, but underneath is a true persona; a thinking machine will have nothing “underneath.” Behind the impressive false front, there will be no one home. The robot will have no inner life, no mental landscape, no true emotions, no awareness of anything.

The question Gelernter asks is: what is our moral obligation to the android? How should we treat these machines? Shall we wait for a new PETA? Will a People for the Ethical Treatment  of Androids tell us that we can’t delete androids, and other intelligent agents, like we used to delete unwanted software? Anthropomorphism is even more potent when human characteristics are projected on to a human form. The humanity we see in the android will be whatever spirit we project into the relationship. It’s a moral dilemma we will create for ourselves by choosing build machines in our own image. Gelernter explains:

Thinking machines will present a new challenge. “Cruelty” to thinking machines or anthropoid robots will be wrong because such machines will seem human. We should do nothing that elicits expressions of pain from a thinking machine or human-like robot. (I speak here of a real thinking machine, not the weak imitations we see today; true thinking machines are many decades in the future.) Wantonly “hurting” such a machine will damage the moral atmosphere by making us more oblivious of cruelty to human beings.

Where Lanier wants us to see mock intelligence with clear eyes as a machine running a routine; Gelernter observes that once we surround ourselves with machines created in our image, the fact that they are without consciousness will not relieve us of moral responsibility regarding their treatment. Lanier warns that new religions are being created out of the fantasy of technological achievement without boundaries, and Gelernter invokes the Judeo-Christian tradition to warn that cruelty to pseudo-humans will be cruelty all the same.

We seem compelled to create machines that appear to relieve us of the burden of thinking. In the end, it’s Jaron Lanier who uncovers the key concept. There’s a gap between aesthetic and moral judgement and the output of an algorithm that simulates, for instance, your taste in music. We have to ask to what extent are we allowing our judgements to be replaced by algorithmic output? Some would say the size of that gap is growing smaller every day, and will eventually disappear. However, in other areas, the line between the two appears brightly drawn. For instance civil and criminal laws are a set of rules, but we wouldn’t feel comfortable installing machines in our courtrooms to hand down judgements based on those rules. We wouldn’t call the output of that system, justice. While it seems rather obvious, judgement and algorithms are not two things of the same type separated by a small gap in efficiency. They are qualitatively different things separated by a difference of kind. But the alchemists of technology tell us, once again, that they can turn lead into gold. What they don’t mention, is what’s lost in the translation.

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?

After all this time, it’s still difficult to say what a tweet is. The generic form of the word has been expressed as microblogging, but this is the wrong metaphor. Blogging and RSS advocates see Twitter as a short-form quick publishing platform. What blogging tools made easy, Twitter, and other similar systems, make even easier. Given this definition, the 140 character limit on tweets seems to be an unnecessary constraint— microblogging could simply be expanded to miniblogging and a 500 character limit for individual posts. Blog posts can be any length, they are as small or large as they need to be.

“All my plays are full length, some are just longer than others.”
- Samuel Beckett

But Twitter didn’t start with blogging or blogging tools as its central metaphor, it began with the message streams that flow through dispatching systems. The tweet isn’t a small blog post, it’s a message in a communications and logistics system. There’s a tendency to say that the tweet is a “micro” something— a very small version of some normally larger thing. But tweets are full sized, complete and lack nothing. Their size allows them to flourish in multiple communications environments, particularly the SMS system and the form factor of the mobile network device (iPhone).

The best metaphor I’ve found for a tweet is the yellow sticky. The optimal post-it note is 3 inches square and canary yellow in color. It’s not a small version of something else, its size is perfect for its purpose. There are no limitations on what can be written on a yellow sticky, but its size places constraints on the form of communication. Generally, one expects a single thought per yellow sticky. And much like Twitter, explaining what a yellow sticky is to someone who’s never used one is a difficult task. Initial market tests for the post-it note showed mixed reactions. However after extensive sampling, 90% of consumers who tried the product wanted to buy it. Like the tweet, the post-it note doesn’t have a specific purpose. Arthur Fry, one of the inventors of the post-it note, wanted a bookmark with a light adhesive to keep his place in his hymnal during church choir. The rapid acceptance of the yellow sticky, in part, had to do with not defining what it should be used for. It’s hard to imagine someone saying that you’re not using a post-it note correctly, although people say that about Twitter all the time.

One thing people use yellow stickies for is as a transmittal. I find a magazine article that I like and I pass it on to you with a short message on a yellow sticky that marks the page. I might send this package to you through the mail, use inter-office mail at work, or I might just leave it on your desk. More formal routing slips might request specific actions be taken on the attached item. Fax cover sheets are another example of this kind of communication. And Twitter is often used in a similar way. The hyperlink is the adhesive that binds the message to article I’d like to pass on to you. With Twitter, and other directed social graph services, the you I pass things on to includes followers, potentially followers of followers and users who track keywords contained in my message. At any given time, the who of the you will describe a different group. The message is passed on without obligation, the listeners may simply let it pass through, or they may take up the citation and peruse its contents.

Just as the special low-tack adhesive on the back of a yellow sticky allows you to attach it to anything without leaving marks or residue, the hyperlink allows the user of Twitter to easily point at something. Hey, look at this! Rather than a long explanation or justification, it’s just my finger pointing at something of interest. That’s interesting to me. It’s the way we talk to each other when the words aren’t the most important part of the communication.

This model of passing along items of interest is fundamentally different from web syndication. Syndication extends the distribution of published content to additional authorized contexts. Some may argue that the mostly defunct form of the ‘link blog‘, or an aggregation of link blogs, offers exactly the same value. The difference is that the tweet, as electronic routing slip, exists in a real-time social media communications system. It operates like the messages in a dispatching system. There’s an item at 3rd and Webster about cute kittens, here’s the hyperlink for interested parties. Syndication implies that I think what I’ve published is valuable, I’ve extended my distribution area and you should have a look at it. With a tweeted electronic routing slip, the value is assigned by the reader who decides to pass something along and the readers who choose to take it up within a real-time (instant) messaging system. Value is external to the thing being evaluated.

As we start to look at new applications like Flipboard, an app that collects routing slips from your social network and lays them out into a magazine format, it’s important to understand the basic unit from which the experience is built. We’re used to a newspaper filled with a combination of syndicated wire stories and proprietary ones. We know about magazines where all the stories are proprietary. A few of us are familiar with web syndication aggregators that allow us to pull in, organize and read feeds from thousands of publication sources. Building an electronic publication from sets of real-time routing slips is a fundamentally different editorial process than we’ve seen before. Of course, it could be that you don’t find the stories that your friends pass on to be very interesting. In the end, this method of  assembling a real-time publication will be judged based on the value it provides. A magazine with a thousand stories isn’t really very useful, just as a Google search result with a million answers doesn’t help you find something. Can you imagine a real-time magazine that captures the ten stories that are worth reading right now? Can you imagine a time when such a thing didn’t exist?

I thought it prudent to write something about Jeffrey Rosen’s Sunday NY Times essay, The Web Means The End Of Forgetting, before it slipped into the past and we’d all forgotten about it. Scott Rosenberg was disappointed in Rosen’s essay and wrote about how it didn’t live up to the large themes it outlined. The essence of Rosen’s piece is that the public information we publish to the web through social network systems like Facebook are permanent markers that may come back to haunt us in unanticipated contexts. Rosenberg’s critique seems to be that there’s not too much evidence of this happening, and that a greater concern is link rot, preservation of the ephemera of the web and general digital preservation.

Of course, there’s a sense in which we seem to have very poor memories indeed. Our universities feature a discipline called archeology in which we dig up our ancestors with the purpose of trying to figure who they were, what they did and how they lived. We lack the ability to simply rewind and replay the ancient past. As each day advances, another slips into time out of mind— or time immemorial as it’s sometimes called.

We use the metaphors memory and forgetting when talking about what computer systems do when they store and retrieve bits from a file system. The human activity of memory and forgetting actually has very little in common with a computer’s storage and retrieval routines. When we say that the Web doesn’t forget, what we mean is that if something is stored in a database, unless there’s a technical problem, it can be retrieved through some kind of search query. If the general public has access to that data store, then information you’ve published will be available to any interested party. It’s not a matter of human remembering or forgetting, but rather one of discovery and random access through querying a system’s indexed data.

At issue in Rosen’s piece, isn’t the fact of personal data retrieved through a search query, but rather the exposure of personal transgressions. Lines that were crossed in the past, behavior from one context made inappropriate by placing it into a new context, some departure from the Puritan norm detected and added into a summary valuation of a person. Rosen even describes this mark as a “scarlet letter in your digital past.” The technical solutions he explores have to do with changing the data or the context of the data to prevent retrieval: the stains of data are scrubbed and removed from relevant databases; additional data is piled in to divert attention from the offending bits; or an expiration policy is enforced on bits that make them unreadable after a set period of time. There’s an idea that at some future point you will own all your personal data (that you’ve published into publicly networked systems) and will have granular access controls over it.

Absent a future of totalitarian personal data control, Rosen moves on to the act of forgiveness. Can we forgive each other in the presence of permanent reminders? I wrote a post about this on the day that MSNBC replayed the events of morning of September 11, 2001. Sometimes we can rewind the past and press play, but wounds cannot heal if we’re constantly picking at them.

While we’re enraptured by the metaphors of memory and forgetting, intelligence and thinking, as we talk about computers, when we speak of forgiveness we tamp down the overtones and resonance of the metaphor. It’s in the cultural practice of western religion that we have the mechanisms for redemption, forgiveness, indulgences and absolution. In the secular rational context of computerized networks of data there’s no basis for forgiveness. It’s all ones or zeros, it’s in the database or it’s not.

Perhaps in our digital secular world we need a system similar to carbon offsets. When we’ve sinned against the environment by virtue of the size of our carbon footprint, we purchase indulgences from TerraPass to offset our trespass. Rather than delete, obscure or divert attention from the bits in question, we might simply offset them with some act of kindness. While the Catholic Church frowns on the idea of online confession, in this model, there would be no person listening to your confession and assigning penance. The service would simply authenticate your good deeds and make sure they were visible as a permanent marker on the Network. It would be up to you to determine the size of the offset, or perhaps you could select from a set of standard offset sizes.

The problem that Rosen describes is not one of technology, but rather one of humanity and human judgment. The question of how we treat each other is fundamental and has been with us since the beginning.

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.