Information Terms

I'll use Kurzweil again as I find that he's a spokesperson for the latest sci-fi thinking on smart machines and the like.  He does his homework, I mean, so when he draws all the wrong conclusions he does it with impressive command of facts.  He's also got an unapologetic vision, and he articulates it in his books in a way that critics and enthusiasts alike really know where he's coming from.  I like the guy, really.  He's just wrong.

For example, in his eminently skimmable "The Singularity is Near", he quips on page who-cares that the project of Strong AI is to reverse engineer the human brain in "information terms."  What is this?  Everything is information these days, but the problem with seeing the world through the lens of "information" or even "information theory" is that it's just a theory about transmitting bits (or, "yes/no"s).  Then, computation is just processing bits (which is really what a Turing Machine does, is traverse a graph with a deterministic, discreet decision at each node), and communications is just, well, communicating them.  But information in this sense is just a way of seeing process discretely.  You can then build a mathematics around it and processes like communication can be handled in terms of "throughput" (of bits) and "loss" (of bits) and compression and so on.  Nothing about this is "smart" or should even really generate a lot of excitement about intelligence.  It's a way of packaging up processes so we can handle them.  But intelligence isn't really a "process" in this boring, deterministic way, and so we shouldn't expect "information terms" to shed a bunch of theoretical light on it.

Intelligence is about skipping all those "yes/no" decisions and intuitively reaching a conclusion from background facts or knowledge in a context.  It's sort of anti-information terms, really.  Or to put it more correctly, after intelligence has reached its conclusions, we can view what happened as a process, discretize the process, graph it in "information terms" and wallah!, we've got something described in information terms.

So my gripe here is that "information" may be a ground breaking way of understanding processes and even of expressing results from science (e.g., thermodynamics, or entropy, or quantum limitations, or what have you), but it's not in the drivers seat for intelligence, properly construed.  Saying we're reverse engineering the brain is a nice buzz-phrase for doing some very mysterious thinking about thinking; saying "oh, and we're doing it in information terms" doesn't really add much.  In fact, whenever we have a theory of intelligence (whatever that might look like, who knows?), we can be pretty confident that there'll be some way of fitting it into an information terms framework.  My point here is that it's small solace for finding that illusive theory in the first place.

Shannon himeself--the pioneer of information theory (Hurray!  Boo!)--bluntly dismissed any mystery when formalizing the theory, saying in effect that we should ignore what happens with the sender and receiver, and how it gets translated into meaning and so on.  This is the "hard problem" of information--how we make meaning in our brains out of mindless bits.  That problem is not illuminated by formalizing the transmission of bits in purely physical terms between sender and receiver.  As Shannon knew, drawing the boundary at the hard problem meant he could make progress on the easier parts.  And so it is with science when it comes face to face with the mysteries of mind.  Ray, buddy, you're glossing it all with your information terms.  But then, maybe you have to, to have anything smart sounding to say at all.

Kurzweil's Confusion

The real mystery about intelligence is how the human brain manages to do so much, with so little.  As Kurzweil himself notes, the human brain "uses a very inefficient electrochemical, digital-controlled analog computational process.  The bulk of its calculations are carried out in the interneuronal connections at a speed of only about two hundred calculations per second ((in each connection), which is at least one million times slower than contemporary electronic circuits."

Kurzweil is making a case for the mystery of human intelligence.   Whenever the human brain, when viewed as a computational device, comes up so short, what needs to be explained then is how our vast superiority intelligent thinking is possible.  The more a purely computational comparison shows brains as inferior to computation, the more computation itself seems a poor model for intelligence.

When supercomputers like Crey's Jaguar achieve petaFLOP performance (a million billion floating point operations per second), and we still can't point to anything intuitive or insightful or human-like that they can do (like understand natural language), it suggests pretty strongly that brute computational power is not a good measure of intelligence.  In fact, Kurzweil himself makes this point pretty well, though of course it's not his intent.  To put it another way, when everything is computational speed, and humans lose the game, then true intelligence is clearly not computational speed.

To put it yet another way, the slower and crappier our "architecture" is when viewed as a glorified computer, the more impressive our actual intelligence is--and of course, the more the very notion of "intelligence" is manifestly not analyzable by computational means.

So much for Moore's Law leading us to Artificial Intelligence.  Next thought?

Limiting Results in Science

Nicolaus Copernicus published his magnus opus, De Revolutionibus Orbium Coelestium, in 1543, shortly before he died.  With it, a series of sea changes rippled through Western Europe, and in the relatively miniscule span of about two hundred years, with Isaac Newton's publication of the Principia Mathematica, the Scientific Revolution had transformed the western world.   Before Copernicus the average 16th Century European believed the Earth was at the center of the cosmos, and the universe was governed by teleological principles first elucidated by Aristotle thousands of years ago.  The fusion of Aristotelian cosmology and physics with the Judeo-Christian tradition in Scholastic thinkers like Saint Thomas Aquinas provided Western Europe with a universe filled with purpose and destiny in the coming of Christ, and in the artistic vision and genius of Dante the great story of the universe and our place in it found a common expression.  This was the world into which Copernicus published his heliocentric model of the universe.

From the beginning, though, the Copernican Revolution as it came to be called, was a strange fusion of religious vision and empirical science.  On the one hand, Copernicus realized that Tycho Brahe's cosmology was hopelessly convoluted.  Since Plato, astronomers had assumed that celestial orbits would follow perfect circles, because such Platonic Forms were more exalted and therefore were proper concepts for the description of the cosmos.  Yet, perfect circles hopelessly complicated geo-centric models.  Tycho Brahe's geo-centric model--the model that Copernicus realized was pointlessly convoluted--predicted the movements of heavenly bodies only with the aid of epicycles, equants, and other mathematical devices that were designed (somewhat ironically, as it turns out) to accommodate the many deviations from perfect circle orbits postulated in the model.  (Think of an epicycle as a smaller hoop affixed to a larger hoop, so that deviations from traversing the larger hoop can be explained by placing the traversing object somewhere on the smaller hoops orbit.)  Yet even with the addition of such fudge-factors in Brahe's geocentric model, limits to the prediction of celestial motion were a commonplace.  In short, the models, though complicated, were also frustratingly inaccurate.

A heliocentric model was virtually unthinkable at the time of Copernicus, however, as the Earth was imported a divinely special status--it was the planet, after all, where Jesus had lived and where it was thought that the Divine Plan of the entire universe was unfolding.  This observation about the barriers to doing science in the culture of the late Middle Ages in Europe, under the cloak of Catholicism, as it were, has formed part of the story of the Scientific Revolution ever since.  And, more or less, it's correct.  What's less appreciated, however, is that Copernicus himself felt divinely inspired; his religious views of the glory of the sun inspired or rther sustained his belief that the sun must be the center of the cosmos.  Only an object as glorified as the sun could fill such a role.  The Copernican Revolution then, the kick-off of what came to be called the Scientific Revolution, was a triumph of religious vision and fervor as much as empirical reality.

Copernicus was right, of course.  He needn't have held such elevated views of the sun.  He needn't necessarily have been infused with Greek neo-platonism or other-worldly thoughts at all.  His helio-centric model though carefully written to avoid conflict with the Catholic Church, would chip at the monolithic synthesis of religion and science under Scholasticism until a fissure formed, deepened with Galileo, and eventually split the entire intellectual world open with Newton.  After Newton, religion was divorced from science, and science was indeed liberated from the constraints of religious tenants and other non-empirical world views.  It was, indeed, a revolution.

But although early thinkers like Copernicus and even Newton were intoxicated with visions of a cosmos full of wonder and deity and immaterial reality (Newton once famously speculated that angels were partially responsible for gravitation, which he claimed he only described mathematically, and had not explained in any deeper sense), by the beginning of the 19th Century the flight from immaterial conceptions of the universe was nearly complete.  The philosopher and mathematician Rene Descartes laid much of the groundwork for what would become known as the "official" scientific worldview in the 19th Century:  Scientific Materialism.  There never was a consensus about the metaphysical presuppositions after the Scientific Revolution, but in practice the cultural and intellectual consequences of the revolution were a profoundly materialistic underpinning for Science, conceived now as a distinct and privileged activity apart from religion or the humanities.  Matter and energy was all that existed.  And to provide a conceptual framework for "matter and energy" was Descartes' life work.

Descartes himself was a theist, and the Cartesian conception of reality is known as Substance Dualism:  there are two basic substances, matter (or matter and energy), and an immaterial substance conceived of as mind or soul.  Everything fits into one of these two categories in the Cartesian framework.  Before, the "material world" was not separable from a mental realm completely.   []

Galileo's focus on primary properties like quantity, mass, and so on.  The other secondary properties were relegated to the Immaterial Realm.  Descartes would later attempt to prove, famously, that as God must exist (his "cogito, ergo sum") because the idea of God could not be doubted (and God would not deceive us), that therefore the human mind existed apart from the material world.  In practice, however, as science achieved impressive and ever growing mastery of knowledge about the world, the Immaterial Realm became less and less important, and implausible.  What we couldn't explain scientifically would end up in the immaterial realm.  But the progress of science seemed to suggest that such a strategy was a mere placeholder, for as the consequences of the Scientific Revolution were fully felt, even something as sacrosanct as the human mind would eventually yield to the march of science, and be explainable in purely material terms.  Hence, the original substantive division of body and mind -- material and immaterial -- tended to collapse into a monistic materialism.  Mind, it seemed, was a mere fiction, much like religious notions about the cosmos turned out to be after Copernicus.

Yet, once one half of the Cartesian framework is removed, the remaining Material Realm is relatively simplistic.  Whereas Aristotle and Greek thinking generally postulated secondary qualities like tastes and smells and colors as part of the purely physical world, along with rich conceptual structures like forms, the Caresian materialist framework was minimalist, and consisted only in a void full of atoms--uncuttables-- and the mathematics necessary to measure and count and explain the movements of these particles.  The universe in the Cartesian framework was suddenly dry, and simple, and, well, bleak.  Hence what began as a full throated metaphysical dualism capable of sustaining a belief in an infinite Deity ended in a simple view of materialism amenable to doing science as it was done by the great minds of the Revolution.  Mathematics, and matter, and all else was fiction.

By the nineteenth century, then, the philosopher and scientist Simon-Pierre Laplace could proclaim that "he had no need of that hypothesis" when confronted with questions about how God fit into science.  Science, which began in a maelstrom of broad and speculative metaphysics, in grand, exalted concepts of things had in the span of a hundred years adopted not only a distinct and often hostile stance opposite of Western religion (and in particular the Judeo-Christian tradition), but had eschewed the "mind" or immaterial half of the Cartesian framework, adopting the other, minimalist, half instead.

Yet, Cartesian materialism has proven remarkably fruitful over the years.  If we think of the universe as simply matter and energy, and go about observing it, formulating hypotheses expressible in mathematics, and confirming these hypotheses with experiments (ideally), we end up with modern science.

Are there any limits to scientific enquiry?  Yes, and in fact the actual practice of science exposes limits seemingly as much (or as significantly)

What Copernicus, Galileo, Newton, and the other heroes of the Scientific Revolution gave us were Progressive Theories--pieces of knowledge about the physical world that showed us, in a postive way how things went, and how we could explain them.

[bridge into discussion of limits]


The Puzzle of Limits



In a trivial sense, scientific knowledge about the physical world is always limiting.  The inverse square law specifies the force of gravity in Newtonian mechanics:  for two objects the gravitational force between them is inversely proportional to the square of their distance.  This is a limit in a trivial sense because gravity can't now be described using some other equation (or any equation); it's not, for instance, inversely proportional to the triple of their distance.  But nothing really turns on this notion of limits, and indeed the very point of science is to find actual laws that govern the behavior of the world, and these laws will have some definite mathematical description or other.  When we say that pressure is related to volume in Boyle's Law, for instance, we don't feel we have a law until we've expressed the relationship to gas pressure and volume as a specific equation, which necessarily precludes other, different, equations.  All of this is to say we can dispense with the trivial notion of limits.

What's more interesting are cases where scientific investigation reveals fundamental limits to our knowledge of certain phenomena in the world.  Like with the Newton's Inverse Square Law or Boyle's Law for gases, we've isolated a physical systems and described it's causal or law-like behavior in mathematical terms (algebraic equations in the two examples), but once we have this correct account, it turns out that there are inherent limitations to how we can use this knowledge to further explain or predict events or outcomes in the system.  The system itself, one might say, once correctly described in the language of science, has characteristics that prevent us from knowing what we wish to know about it, or using the knowledge we do have in certain desired ways.

The first major limiting result in this non-trivial sense probably came from Maxwell's work in thermodynamics in the 19th century.  Entropy, as it came to be known, is perhaps the ultimate limiting result.
[]

The 19th Century had other surprises,  Henri Poincare, the great French mathematician, proved that the famous "Three Body Problem" was unsolvable, and in so doing anticipated much of the modern field of Chaos Theory.  The Three Body Problem states that ...

By comparison to the 20th Century, however, limiting results emerging from work in the 19th Century have been tame.  Two major 20th Century advances--one in physics and the other in mathematics--have ushered in sea changes to modern science that have greatly altered our Enlightenment notion of the nature and limits of science.  In physics, Heisenberg's Uncertainty Principle demonstrated that at the quantum level, we can't isolate the position and momentum of a particle simultaneously.  To get arbitrary precision of a particle's position, we necessarily change it's momentum, and likewise isolating the momentum of a subatomic particle limits our ability to pinpoint its position.  The Uncertainty Principle thereby established that as scientific investigation turns to the "really small", or subatomic scale of the universe, there are boundaries to our knowledge of physics.  It's important to note here that the Uncertainty Principle is not provisional, a result based on current limits to technology or to the state of physics in the early 20th century.  Rather, it's a valid result in general; it holds for any measurement of subatomic phenomena, anywhere, at any time.  It's a fundamental limit that we've discovered about the nature of our world, when we turn our investigation to subatomic scales.

Yet, for all the humbling implications of Heisenberg's principle, it helped launch modern quantum mechanics.  As is often the case, discovering what we can't know ends up more fruitful for science than discoveries about what we can.  Armed with the Uncertainty Principle, scientists were able to frame hypotheses and investigations into the nature of quantum phenomena and further develop the statistical framework of modern quantum mechanics.  Indeed, the notion that deterministic knowledge isn't fully possible in subatomic realm, and thus that a statistical distribution of possible outcomes must be provided, is one of the key insights of the new physics.  Not knowing our limitations, the statistical framework for quantum mechanics may not have fit into place so rapidly and intuitively as it did in the last century.  Again, limiting results in science have proven art of the backbone of progress in science, however paradoxical this may seem.

To wrap our minds around the significance of the productivity of limiting results in science, we might employ some metaphors.  Call the "limitless progress" assumptions undergirding the Scientific Revolution and 19th century scientific materialism (with Mach and others) Limitless Science.  A metaphor for Limitless Science will be something constructive, evoking the notion of continual progress by building something.  We might conceive of the results of Limitless Science as a crisscrossing of roads and infrastructure on a smooth round sphere (like the Earth, say, but without land marks like canyons or mountains that obstruct road-building).  To get to any location on the sphere of Limitless Science, you simply plot out the distance, allow for conditions like rain or say hills or sand or swamps, and lay out your roadway.  Each time a road is built, another location is accessible on the globe.  Continue in this way and eventually anyone can get anywhere on Limitless Science planet.  (To avoid having every square inch of the planet covered in roadways, we might stipulate that places within a bike ride or a walk from some road don't need their own roads.)

Beginning with the Second Law of Thermodynamics and moving through the 19th century to Poincare's insight into the chaotic behavior of complex systems on up through the 20th century we see that the limiting results stand in stark contradistinction to Limitless Science.  In fact, we'll need a different metaphor to visualize scientific progress in this world.  Our globe of ever-increasingly roadways doesn't capture the fact that many times our inroads to results end up in dead-ends.  So, by contrast, Limiting-Discovery Science isn't a pristine spherical object where any road (theory) can reach any destination, but rather obstructions like a Grand Canyon or a raging river, or a range of impassable mountains dot the landscape.  Building roads on Limiting-Discovery Planet is not a matter of plotting a straight line from a beginning point to a destination, but rather in negotiating around obstructions (limiting results) to get to final destinations.  We can have fun with this metaphor:  if Heisenberg's Uncertainty Principle is a Grand Canyon to be navigated around, then say the Second Law of Thermodynamics is the Himalayas, and Chaos Theory is the Pacific.  The point here is that scientific investigation discovers these impassable landmarks and our knowledge of the world then proceeds along roads we've engineered as detours in light of these discoveries.  And, to push the metaphor a bit, we find out things we can't do--places we can't go--unlike our Limitless Science globe, with its smooth, traversable surface.  It's no use building a road through the Grand Canyon, or over Mount Everest.  The features of this world eliminate options we thought we had, before the discoveries.  Likewise, of course, with scientific discovery itself.

To expland on this point a bit more, what's interesting about the metaphor is it helps us see that, in a way, every truth we discovery about the world around us is fruitful and progressive.  Discovering the Grand Canyon is a landmark of the Southwestern United States is only limiting if we'd assumed that our human ambitions to build roads all over our world would never be frustrated, by "facts on the ground" so to speak.  But scientific investigation is in the end about discovering truths, and these truths are fruitful even when limiting (when we first assume perfect, linear progress) because knowing how the world really is, is bound to be fruitful.  If you can't, after all, drive across the Grand Canyon, it's fruitful to know that fact.  You can then build a system of roads that skirts around it, and you're on your way.  Similarly with scientific discovery, when we realize we can't, for instance, isolate with arbitrary precision the position and momentum of a subatomic particle simultaneously, this knowledge about the observational limits of subatomic phenomena paves the way for a formulation of quantum mechanics in terms of statistics and probability, rather than causal laws that presuppose knowledge of impossibilities, and such results generate successful predictions elsewhere, along with technological and engineering innovations based on such results.  We learn, in other words, what we can do, when we discover what we can't.  And so it is with science, just as in other aspects of our lives.

This brings us to our next major point, which is that, unfortunately, scientific materialists hold metaphysical assumptions about the nature of the world that tend to force Limitless Science types of thinking about discovery.  If the entire universe is just matter and energy--if Physicalism is true, in other words--then every impossibility result emerging from scientific investigation is a kind of failure.  Why?  Because there's nothing mystical or immaterial about the universe, anywhere, and so one naturall assumes the sense of mystery and wonder will gradually give way as more and more physical knowledge is accumulated.  If Chaos Theory tells us that some physical systems exhibit a sensitive dependence on initial conditions such that long-range prediction of events in these systems is effectively impossible, this means only that with our current differential techniques (say, Lavier-Stokes equations for fluid dynamics) we have some limits in those types of systems.  Since there's nothing much going on but unpredictability from the properties of chaotic systems, there's sure to be some advances in our ability to build roads that will gradually whittle away the limitations here.  And to the extent that this isn't fully possible, it expresses only a fact about our limited brains, say, or the limits of computation given the complexity of the world.

To put all this another way, scientific materialists are committed to seeing limiting results in science either as placeholders until better methods come around, or as lacunae in our own noetic capabilities.  You might say this is the "we're too primitive" or "we're too stupid" response to limiting results.  What is manifestly not possible with a materialist presupposition is that the limits point to real boundaries in our application of physical concepts to the world.

Roughly, there are two two possibilities that materialists will ignore when confronted with Grand Canyons or the Himalayas on Limiting-Discovery Planet.  One, the Cartesian materialism presupposed in science since the Enlightenment might be wrong or incomplete, so that some expanded framework for doing science is necessary.  Two, there may be immaterial properties in the universe.  In this latter case, the reason we can't lay roadwork down through the parts of Arizona that intersect with the Grand Canyon is simply because there's no physical "stuff" there to work with; the Grand Canyon is a metaphor for something that is not reducible to matter and energy.  This is an entirely possible and even reasonable response when contemplating the meaning of the limiting results (i.e., we're up against a part of the universe that isn't purely material, which is why material explanations fall short), but they won't be entertained seriously by scientific materialists.  Again, since all of the universe is just assumed to be matter and energy (and we have historical, roughly Cartesian accounts of matter and energy, to boot), limiting results will always end up as commentary about humans-when-doing-science (that we're either too primitive still to get it, or just too stupid, which is close to the same idea sans the possibility of future progress).

But we can reign all this philosophical speculation in, for the moment (though we'll have to return to it later).   We've left out maybe the most interesting limiting result not only of the last century, but perhaps ever.  It's arguably the most fruitful, as well, as its publication in 1931 led step by step to the birth of modern computation.  This is rather like discovering the Grand Canyon, reflecting on it for a while, kicking around in the hot desert sands, and then realizing a design for an airplane.  The result is Godel's Incompleteness Theorems.  As its the sin qua non of our thesis--limiting results resulting in fruitful scientific research--we'll turn to it in some detail next.

[Godel's Theorem, Halting Problem, modern computation, computational complexity versus undecideability, AI]

 AI-A Giant Limiting Result

Yet, the lessons of science seem lost on the Digital Singularity crowd.  The apposite metaphor here is in fact the one we rejected in the context of actual science--Limitless Science Planet.  Everything is onward and upward, with progress, progress, progress.  Indeed futurists and AI enthusiast Ray Kurzweil insists that the lesson of the last few hundred years of human society is that technological innovation is not only increasingly but exponentially increasing.  Such a nose thumbing take on intellectual culture (including scientific discovery, of course, which drives technological innovation) excludes any real role for limiting results, and suggests instead the smooth, transparent globe of Limitless Science.  Indeed, as we build roads we become more and more capable of building better roads more quickly.  In such a rapidly transfiguring scenario, it's no wonder that Kurzweil and other Digital Singularity types expect Artificial Intelligence to "emerge" from scientific and technological progress in a few years time.  It's assumed--without argument--that there are no Grand Canyons, or Mount Everests, or Pacific Oceans to fret about, and so there's nothing theoretical or in principle that prevents computation--networks of computers, say--from coming alive into a super intelligence in the near future (we get the "near" part of "near future" from the observation that technological innovation is exponentially increasing).  This is Limitless Science at its finest.

But, in general, we've seen that Limitless Science isn't true.  In fact, there are lots of features of the actual world that the accretion of scientific knowledge uncovers to be limiting.  And indeed, the history of science is replete with examples of such limiting results bearing scientific and technological fruit.  The actual world we discover using science, in other words, is vastly different than Digital Singularitists assume.  It's time now to return to the question of whether (a) an expanded framework for science or (b) an actual boundary to materialism in science is required.  But to do this, we'll need to tackle one final limiting result, the problem of consciousness.

Prolegomena to a Digital Humanism

"What makes something fully real is that it's impossible to represent it to completion."  - Jaron Lanier

The entire modern world is inverted.  In-Verted.  The modern world is the story of computation (think:  the internet), and computation is a representation of something real, an abstraction from real particulars.  The computation representing everything and connecting it together on the internet and on our digital devices is now more important to many of us than the real world.  I'm not making a retrograde, antediluvian, troglodyte, Luddite point; it's deep, what I'm saying.  It's hard to say clearly because my limited human brain doesn't want to wrap around it.  (Try, try, try -- if only some computation would help me.  But alas.)

The modern world is inverted to the extent that abstractions of reality become more important than the real things.  A computer representation of an oil painting is not an oil painting.  Most people think the representation is the wave of the future.  The oil painting is, actually.

What's a computer representation of a person?  This is the crux of the problem.  To understand the problem we have to understand two big theory problems here, and I'll be at some pains to explain them.  First, suppose I represent you -- or "model" you, in the lingo -- in some software code.  Suppose for example I model all the employees working at a company because I want to predict who fits best for a business project happening in, say, Europe (It's a big corporation with huge global reach and many business units, like IBM.  No one really knows all the employees and who's qualified for what, except locally perhaps.  The example is real-world).  That necessarily means I have a thinner copy of the "real" you-- I may not know you at all, so I'm abstracting away some data stored in a database about you--your position, salary, latest performance reports, the work you do, a list of job skills.  Because abstractions are simplified models of real things, they can be used to do big calculations (like a database operation that returns all the people who know C++); it also means they leave out details.  Abstractions conceived of as accurate representations are a lie, to put it provocatively, as the philosopher Nietzsche remarked once (He said that Words Lie.  You say "this is a leaf", pointing to a leaf.  There is no such thing as a "leaf" as an abstract concept.  There are only leaves...).

All representations are in a language, and every language has limits to its expressiveness.  Natural language like English is most expressive, which is why a novel or a poem can capture more about the human experience than mathematics can, or computer code.  This point is lost on many Silicon Valley 'Singularity' types--technologists and futurists who want computation to replace the messy real word.

Change the example if you want, because abstracting the real world and especially human behavior into slick computer models is all the rage today.  Examples abound.  Say I go shopping at the same store.  I shop at Safeway.  Whenever I go to Safeway, I get a bunch of coupons when I check out at the self check out.  The coupons are strangely relevant--I get deals on protein bars and chocolate milk and so on.  Funny thing is that I buy all those items, but I didn't necessarily buy any of the coupon items when I received those coupons.  What's happening here is that Safeway has made a model of "me" in its databases, and it runs some simple statistics on my purchases as a function of time (like:  month by month by item type, say), and from this data it makes recommendations.  People like this sort of service, generally.  You talk to technologists and the people who're modernizing the consumer experience and you'll get a vision of our future:  walk into the supermarket and scan your ID into the cart.  It starts directing you to items you need, and recommending other items "I noticed you bought tangerines the other day, you might like tangelos too.  They're on sale today on Aisle 5."

Now, nothing is wrong here, it's just a "lie" of sorts is all.  I'm generally not my Safeway model is all.  Not completely.  The model of me that Safeway has is based on my past buying patterns, so if I change anything or if the world changes, it's suddenly irrelevant and it starts bugging me instead of helping me.  It's a lie, like Nietzsche said, and so it gets out of sync eventually with the actual me that's a real person.  I don't buy chocolate but on Valentines Day I do, say.  Or I'm always buying ice cream but last week I started the Four Hour Body diet and so now I only buy that on Saturdays, and I buy beans all the time now.  But right when I get sick of them and start buying lentils the system has a good representation of me as a bean-buyer, so now I'm getting coupons for beans at precisely the time I'm trying to go no-beans (but I'm still into legumes).  Or I'm running errands for someone else, who loves Almond Milk.  Almond Milk is on sale but I don't get that information; I only see 2% Lactose Free milk is on sale because I usually buy that.  The more the model of me is allowed to lord over me, too, the worse things get.  If the cart starts pulling me around to items that I "need", and it's wrong, I'm now fighting with a physical object -- the shopping cart -- because it's keeping me from buying lentils and Almond Milk.  None of this has happened yet, but welcome to creating mathematical objects out of real things.  The computer can't help with any of my buying behavior today, because it's got a stale, simple model of me based on my buying behavior yesterday.  That's how computers work.  (Would it be a surprise to learn that the entire internet is like this?  I mean:  a shallow, stale, simple model of everything?  Well, it is.  Read on.)

Let's finish up with abstraction.  Someone like Mathew Crawford, who wrote the best-selling "Shop Class as Soul Craft", dropped out of a six figure a year job at a think tank writing about politics in D.C. to fix motorcycles, because when he realized the modern world is inverted, and abstractions are becoming more important than real things and experiences, he was desperate to find something meaningful.  He wasn't persuaded, like the Silicon Valley culture seems to be, that all these abstractions are actually getting smarter and smarter and making us all better and better.  He opened a motorcycle repair shop in Virginia and wrote a book about how you can't rely on abstractions and be any good at all fixing real things like motorcycles.

This is an interesting point, actually.  Crawford's an interesting guy.  You could write a dissertation alone on how difficult it is to diagnose and fix something complicated.  You can download instructions and diagnostics from the internet, but you're not a real mechanic if you don't feel your way through the problem.  Computation is supposed to replace all of this embarrassing human stuff like intuition and skill and judgement.  "Feeling our way" through things is supposed to be in the past now, and really the pesky "human element" is supposed to go away too.

A confusion of the modern inverted age is that as computers get smarter (but they don't, not like people do), we're supposed to get smarter and better, too.  But all this sanguine optimism that everything is getting "smarter" disguises the truth, which is that it's impossible for us to get "smarter" by pretending that computers are smarter--we have to choose.  For example, if we pretend that abstractions are "smart", we have to fit into them to continue the illusion.  If we start imposing the messy reality of life onto things, the abstractions will start looking not-so-smart, and then the entire illusion is gone.  Poof!  To the extent that we can't handle exposing our illusions, we're stooping down to accommodate them.  All this becomes clear when you open a motorcycle repair shop and discover that you have to feel your way through the problem and abstractions of fixes don't really help.

So much for Crawford.  There are so many Crawford's today, actually.  I think it's time to start piecing together the "counter-resistance" to what Lanier calls the Digital Maoists or Cybernetic Totalists--the people saying that the abstractions are more real and smart than what's actually real and smart.  The people saying the human element is old news and not important.  The people saying that the digital world is getting smarter and coming alive.  If it sounds crazy (and it should), it's time to start pointing it out.

I can talk about Facebook now because I don't like Facebook at all and almost everyone I know seems to be obsessed with it.  (It makes me reluctant to complain too much or when I'm aggravated it emboldens me to complain with a kind of righteous indignation.)  Facebook is a model of you for the purposes of telling a story about you.  Who is reading the story and why?  On the internet this is called "sharing" because you connect to other models called "friends" and information about your model is exchanged with your Friend-Models.  Mostly, this trickles down to the actual things--the people--so that we feel a certain way and receive a certain satisfaction.  It's funny that people who use Facebook frequently and report that they have many friends on Facebook also report greater degrees of loneliness in the real world.  Which way does the arrow of causality go?  Were they lonely types of people first?  Or does abstraction into shallow models and using emotional words like "friends" and "social" make their actual social existence worse somehow?

I don't think there's much wrong with a shallow Facebook model of me or you, really.  Facebook started out as a way to gawk at attractive nineteen year old Harvard women, and if you want to do this, you need an abstraction that encourages photo sharing.  I don't necessarily want this experience to be deep either.  I don't want three hundred friends to have a deep model of me online, necessarily, either.

Theoretically, though, the reason Facebook models are shallow is the same reason that Safeway only wants my buying behavior in my Supermarket Model.  Since "Facebook" is really a bunch of servers (a "server" is a computer that services other computers--it's a computer is all), then what the real people who own Facebook can do is determined by what Facebook computers can do, with our models.  Since computers are good at doing lots of shallow things quickly (think the Safeway database), why would Facebook have rich models of us?  Then they couldn't do much with them.  It's an important but conspiratorial-sounding point that most of what Facebook wants to do with your Facebook model, connected to your Facebook friend models, is run statistics on what ads to sell you.  It's another significant but bomb-shell type of observation here that all the supposed emerging smartness of the World Wide Web is laser-focused on targeted advertising.  All this liberation we think we feel is really disguising huge seas of old-fashioned persuasion and advertising.  Because everything we get online is (essentially) free -- think Facebook -- it's no wonder that actual money will be concentrated on ads.  (Where does the the actual earned money come from still, to buy the advertised goods and services?  That gets us back into the messy real world.)

So much for abstraction.  Let's say that abstraction is often shallow, and even vapid.  It's incomplete.  This says something true.  It means that we ought to think of computation as a shallow but convenient way to do lots of things quickly.  We shouldn't confuse it with life.  Life here includes the mystery of human life:  our consciousness, our thoughts, and our culture.  We confuse abstractions with the fullness of life at our peril.  It's interesting to ask what vision of digital technology would support a better cultural experience, or whether shallow, infantile, ad-driven models are the best we can do.  Maybe why we cheer lead so loudly for Facebook and the coming "digital revolution" is because we think it's the only way things can turn out, and it's better than horses and buggies.  This is a really unfortunate way to think about technology and innovation I would say...

The second way the modern world is inverted-- the second theoretical problem with treating computer models as reality -- is known as the Problem of Induction (POI).  Someone like former trader Nasam Nicholas Taleb describes the POI as the problem of the Black Swan.  Most swans -- the vast majority of swans -- are white, so eventually you generalize in your code or your database or your mind to think something like "All swans are white."  Taleb calls this a Gaussian distribution (or normal distribution) because you don't expect there to be outliers that screw everything up.  Taleb says that sometimes the real events in the world are not so much like Gaussian distributions but are like exponential ones.  He calls this the Black Swan phenomenon.  It's tied to the ancient POI as I'll explain.  I mean:  when a Black Swan shows up and we all thought "All swans are white."

We'll say first that a Gaussian or normal distribution is like the height of people in the real world.  Most people are between 5 and 6 feet tall, the vast majority in fact.  This is a normal distribution.  It's rare to have a 7' guy or a 4' one, and essentially impossible to have a 9' one or a 3' one.  If human height was like an Exponential Distribution, though, or a "Black Swan", then occasionally there'd be a guy that was a hundred feet tall.  He'd be rare, but unlike with the Gaussian, he'd be guaranteed to show up one day.  This would screw something up no doubt, so it's no wonder that we prefer the Gaussian for most of the representing we do of the actual world.

Taleb explains, however, that when it comes to social systems like the economy, we unfortunately get Black Swans.  We get 100 feet tall people occasionally, or in other words we get unpredictable market crashes. We can't predict when they'll happen, he says, but we can predict that they'll come around eventually, and screw everything up.  He says that when we create shallow abstractions of real economic behavior like with credit default swaps and derivatives and other mathematical representations of the real world, we are guaranteed to get less predictable behavior and really large anomalies (like 100 feet tall people).  So he says that the economy is not Gaussian.

All of this is well and good but all the computer modeling is based on Gaussian principles.  This is what's called Really Bad, because we're relying on all that modeling, remember.  It means that as we make the economy "digital" with shallow mathematical abstractions (like default swaps), we also make it more of a "lie", in so far as the Black Swan feature will tend to get concealed in the layers of Gaussian computation we're using to make money.  All the money is made possible when we get rid of the rich features of reality, like actual real estate, and digitize it.  If we know that, sooner or later, we're guaranteed to lose all the money we've made, because the future behavior of these systems contains a Black Swan, but our computer models assure us that the swans are all white, do we care?  As long as we make the money now, maybe we don't.  If we know we're getting lonely on Facebook but we still have something to do at night with all of our representations of friends, do we care?  It takes some thought to figure out what we care about and whether we care at all.  (It's interesting to ask whether we start caring, as a rule, only after things seem pretty bad.)

This is the case with the economy, it seems.

So the second big theory problem with the inverted modern world is that computation is inductive.  This is a fancy way of saying that the Safeway database cannot figure out what I might like unless it's based on what I've already proven I like.  It doesn't know the real me, for one.   It knows the abstraction.  And even more importantly, because computation is inductive, it must always infer something about me or my future based on something known about me and in my past.  Human thought itself is partly inductive, which is why I'll expect you to show up at around 5 pm at the coffee shop on Thursdays, because you always do.  But I might also know something about you, like say that you're working at 5.

Knowing that you're working at 5 on Thursday is called "causal knowledge", because I know something about you instead of just the past observations of you showing up.  I have some insight about you.  It's "causal" because if you work at 5 on Thursday, that causes you to be there regardless of whether you've shown up in the past.  It's a more powerful kind of knowledge about you.  We want our computers to have insights like this but really, they are more at home with a database full of entries about your prior arrivals on Thursdays at 5.  The computer really doesn't care or know why you show up.  This is induction.

Induction applies to the Black Swans in stock market crashes because we were all thinking that "All swans were white" based on our computer models of the past.  Those models were wrong, it turns out, so we didn't see the Black Swan coming.  If we hadn't been convinced the computer models were so smart, we might have noticed the exponential properties of the system.  Or:  we might have noticed the inherent, real world volatility that we were amplifying by abstracting it, and relying on inductive inferences instead of causal knowledge or insight.  Computers are very good at convincing us we're being very smart about things by analyzing all those huge data sets from the past.  When something not in that past shows up, they're also very good at making things become chaotic.  This is a reminder that the real world is actually in charge.

It's very complicated to explain why computers don't naturally have the "insight" or "causal knowledge" part of thinking that we do (and why they can't really be programmed to have it in future "smarter" versions either).  Generally Artificial Intelligence enthusiasts will insist that computers will get smarter and eventually will have insights that predict the Black Swans (the very ones they've also made possible).  In general however the Problem of Induction, which is a kind of blind spot (to go along with the "lie" of abstraction) is part and parcel of computation.  If you combine this inductive blindness with the shallowness of the models, you get a world that is really good at doing simple things quickly.  If you question whether this is our inevitable future, and whether perhaps there are entirely new vistas of human experience and culture available to us (including the technology we make), I think you're on the right track.

Here is a representation of me: 42 73 1 M.  What does it mean?  I once used something called "log linear" modeling to predict who would pay traffic tickets in data provided by the state of Iowa (true). We used the models of hundreds of thousands of people with database entries like this example, but more complicated, to predict those with greater than some number n likelihood to never pay.  Then we recommended to the state of Iowa not to bother with these people.  It worked pretty well, actually, which is why we make shallow representations for tasks like this...

What's funny about technologists is how conservative they are.  A hundred fifty years ago, the technologists were passionately discussing the latest, powerful methods for extracting whale oil from the blubber of Sperm and Baleen whales harpooned and gutted against the wooden hulls of Atlantic Whalers.  No one stopped to wonder whether the practice was any good, because it seemed inevitable.  There was money to be made, too.  No one even considered that perhaps there was something better, until petroleum showed up.  This is why you see techno-futurists like Kevin Kelly, co-founder of Wired magazine, or author and futurist Ray Kurzweil always talk as if they can extrapolate our future in the digital world from observations of the past.  They pretend it's simple and like a computation to see into the future.  Kelly is also eager to explain that technological innovation is not the product of individual, human insight and genius but rather a predictable and normal process.  The great philosopher of science Karl Popper explained how technological innovation is intrinsically unpredictable.  But you can see that Kelly and folks like Clay Shirky (Here Comes Everybody, Cognitive Surplus) already see the future and already have concluded that humans have less and less to do with it, as digital technology gets smarter and smarter.  All these predictions and all those books sold (and real paper books, too!) would be wrong if someone just invented a better mouse trap, like people always do.  When petroleum became readily available all the whale-oil-predictions became silly and retrograde, almost overnight.

If you believe there are no Black Swans and things are moving in a direction, you don't like these comparisons (do you?).  But the real world is messy and technology is not smart in the way that human minds are, so we have to pretend if we want to predict the future that's described.  When everything is shallow (abstraction) and quick but limited (induction) you need something to grab onto to compensate, which is why we say all the computation will get "smarter."  If it doesn't, we're stuck pretending that shallow and quick is human culture.  That's too hard to do, eventually, which is why we have innovation and why the Atlantic Whalers eventually became obsolete and why we're due for some different digital designs than what we have now.  I have some thoughts on this, but that's the subject of another discussion.

The End of Stupidity

That the links between web pages, rather than the words on the pages, are a good guide to quality (roughly, the more links a page has, the better it is) was, as we've discussed, the key insight that propelled Google into the limelight on the Web.  But this makes the Trivial Content objections all the more puzzling:  certainly, there are lots of quality Web pages on the Web, and the success of Google seems to lie in the confirmation that, mostly, it finds them for us.  And it does, much of the time.  But the Achilles Heel of Google's ranking system--ordering the results of a search with the best first--is in the same insight that made it so popular.

Popular.  That's the Achilles Heel.  Simply put, the results on the first page of a Google search are the ones everyone else on the Web thinks are "good."  But we don't know anything about all those other Web users, except that they liked the content (by linking to it) that we're now getting.  To make the point here, in the academic journal situation (which inspired PageRank, remember), we know lots about the authors of the articles.  We know for instance, that if Person A references Article Z, written by Person B, that both A and B are published authors in peer reviewed journals--they're experts.  Hence if we collect all the references to Article Z by simply counting up the experts, we've got a good idea of the value of Z to the community of scholars who care about whatever Z's about (Z's topic).  Since we're dealing with expert authors, counting them all up (recursively, but this is a detail) makes a ton of sense.

Skip to the Web, now, and first thing that goes is "expert."  Who's to say why someone likes Web page Z?  Who's to say, if Person A likes Z, and Person B likes Z, and so on, that anyone is an expert about "Z" at all?  The Web case is different than the academic article case, then, because the users have no intrinsic connection to the content--they're not credentialed in any measurable way as experts or authorities on whatever Web page Z's about.  Lots of anonymous folks like Z; that's what we know.

This feature of Web ranking has a number of consequences.  One is that large, commercial sites tend to end up on the first page of Google results.  If I query "hiking boots", I'm likely to see lots of Web sites for big stores trying to sell me hiking boots, like REI, or Timberland, or what have you.  Of course, many Web users simply want big commercial web sites (and not, say, a blog about hiking boots, or an article about the history of hiking boots).  Most people using the Web want what most people linking things on the Web want--this is just to say that what's popular is by and large what most people want (a truism).  This is why Google works, and for the very same reason it's why it doesn't (when in fact it doesn't).

The next consequence is really a corollary of the Big Business consequence just noted.  We can call this the "Dusty Books" objection, because it's about content that is exactly what you want, but isn't exactly the most popular content.  This'll happen whenever you're looking for something that not a lot of people think about, or care about, or for whatever reason isn't popular enough to get a high ranking.  It's a dusty book, in other words, like the book you find hidden away on a shelf of the library, last checked out three years ago, say, with dust on its cover from disuse.  Only, that's what you're looking for, it turns out.  You'll never see the dusty books in Google searches.  This is the point; if you think about how Google works for a second, it's an obvious point too.  Dusty books, by definition, aren't popular.  They're the Web pages that you want, but never find, and there are lots of them.  Think for another second about Google and you'll see the deeper problem, too:  works so well most of the time for popular content means that some of the time it doesn't work at all.  All that popular, unwanted content, is guaranteed to keep your dusty book hidden forever, back on the tenth or hundredth page of search results (and who looks at those?).  Google, in other words, gives us what we want whenever it's what everyone else wants too; if it's just what you want, all those other people on the Web are now your enemies.  They're hiding your dusty book from you.

But what could we want, that's not popular?  Oh, lots of things.  If I'm thinking of driving Highway 101 from Washington to California, say, I may want a big travel planner site telling me where the hotels are, or the camping grounds, or I may want a personal blog from someone who can write, who's actually driven the route, and can tell me all sorts of "expert" things that commercial Web sites don't bother with.  This fellow's blog may or may not be popular, or linked to a big travel site, so it's a crap shoot if I find it with Google (even if it's popular as a homegrown blog, it isn't popular compared to Trip Advisor).

Faced with this scenario, many people take to a blog search engine like Google Blog Search, or Technorati, or Ice Rocket (Google Blog Search is probably the best).  Only, the popularity-as-quality approach screws this up too, if you're looking for the expert opinion from the experience traveler of 101 who writes a personal and informative blog.  Why?  Because the most linked to stories about "Highway 101" are a litany of traffic accidents in local newspaper articles (somehow considered "blogs" by Google Blog Search).  For instance, the second result for the query "driving Highway 101" to Google Blog Search is: "Woman killed on Highway 101 near Shelton."  And lest we think this is a fluke, the third result is "Can toll lanes on Highway 101 help pay for Caltrain?", and the fourth is the helpful "Man Who Had Heart Attack in Highway 101 Crash Dies in Hospital."  Clearly, what's popular to Google Blog Search has little to do with what our user interested in driving 101 has in mind.  (Incidentally, the first result is three paragraphs from northwestopinions.com about the Christmas light show on 101 every year.  At least "northwestopinions.com" might be a find.)

What's going on here?  Well, you're getting what everyone links to, that's what.  The more interesting question is how we've all managed to be in the dark about the limitations of the approach that we use day in and day out.  Even more interesting:  exactly how do you find good blogs about driving Highway 101 (or hiking boots, or lamp shades, or whatever)?  Well, most people "Google around" still, and when they happen upon (in the search biz: "discover") an interesting site, or a portal site like Fodors or Trip Advisor, they save the URL or remember how to find it again.  Mostly, they just miss dusty books, though.

To continue with the Dusty Books metaphor, and to see the problem in a different way, imagine the public library organized according to popularity, rather than expertise on the topic, or authority (books that are published are ipso facto books with authority).  Someone wrote a definite history of 101, or the guide to driving 101, but it's so detailed that most people don't bother to read it.  They get the lighter version, with the glossy cover.  Ergo, the definite guide just disappeared from the library shelf.  It's not even a dusty, seldom read book, it's simply not there anymore (this is akin to being on page 1323, say, of a Google search).  This is swell for all those 101 posers and dilettantes, but for you, you're really looking for the full, 570 page exposition on 101.  This is a ridiculous library, of course, because (we're all tempted to say, in chorus) what else is a library for, but to give you all the expertise and authoritative books on a topic?  Who cares what's darned popular?  Indeed.  Returning then to the Web world, it's easy enough to see the limits of the content we're getting (and why, most of the time, we're all happy with it).  Put it another way, the Web is skewed toward Trivial Content--every time what's popular trumps what's substantive, you get the popular.  (To be sure, when what's popular is also substantive--say, because "popular" expositions of Quantum Mechanics are those written by Scientific American writers, or MIT professors--there's no problem.)

But is this why Google is making us stupid?  Well, sort of, yes.  It's easier to see with something like "politics" or "economics", say.  If Web 2.0 liberated millions of people to write about politics, and Google simply delivers the most popular pages on this topic for us, then generally speaking all the "hard" discussions are going to fall off of the first page of a Google search.  "Popular politics" on the Web isn't William Jennings Bryan, it's usually a lot of surface buzz and griping and polarization.  Good versus evil.  Good guys, bad guys.  Doomsday predictions and everything else that crowds seize upon.  True, large media sites like the New York Times will pop up on the first page of a query about "health care crisis."  This is a consequence of popularity too (same reason that Trip Advisor shows up with hotel prices on your Highway 101 search).  But if you're looking for interesting, informed opinions at their in the public (say, from good bloggers or writers), you don't care about the NYT anyway.  Since Google doesn't care about the quality of an article, whatever has shock value is likely to be what you get for all the rest.  We might say here that, if Google isn't actively making us stupid for Trivial Content reasons alone, if we're already uninformed (or "stupid"), it's not helping us get out of this situation by directing us to the most thoughtful, quality discussions.  It's up to us to keep looking around for it, full of hope, as it were.  (And, if we don't know what to look for, we're likely to think the Google results are the thoughtful ones, which explains why half my friends in the programming world are now conspiracy theorists, too.  Four years of learning to program a computer in "real" college, and their politics on the Web, and that's what you get.  Alas.)

To sum this up, then, the full answer to the question we began with ("is Google making us stupid?") is something like, yes.  While we didn't address all the reasons, we can blanket this with:  it's a Crappy Medium with Lots of Distractions that tends to encourage reading Trivial Content.  Mostly, then, it's not helping us become classically trained scholars, or better and more educated in the contemplative and thoughtful sense.  I've chosen to focus mostly on Trivial Content in this piece, because of the three, if you're staying on the Web (and most of us will, me included), improving the quality of search results seems the most amenable to change.  It takes, only, another revolution in search.  While it's outside the scope of this article to get into details (and like Popper once said, you can't predict innovation, because if you could, you'd already have innovated), a few remarks on the broad direction of this revolution are in order, by way of closing.

Search.next()

Google's insight, remember, was that the links between Web pages, and not only the words in pages were good guides to quality.  It's interesting to note here that both the method Google replaced (the old Alta Vista search approaches that looked at correlations between words on a page and your query words) and its PageRank method relay on majority rules calculations.  In the old-style approach--what's called "term frequency - inverse document frequency or tf-idf calculation--the more frequent your query terms occur in the terms of the documents, the higher the rank they receive.  Hence, "majority rules" equals word frequency.  In the Google approach, as we've seen, "majority rules" equals link-to frequency.  In either case, the exceptions or minorities are always ignored.  This is why Google (or Alta Vista) has a tough time with low frequency situations like sarcasm:  if I write that "the weather here is great, as usual" and it's Seattle in December, most human readers recognize this as sarcasm.  But sarcasm isn't the norm, so mostly your query about great weather places in December will take you to Key West, or the Bahamas.  More to the point, if I'm looking for blogs about how the weather sucks in Seattle in December, the really good, insightful blog with the sarcasm may not show up.  

So interestingly the Google revolution kept the same basic idea, which is roughly that converting human discourse or writing into computation involves looking for the most-of-the-time cases and putting them first.  Human language is trickier and more interesting and variegated than this approach, of course, which is the key to understanding what may be next in search.  Intrinsic quality is a property of the way a document is written.  Many computer scientists avoid this type of project, feeling it's too hard for computation, but in principle it's a syntactic property of language (and hence should be translated into computer code).  Consider the following writing about, say, "famous writers who visited or lived in Big Sur, California."

Exhibit A

"I heard lots of really good writers go to Big Sur.  This makes sense to me, because the ocean is so peaceful and the mountains would give them peace to write.  Plus the weather is warm.  I can imagine sitting on the beach with a notepad and writing the next great novel at Big Sur.  And later my girlfriend and I would eat S'mores and build a fire.  My girlfriend likes to camp, but she doesn't hike very much.  So when I write she'd be at the camp maybe I don't know.  Anyway I should look up all the writers who went there because there must be something to it."

What's wrong with Exhibit A?  Nothing, really.  It's just, well, trivial.  It's Trivial Content.  But why?  Well, the author doesn't really say that much, and what he does say is general and vague.  He doesn't seem to know much about Big Sur, except that it's located near the ocean and it's forested, and other common pieces of knowledge like that you can camp and hike there.  He also doesn't seem to know many details (if any) about the writers who've spent time in Big Sur, or why they did.  In short, it's a vague piece of writing that demonstrates no real knowledge of the topic.  Enough of Exhibit A then.  

Exhibit B 

"BIG SUR, Calif. — The road to Big Sur is a narrow, winding one, with the Pacific Ocean on one side, spread out like blue glass, and a mountainside of redwood trees on the other.

The area spans 90 miles of the Central Coast, along Highway 1. Los Angeles is 300 miles south. San Francisco is 150 miles north. There are no train stations or airports nearby. Cell phone reception is limited. Gas and lodging are pricey."

"Venerated in books by late authors Henry Miller and Jack Kerouac, it's no wonder then that Big Sur continues to be a haven for writers, artists and musicians such as Alanis Morissette and the Red Hot Chili Peppers, all inspired by a hybrid landscape of mountains, beaches, birds and sea, plus bohemian inns and ultra-private homes."

"In the 1920s, American poet Robinson Jeffers meditated about Big Sur's "wine-hearted solitude, our mother the wilderness" in poems like "Bixby's Landing," about a stretch of land that became part of Highway 1 and the towering Bixby Bridge 13 miles south of Carmel. (Part of the highway near that bridge collapsed due to heavy rains this past spring, followed by a landslide nearby; the roadway reopened recently.)"

"Among literary figures, Miller probably has the strongest association with the area. "Big Sur has a climate all its own and a character all its own," he wrote in his 1957 autobiographical book "Big Sur and the Oranges of Hieronymus Bosch." "It is a region where extremes meet, a region where one is always conscious of weather, of space, of grandeur, and of eloquent silence."

Miller, famed for his explicit novel "Tropic of Cancer," lived and worked in Big Sur between 1944 and 1962, drawn to the stretch of coast's idyllic setting and a revolving cadre of creative, kind, hard-working residents."

 What's better about Exhibit B?  Well, it's specific.  Qualitatively, the author (Solvej Schou, from the AP.  The full story appears in the Huffington Post) has specific facts about Big Sur and about the writers who've spent time there.  The paragraphs are full of details and discussion that would, presumably, be appreciated by anyone who queried about writers at Big Sur.  But quantiatively, or we should say here syntactically, the paragraphs are different than Exhibit A too.  Exhibit A is full of common nouns ("camp", "hike", "ocean", "writers") and it's relatively devoid of proper nouns that pick out specific places or people (or times, or dates).  Also, there are no links going out of Exhibit A--not links to Exhibit A, but links from Exhibit A--to other content, which would embed the writing in a broader context and serve as an external check on its content.  Syntactically, there's a "signature" in other words, that serves as a standard for judging Exhibit B superior to Exhibit A.  Key point here is "syntactic", because computers process syntax--the actual characters and words written--and so the differences between the two examples are not only semantic, and meaningful only to human minds.  In other words, there's a perfectly programmable, syntactic "check" on page quality, it seems, which is intrinsic to the Web page.  (Even in the case of the links we mentioned in Exhibit B, they're outbound links from the document, and hence are intrinsic to the document as well.)

In closing, I'd like to make a few broadly philosophical comments about the terrain we've covered here with our discussion of intrinsic quality above.  If you've spent time reading about "Web revolutions" and movements and fads (they're usually "revolutions") from thinkers like Shirky or any of a number of Web futurists, you're always led down the road toward democratization of content, and the "wisdom of crowds" type of ideas, that tend naturally to undervalue or ignore individual expertise in favor of large collaborative projects, where content quality emerges out of the cumulative efforts of a group.  Whereas group-think is terrible in, say, entrepreneurial ventures (and at least in lip service is bad in large corporations), it's all the rage for the Web enthusiasts.  I mentioned before that an iconoclast like Lanier calls this the "hive mind" mentality, where lots of individually irrelevant (if not mindless) workers collectively can move mountains, creating Wikipedia, or developing open source software like Linux.  The Web ethos, in other words, doesn't seem too inviting for the philosophical themes introduced here:  a verifiable check on document quality (even if not perfect, it separates tripe like Exhibit A from something worthy of reading like Exhibit B), and along with it some conceptual tip of the hat to actual expertise.  It doesn't seem part of the Web culture, in other words, to insist that some blogs are made by experts on the topics they address, and many others are made by amateurs who have little insights, knowledge, or talents.  It's a kind of Web Eliticism, in other words, and that seems very un-Web-like.

Only, it's not.  Like with the example of Yelp, where a reviewer has a kind of "circumstantial" expertise if they've actually gone to the cafe in the Mission District and sat and had an Espresso and a Croissant, there's expertise and authority stamped all over the Web.  In fact, if you think about it, often what makes the Web work is that we've imported the skills and talents and knowledge in the real world into the cyber realm.  That's why Yelp works.  And so the notion of "authority" and "expertise" we're dealing with here is relatively unproblematic.  No one gripes that they don't like their car mechanic to be an "expert" for instance;  rather, we're overjoyed when the person who fixes our ailing Volvo actually does have mechanical expertise--it saves us money, and helps assure a successful outcome.  Likewise we don't read fiction in the New Yorker because we think it's a crap shoot if it's any better than someone could write, pulled off of the street outside our apartment.  Not that New Yorker fiction is someone "better" in an objectionable, elitist way (or that the woman walking her dog out on the street couldn't be a fantastic short story writer), but only that the editors of the New Yorker should (we hope) have some taste for good fiction.  And same goes for the editorial staff of the New York Times, or contributing writers to, say, Wired magazine.  

We're accustomed to expecting quality in the real world, in other words, and so there's nothing particularly alarming about expecting or demanding it online, too.  For, from the fact that everyone can say anything about anything on the Web (which is the Web 2.0 motto, essentially), it simply doesn't follow that we all want to spend our day reading it.  For one, we can't, because there's simply too much content online these days.  But for two, and more importantly, we don't want to.  First, because life is short, and we'd rather read something that improved or enlightened or even properly amused or entertained us.  And second, because, as the recent backlash against the Web culture from Carr, Lanier, and others suggest, it's making us stupid.  And, of course, life should be too short for that.

The Triumph of Triviata

Almost as soon as user generated content became an acronym, two rival interpretations appeared among cultural critics and technologists and seemingly everyone else.  On the one hand, someone like Web guru turned NYU professor Clay Shirky (Here Comes Everybody, Cognitive Surplus) seized on the democratizing, collaborative possibilities of the social, Web 2.0 movement.  Whereas Big Media once told everyone what was important (epitomized in antediluvian declarations like Cronkite's "and that's the way it is"), the Web was making it possible now for us to tell each other what we cared about; what was important.  To someone like Shirky, or Stanford law professor Lawrence Lessig (Free Culture), or Harvard technology theorist Yochai Benkler (The Wealth of Networks), it seemed that the Web was a kind of information liberation movement, destined to make all those passive readers of yesterday tomorrow's writers and trend setters and innovators.  It wasn't simply that we had more options with UGC--more things to look at and to enjoy--it was that we had an entire, revolutionary, technological means for large-scale social change and improvement.  "What gives?" was missing the point, and borderline nonsensical.  "What's next?" was the only relevant question.  As the popular Microsoft ad of the time put it (ironically referring to someone sitting at a computer):  Where do you want to go today? The answer, to the Web 2.0 enthusiasts and visionaries, was a resounding anywhere.

On the other hand, folks began noticing before long that much of the content generated by all these newly liberated creators wasn't worth much, to put it bluntly.  The LA Times attempted to capitalize on the new Web culture by allowing anyone to comment and even contribute to its stories; this lasted a few days, until the sheer magnitude of silliness and irrelevance and tastelessness peppering its woebegone pages forced an about face, and they discontinued the feature in disgrace (albeit quietly).  Other media giants like the New York Times or the Wall Street Journal of course launched "Web 2.0" online versions with comments sections, but they were notably safeguarded from the "mob rules" type of scenario that embarrassed the LA Times.  In general, it became apparent that while anyone could say anything and publish it online, editorial standards in the traditional sense were more, not less, necessary in such an environment.

Blogging became ubiquitous, entering into our lexicon shortly after appearing first as "Web logs", and gave voice to the common person, to be sure.  But most blogs were silly missives written by uninformed amateurs who either borrowed from actual reporting to regurgitate or expound on ideas and stories, or simply neglected serious discussion altogether, journalistic or otherwise, in favor of mindless off-the-cuff chatter about their significant others, their sports cars, or other desiderata that few others found worthy of reading.  A few blogs became important in serious discussions; most of the millions of others were scarcely worth knowing about.  Still, they were, all of them, "published" on do-it-yourself blogging platforms like Live Journal or Google's Blogger, and it was all readable to anyone who cared, and all UGC.  Similar observations apply here to amateur videos on YouTube, to "mashing up" content like songs by combining existing artists' singles, and on and on.  In short, sans the social change rhetoric, "UGC" was largely what one might expect, by the end of the 2000s:  lots of amateurish, often inaccurate, often mendacious, and rarely publishable (in the traditional sense) written and multi-media content, everywhere.  Crap, in other words.

The sobering reality of Web 2.0 when judged by traditional media standards should not, in retrospect, have been much of a surprise.  Viewed statistically, any large sample of the population will generally not happen to be award-winning journalists, novelists, musicians, or movie makers.  That's life.  But what was, perhaps, a surprise were the success stories, like Wikipedia.  Here, anonymous users collaborated in an open "Wiki" environment to produce encyclopedia entries, and as the project exploded in the early 2000s, with some famous exceptions, the quality of the articles appearing on Wikipedia seemed to confirm, not challenge, the idea that there could be "wisdom in crowds", and that Shirky et al really were prescient in seeing the transformative social potential of Web 2.0.  Fair enough.  But notwithstanding the successes, there was a deeper problem emerging that would pose more fundamental challenges to the technological revolution of the Web.  To see it clearly and at its root, we'll need to return to the issue of search, and to Google search in particular.

Whoops! Idiocracy

In the last section, we surveyed the rise of search, focusing on (who else?) Google, and saw how Google's insight about human judgments in HTML links propelled Web search into the modern era.  In this vein, then, we can see the beginning of the entire social revolution (roughly, from Web 1.0 to Web 2.0 and on) as a story of the beginning of "real" Web search with Google's PageRank idea.  Yet we ended this feel-good section back where we started, with all the original worry about the Web making us stupid, a view given recent voice by folks like Carr and Lanier, and even more recently with the latest The Atlantic Cities article on the dangers of photo sharing; fretting now about our memories and memory formation in the Instagram-age (always, alas, worried about our brains online).  What gives?  This is our question.

Before answering it, though, it'll be helpful to review the general landscape we've been traversing.  Back to the beginning, then, we have:
(1) Increasingly, smart people are worrying about the downside of modern technological culture (basically, "Web culture").  Indeed, studies now emerging from cognitive psychology and neuroscience suggest that there's a real, actual threat to our cognitive selves on the Web (our brains and brain activities like memory, attention, and learning).
(2) As a corollary of (1), the picayune dream of something like instrumentalism--we use a technology as we wish, and it doesn't really change us in the process--is almost certainly false with respect to Web culture.
(3)  From (1) and (2), the Web seems to be changing us, and not entirely (or even mostly, depending on how moody one is) for the better.
(4) But the Web seems like the very paragon of progress, and indeed, we've been at pains in the last section to explain how the Web (or Web search with Google) is really all about people.  It's all about people-smarts, we've argued, and so how can something about us turn out to be bad for us?  Isn't the "Web" really just our own, ingenious way of compiling and making searchable and accessible all the content we think and write and communicate about, anyway?
(5) And so, from (1)-(4), we get our question:  what gives?

That's our summary, then.  And now we're in a position to address (5), or at least we've got enough of a review of the terrain to have a fresh go at it now.  To begin, let's make some more distinctions.

More Distinctions (or, Three Ways the Web Might be Bad).  These are general points about Web culture, and we might classify them roughly as (1) Bad Medium (2) Distracting Environment, and (3) Trivial Content.

(1) Bad Medium
For years, people have noted in anecdotes and general hunches or preferences the differences between physical books and electronic Web pages.  Back in 2000, for instance, in the halcyon days of the Web, noted researchers like John Seeley Brown (who admittedly worked for Zerox) and Paul Diguid argued in The Social Life of Information that "learning" experiences from printed material seem to be of a qualitatively different sort then "learning" experiences we get from reading lighted bits on an artificial screen.  Books, somehow, are more immersive; we tend to engage a book, where we're tempted reading text on a Web page to skim, instead.  We might call this an umbrella objection to taking the Web too seriously, right from the get go, and I think there's some real teeth in it.  But onward...
(2) Distracting Environment
Much of Carr's points in his original Atlantic article "Is Google Making Us Stupid?" and later in his book The Shallows are (2) type objections.  Roughly speaking, you can view Carr's point (and the research he points to that suggests his point is valid) as something akin to the well-known psychological result that people faced with endless choices tend to report less intrinsic satisfaction in their lives.  It's like that on the Web, roughly.  If I can read my email, take in a number of tweets, get Facebook updates, field some IM, and execute a dozen searches all in fifteen minutes, it's hard to see in practical terms how I'm doing anything, well, deep.  Any real cognitive activity that requires focus and concentration is already in pretty bad straights in this type of I-can-have-anything-all-the-time information environment.  And, again, for those tempted to play the instrumentalist card (where we argue that in theory we can concentrate, we just need to discipline ourselves online), we have a growing number of brain and behavioral studies surfacing that suggest the problem is actually intrinsic to the Web environment.  In other words, we can't just "try harder" to stay on track (though it's hard to see how this would hurt); there's something about our connection to information on the Web that actively mitigates against contemplation and concentration of the kind required to really, thoroughly engage or learn something.  As Carr summarizes our condition, we're in The Shallows.  And since we're online more and more, day after day, we're heading for more shallows.
(3)  Trivial Content
Much of Lanier's arguments in his You Are Not a Gadget are explorations of (3).  Likewise, someone like former tech-guy Andrew Keen advances objections of the Trivial Content sort in his The Cult of the Amateur. As I think Lanier's observations are more trenchant, we'll stick mostly to his ideas.  Trivial Content is really at the heart of what I wish to advance in this piece, actually, so to this we'll turn in the next section.

Whoops!  Idiocracy