What the Internet is doing to our brains

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[Michael]

Is Google Making Us Stupid? - part 1

by Nicholas Carr

"Dave, stop. Stop, will you? Stop, Dave. Will you stop, Dave?” So the supercomputer HAL pleads with the implacable astronaut Dave Bowman in a famous and weirdly poignant scene toward the end of Stanley Kubrick’s 2001: A Space Odyssey. Bowman, having nearly been sent to a deep-space death by the malfunctioning machine, is calmly, coldly disconnecting the memory circuits that control its artificial brain. “Dave, my mind is going,” HAL says, forlornly. “I can feel it. I can feel it.”

I can feel it, too. Over the past few years I’ve had an uncomfortable sense that someone, or something, has been tinkering with my brain, remapping the neural circuitry, reprogramming the memory. My mind isn’t going—so far as I can tell—but it’s changing. I’m not thinking the way I used to think. I can feel it most strongly when I’m reading. Immersing myself in a book or a lengthy article used to be easy. My mind would get caught up in the narrative or the turns of the argument, and I’d spend hours strolling through long stretches of prose. That’s rarely the case anymore. Now my concentration often starts to drift after two or three pages. I get fidgety, lose the thread, begin looking for something else to do. I feel as if I’m always dragging my wayward brain back to the text. The deep reading that used to come naturally has become a struggle.

I think I know what’s going on. For more than a decade now, I’ve been spending a lot of time online, searching and surfing and sometimes adding to the great databases of the Internet. The Web has been a godsend to me as a writer. Research that once required days in the stacks or periodical rooms of libraries can now be done in minutes. A few Google searches, some quick clicks on hyperlinks, and I’ve got the telltale fact or pithy quote I was after. Even when I’m not working, I’m as likely as not to be foraging in the Web’s info-thickets—reading and writing e-mails, scanning headlines and blog posts, watching videos and listening to podcasts, or just tripping from link to link to link. (Unlike footnotes, to which they’re sometimes likened, hyperlinks don’t merely point to related works; they propel you toward them.)

For me, as for others, the Net is becoming a universal medium, the conduit for most of the information that flows through my eyes and ears and into my mind. The advantages of having immediate access to such an incredibly rich store of information are many, and they’ve been widely described and duly applauded. “The perfect recall of silicon memory,” Wired’s Clive Thompson has written, “can be an enormous boon to thinking.” But that boon comes at a price. As the media theorist Marshall McLuhan pointed out in the 1960s, media are not just passive channels of information. They supply the stuff of thought, but they also shape the process of thought. And what the Net seems to be doing is chipping away my capacity for concentration and contemplation. My mind now expects to take in information the way the Net distributes it: in a swiftly moving stream of particles. Once I was a scuba diver in the sea of words. Now I zip along the surface like a guy on a Jet Ski.

I’m not the only one. When I mention my troubles with reading to friends and acquaintances—literary types, most of them—many say they’re having similar experiences. The more they use the Web, the more they have to fight to stay focused on long pieces of writing. Some of the bloggers I follow have also begun mentioning the phenomenon. Scott Karp, who writes a blog about online media, recently confessed that he has stopped reading books altogether. “I was a lit major in college, and used to be [a] voracious book reader,” he wrote. “What happened?” He speculates on the answer: “What if I do all my reading on the web not so much because the way I read has changed, i.e. I’m just seeking convenience, but because the way I THINK has changed?”

Bruce Friedman, who blogs regularly about the use of computers in medicine, also has described how the Internet has altered his mental habits. “I now have almost totally lost the ability to read and absorb a longish article on the web or in print,” he wrote earlier this year. A pathologist who has long been on the faculty of the University of Michigan Medical School, Friedman elaborated on his comment in a telephone conversation with me. His thinking, he said, has taken on a “staccato” quality, reflecting the way he quickly scans short passages of text from many sources online. “I can’t read War and Peace anymore,” he admitted. “I’ve lost the ability to do that. Even a blog post of more than three or four paragraphs is too much to absorb. I skim it.”

Anecdotes alone don’t prove much. And we still await the long-term neurological and psychological experiments that will provide a definitive picture of how Internet use affects cognition. But a recently published study of online research habits, conducted by scholars from University College London, suggests that we may well be in the midst of a sea change in the way we read and think. As part of the five-year research program, the scholars examined computer logs documenting the behavior of visitors to two popular research sites, one operated by the British Library and one by a U.K. educational consortium, that provide access to journal articles, e-books, and other sources of written information. They found that people using the sites exhibited “a form of skimming activity,” hopping from one source to another and rarely returning to any source they’d already visited. They typically read no more than one or two pages of an article or book before they would “bounce” out to another site. Sometimes they’d save a long article, but there’s no evidence that they ever went back and actually read it. The authors of the study report:

It is clear that users are not reading online in the traditional sense; indeed there are signs that new forms of “reading” are emerging as users “power browse” horizontally through titles, contents pages and abstracts going for quick wins. It almost seems that they go online to avoid reading in the traditional sense.

Thanks to the ubiquity of text on the Internet, not to mention the popularity of text-messaging on cell phones, we may well be reading more today than we did in the 1970s or 1980s, when television was our medium of choice. But it’s a different kind of reading, and behind it lies a different kind of thinking—perhaps even a new sense of the self. “We are not only what we read,” says Maryanne Wolf, a developmental psychologist at Tufts University and the author of Proust and the Squid: The Story and Science of the Reading Brain. “We are how we read.” Wolf worries that the style of reading promoted by the Net, a style that puts “efficiency” and “immediacy” above all else, may be weakening our capacity for the kind of deep reading that emerged when an earlier technology, the printing press, made long and complex works of prose commonplace. When we read online, she says, we tend to become “mere decoders of information.” Our ability to interpret text, to make the rich mental connections that form when we read deeply and without distraction, remains largely disengaged.

Reading, explains Wolf, is not an instinctive skill for human beings. It’s not etched into our genes the way speech is. We have to teach our minds how to translate the symbolic characters we see into the language we understand. And the media or other technologies we use in learning and practicing the craft of reading play an important part in shaping the neural circuits inside our brains. Experiments demonstrate that readers of ideograms, such as the Chinese, develop a mental circuitry for reading that is very different from the circuitry found in those of us whose written language employs an alphabet. The variations extend across many regions of the brain, including those that govern such essential cognitive functions as memory and the interpretation of visual and auditory stimuli. We can expect as well that the circuits woven by our use of the Net will be different from those woven by our reading of books and other printed works.

Sometime in 1882, Friedrich Nietzsche bought a typewriter—a Malling-Hansen Writing Ball, to be precise. His vision was failing, and keeping his eyes focused on a page had become exhausting and painful, often bringing on crushing headaches. He had been forced to curtail his writing, and he feared that he would soon have to give it up. The typewriter rescued him, at least for a time. Once he had mastered touch-typing, he was able to write with his eyes closed, using only the tips of his fingers. Words could once again flow from his mind to the page.

But the machine had a subtler effect on his work. One of Nietzsche’s friends, a composer, noticed a change in the style of his writing. His already terse prose had become even tighter, more telegraphic. “Perhaps you will through this instrument even take to a new idiom,” the friend wrote in a letter, noting that, in his own work, his “‘thoughts’ in music and language often depend on the quality of pen and paper.”

“You are right,” Nietzsche replied, “our writing equipment takes part in the forming of our thoughts.” Under the sway of the machine, writes the German media scholar Friedrich A. Kittler, Nietzsche’s prose “changed from arguments to aphorisms, from thoughts to puns, from rhetoric to telegram style.”

The human brain is almost infinitely malleable. People used to think that our mental meshwork, the dense connections formed among the 100 billion or so neurons inside our skulls, was largely fixed by the time we reached adulthood. But brain researchers have discovered that that’s not the case. James Olds, a professor of neuroscience who directs the Krasnow Institute for Advanced Study at George Mason University, says that even the adult mind “is very plastic.” Nerve cells routinely break old connections and form new ones. “The brain,” according to Olds, “has the ability to reprogram itself on the fly, altering the way it functions.”

As we use what the sociologist Daniel Bell has called our “intellectual technologies”—the tools that extend our mental rather than our physical capacities—we inevitably begin to take on the qualities of those technologies. The mechanical clock, which came into common use in the 14th century, provides a compelling example. In Technics and Civilization, the historian and cultural critic Lewis Mumford described how the clock “disassociated time from human events and helped create the belief in an independent world of mathematically measurable sequences.” The “abstract framework of divided time” became “the point of reference for both action and thought.”

The clock’s methodical ticking helped bring into being the scientific mind and the scientific man. But it also took something away. As the late MIT computer scientist Joseph Weizenbaum observed in his 1976 book, Computer Power and Human Reason: From Judgment to Calculation, the conception of the world that emerged from the widespread use of timekeeping instruments “remains an impoverished version of the older one, for it rests on a rejection of those direct experiences that formed the basis for, and indeed constituted, the old reality.” In deciding when to eat, to work, to sleep, to rise, we stopped listening to our senses and started obeying the clock.

The process of adapting to new intellectual technologies is reflected in the changing metaphors we use to explain ourselves to ourselves. When the mechanical clock arrived, people began thinking of their brains as operating “like clockwork.” Today, in the age of software, we have come to think of them as operating “like computers.” But the changes, neuroscience tells us, go much deeper than metaphor. Thanks to our brain’s plasticity, the adaptation occurs also at a biological level.

The Internet promises to have particularly far-reaching effects on cognition. In a paper published in 1936, the British mathematician Alan Turing proved that a digital computer, which at the time existed only as a theoretical machine, could be programmed to perform the function of any other information-processing device. And that’s what we’re seeing today. The Internet, an immeasurably powerful computing system, is subsuming most of our other intellectual technologies. It’s becoming our map and our clock, our printing press and our typewriter, our calculator and our telephone, and our radio and TV.

[Michael]

part 2

When the Net absorbs a medium, that medium is re-created in the Net’s image. It injects the medium’s content with hyperlinks, blinking ads, and other digital gewgaws, and it surrounds the content with the content of all the other media it has absorbed. A new e-mail message, for instance, may announce its arrival as we’re glancing over the latest headlines at a newspaper’s site. The result is to scatter our attention and diffuse our concentration.

The Net’s influence doesn’t end at the edges of a computer screen, either. As people’s minds become attuned to the crazy quilt of Internet media, traditional media have to adapt to the audience’s new expectations. Television programs add text crawls and pop-up ads, and magazines and newspapers shorten their articles, introduce capsule summaries, and crowd their pages with easy-to-browse info-snippets. When, in March of this year, TheNew York Times decided to devote the second and third pages of every edition to article abstracts, its design director, Tom Bodkin, explained that the “shortcuts” would give harried readers a quick “taste” of the day’s news, sparing them the “less efficient” method of actually turning the pages and reading the articles. Old media have little choice but to play by the new-media rules.

Never has a communications system played so many roles in our lives—or exerted such broad influence over our thoughts—as the Internet does today. Yet, for all that’s been written about the Net, there’s been little consideration of how, exactly, it’s reprogramming us. The Net’s intellectual ethic remains obscure.

About the same time that Nietzsche started using his typewriter, an earnest young man named Frederick Winslow Taylor carried a stopwatch into the Midvale Steel plant in Philadelphia and began a historic series of experiments aimed at improving the efficiency of the plant’s machinists. With the approval of Midvale’s owners, he recruited a group of factory hands, set them to work on various metalworking machines, and recorded and timed their every movement as well as the operations of the machines. By breaking down every job into a sequence of small, discrete steps and then testing different ways of performing each one, Taylor created a set of precise instructions—an “algorithm,” we might say today—for how each worker should work. Midvale’s employees grumbled about the strict new regime, claiming that it turned them into little more than automatons, but the factory’s productivity soared.

More than a hundred years after the invention of the steam engine, the Industrial Revolution had at last found its philosophy and its philosopher. Taylor’s tight industrial choreography—his “system,” as he liked to call it—was embraced by manufacturers throughout the country and, in time, around the world. Seeking maximum speed, maximum efficiency, and maximum output, factory owners used time-and-motion studies to organize their work and configure the jobs of their workers. The goal, as Taylor defined it in his celebrated 1911 treatise, The Principles of Scientific Management, was to identify and adopt, for every job, the “one best method” of work and thereby to effect “the gradual substitution of science for rule of thumb throughout the mechanic arts.” Once his system was applied to all acts of manual labor, Taylor assured his followers, it would bring about a restructuring not only of industry but of society, creating a utopia of perfect efficiency. “In the past the man has been first,” he declared; “in the future the system must be first.”

Taylor’s system is still very much with us; it remains the ethic of industrial manufacturing. And now, thanks to the growing power that computer engineers and software coders wield over our intellectual lives, Taylor’s ethic is beginning to govern the realm of the mind as well. The Internet is a machine designed for the efficient and automated collection, transmission, and manipulation of information, and its legions of programmers are intent on finding the “one best method”—the perfect algorithm—to carry out every mental movement of what we’ve come to describe as “knowledge work.”

Google’s headquarters, in Mountain View, California—the Googleplex—is the Internet’s high church, and the religion practiced inside its walls is Taylorism. Google, says its chief executive, Eric Schmidt, is “a company that’s founded around the science of measurement,” and it is striving to “systematize everything” it does. Drawing on the terabytes of behavioral data it collects through its search engine and other sites, it carries out thousands of experiments a day, according to the Harvard Business Review, and it uses the results to refine the algorithms that increasingly control how people find information and extract meaning from it. What Taylor did for the work of the hand, Google is doing for the work of the mind.

The company has declared that its mission is “to organize the world’s information and make it universally accessible and useful.” It seeks to develop “the perfect search engine,” which it defines as something that “understands exactly what you mean and gives you back exactly what you want.” In Google’s view, information is a kind of commodity, a utilitarian resource that can be mined and processed with industrial efficiency. The more pieces of information we can “access” and the faster we can extract their gist, the more productive we become as thinkers.

Where does it end? Sergey Brin and Larry Page, the gifted young men who founded Google while pursuing doctoral degrees in computer science at Stanford, speak frequently of their desire to turn their search engine into an artificial intelligence, a HAL-like machine that might be connected directly to our brains. “The ultimate search engine is something as smart as people—or smarter,” Page said in a speech a few years back. “For us, working on search is a way to work on artificial intelligence.” In a 2004 interview with Newsweek, Brin said, “Certainly if you had all the world’s information directly attached to your brain, or an artificial brain that was smarter than your brain, you’d be better off.” Last year, Page told a convention of scientists that Google is “really trying to build artificial intelligence and to do it on a large scale.”

Such an ambition is a natural one, even an admirable one, for a pair of math whizzes with vast quantities of cash at their disposal and a small army of computer scientists in their employ. A fundamentally scientific enterprise, Google is motivated by a desire to use technology, in Eric Schmidt’s words, “to solve problems that have never been solved before,” and artificial intelligence is the hardest problem out there. Why wouldn’t Brin and Page want to be the ones to crack it?

Still, their easy assumption that we’d all “be better off” if our brains were supplemented, or even replaced, by an artificial intelligence is unsettling. It suggests a belief that intelligence is the output of a mechanical process, a series of discrete steps that can be isolated, measured, and optimized. In Google’s world, the world we enter when we go online, there’s little place for the fuzziness of contemplation. Ambiguity is not an opening for insight but a bug to be fixed. The human brain is just an outdated computer that needs a faster processor and a bigger hard drive.

The idea that our minds should operate as high-speed data-processing machines is not only built into the workings of the Internet, it is the network’s reigning business model as well. The faster we surf across the Web—the more links we click and pages we view—the more opportunities Google and other companies gain to collect information about us and to feed us advertisements. Most of the proprietors of the commercial Internet have a financial stake in collecting the crumbs of data we leave behind as we flit from link to link—the more crumbs, the better. The last thing these companies want is to encourage leisurely reading or slow, concentrated thought. It’s in their economic interest to drive us to distraction.

Maybe I’m just a worrywart. Just as there’s a tendency to glorify technological progress, there’s a countertendency to expect the worst of every new tool or machine. In Plato’s Phaedrus, Socrates bemoaned the development of writing. He feared that, as people came to rely on the written word as a substitute for the knowledge they used to carry inside their heads, they would, in the words of one of the dialogue’s characters, “cease to exercise their memory and become forgetful.” And because they would be able to “receive a quantity of information without proper instruction,” they would “be thought very knowledgeable when they are for the most part quite ignorant.” They would be “filled with the conceit of wisdom instead of real wisdom.” Socrates wasn’t wrong—the new technology did often have the effects he feared—but he was shortsighted. He couldn’t foresee the many ways that writing and reading would serve to spread information, spur fresh ideas, and expand human knowledge (if not wisdom).

The arrival of Gutenberg’s printing press, in the 15th century, set off another round of teeth gnashing. The Italian humanist Hieronimo Squarciafico worried that the easy availability of books would lead to intellectual laziness, making men “less studious” and weakening their minds. Others argued that cheaply printed books and broadsheets would undermine religious authority, demean the work of scholars and scribes, and spread sedition and debauchery. As New York University professor Clay Shirky notes, “Most of the arguments made against the printing press were correct, even prescient.” But, again, the doomsayers were unable to imagine the myriad blessings that the printed word would deliver.

So, yes, you should be skeptical of my skepticism. Perhaps those who dismiss critics of the Internet as Luddites or nostalgists will be proved correct, and from our hyperactive, data-stoked minds will spring a golden age of intellectual discovery and universal wisdom. Then again, the Net isn’t the alphabet, and although it may replace the printing press, it produces something altogether different. The kind of deep reading that a sequence of printed pages promotes is valuable not just for the knowledge we acquire from the author’s words but for the intellectual vibrations those words set off within our own minds. In the quiet spaces opened up by the sustained, undistracted reading of a book, or by any other act of contemplation, for that matter, we make our own associations, draw our own inferences and analogies, foster our own ideas. Deep reading, as Maryanne Wolf argues, is indistinguishable from deep thinking.

If we lose those quiet spaces, or fill them up with “content,” we will sacrifice something important not only in our selves but in our culture. In a recent essay, the playwright Richard Foreman eloquently described what’s at stake:

I come from a tradition of Western culture, in which the ideal (my ideal) was the complex, dense and “cathedral-like” structure of the highly educated and articulate personality—a man or woman who carried inside themselves a personally constructed and unique version of the entire heritage of the West. [But now] I see within us all (myself included) the replacement of complex inner density with a new kind of self—evolving under the pressure of information overload and the technology of the “instantly available.”

As we are drained of our “inner repertory of dense cultural inheritance,” Foreman concluded, we risk turning into “‘pancake people’—spread wide and thin as we connect with that vast network of information accessed by the mere touch of a button.”

I’m haunted by that scene in 2001. What makes it so poignant, and so weird, is the computer’s emotional response to the disassembly of its mind: its despair as one circuit after another goes dark, its childlike pleading with the astronaut—“I can feel it. I can feel it. I’m afraid”—and its final reversion to what can only be called a state of innocence. HAL’s outpouring of feeling contrasts with the emotionlessness that characterizes the human figures in the film, who go about their business with an almost robotic efficiency. Their thoughts and actions feel scripted, as if they’re following the steps of an algorithm. In the world of 2001, people have become so machinelike that the most human character turns out to be a machine. That’s the essence of Kubrick’s dark prophecy: as we come to rely on computers to mediate our understanding of the world, it is our own intelligence that flattens into artificial intelligence.

[jimtzu]

great article.  there is definetly a problem with information overload with all the new technologies and medias. too much of everything and no time to absorb it all, unless you take the time, unplug and let things organically germinate into our being.  almost all creative breakthroughs of any kind are done in a period of subjective subconscious mindless distraction, where all the possibilities have been explored waiting for an organic process to bring them together in some insight or invention.

i read a similar piece on the effects of television and part of it's focus was on the flickering tube that is stared at and how the frequency that it emits entrains us into an alpha state that makes us even more complacent than the  hypnotizing content that is broadcasted thru the screen.  i'm not sure what the frequency of computer monitors is, but i'm sure that is having an effect too.

[Michael]

I found this article on on one of the meta-feed services I look at daily.  I skimmed it as usual, and began to notice that it describes very well some symptoms and changes I've been noticing about myself.  I can barely read a full book anymore, though I listen to audio books a lot, such as when I'm doing manual labor or driving.  But to actually read books the way I used to...those days are gone, - and missed.

Contemplation time is a luxury that must be bought or traded in some fashion. 

This is SO different than even a few short years ago for me.  Strange changes...mostly wrought by the internet and the way I use it.

Anyone else?

[henry]

yes mD. for years(lots) i had to read everything and then i stopped. today i renewed my subscription to WIE which i had let lapse for a couple of years. i keep a list of books i might order/read. i have much more searched the internet the last few years. and that is winding down now also. lots of help from books and the internet, but i don't have much of a transformational resume. maybe i'm not done yet ...henry

[Michael]

A response to Carr's thesis is here

I'm not impressed by either this response or the comments.  They seem to miss the point IMO.

Time and honest studies will tell...

I'm currently reading (read: listening to the audio version) Michael Crichton's latest pulp-fiction...don't ask me why...just curiosity really...called Next.  It's about the craziness of cutting-edge genetic engineering, and it's insane and alarming implications.  But what's actually most interesting to me about this book, is the way it's written.  I began to see this style in his previous effort, about global warming.  What I'm talking about is a style of "storytelling" that seems to exemplify the above article in some interesting ways.

The book seems to be a series of anecdotes, both actual and constructed (composited out of actual and imagined facts, factoids, incidents and events), strung together in a choppy, shallow and awkward storyline, interspersed with "pop-up ads" of fake newspaper headlines loosely related to the subject at hand.  Just as though it was constructed in the same manner as an internet search/browse.

Interesting...

[Michael]

How we read online.

By Michael Agger
Posted Friday, June 13, 2008, at 1:00 PM ET

You're probably going to read this.

It's a short paragraph at the top of the page. It's surrounded by white space. It's in small type.

To really get your attention, I should write like this:

• Bulleted list
• Occasional use of bold to prevent skimming
• Short sentence fragments
• Explanatory subheads
• No puns
• Did I mention lists?

What Is This Article About?
For the past month, I've been away from the computer screen. Now I'm back reading on it many hours a day. Which got me thinking: How do we read online?

It's a Jungle Out There
That's Jakob Nielsen's theory. He's a usability expert who writes an influential biweekly column on such topics as eye-tracking research, Web design errors, and banner blindness. (Links, btw, give a text more authority, making you more likely to stick around.)

Nielsen champions the idea of information foraging. Humans are informavores. On the Internet, we hunt for facts. In earlier days, when switching between sites was time-consuming, we tended to stay in one place and dig. Now we assess a site quickly, looking for an "information scent." We move on if there doesn't seem to be any food around.

Sorry about the long paragraph. (Eye-tracking studies show that online readers tend to skip large blocks of text.)

Also, I'm probably forcing you to scroll at this point. Losing some incredible percentage of readers. Bye. Have fun on Facebook.

Screens vs. Paper
What about the physical process of reading on a screen? How does that compare to paper?

When you look at early research, it's fascinating to see that even in the days of green phosphorus monitors, studies found that there wasn't a huge difference in speed and comprehension between reading on-screen and reading on paper. Paper was the clear winner only when test subjects were asked to skim the text.

The studies are not definitive, however, given all the factors that can affect online reading, such as scrolling, font size, user expertise, etc. Nielsen holds that on-screen reading is 25 percent slower than reading on paper. Even so, experts agree on what you can do to make screen reading more comfortable:

• Choose a default font designed for screen reading; e.g., Verdana, Trebuchet, Georgia.
• Rest your eyes for 10 minutes every 30 minutes.
• Get a good monitor. Don't make it too bright or have it too close to your eyes.
• Minimize reflections.
• Skip long lines of text, which promote fatigue.
• Avoid MySpace.

Back to the Jungle
Nielsen's apt description of the online reader: "[U]sers are selfish, lazy, and ruthless." You, my dear user, pluck the low-hanging fruit. When you arrive on a page, you don't actually deign to read it. You scan. If you don't see what you need, you're gone.

And it's not you who has to change. It's me, the writer:

• One idea per paragraph
• Half the word count of "conventional writing"! (Ouch!)
• Other stuff along these lines

Nielsen often sounds like a cross between E.B. White and the Terminator. Here's his advice in a column titled "Long vs. Short Articles as Content Strategy": "A good editor should be able to cut 40 percent of the word count while removing only 30 percent of an article's value. After all, the cuts should target the least valuable information."

[Ed. Note: Fascinating asides about the writer's voice, idiosyncrasies, and fragile ego were cut here.]

He's Right
I kid about Nielsen, but he's very sensible. We're active participants on the Web, looking for information and diversion. It's natural that people prefer short articles. As Nielsen states, motivated readers who want to know everything about a subject (i.e., parents trying to get their kid into a New York preschool) will read long treatises with semicolons, but the rest of us are snacking. His advice: Embrace hypertext. Keep things short for the masses, but offer links for the Type A's.

No Blogs, Though
Nielsen may be ruthless about brevity, but he doesn't advocate blogging. Here's his logic: "Such postings are good for generating controversy and short-term traffic, and they're definitely easier to write. But they don't build sustainable value."

That's a debatable point. My experience has been that a thoughtful blogger who tags his posts can cover a subject well. But Nielsen's idea is that people will read (and maybe even pay) for expertise that they can't find anywhere else. If you want to beat the Internet, you're not going to do it by blogging (since even OK thinkers occasionally write a great blog post) but by offering a comprehensive take on a subject (thus saving the reader time from searching many sites) and supplying original thinking (offering trusted insight that cannot be easily duplicated by the nonexpert).

Like a lot of what Nielsen says, this is both obvious and thoughtful.

Ludic Reading
Nielsen focuses on how to hold people's attention to convey information. He's not overly concerned with pleasure reading.

Pleasure reading is also known as "ludic reading." Victor Nell has studied pleasure reading (PDF). Two fascinating notions:

• When we like a text, we read more slowly.
• When we're really engaged in a text, it's like being in an effortless trance.

Ludic reading can be achieved on the Web, but the environment works against you. Read a nice sentence, get dinged by IM, never return to the story again.

I suppose ludic readers would be the little sloths hiding in the jungle while everyone else is out rampaging around for fresh meat.

Final Unnecessary Thought
We'll do more and more reading on screens, but they won't replace paper—never mind what your friend with a Kindle tells you. Rather, paper seems to be the new Prozac. A balm for the distracted mind. It's contained, offline, tactile. William Powers writes about this elegantly in his essay "Hamlet's BlackBerry: Why Paper Is Eternal." He describes the white stuff as "a still point, an anchor for the consciousness."

Moby Dick has become a spa.

Slate is Grand Central Station.

OK, you may leave now.

[jimtzu]

great observations on the form of this medium.  after reading this i've been more aware of the styles and points he brought up in his piece, which was very easy to read and kept my attention 

i do find reading online to be somewhat easier just because i have a decent monitor and can make the font size bigger if need be.  i still read on paper but usually have to put on a pair of reading glasses over my regular glasses to be able to see 

[Michael]

Hi Jim,

I was wondering about how this stuff played out for you, with the eyesight thing...

So I've decided to take an internet break and dive back into reading books.  Here's what I'll be reading:

[jimtzu]

hey michael, i checked out the Hofstedter interview link..  i'd never heard of him. the I am a strange loop looks pretty interesting.

don't worry, going back to long form reading will just take a bit of getting used to. all abilities have their "muscle memory", just have to exercise it from time to time.  i just got a new guitar after concentrating on the flute for a couple years and  having to relearn and remember (and get crickety fingers working) again.  it all comes back back 

[Michael]

I've started reading I Am A Strange Loop, which arrived yesterday.  Looks like a great and fun book, written by an author who is a true pleasure to read.

Now this:

WIRED MAGAZINE: 16.07

 

The End of Theory: The Data Deluge Makes the Scientific Method Obsolete

 

By Chris Anderson 18 hours ago

     

   

       

     

   

   

 

   

 

 

   

   

The End of Theory:
     Essay: The Data Deluge Makes the Scientific Method Obsolete
   

   

   

 

 

       

"All models are wrong, but some are useful."

   

So proclaimed statistician George Box 30 years ago, and he was  right. But what choice did we have? Only models, from cosmological  equations to theories of human behavior, seemed to be able to  consistently, if imperfectly, explain the world around us. Until now.  Today companies like Google, which have grown up in an era of massively  abundant data, don't have to settle for wrong models. Indeed, they  don't have to settle for models at all.

   

Sixty years ago, digital computers made information readable. Twenty  years ago, the Internet made it reachable. Ten years ago, the first  search engine crawlers made it a single database. Now Google and  like-minded companies are sifting through the most measured age in  history, treating this massive corpus as a laboratory of the human  condition. They are the children of the Petabyte Age.

   

The Petabyte Age is different because more is different. Kilobytes  were stored on floppy disks. Megabytes were stored on hard disks.  Terabytes were stored in disk arrays. Petabytes are stored in the  cloud. As we moved along that progression, we went from the folder  analogy to the file cabinet analogy to the library analogy to — well,  at petabytes we ran out of organizational analogies.

   

At the petabyte scale, information is not a matter of simple three-  and four-dimensional taxonomy and order but of dimensionally agnostic  statistics. It calls for an entirely different approach, one that  requires us to lose the tether of data as something that can be  visualized in its totality. It forces us to view data mathematically  first and establish a context for it later. For instance, Google  conquered the advertising world with nothing more than applied  mathematics. It didn't pretend to know anything about the culture and  conventions of advertising — it just assumed that better data, with  better analytical tools, would win the day. And Google was right.

   

Google's founding philosophy is that we don't know why this page is  better than that one: If the statistics of incoming links say it is,  that's good enough. No semantic or causal analysis is required. That's  why Google can translate languages without actually "knowing" them  (given equal corpus data, Google can translate Klingon into Farsi as  easily as it can translate French into German). And why it can match  ads to content without any knowledge or assumptions about the ads or  the content.

   

Speaking at the O'Reilly Emerging Technology Conference this past  March, Peter Norvig, Google's research director, offered an update to  George Box's maxim: "All models are wrong, and increasingly you can  succeed without them."

   

This is a world where massive amounts of data and applied  mathematics replace every other tool that might be brought to bear. Out  with every theory of human behavior, from linguistics to sociology.  Forget taxonomy, ontology, and psychology. Who knows why people do what  they do? The point is they do it, and we can track and measure it with  unprecedented fidelity. With enough data, the numbers speak for  themselves.

   

The big target here isn't advertising, though. It's science. The  scientific method is built around testable hypotheses. These models,  for the most part, are systems visualized in the minds of scientists.  The models are then tested, and experiments confirm or falsify  theoretical models of how the world works. This is the way science has  worked for hundreds of years.

   

Scientists are trained to recognize that correlation is not  causation, that no conclusions should be drawn simply on the basis of  correlation between X and Y (it could just be a coincidence). Instead,  you must understand the underlying mechanisms that connect the two.  Once you have a model, you can connect the data sets with confidence.  Data without a model is just noise.

   

But faced with massive data, this approach to science — hypothesize,  model, test — is becoming obsolete. Consider physics: Newtonian models  were crude approximations of the truth (wrong at the atomic level, but  still useful). A hundred years ago, statistically based quantum  mechanics offered a better picture — but quantum mechanics is yet  another model, and as such it, too, is flawed, no doubt a caricature of  a more complex underlying reality. The reason physics has drifted into  theoretical speculation about n-dimensional grand unified  models over the past few decades (the "beautiful story" phase of a  discipline starved of data) is that we don't know how to run the  experiments that would falsify the hypotheses — the energies are too  high, the accelerators too expensive, and so on.

   

Now biology is heading in the same direction. The models we were  taught in school about "dominant" and "recessive" genes steering a  strictly Mendelian process have turned out to be an even greater  simplification of reality than Newton's laws. The discovery of  gene-protein interactions and other aspects of epigenetics has  challenged the view of DNA as destiny and even introduced evidence that  environment can influence inheritable traits, something once considered  a genetic impossibility.

   

In short, the more we learn about biology, the further we find ourselves from a model that can explain it.

   

There is now a better way. Petabytes allow us to say: "Correlation  is enough." We can stop looking for models. We can analyze the data  without hypotheses about what it might show. We can throw the numbers  into the biggest computing clusters the world has ever seen and let  statistical algorithms find patterns where science cannot.

   

The best practical example of this is the shotgun gene sequencing by  J. Craig Venter. Enabled by high-speed sequencers and supercomputers  that statistically analyze the data they produce, Venter went from  sequencing individual organisms to sequencing entire ecosystems. In  2003, he started sequencing much of the ocean, retracing the voyage of  Captain Cook. And in 2005 he started sequencing the air. In the  process, he discovered thousands of previously unknown species of  bacteria and other life-forms.

   

If the words "discover a new species" call to mind Darwin and  drawings of finches, you may be stuck in the old way of doing science.  Venter can tell you almost nothing about the species he found. He  doesn't know what they look like, how they live, or much of anything  else about their morphology. He doesn't even have their entire genome.  All he has is a statistical blip — a unique sequence that, being unlike  any other sequence in the database, must represent a new species.

   

This sequence may correlate with other sequences that resemble those  of species we do know more about. In that case, Venter can make some  guesses about the animals — that they convert sunlight into energy in a  particular way, or that they descended from a common ancestor. But  besides that, he has no better model of this species than Google has of  your MySpace page. It's just data. By analyzing it with Google-quality  computing resources, though, Venter has advanced biology more than  anyone else of his generation.

   

This kind of thinking is poised to go mainstream. In February, the  National Science Foundation announced the Cluster Exploratory, a  program that funds research designed to run on a large-scale  distributed computing platform developed by Google and IBM in  conjunction with six pilot universities. The cluster will consist of  1,600 processors, several terabytes of memory, and hundreds of  terabytes of storage, along with the software, including Google File  System, IBM's Tivoli, and an open source version of Google's MapReduce.  Early CluE projects will include simulations of the brain and the  nervous system and other biological research that lies somewhere  between wetware and software.

   

Learning to use a "computer" of this scale may be challenging. But  the opportunity is great: The new availability of huge amounts of data,  along with the statistical tools to crunch these numbers, offers a  whole new way of understanding the world. Correlation supersedes  causation, and science can advance even without coherent models,  unified theories, or really any mechanistic explanation at all.

   

There's no reason to cling to our old ways. It's time to ask: What can science learn from Google?

 

[Michael]

A response:

Why the cloud cannot obscure the scientific method

By John Timmer | Published: June 25, 2008 - 11:00PM CT

Every so often, someone (generally not a practicing scientist) suggests that it's time to replace science with something better. The desire often seems to be a product of either an exaggerated sense of the potential of new approaches, or a lack of understanding of what's actually going on in the world of science. This week's version, which comes courtesy of Chris Anderson, the Editor-in-Chief of Wired, manages to combine both of these features in suggesting that the advent of a cloud of scientific data may free us from the need to use the standard scientific method.

It's easy to see what has Anderson enthused. Modern scientific data sets are increasingly large, comprehensive, and electronic. Things like genome sequences tell us all there is to know about the DNA present in an organism's cells, while DNA chip experiments can determine every gene that's expressed by that cell. That data's also publicly available—out in the cloud, in the current parlance—and it's being mined successfully. That mining extends beyond traditional biological data, too, as projects like WikiProteins are also drawing on text-mining of the electronic scientific literature to suggest connections among biological activities.

There is a lot to like about these trends, and little reason not to be enthused about them. They hold the potential to suggest new avenues of research that scientists wouldn't have identified based on their own analysis of the data. But Anderson appears to take the position that the new research part of the equation has become superfluous; simply having a good algorithm that recognizes the correlation is enough.

The source of this flight of fancy was apparently a quote by Google's research director, who repurposed a cliché that most scientists are aware of: "All models are wrong, and increasingly you can succeed without them." And Google clearly has. It doesn't need to develop a theory as to why a given pattern of links can serve as an indication of valuable information; all it needs to know is that an algorithm that recognizes specific link patterns satisfies its users. Anderson's argument distills down to the suggestion that science can operate on the same level—mechanisms, models, and theories are all dispensable as long as something can pick the correlations out of masses of data.

I can't possibly imagine how he comes to that conclusion. Correlations are a way of catching a scientist's attention, but the models and mechanisms that explain them are how we make the predictions that not only advance science, but generate practical applications. One only needs to look at a promising field that lacks a strong theoretical foundation—high-temperature superconductivity springs to mind—to see how badly the lack of a theory can impact progress. Put in more practical terms, would Anderson be willing to help test a drug that was based on a poorly understood correlation pulled out of a datamine? These days, we like our drugs to have known targets and mechanisms of action and, to get there, we need standard science.

Anderson does provide two examples that he feels support his position, but they actually appear to undercut it. He notes that we know quantum mechanics is wrong on some level, but have been unable to craft a replacement theory after decades of work. But he neglects to mention two key things: without the testable predictions made by the theory, we'll never be able to tell how precisely it is wrong and, in those decades where we've failed to find a replacement, the predictions of quantum mechanics have been used to create the modern electronics industry, with the data cloud being a consequence of that.

If anything, his second example is worse. We can now perform large-scale genetic surveys of the life present in remote environments, such as the far reaches of the Pacific. Doing so has informed us that there's a lot of unexplored biodiversity on the bacterial level; fragments of sequence hint at organisms we've never encountered under a microscope. But as Anderson himself notes, the only thing we can do is make a few guesses as to the properties of the organisms based on who their relatives are, an activity that actually requires a working scientific theory, namely evolution. To do more than that, we need to deploy models of metabolism and ecology against the bacteria themselves.

Overall, the foundation of the argument for a replacement for science is correct: the data cloud is changing science, and leaving us in many cases with a Google-level understanding of the connections between things. Where Anderson stumbles is in his conclusions about what this means for science. The fact is that we couldn't have even reached this Google-level understanding without the models and mechanisms that he suggests are doomed to irrelevance. But, more importantly, nobody, including Anderson himself if he had thought about it, should be happy with stopping at this level of understanding of the natural world.

[Michael]

Your Brain Lies to You

By SAM WANG and SANDRA AAMODT

FALSE beliefs are everywhere. Eighteen percent of Americans think the sun revolves around the earth, one poll has found. Thus it seems slightly less egregious that, according to another poll, 10 percent of us think that Senator Barack Obama, a Christian, is instead a Muslim. The Obama campaign has created a Web site to dispel misinformation. But this effort may be more difficult than it seems, thanks to the quirky way in which our brains store memories — and mislead us along the way.

The brain does not simply gather and stockpile information as a computer’s hard drive does. Facts are stored first in the hippocampus, a structure deep in the brain about the size and shape of a fat man’s curled pinkie finger. But the information does not rest there. Every time we recall it, our brain writes it down again, and during this re-storage, it is also reprocessed. In time, the fact is gradually transferred to the cerebral cortex and is separated from the context in which it was originally learned. For example, you know that the capital of California is Sacramento, but you probably don’t remember how you learned it.

This phenomenon, known as source amnesia, can also lead people to forget whether a statement is true. Even when a lie is presented with a disclaimer, people often later remember it as true.

With time, this misremembering only gets worse. A false statement from a noncredible source that is at first not believed can gain credibility during the months it takes to reprocess memories from short-term hippocampal storage to longer-term cortical storage. As the source is forgotten, the message and its implications gain strength. This could explain why, during the 2004 presidential campaign, it took some weeks for the Swift Boat Veterans for Truth campaign against Senator John Kerry to have an effect on his standing in the polls.

Even if they do not understand the neuroscience behind source amnesia, campaign strategists can exploit it to spread misinformation. They know that if their message is initially memorable, its impression will persist long after it is debunked. In repeating a falsehood, someone may back it up with an opening line like “I think I read somewhere” or even with a reference to a specific source.

In one study, a group of Stanford students was exposed repeatedly to an unsubstantiated claim taken from a Web site that Coca-Cola is an effective paint thinner. Students who read the statement five times were nearly one-third more likely than those who read it only twice to attribute it to Consumer Reports (rather than The National Enquirer, their other choice), giving it a gloss of credibility.

Adding to this innate tendency to mold information we recall is the way our brains fit facts into established mental frameworks. We tend to remember news that accords with our worldview, and discount statements that contradict it.

In another Stanford study, 48 students, half of whom said they favored capital punishment and half of whom said they opposed it, were presented with two pieces of evidence, one supporting and one contradicting the claim that capital punishment deters crime. Both groups were more convinced by the evidence that supported their initial position.

Psychologists have suggested that legends propagate by striking an emotional chord. In the same way, ideas can spread by emotional selection, rather than by their factual merits, encouraging the persistence of falsehoods about Coke — or about a presidential candidate.

Journalists and campaign workers may think they are acting to counter misinformation by pointing out that it is not true. But by repeating a false rumor, they may inadvertently make it stronger. In its concerted effort to “stop the smears,” the Obama campaign may want to keep this in mind. Rather than emphasize that Mr. Obama is not a Muslim, for instance, it may be more effective to stress that he embraced Christianity as a young man.

Consumers of news, for their part, are prone to selectively accept and remember statements that reinforce beliefs they already hold. In a replication of the study of students’ impressions of evidence about the death penalty, researchers found that even when subjects were given a specific instruction to be objective, they were still inclined to reject evidence that disagreed with their beliefs.

In the same study, however, when subjects were asked to imagine their reaction if the evidence had pointed to the opposite conclusion, they were more open-minded to information that contradicted their beliefs. Apparently, it pays for consumers of controversial news to take a moment and consider that the opposite interpretation may be true.

In 1919, Justice Oliver Wendell Holmes of the Supreme Court wrote that “the best test of truth is the power of the thought to get itself accepted in the competition of the market.” Holmes erroneously assumed that ideas are more likely to spread if they are honest. Our brains do not naturally obey this admirable dictum, but by better understanding the mechanisms of memory perhaps we can move closer to Holmes’s ideal.

Sam Wang, an associate professor of molecular biology and neuroscience at Princeton, and Sandra Aamodt, a former editor in chief of Nature Neuroscience, are the authors of “Welcome to Your Brain: Why You Lose Your Car Keys but Never Forget How to Drive and Other Puzzles of Everyday Life.”

[Michael]

Is the Internet really a blessing for democracy?
Cass R. Sunstein

Is the Internet a wonderful development for democracy? In many ways it certainly is. As a result of the Internet, people can learn far more than they could before, and they can learn it much faster. If you are interested in issues that bear on public policy—environmental quality, wages over time, motor vehicle safety—you can find what you need to know in a matter of seconds. If you are suspicious of the mass media, and want to discuss issues with like-minded people, you can do that, transcending the limitations of geography in ways that could barely be imagined even a decade ago. And if you want to get information to a wide range of people, you can do that via email and websites; this is another sense in which the Internet is a great boon for democracy.

But in the midst of the celebration, I want to raise a note of caution. I do so by emphasizing one of the most striking powers provided by emerging technologies: the growing power of consumers to "filter" what they see. As a result of the Internet and other technological developments, many people are increasingly engaged in a process of "personalization" that limits their exposure to topics and points of view of their own choosing. They filter in, and they also filter out, with unprecedented powers of precision. Consider just a few examples:

1. Broadcast.com has "compiled hundreds of thousands of programs so you can find the one that suits your fancy…. For example, if you want to see all the latest fashions from France 24 hours of the day you can get them. If you're from Baltimore living in Dallas and you want to listen to WBAL, your hometown station, you can hear it."

2. Sonicnet.com allows you to create your own musical universe, consisting of what it calls "Me Music." Me Music is "A place where you can listen to the music you love on the radio station YOU create…. A place where you can watch videos of your favorite artists and new artists."

3. Zatso.net allows users to produce "a personal newscast." Its intention is to create a place "where you decide what's news." Your task is to tell "what TV news stories you're interested in," and Zatso.net turns that information into a specifically designed newscast. From the main "This is the News I Want" menu, you can choose stories with particular words and phrases, or you can select topics, such as sports, weather, crime, health, government/politics, and much more.

4. Info Xtra offers "news and entertainment that's important to you," and it allows you to find this "without hunting through newspapers, radio and websites." Personalized news, local weather, and "even your daily horoscope or winning lottery number" will be delivered to you once you specify what you want and when you want it.

5. TiVo, a television recording system, is designed, in the words of its website, to give "you the ultimate control over your TV viewing." It does this by putting "you at the center of your own TV network, so you'll always have access to whatever you want, whenever you want." TiVo "will automatically find and digitally record your favorite programs every time they air" and will help you create "your personal TV line-up." It will also learn your tastes, so that it can "suggest other shows that you may want to record and watch based on your preferences."

6. Intertainer, Inc. provides "home entertainment services on demand," including television, music, movies, and shopping. Intertainer is intended for people who want "total control" and "personalized experiences." It is "a new way to get whatever movies, music, and television you want anytime you want on your PC or TV."

7. George Bell, the chief executive officer of the search engine Excite, exclaims, "We are looking for ways to be able to lift chunks of content off other areas of our service and paste them onto your personal page so you can constantly refresh and update that 'newspaper of me.' About 43 percent of our entire user data base has personalized their experience on Excite."

Of course, these developments make life much more convenient and in some ways much better: we all seek to reduce our exposure to uninvited noise. But from the standpoint of democracy, filtering is a mixed blessing. An understanding of the mix will permit us to obtain a better sense of what makes for a well-functioning system of free expression. In a heterogeneous society, such a system requires something other than free, or publicly unrestricted, individual choices. On the contrary, it imposes two distinctive requirements. First, people should be exposed to materials that they would not have chosen in advance. Unanticipated encounters, involving topics and points of view that people have not sought out and perhaps find irritating, are central to democracy and even to freedom itself. Second, many or most citizens should have a range of common experiences. Without shared experiences, a heterogeneous society will have a more difficult time addressing social problems and understanding one another.

Continued...

[Michael]

This is a continuation of the conversation starting with The End of Theory: The Data Deluge Makes the Scientific Method Obsolete.  I find this fascinating... In particualr the part below that I've highlighted in red:

The Google Way of Science

There's a dawning sense that extremely large databases of information,  starting in the petabyte level, could change how we learn things. The  traditional way of doing science entails constructing a hypothesis to  match observed data or to solicit new data. Here's a bunch of  observations; what theory explains the data sufficiently so that we can  predict the next observation?

It may turn out that tremendously large volumes of data are  sufficient to skip the theory part in order to make a predicted  observation. Google was one of the first to notice this. For instance,  take Google's spell checker. When you misspell a word when googling,  Google suggests the proper spelling. How does it know this? How does it  predict the correctly spelled word? It is not because it has a theory  of good spelling, or has mastered spelling rules. In fact Google knows  nothing about spelling rules at all.

Instead Google operates a very large dataset of observations  which show that for any given spelling of a word, x number of people  say "yes" when asked if they meant to spell word "y." Google's spelling  engine consists entirely of these datapoints, rather than any notion of  what correct English spelling is. That is why the same system can  correct spelling in any language.

In fact, Google uses the same philosophy of learning via  massive data for their translation programs. They can translate from  English to French, or German to Chinese by matching up huge datasets of  humanly translated material. For instance, Google trained their  French/English translation engine by feeding it Canadian documents  which are often released in both English and French versions. The  Googlers have no theory of language, especially of French, no AI  translator. Instead they have zillions of datapoints which in aggregate  link "this to that" from one language to another. 

Once you have such a translation system tweaked, it can translate from  any language to another. And the translation is pretty good. Not expert  level, but enough to give you the gist. You can take a Chinese web page  and at least get a sense of what it means in English. Yet, as Peter  Norvig, head of research at Google, once boasted to me, "Not one person  who worked on the Chinese translator spoke Chinese."  There was no  theory of Chinese, no understanding. Just data. (If anyone ever wanted  a disproof of Searle's riddle of the Chinese Room, here it is.)

If you can learn how to spell without knowing anything about the rules  or grammar of spelling, and if you can learn how to translate languages  without having any theory or concepts about grammar of the languages  you are translating, then what else can you learn without having a  theory?

In a cover article in Wired this month Chris Anderson explores the idea that perhaps you could do science without having theories.

This is a world where massive amounts of data and  applied mathematics replace every other tool that might be brought to  bear. Out with every theory of human behavior, from linguistics to  sociology. Forget taxonomy, ontology, and psychology. Who knows why  people do what they do? The point is they do it, and we can track and  measure it with unprecedented fidelity. With enough data, the numbers  speak for themselves.
   
  Petabytes allow us to say: "Correlation is enough." We can stop  looking for models. We can analyze the data without hypotheses about  what it might show. We can throw the numbers into the biggest computing  clusters the world has ever seen and let statistical algorithms find  patterns where science cannot.

There may be something to this observation. Many  sciences such as astronomy, physics, genomics, linguistics, and geology  are generating extremely huge datasets and constant streams of data in  the petabyte level today. They'll be in the exabyte level in a decade.  Using old fashioned "machine learning," computers can extract patterns  in this ocean of data that no human could ever possibly detect. These  patterns are correlations. They may or may not be causative, but we can  learn new things. Therefore they accomplish what science does, although  not in the traditional manner.

What Anderson is suggesting is that sometimes enough correlations are  sufficient. There is a good parallel in health. A lot of doctoring  works on the correlative approach. The doctor may not ever find the  actual cause of an ailment, or understand it if he/she did, but he/she  can correctly predict the course and treat the symptom. But is this  really science? You can get things done, but if you don't have a model,  is it something others can build on?
   
  We don't know yet. The technical term for this approach in science  is Data Intensive Scalable Computation (DISC). Other terms are "Grid  Datafarm Architecture" or "Petascale Data Intensive Computing." The  emphasis in these techniques is the data-intensive nature of  computation, rather than on the computing cluster itself. The online  industry calls this approach of investigation a type of "analytics."  Cloud computing companies like Google, IBM, and Yahoo(pdf), and some universities have been holding workshops on the topic. In essence these pioneers are trying to exploit cloud  computing, or the OneMachine, for large-scale science. The current  tools include massively parallel software platforms like MapReduce and  Hadoop (see my earlier post),  cheap storage, and gigantic clusters of data centers. So far, very few  scientists outside of genomics are employing these new tools. The  intent of the NSF's Cluster Exploratory program is to match scientists owning large databased-driven  observations with computer scientists who have access and expertise  with cluster/cloud computing.

My guess is that this emerging method will be one additional  tool in the evolution of the scientific method. It will not replace any  current methods (sorry, no end of science!) but will compliment  established theory-driven science. Let's call this data intensive  approach to problem solving Correlative Analytics. I think Chris  squander a unique opportunity by titling his thesis "The End of Theory"  because this is a negation, the absence of something. Rather it is the  beginning of something, and this is when you have a chance to  accelerate that birth by giving it a positive name. A non-negative name  will also help clarify the thesis. I am suggesting Correlative  Analytics rather than No Theory because I am not entirely sure that  these correlative systems are model-free. I think there is an emergent,  unconscious, implicit model embedded in the system that generates  answers. If none of the English speakers working on Google's Chinese  Room have a theory of Chinese, we can still think of the Room as having  a theory. The model may be beyond the perception and understanding of  the creators of the system, and since it works it is not worth trying  to uncover it. But it may still be there. It just operates at a level  we don't have access to.

But the models' invisibility doesn't matter because they work.  It is not the end of theories, but the end of theories we understand.  Writing in response to Chris Anderson's article George Dyson says this  much better:

For a long time we were stuck on the idea that the  brain somehow contained a "model" of reality, and that AI would be  achieved by constructing similar "models." What's a model? There are 2  requirements: 1) Something that works, and 2) Something we understand.  Our large, distributed, petabyte-scale creations, whether GenBank or  Google, are starting to grasp reality in ways that work just fine but  that we don't necessarily understand.

Just as we will eventually take the brain  apart, neuron by neuron, and never find the model, we will discover  that true AI came into existence without ever needing a coherent model  or a theory of intelligence. Reality does the job just fine.
 
  By any reasonable definition, the "Overmind" (or Kevin's  OneComputer, or whatever) is beginning to think, though this does not  mean thinking the way we do, or on any scale that we can comprehend.
 
  What Chris Anderson is hinting at is that Science (and some very  successful business) will increasingly be done by people who are not  only reading nature directly, but are figuring out ways to read the  Overmind.

What George Dyson is suggesting is that this new  method of doing science -- gathering a zillion data points and then  having the OneMachine calculate a correlative answer  -- can also be  thought of as a method of communicating with a new kind of scientist,  one who can create models at levels of abstraction (in the zillionics realm) beyond our own powers.

So far Correlative Analytics, or the Google Way of Science, has  primarily been deployed in sociological realms, like language  translation, or marketing. That's where the zillionic data has been.  All those zillions of data points generated by our collective life  online. But as more of our observations and measurements of nature are  captured 24/7, in real time, in increasing variety of sensors and  probes, science too will enter the field of zillionics and be easily  processed by the new tools of Correlative Analytics.  In this part of  science, we may get answers that work, but which we don't understand.  Is this partial understanding? Or a different kind of understanding?

Perhaps understanding and answers are overrated. "The problem  with computers," Pablo Picasso is rumored to have said, "is that they  only give you answers."  These huge data-driven correlative systems  will give us lots of answers -- good answers -- but that is all they  will give us. That's what the OneComputer does --  gives us good  answers. In the coming world of cloud computing perfectly good answers  will become a commodity. The real value of the rest of science then  becomes asking good questions.