Features

1. Two modes

User can select to replace mootools with jQuery completely or use jQuery in conjunction with mootools. Using jQuery with mootools introduces noConflict features of jQuery and replaces ‘$’ operator with jquery. This allows other libraries to use ‘$’ operator without conflict.

2. Where to use selection

Use can select where to use jQuery. User can choose to use jQuery in frontend and/ or in administrator.

3. Allows other application to check if jquery is loaded.

This plugin also introduces a variable in application session that allows others to check if jquery is loaded or not. The illustration of use is as below\

    $app =& JFactory::getApplication();

        //Check to see if jquery is already included
        if( $app->get('jquery') === true ) {

            //Code here

        }
    else
    {

    //Code here

    }

Here is the screenshot of plugin parameters

License

The module is released under GNU/GPL license and hence users are allowed distribute and modify the module without warranty what so ever. The developer does not bear any liability for the extension.

Download

Download the latest release from the download section of joomlaguru.com.np or you can click here to download the plugin.

Send in your comments and feedbacks on the plugin. Hope you will like the plugin. Happy reading.

Features

Website ID – Website ID from Google Analytics. These website ID are unique to each website profile.index.php?option=com_content

Domain – Enter domain without http://www e.g. joomlaguru.com.np

Track – Select tracker type. User can select from tracker type. There are 3 types of tracker available.

1. Single Domain Tracker
   Tracker Tracks single domain only.
2. One Domain With Multiple Subdomains
   Tracker tracks multiple subdomains under single domain
3. Multiple Top Level Domains
   Tracker tracks multiple top level domains.

Download the plugin from the joomlaguru.com.np download page.

If you find this plugin useful please consider contributing. Your contribution can make a difference

Hope you like this plugin. Happy reading!!

I have already launched twitter box – a free and easy twitter module for joomla through Joomlaguru.com.np.

I hope overwhelming response from readers. Let us pray for its bright future.

Thanks
Sanjeev

JCal Pro is a fantastic extension to manage events on Joomla enabled Sites. Last week I used JCal Pro for a project and required some modification. JCal Pro is a great extension but lacks spam protection like CAPTCHA or any thing that stops spam posting. But I needed spam prevention in event posting, So I set out to add a CAPTCHA to the fantastic component.

I used reCpatcha as it is audio enabled, a free service and is also owned by google now. ReCaptcha has enabled more than 100,000 sites and I added one more to the list.

The component now has a reCaptcha for spam filtering and JCal Pro has a missing gem installed on it.
You can view a of modified JCal Pro here

I wanted to distribute this modified extension for free but cannot distribute it as it is developed and distributed by dev.anything-digital.com and the extension is subscription based.

However you can contact me for modified version of JCal Pro or if you want to add reCaptcha to your existing JCal Pro or any other extension. Yes I charge a little for my time.

Happy Reading.

If you find this post or modified JCal Pro Useful, please consider contributing. Your contribution can make a difference

The media manager is cool with multiple upload and that too with ajax but I could not get it running. Another inspiration for me

This is an opportunity and challenge for component developers like me to upgrade and update. I am getting my hands dirty on the new arrived code, better get yours too.

Download the latest release of joomla from here
http://joomlacode.org/gf/project/joomla/frs/?action=FrsReleaseBrowse&frs_package_id=3585

Happy coding with joomla.

I have been using hotmail for last 9 years and today It says I don’t have the inbox! strange and annoying.

Anyone faced such things!

Happy hotmailing!

JoomRSS is a RSS aggregator that runs on top of One of the finest CMS – Joomla. This wonderful component is built by Joomclan.com and chops off most of the manual work that would generally be needed to do update articles in Joomla. Just save the RSS Url you want the feeds from and leave the rest to JoomRSS. It will periodically pull feeds from the source and puts it nicely in different sections and categories. Yes, without a click to any button anywere you will have your site updated regularly.

This cool component has some cool features you will not find in other RSS Aggregator.

1. Pull RSS from different source and feed it directly into frontpage.
2. Put a file in a cron and leave the rest to JoomRSS. It will pull the
   feeds and post it in sections and categories you have assigned.
3. Oh! your web host does not provide you the cron! No, Problem enable the template cron. This works like the cpanel cron.
4. Create custom source links and display it professionaly at the end of the every post.
5. Create Ad sections for the post and enable it in the post (article). This is cool
6. Set an expiry date so that the feed is not pulled beyond that.
7. Pull media; Yes you can pull media and play it there in the post. Try it with Google Video Feed.
8. Open the link in different window or within an Wrapper

Yes, these and many more cool features. Just take JoomRSS for a roll and that too for $25/Year.
For more information on JoomRSS, visit www.joomclan.com/joomrss

Yes you found a savior! This component is a savior who want to bring more traffic to site. Yes, fresh articles, news bring more people. This savior works for one and all. Great isn’t it?

I will let you know If I find any other cool stuffs from joomclan.com

Our system had been infested with lots of viruses and it is natural when we use Microsoft windows in almost all the systems. This infestation, really introduced the slag in projects. So we decided to switch our systems to linux. (We knew linux is secure and least infested by viruses and worms)

One fine morning of Saturday we set out to remove all the windows OS and install Linux in almost all of the systems. We still needed few windows OS, as we have applications that run on .net framework.

We have 10 systems that are used within my team members that was to be migrated. The number though small (10), meant 10 fresh installations of Linux that could take almost a day for few people with few Fedora DVDs to install and configure the system with networks. So we decided to use DVD to install the system in 2 PCs and for others we Used network install. Network installation was tough. indeed! but we managed to install the system with USB drive and Cobbler. (I did not know about Cobbler the day before). Luckily our systems supported PXE boot. So it was easy after few hours of self study and search through internet. We managed to install other 7 system from Network.

After all successfull installation (except for 1 PC), we managed to install network. We use static IP given to us by our ISP so installing a DHCP meant more incoming  problems so we decided to use static IPs for our system. We switched off the Network Manager, switched on the network services (This is necessary if you use Static IP otherwise your ip configuration is lost on every reboot.), configured our IPs, Configured  HTTP, FTP, Mysql other servers in our central development server. So we are ready to roll in linux (Fedora 9 Sulphur).

We tested our systems it was working all right with the freshness in the air. This felt good!

All well,  untill we faced a small problem with firewalls and http configurations. We were not able to browse our local server through IP. That was bad! So we decided to set up a DNS (bind) Server for easy browsing of local sites. This was how we came up with our internal TLD (.bd) that would be suffixed in every development stage for every projects.

This .bd is not related to TLD of bangladesh by any means, it is just a simple acronym for braindigit and is used for our convenience only. These .bd sites are not browseable outside our intranet.

So bind was setup and few primary zones were created for testing. All good till this part.

Yes we migrated to Linux and we felt good about it. But still, there was a huge problem ahead. Development tools. My team had been accustomed to Adobe Dreamweaver and other windows tools for long, so they wanted something similar to Dreamweaver in linux too.  I used eclipse since long so it was easy to decide the development IDE. And we have eclipse in linux too. So I advised all my team members to use eclipse. It will take some time for them to get accustomed to eclipse but for now this is solved.

We still needed few more applications to collaborate and work in central server. This was tough one. We hardly used SVN, CVS for development internally and setting up CVS, SVN server was though easy but useless, unless the team get accustomed to SVN or CVS. This was a huge problem. We thought for a while and I asked for suggestion from my team. As a response, they wanted something similar to windows. A folder browsing through IP (I don’t know what it is called exactly. maybe Network sharing). I knew this can be done with SSH but using SSH every now and then for development that is ridiculous and stupid (in my view). So to remedy this problem, I created NFS shares for most of the used folders in central server and mounted these folder in every team member PC. Yes I used fstab. So now they are happy developing directly in central server.

I know this solution is stupid but we need to get running quickly so this was the work around.

We set up streber for our small project collaboration. We have openfire for internal collaboration.

So we have successfully migrated to Linux in machines but we need more time to migrate completely to Linux in thoughts.

This is my first experience with Linux in workplace and I feel good that I took the initiative to migrate to Linux. I am not against Windows or Gates. This is only a quest for better and stable system and ofcourse environment.

I will write about all the installation steps I took to setup central servers, setting up NFS and creating a working development environment in my later posts.

Till then Happy Reading

By Steven Levy

Word had been out that Stephen Wolfram, the onetime enfant terrible of the science world, was working on a book that would Say It All, a paradigm-busting tome that would not only be the definitive account on complexity theory but also the opening gambit in a new way to view the universe. But no one had read it.

Though physically unimposing with a soft, round face and a droll English accent polished at Eton and Oxford, Wolfram had already established himself as a larger-than-life figure in the gossipy world of science. A series of much-discussed reinventions made him sort of the Bob Dylan of physics. He’d been a child genius, and at 21 had been the youngest member of the storied first class of MacArthur genius awards. After laying the groundwork for a brilliant career in particle physics, he’d suddenly switched to the untraditional pursuit of studying complex systems, and, to the establishment’s dismay, dared to pioneer the use of computers as a primary research tool. Then he seemed to turn his back on that field. He started a software company to sell Mathematica, a computer language he’d written that did for higher math what the spreadsheet did for business. It made him a rich man. Now he had supposedly returned to science to write a book that would make the biggest splash of all. And, as someone who’d followed his progress since the mid-1980s, I was going to see some of it.

We agreed to meet for dinner in Berkeley. As I drove to the restaurant, rain started coming down in sheets; on the pavement, water ran toward the gutter in twisted, chaotic rivulets – seemingly unfathomable patterns that I would never view in the same way after Stephen Wolfram was done with me. We chatted through dinner, remembering some of our history. And then he handed over a stack of papers. The type was set and the diagrams were sharp – apparently he was almost at the page-proof stage, with publication pending. I’d known about his work in a former backwater of physics called cellular automata, and as I read the first few paragraphs, it was clear he was using that research as a background to make more profound statements. Very profound statements. As best I could make out in my quick flip through the pages, he seemed to be saying that the key to the universe was computation: The entire cosmos, from quantum particles to the formation of galaxies, was a perpetual runtime flowing from simple rules. Yet despite all our learning, human beings have missed the point of it all, because of the elusive nature of complexity. That is, until Stephen Wolfram came along and uncovered what a few millennia’s worth of scientists had somehow failed to comprehend. Whoa.

I wondered if the pages I was holding would actually be a part of history. Or would they be regarded as folly, an act of hubris by a brain-punk who’d been thumbing his nose at the scientific establishment even before he began to shave? I handed it back to him, with the assurance that upon its completion within a few months, I’d get a chance to go through it at my own pace. And so would the world.

That was 10 years ago.

What happened to Stephen Wolfram in the interim has become sort of an urban legend in the scientific community. Not long after our dinner, which occurred in the spring of 1992, he became, in his own words, a “recluse.” He moved, with the woman he had recently married (a mathematician), to the Chicago area and started a family. He rarely made the two-hour drive to Wolfram Research, his thriving software company. Instead, he put himself in a kind of voluntary house arrest, single-mindedly devoted to the completion of the book. “He dropped totally out of the scene in every sense of the word,” says his friend Terrence Sejnowski, a neuroscientist at the Salk Institute. “He hasn’t published a word, he doesn’t go to meetings. He’s in a self-made isolation center.” To maximize his concentration, Wolfram became nocturnal: He worked at night, when the world was asleep, and retired at 8 in the morning.

As the Web emerged and exploded, as dotcoms boomed and busted, as the White House went from Bush to Clinton to Bush, he worked. At some point he had decided that no conventional publisher would provide the attention and exacting standards that his book demanded. (He had no lack of offers.) So he decided to do it himself, using the resources of his software company. It would result in one of the most expensive vanity projects in history. Or as one friend, Gregory Chaitin, an information theorist at IBM, puts it, “He reminds me of the noblemen who worked in science during the 1800s – they did it for the love of it.”

Wolfram’s days would begin in mid-afternoon. He’d usually do an hour or two of official business, operating a multimillion-dollar company by email and conference call. Early evening hours offered an opportunity for some family time. Then, as the world retired and distractions fell away, he’d enter the professionally soundproofed, wood-lined office on the top floor of his house and immerse himself in the act of remaking science.

He spent hours running thousands of computer simulations and noting the results. Because part of his project involved nailing down the conceptual history of dozens of scientific branches, he’d surf the Web. “One can devour lots of papers in very short amounts of time in the middle of the night,” he would later explain to me. He’d begin with an idea, and start downloading papers. Eventually, “you feel kind of depressed that it’s too big a field and you’re never going to understand it.” But then, “usually in a few days it all starts to kind of crystallize and you realize that there really are only three ideas in this field, and two of them you don’t believe. And sometimes at that stage, when I’m checking that I’ve really got all of the ideas, I find it useful to chat with people. Sometimes you hear about something else. And sometimes you don’t.”

Wolfram’s friends came to know the drill. “You get a call at 2 in the morning,” says Sejnowski. “By the morning he knows more than you do.” Every two weeks or so, Wolfram would call an outside expert, but usually found these sessions unsatisfying. All too often he’d be disappointed that the alleged master couldn’t provide him with the information he needed.

He pressed on, never a day off. “I wanted a straight line from where I started to where I wanted to get to,” he says. “I cut off interaction with the outside world – not that it wouldn’t have been fun, I personally like it – but those little perturbations would make the thing take longer.” On a good night, he’d get a page written, and he’d be a few hundred words closer to finishing. And so it went, night after night, a lone explorer inventing his own brand of science while the world slept.

At various times, it appeared publication was imminent. Those who purchased a collection of his scientific papers, issued in hardback in 1994, saw an image of the cover art for his book, then titled A Science of Complexity (“coming soon,” the caption said, “sure to become a landmark in the history of modern science”). Over the next few years, Wolfram teased his public by hinting at the contents in occasional interviews. But the publication date kept moving back. Wolfram’s friends seriously feared that it would never be completed.

Wolfram predicts an algorithmic key to the universe that can compute quantum physics – or, say, reality TV – in four lines of code.

Early last year, Wolfram told me he was almost finished, this time for real. He promised to send me an early copy, if I would sign a nondisclosure agreement. A few days later, A New Kind of Science arrived. My copy (number 26) was broken up into three thick sections. Together they dwarfed a phone book. A sticker on the otherwise blank cover was printed with my name on it. There was a disconcerting warning: “CONFIDENTIAL: Receipt and perusal of this document permitted pursuant to nondisclosure agreement … If you do not have such an agreement please return this immediately….”

If I thought that the draft I had glimpsed in 1992 was provocative, it was nothing compared with the scope and sheer chutzpah of the finished product. Scheduled to reach stores in May, A New Kind of Science will ignite controversy in the scientific world. The self-conscious comparisons with Newton’s 1687 Principia will undoubtedly earn Wolfram both attention and derision. Some early readers are drawing analogies instead to Galileo – not in terms of scientific achievement, but heresy.

At 1,280 pages, the book pushes the limit of what can be physically bound between two covers. Inside, it recognizes no boundaries, not only ranging through traditional fields of science but venturing into the realms of philosophy, theology, the social sciences, and even extraterrestrial policy. There are two sections, the larger being a main text of 12 chapters written in everyday English, with almost no equations, in order to reach an audience of nonspecialists. (One of his friends, Carnegie Mellon mathematical logician Dana Scott, complained to Wolfram that A New Kind of Science reads like USA Today. As if.) Just as important as the text are hundreds of detailed diagrams, the majority of them visual representations of experiments run from Mathematica programs.

The second section is a collection of notes, which includes a piecemeal yet concise history of science through the filter of a didactic middle-aged, MacArthur-winning Jedi mind-warrior. It also contains personal notes, bits of Mathematica code, various mentions of previous work (though bibliographic comments are scrupulously avoided), and an index of 15,000 entries.

To Wolfram, adopting a relatively readable style also meant jettisoning all pretense of humility, a trait that in any case he believes is a waste of time. In a note titled “Clarity and modesty,” he admits to having once subscribed to the “common style of understated scientific writing” but concluded that unless he explicitly identified his findings as the earth-shattering concepts he believed them to be, readers wouldn’t grasp their significance. Of course, the very nature of his approach – laying his theory out in one Brobdingnagian salvo – is by nature immodest.

By rejecting the standard protocols of scientific publication – the release of findings in a series of refereed, jargon-laden papers with rigorous mathematical proofs – Wolfram is consciously bypassing the establishment, engaging in a form of retail science that aims straight for the people. Wolfram insists that “doing a small piece and telling the world about it” would have taken him three times longer, and besides, “if you give them little pieces, they’re not going to come up with grand conclusions.”

The book begins with a thunderclap:

“Three centuries ago science was transformed by the dramatic new idea that rules based on mathematical equations could be used to describe the natural world. My purpose in this book is to initiate another such transformation, and to introduce a new kind of science that is based on the much more general types of rules that can be embodied in simple computer programs.”

He goes on to explain that by applying a single key observation – that the most complicated behavior imaginable arises from very simple rules – one can view and understand the universe with previously unattainable clarity and insight. The idea of complexity arising from simple rules – and that the universe can best be understood by way of the computation it requires to grind out results from those rules – is at the center of the book. The big idea is that the algorithm is mightier than the equation. “Stephen makes the point that Newton developed calculus before Babbage invented computing – but what if it had been the other way?” says Rocky Kolb, a physicist at the Swiss physics laboratory CERN.

Wolfram is not satisfied with simply explaining and justifying his contentions, but instead makes substantial efforts to apply his insights to dozens of fields. “What’s basically happened is that I had this idea of how to use simple programs to understand things about nature, the universe, other stuff,” he says. “And you can start looking at questions that have been around forever, and you really get somewhere.” He invariably introduces each topic in a similar fashion: Curious to know about _______ [CHOOSE ANY SCIENTIFIC DISCIPLINE] and how his new theories might apply, he decides to take a look at the history of the field. Amazingly, he concludes, for hundreds of years so-called experts have failed to answer key questions that should have been easily resolved centuries ago. (Wolfram’s disappointment in his predecessors is bottomless.) But when Wolfram applies the ideas from A New Kind of Science, he begins making progress and expresses the hunch that not long after his ideas are understood, the biggest problems will quickly be resolved, transforming the field.

To list only a few examples: Wolfram finds an exception to the second law of thermodynamics; conjectures why extraterrestrials might be communicating with us in messages we can’t perceive; explains seeming randomness in financial markets; defines randomness; elaborates on why the “apparent freedom of human will” is so convincing; reconstructs the foundations of mathematics; devises a new way to perform encryption; insists that Darwinian natural selection is an overrated component in evolution; and, oh, theorizes that there’s a “definite ultimate model for the universe.” What might this be? The mother of all rules; a single, simple “ultimate rule” that computes everything from quantum physics to reality television.

The climax of the book is the principle of computational equivalence, which may as well be called “Wolfram’s law.” After hundreds of pages of laying groundwork, presenting case after case of visual examples where simple rules generate counterintuitively complex results, Wolfram concludes that this phenomenon is overwhelmingly commonplace – it’s at the base of everything from morphology to traffic jams. Then he goes further, stating that once a system achieves a certain, easily attainable degree of complexity, it’s reached the point of maximum complexity, as measured by the computation required to crank out the end result. Everything at that level of complexity – and that means almost everything you can think of, from human thought to rain hitting pavement – is exactly as complex as anything else.

It’s an idea that is at once liberating and humbling. Wolfram himself considers it the logical next step from earlier scientific revolutions, each of which disabused humanity of the notion that there is something “special” about our species and its place in the scheme of things. (Copernicus showed we weren’t the center of the universe; Darwin proved we were just another product of evolution.) Basically, he’s saying that all we hold dear – our minds, if not our souls – is a computational consequence of a simple rule. “It’s a very negative conclusion about the human condition,” he admits. “You know, consider those gas clouds in the universe that are doing a lot of complicated stuff. What’s the difference [computationally] between what they’re doing and what we’re doing? It’s not easy to see.”

The principle of computational equivalence also puts limits on science itself, ruling many questions unanswerable because the only way to discover the consequences of many complex processes is to let things proceed naturally. There’s no shortcut, since our own computational tools are at best only as powerful as the complicated systems we hope to study.

On the other hand, if the concept is valid, it portends amazing technological developments. “You might think machines can’t capture nature because these programs are too simple,” Wolfram says. “But the principle of computational equivalence says that’s just not true. These programs can do all the stuff that happens in nature.” By that reasoning, no barriers exist to prevent machines from thinking as humans do. “I have little doubt,” he writes, “that within a matter of a few decades what I have done will have led to some dramatic changes in the foundations of technology – and in our basic ability to take what the universe provides and apply it for our own human purposes.”

Only a few people – mainly friends of his in the scientific community – have read the book before its publication. They are vastly impressed, but at this point generally reluctant to endorse all of it; they say people will take decades to absorb everything Wolfram is proposing. Not heard from yet are the voices of the establishment, which undoubtedly will have problems with the unconventional work and its author. “Most scientists will find it difficult to believe that there’s a better way to do science,” says CERN’s Kolb. “It’s not the way we’ve been trained to think.”

Probably the toughest criticism will come from those who reject Wolfram’s ideas because the evidence for his contentions is based on observing systems contained inside computers. “When it comes to computer experiments,” he says, “I can just do them and can know absolutely – definitively – I got the right answer and understand what’s going on.” Wolfram can argue at length why this is a valid approach. Ultimately, he believes, he and his future followers will generate a wealth of computer-related systems that create phenomena identical to those found in the natural world – and the weight of the evidence will convince all but the most hardened skeptics that his ideas are dead-on. The beginnings of this are rules that seem to produce on a computer the same results as pigmentation patterns on jaguars and seashells, the behavior of financial markets, or the growth of leaves.

For now, the skeptics aren’t having it. “Worthless!” says renowned physicist Freeman Dyson, who received an early copy of A New Kind of Science and required only a glance before dismissing it. “It’s a case of style over substance.”

If Wolfram’s ideas ultimately are refuted, he will be remembered as one more brilliant guy who went overboard, verging on megalomania. But even if he is wrong, A New Kind of Science is an incredible achievement, one that will richly reward adventuresome readers. Of course, if he is right, his book indeed belongs to history. Either way, the world is about to reckon with a scientist who’s making the biggest leap imaginable: remaking science itself, with only his computer and his brain.

In a sense, A New Kind of Science is the result of a journey that began with a computer printout produced by an early Sun workstation on June 1, 1984. Stephen Wolfram, then 25, was already on his second career. Born in 1959 to a father who was both a textile manufacturer and a minor novelist, and a mother who taught philosophy at Oxford, the young Wolfram was clearly a prodigy – and a handful. “I guess I was not a very easy kid,” Wolfram told me when we first met in 1984. His baby-sitters would typically leave after a week or so “because I was terrible to them.”

At age 10, he decided to become a scientist and began operating in much the same isolated manner that would characterize his later methodology. Almost from the start, he developed an allergy to the establishment. At 12, he won a scholarship to Eton, where he astonished teachers with his brilliance and frustrated them by taking no instruction whatsoever. He made money by doing other kids’ math homework. At 14, he became interested in a particle physics problem and wound up writing a paper that was accepted by a prestigious professional journal. He entered Oxford at age 17, but it is an exaggeration to say he attended it – by his account, he went to first-year lectures on his first day and found them “awful.” The next two days he dropped in on second- and then third-year lectures, quickly deciding “it was all too horrible – I wasn’t going to go to any more lectures.” So he worked independently, making no secret of his disdain for the professors he considered his intellectual inferiors. When he took end-of-year exams, he finished at the top of his class.

Eventually, after publishing 10 papers, he left Oxford for Caltech, which presented him with a PhD in theoretical physics just weeks after he turned 20 and hired him as a faculty member alongside luminaries like Richard Feynman and Murray Gell-Mann. A year later, he won the MacArthur award. He considered the surrounding hubbub an annoyance, and during a network TV interview he conspicuously picked his nose.

At Caltech, he ran into his first serious professional flap. Wolfram had become interested in how computers could help the scientific process; he developed SMP, a computer language that performed tasks like algebra. Because of Caltech’s patent rules, an ugly dispute broke out, and Wolfram was forever embittered that he was denied sole ownership of what he considered his creation. He left Caltech for a sinecure at the Institute for Advanced Study, the Princeton, New Jersey-based former home of Albert Einstein. But by that time, he was no longer interested in particle physics. Instead, he began pursuing what he viewed as more creative areas, “things that people would consider crazy.” Specifically, he became interested in cellular automata.

At the time, the field of cellular automata, or CAs, oscillated between a science and a computer geek’s plaything. CAs themselves are abstract systems that pose a spreadsheetlike universe in which individual cells move from one condition to another – for example, from dark to light – one click at a time, according to what rules have been set for this evolution. These rules determine the color of the cells in the next iteration, depending on the conditions of the current pattern. The word automata refers to the nature of the process, in which the patterns on the grid evolve depending not on human intervention but on the rules themselves: Once the initial condition and those rules are set, all a person can do is sit back and watch.

The field was the brainchild of the legendary mathematician John von Neumann, at the suggestion of his friend Stanislaw Ulam. Von Neumann was interested in the idea of artificial life, particularly self-reproduction. His claim – which would be echoed by those who went on to study CAs – was that these systems should not be seen solely as mathematical abstractions but as stripped-down versions of the universe itself, wherein the pageant of cells turned on and off on a checkerboard (or computer screen) could actually stand for the mechanisms in the physical world. One computer scientist, Ed Fredkin, the former head of MIT’s famous Project MAC, bent some minds by suggesting that the universe itself was a giant cellular automaton.

Not surprisingly, Wolfram regarded the early work in the field as “just awful” and proceeded to brand the category as his own, somewhat to the dismay of the small CA community, which appreciated the attention Wolfram brought but resented his imperious attitude. (“Wolfram is an absolutely brilliant guy, and he’s right about the new kind of science that underlies everything,” says Fredkin. “But he can’t escape a compulsion to take credit.”) Wolfram methodically analyzed sets of rules, developing a classification system that rated the complexity of various CAs – all with the intention of clarifying the way we view complexity in the real world. He did this by studying and numbering all possible rule sets in one-dimensional CAs. These were elementary systems in which the CA grows one line at a time; the state – dark or light – of each cell on the new line is determined by a rule that depends on the conditions on the previous line.

Wolfram also began to build a case that the same mechanisms that determined the outcome of cellular-automata experiments were omnipresent in nature itself. He was often photographed with seashells whose pigment displayed a pattern that was eerily similar to those produced in his computer printouts of simple CA experiments.

Wolfram was a controversial figure at the Princeton institute in the mid-1980s. Established scientists considered his operation on the third floor of Fuld Hall, where he and his assistants sat in front of workstations and performed digital experiments, as somehow unseemly, not the way serious research should be conducted. “I’m not sure that what he does can be called science,” the institute’s Dyson told me around that time. “It’s more in the nature of mathematical games. He clearly is not a physicist anymore.” And Heinz Pagels, the late physicist who headed the New York Academy of Sciences, told me, “The wunderkind has no clothes.”

For his part, Wolfram felt he could have used more outrage – it would have meant people were thinking about those ideas and taking them seriously. In Wolfram’s mind, studying the results of cellular-automata runs on the computer could unlock deep truths about the universe itself. The proof for him came one fateful day in June 1984 when he printed out the results of a 2-D cellular-automata experiment using Rule 30.

When Wolfram studied the printouts on an airline flight from New York to London, he was thunderstruck. This experiment used the simplest of initial conditions – one darkened cell on the top row. And the process of generating future states was elementary. Yet Rule 30 yielded an eruption of the most complicated, seemingly random output imaginable. (See page 135.) In fact, there seemed no end to it. As Wolfram studied it, he began to realize that there was something profound about how such complexity would arise from a simple program and began to wonder about the implications. Eventually, he would conclude that Rule 30 was not an anomaly but a crucial window onto the way the world operated.

Wolfram’s cellular-automata work came to be cited in more than 10,000 papers. He felt, however, that even his enthusiasts were missing the point – that CAs held the key to a vast understanding of the world. Aware that the Institute for Advanced Study was not eager to host his explorations, he left for the University of Illinois at Urbana-Champaign, which gave him his own institute, the Center for Complex Systems Research. But after two years, he left the center – among his many complaints, he says, “the goofiest thing was that I was supposed to be the guy who went out to raise money, while other people got to do science.” By then, he had seemingly been diverted by another project – creating a computer language called Mathematica, which took his SMP work at Caltech to a much higher level. He started Wolfram Research and hired top scientists and mathematicians to staff its Champaign headquarters. The software came out in 1988 and was an instant success. By 1995, more than a million people were using it.

Mathematica turned out to be invaluable to Wolfram, allowing him to pursue his real dream of making a mammoth contribution to scientific understanding. On a mundane level, the company brought him the wealth and resources to proceed with his book without having to worry about income or research grants – since Wolfram Research was a private company, with the majority of shares owned by its founder, there was no problem spending millions of dollars on a personal science project. More significantly, the creator of the software turned out to be its most avid consumer. Mathematica was a powerful tool to run the experiments that formed the basis of his “new kind of science.” A couple of years after the program was finished, Wolfram gushed to me that “I’ve been going back and redoing problems, and it’s spectacular – things that once took me a week to do now take a half hour.” Wolfram had given himself the ammunition to remake science, and in 1991, he withdrew his physical presence from the company to concentrate on the book. So began his days as a recluse.

On a crisp morning in February this year, I am off to Champaign to sit down with Wolfram for the first time since that night in Berkeley a decade ago. Only a few days before, he absolutely, positively completed A New Kind of Science. Still trying to acclimate himself to the weird circumstance of being awake at 9 in the morning, the CEO is making a rare appearance at Wolfram Research, located in an six-story office building not far from the university campus, to review some projects. (The book itself – 50,000 copies – is about to roll off presses at a Canadian printer, the only operation in the western hemisphere that Wolfram judged capable of rendering the high-definition graphics and illustrations. It will cost $12 a copy to print – five or six times that of a conventional book – making its $45 cover price somewhat of a bargain.) What was a mop of unruly hair when we last met is now a balding pate. He wears a tweed jacket, slacks, and sneakers, the picture of a software executive.

For someone with so little patience for human failing, his management style is fairly loose, though clearly his employees are deferential to him. At a Mathematica design review, he flirts with sarcasm – “Why would anyone want to do this?” he says of a proposed feature – but listens to the answer and finally concludes that the proposal is impressive. “I wouldn’t have been here for 11 years if he was the terror that people say he is,” says marketing exec Jean Buck, who assumes a maternal tolerance toward the quirks of her employer. (She finds it humorous that when she told her boss she’d be busy on Super Bowl Sunday, he asked, “What’s that?”) The 300 people at Wolfram Research know they are free to act independently, but only in the spirit of their leader. Though during the Internet boom some hoped that Wolfram Research would go public, Theo Gray, a scientist who helped Wolfram form the business, says that was never a possibility. “It wouldn’t be Stephen’s company then,” he says.

Later in the day, I meet with a group who assisted Wolfram on A New Kind of Science. There are perhaps a dozen people in the room, and like prisoners shown the open gate after serving a long sentence, everybody is a little stunned that the book is actually finished. There are fact checkers, proofreaders, graphics specialists, PhDs who helped run the computer experiments, the art director, the production manager – a disparate collection who were part scientific staff, part publishing staff. Each day, while Wolfram was sleeping, this contingent would be busily generating graphics, securing permissions, and looking for the perfect photograph of broccoli. (One tells a story of when Wolfram rejected a picture of a panther “because it had a funny expression.”) As the book got bigger, there were conflicts over how to handle its complexity. At one point there was actually a debate about whether there should be notes to the notes.

In some ways, A New Kind of Science was run like a software project. The work was always to be delivered as a digitally typeset file with all the graphics included: one massive load of bits. So instead of drafts, there were frequent “builds,” some of them buggier than others. There were alpha versions and beta versions. Some of the engineers are developing A New Kind of Science Explorer, a PC application with a mini-Mathematica program that allows people to run the experiments in the book and begin to do research projects of their own. Wolfram feels very strongly that “his” kind science is one through which amateurs will unearth major discoveries, and he has been thinking of various ways to assist them.

Suddenly, it occurs to me that someone might be missing in this group. “Who actually edited the book?” I ask. There is a puzzled silence in the room. An editor? Finally Wolfram says, “No one.” Except, of course, the author. Later on, he explains. “I think in terms of ‘This is my book and I’m fully responsible for it.’”

After Wolfram’s day at his software company, we drive through town to a nondescript steak, chicken, and salad house in Urbana to continue our discussion. I ask him what he thinks the reaction will be to A New Kind of Science. He doesn’t guess, and in a sense doesn’t care. “I think when I started this project I was still very interested in saying, ‘What will other people think?’ After a while I realized, ‘Why am I really doing this? Is it really worth my while to spend 10 years of my life doing something to get other people to say positive things about it?’ No, it’s not. Absolutely not. And actually, from some very cynical point of view, my opinion of the world at large isn’t high enough for me really to be interested in what they have to say.”

So when people complain – and they will – that Wolfram’s “new kind of science” is built not on proofs but on looking at computer readouts, he’ll see their complaints as the howling of dinosaurs. “They’ll probably talk derisively about little programs and games,” he says. “But it’s not really engagement, it’s like, ‘Let’s just hope it goes away.’ It’s like the print publishers hoping the Web goes away.” He prefers to take the long view. He’s absolutely confident that his work is sound and is ready to let people absorb it over a period of decades. He believes that in each area he discusses, other researchers will confirm his findings. He thinks that eventually the principle of computational equivalence will be as commonly accepted as gravity. Meanwhile, he says, his main concern is that people actually read the book, and he professes to fear not those who will attack him but bandwagon-riders who will scan a chapter or two and then generate garbage based on their misimpressions.

As the meal progresses, our talk turns to an enigma that is almost certainly a computational equivalent of the mysteries of the universe: Wolfram himself. I point out that in a strange way, this 1,200-page tome with pictures and diagrams of computer experiments and animal skins and seashells and axioms is an extremely personal book. Presented in the guise of science are passionate contentions about religion and free will and the nature of humanity. The discoveries track its author’s obsessions. In a sense, A New Kind of Science is Stephen Wolfram’s autobiography.

“There are definitely elements of expression there,” he admits. “I think 10, 15 years ago, I could not have done a decent job. I’ve seen more of people’s lives now. Back then, I would have said, ‘I don’t care about theology, that’s not my thing.’ But as I kept looking at the historical context, I started realizing that I actually did care about these things and had something to say about them.”

The book also is arguably a rite of passage for him as a man. When I first met Wolfram in 1984, he insouciantly dissed his parents’ careers. “I’ve never read [my father's] novels…. They get good reviews, but they don’t sell terribly many copies,” he told me. Ironically, A New Kind of Science is not just a scientific excursion but also a literary excursion. Like James Joyce, Wolfram believes his ideal reader is one who will devote a lifetime to reading his book, and like Joyce the novelist, Stephen Wolfram (a novelist’s son) has produced an encyclopedic world.

If the expression of the book represents his father’s craft, the application of his ideas to the riddles of human existence reflects the concerns of his mother, the Oxford philosophy professor, who died in 1993. Back in 1984, he said of her, “I have no idea what she does, and the only consequence of her being in that profession is that I will never consider doing anything that’s labeled philosophy.” But A New Kind of Science is nothing if not a book on philosophy. One of his friends suggests it should be called Principia Computatus. And in another irony not lost on the author, Wolfram’s research led him to a textbook on logic written by his mother. “I actually cared about the answers to the questions,” he says.

I think back to Wolfram as a brash, trash-talking 25-year-old. Now he’s a family man (“Having kids has made him much more of a human being,” says a Wolfram Research exec) whose new work, while as iconoclastic as ever, turns out to be a homecoming for him, an outcome that seemed totally unpredictable. Only by nature running its inscrutable computations could the result become apparent.

As dessert is served, I bring up the secret-of-the-universe question. Wolfram’s theory that there is a single rule at the heart of everything – a single simple algorithm that, in effect, generates all the rules of physics and everything else – is bound to be one of his most controversial claims, a theory that even some of his close friends in physics aren’t buying. Furthermore, Wolfram rubs our faces in the dreary implications of his contention. Not only does a single measly rule account for everything, but if one day we actually see the rule, he predicts, we’ll probably find it unimpressive. “One might expect,” he writes, “that in the end there would be nothing special about the rule for our universe – just as there has turned out to be nothing special about our position in the solar system or the galaxy.”

I have some trouble with this.

“I’ve got to ask you,” I say. “How long do you envision this rule of the universe to be?”

“I’m guessing it’s really very short.”

“Like how long?”

“I don’t know. In Mathematica, for example, perhaps three, four lines of code.”

“Four lines of code?”

“That’s what I’m guessing. I mean, I don’t really know, but I think there’s no obvious evidence that it’s any longer than that. Now, in a sense, it will be short if Mathematica was a well-designed language. It will be longer if it doesn’t happen to be as well-designed, in the sense that that doesn’t happen to be the way the universe works. But we’re not looking at 25,000 lines of code or something. We’re looking at a handful of lines of code.”

“So it’s not like Windows?”

“No.” Wolfram laughs. “It’s not like Windows. It’s going to be something small, I think. I’ve certainly wondered. You ask about the theological questions and things. I think there will be a time when one will sort of hold those lines of code in one’s hand, and that is the universe. And what does this mean? You know, how do we then feel about things, if this whole thing is just five lines of code or something? And in a sense, that is a very unsatisfying conclusion, that sort of everything that’s going on, everything out there, is all just this five lines of code we’re running.”

There is a moment of silence between us. In the background are the clatter of dishes and silverware, noises that come from a restaurant in Urbana, Illinois, preparing for closing time. The mundane but complex stuff of equivalent computational processes.

“Well,” I say finally, “I guess we’d feel really bad if it wasn’t well-written.”

Wolfram grins. “Yes, right.”

Another pause. “So do you believe we’ll find this code in your lifetime?”

“I hope so. Yeah.”

“Do you want to find it?”

“Sure. That’d be nice.”

“Is that your next thing to do?”

The self-styled Newton of our times smiles, as if to himself. “I’d like to think about that. Yeah.”

Stephen was kind enough to spend two hours with me last week to demo his new online service — Wolfram Alpha (scheduled to open in May). In the course of our conversation we took a close look at Wolfram Alpha’s capabilities, discussed where it might go, and what it means for the Web, and even the Semantic Web.

Stephen has not released many details of his project publicly yet, so I will respect that and not give a visual description of exactly what I saw. However, he has revealed it a bit in a recent article, and so below I will give my reactions to what I saw and what I think it means. And from that you should be able to get at least some idea of the power of this new system.

A Computational Knowledge Engine for the Web

In a nutshell, Wolfram and his team have built what he calls a “computational knowledge engine” for the Web. OK, so what does that really mean? Basically it means that you can ask it factual questions and it computes answers for you.

It doesn’t simply return documents that (might) contain the answers, like Google does, and it isn’t just a giant database of knowledge, like the Wikipedia. It doesn’t simply parse natural language and then use that to retrieve documents, like Powerset, for example.

Instead, Wolfram Alpha actually computes the answers to a wide range of questions — like questions that have factual answers such as “What is the location of Timbuktu?” or “How many protons are in a hydrogen atom?,” “What was the average rainfall in Boston last year?,” “What is the 307th digit of Pi?,” “where is the ISS?” or “When was GOOG worth more than $300?”

Think about that for a minute. It computes the answers. Wolfram Alpha doesn’t simply contain huge amounts of manually entered pairs of questions and answers, nor does it search for answers in a database of facts. Instead, it understands and then computes answers to certain kinds of questions.

How Does it Work?

Wolfram Alpha is a system for computing the answers to questions. To accomplish this it uses built-in models of fields of knowledge, complete with data and algorithms, that represent real-world knowledge.

For example, it contains formal models of much of what we know about science — massive amounts of data about various physical laws and properties, as well as data about the physical world.

Based on this you can ask it scientific questions and it can compute the answers for you. Even if it has not been programmed explicity to answer each question you might ask it.

But science is just one of the domains it knows about — it also knows about technology, geography, weather, cooking, business, travel, people, music, and more.

It also has a natural language interface for asking it questions. This interface allows you to ask questions in plain language, or even in various forms of abbreviated notation, and then provides detailed answers.

The vision seems to be to create a system wich can do for formal knowledge (all the formally definable systems, heuristics, algorithms, rules, methods, theorems, and facts in the world) what search engines have done for informal knowledge (all the text and documents in various forms of media).

How Smart is it and Will it Take Over the World?

Wolfram Alpha is like plugging into a vast electronic brain. It provides extremely impressive and thorough answers to a wide range of questions asked in many different ways, and it computes answers, it doesn’t merely look them up in a big database.

In this respect it is vastly smarter than (and different from) Google. Google simply retrieves documents based on keyword searches. Google doesn’t understand the question or the answer, and doesn’t compute answers based on models of various fields of human knowledge.

But as intelligent as it seems, Wolfram Alpha is not HAL 9000, and it wasn’t intended to be. It doesn’t have a sense of self or opinions or feelings. It’s not artificial intelligence in the sense of being a simulation of a human mind. Instead, it is a system that has been engineered to provide really rich knowledge about human knowledge — it’s a very powerful calculator that doesn’t just work for math problems — it works for many other kinds of questions that have unambiguous (computable) answers.

There is no risk of Wolfram Alpha becoming too smart, or taking over the world. It’s good at answering factual questions; it’s a computing machine, a tool — not a mind.

One of the most surprising aspects of this project is that Wolfram has been able to keep it secret for so long. I say this because it is a monumental effort (and achievement) and almost absurdly ambitious. The project involves more than a hundred people working in stealth to create a vast system of reusable, computable knowledge, from terabytes of raw data, statistics, algorithms, data feeds, and expertise. But he appears to have done it, and kept it quiet for a long time while it was being developed.

Computation Versus Lookup

For those who are more scientifically inclined, Stephen showed me many interesting examples — for example, Wolfram Alpha was able to solve novel numeric sequencing problems, calculus problems, and could answer questions about the human genome too. It was also able to compute answers to questions about many other kinds of topics (cooking, people, economics, etc.). Some commenters on this article have mentioned that in some cases Google appears to be able to answer questions, or at least the answers appear at the top of Google’s results. So what is the Big Deal? The Big Deal is that Wolfram Alpha doesn’t merely look up the answers like Google does, it computes them using at least some level of domain understanding and reasoning, plus vast amounts of data about the topic being asked about.

Computation is in many cases a better alternative to lookup. For example, you could solve math problems using lookup — that is what a multiplication table is after all. For a small multiplication table, lookup might even be almost as computationally inexpensive as computing the answers. But imagine trying to create a lookup table of all answers to all possible multiplication problems — an infinite multiplication table. That is a clear case where lookup is no longer a better option compared to computation.

The ability to compute the answer on a case by case basis, only when asked, is clearly more efficient than trying to enumerate and store an infinitely large multiplication table. The computation approach only requires a finite amount of data storage — just enough to store the algorithms for solving general multiplication problems — whereas the lookup table approach requires an infinite amount of storage — it requires actually storing, in advance, the products of all pairs of numbers. Even if one simply enumerates this infinite multiplication table as a numeric series it is still an infinite amount of data. In fact, it is even larger than what we consider to be an infinite amount of data. It is not storable at all.

Using the above analogy, we can see why a computational system like Wolfram Alpha is ultimately a more efficient way to compute the answers to many kinds of factual questions than a lookup system like Google. Even though Google is becoming increasingly comprehensive as more information comes on-line and gets indexed, it will never know EVERYTHING. Google is effectively just a lookup table of everything that has been written and published on the Web, that Google has found. But not everything has been published yet, and furthermore Google’s index is also incomplete, and always will be.

Therefore Google does and always will contain gaps. It cannot possibly index the answer to every question that matters or will matter in the future — it doesn’t contain all the questions or all the answers. If nobody has ever published a particular question-answer pair onto some Web page, then Google will not be able to index it, and won’t be able to help you find the answer to that question — UNLESS Google also is able to compute the answer like Wolfram Alpha does (an area that Google is probably working on, but most likely not to as sophisticated a level as Wolfram’s Mathematica engine enables).

While Google only provide answers that are found on some Web page (or at least in some data set they index), a computational knowledge engine like Wolfram Alpha can provide answers to questions it has never seen before — provided however that it at least knows the necessary algorithms for answering such questions, and it at least has sufficient data to compute the answers using these algorithms. This is a big “If” of course.

There are a lot of potential kinds of factual questions out there. But at least it is possible to find general rules for answering broad classes of questions in many cases — for example the basic theorems of geometry. These general rules provide a way to answer broad sets of potential questions about geometry, without having to enumerate each question. They even enable you to derive further rules when you need to. This is the essence of Wolfram Alpha — it contains carefully chosen algorithms for doing useful computations about a broad set of problem domains, plus sufficient data about those domains to actually come up with useful answers to an infinite range of potential questions about those domains.

Google Alpha substitutes computation for storage. It is simply more compact to store general algorithms for computing the answers to various types of factual questions, than to store all possible answers to all possible factual questions. In then end making this tradeoff in favor of computation wins, at least for subject domains where the space of possible factual questions and answers is large. A computational engine is simply more compact and extensible than a database of all questions and answers.

Competition

Where Google is a system for FINDING things that we as a civilization collectively publish, Wolfram Alpha is for COMPUTING answers to questions about what we as a civilization collectively know. It’s the next step in the distribution of knowledge and intelligence around the world — a new leap in the intelligence of our collective “Global Brain.” And like any big next-step, Wolfram Alpha works in a new way — it computes answers instead of just looking them up.

Wolfram Alpha, at its heart is quite different from a brute force statistical search engine like Google. And it is not going to replace Google — it is not a general search engine: You would probably not use Wolfram Alpha to shop for a new car, find blog posts about a topic, or to choose a resort for your honeymoon. It is not a system that will understand the nuances of what you consider to be the perfect romantic getaway, for example — there is still no substitute for manual human-guided search for that. Where it appears to excel is when you want facts about something, or when you need to compute a factual answer to some set of questions about factual data.

I think the folks at Google will be surprised by Wolfram Alpha, and they will probably want to own it, but not because it risks cutting into their core search engine traffic. Instead, it will be because it opens up an entirely new field of potential traffic around questions, answers and computations that you can’t do on Google today.

The services that are probably going to be most threatened by a service like Wolfram Alpha are the Wikipedia, Metaweb’s Freebase, True Knowledge, and any natural language search engines (such as Microsoft’s upcoming search engine, based perhaps in part on Powerset’s technology among others), and other services that are trying to build comprehensive factual knowledge bases.

As a side-note, my own service, Twine.com, is NOT trying to do what Wolfram Alpha is trying to do, fortunately. Instead, Twine uses the Semantic Web to help people filter the Web, organize knowledge, and track their interests. It’s a very different goal. And I’m glad, because I would not want to be competing with Wolfram Alpha. It’s a force to be reckoned with.

Relationship to the Semantic Web

During our discussion, after I tried and failed to poke holes in his natural language parser for a while, we turned to the question of just what this thing is, and how it relates to other approaches like the Semantic Web.

The first question was could (or even should) Wolfram Alpha be built using the Semantic Web in some manner, rather than (or as well as) the Mathematica engine it is currently built on. Is anything missed by not building it with Semantic Web’s languages (RDF, OWL, Sparql, etc.)?

The answer is that there is no reason that one MUST use the Semantic Web stack to build something like Wolfram Alpha. In fact, in my opinion it would be far too difficult to try to explicitly represent everything Wolfram Alpha knows and can compute using OWL ontologies and the reasoning that they enable. It is just too wide a range of human knowledge and giant OWL ontologies are too difficult to build and curate.

It would of course at some point be beneficial to integrate with the Semantic Web so that the knowledge in Wolfram Alpha could be accessed, linked with, and reasoned with, by other semantic applications on the Web, and perhaps to make it easier to pull knowledge in from outside as well. Wolfram Alpha could probably play better with other Web services in the future by providing RDF and OWL representations of it’s knowledge, via a SPARQL query interface — the basic open standards of the Semantic Web. However for the internal knowledge representation and reasoning that takes places in Wolfram Alpah, OWL and RDF are not required and it appears Wolfram has found a more pragmatic and efficient representation of his own.

I don’t think he needs the Semantic Web INSIDE his engine, at least; it seems to be doing just fine without it. This view is in fact not different from the current mainstream approach to the Semantic Web — as one commenter on this article pointed out, “what you do in your database is your business” — the power of the Semantic Web is really for knowledge linking and exchange — for linking data and reasoning across different databases. As Wolfram Alpha connects with the rest of the “linked data Web,” Wolfram Alpha could benefit from providing access to its knowledge via OWL, RDF and Sparql. But that’s off in the future.

It is important to note that just like OpenCyc (which has taken decades to build up a very broad knowledge base of common sense knowledge and reasoning heuristics), Wolfram Alpha is also a centrally hand-curated system. Somehow, perhaps just secretly but over a long period of time, or perhaps due to some new formulation or methodology for rapid knowledge-entry, Wolfram and his team have figured out a way to make the process of building up a broad knowledge base about the world practical where all others who have tried this have found it takes far longer than expected. The task is gargantuan — there is just so much diverse knowledge in the world. Representing even a small area of it formally turns out to be extremely difficult and time-consuming.

It has generally not been considered feasible for any one group to hand-curate all knowledge about every subject. The centralized hand-curation of Wolfram Alpha is certainly more controllable, manageable and efficient for a project of this scale and complexity. It avoids problems of data quality and data-consistency. But it’s also a potential bottleneck and most certainly a cost-center. Yet it appears to be a tradeoff that Wolfram can afford to make, and one worth making as well, from what I could see. I don’t yet know how Wolfram has managed to assemble his knowledge base in less than a very long time, or even how much knowledge he and his team have really added, but at first glance it seems to be a large amount. I look forward to learning more about this aspect of the project.

Building Blocks for Knowledge Computing

Wolfram Alpha is almost more of an engineering accomplishment than a scientific one — Wolfram has broken down the set of factual questions we might ask, and the computational models and data necessary for answering them, into basic building blocks — a kind of basic language for knowledge computing if you will. Then, with these building blocks in hand his system is able to compute with them — to break down questions into the basic building blocks and computations necessary to answer them, and then to actually build up computations and compute the answers on the fly.

Wolfram’s team manually entered, and in some cases automatically pulled in, masses of raw factual data about various fields of knowledge, plus models and algorithms for doing computations with the data. By building all of this in a modular fashion on top of the Mathematica engine, they have built a system that is able to actually do computations over vast data sets representing real-world knowledge. More importantly, it enables anyone to easily construct their own computations — simply by asking questions.

The scientific and philosophical underpinnings of Wolfram Alpha are similar to those of the cellular automata systems he describes in his book, “A New Kind of Science” (NKS). Just as with cellular automata (such as the famous “Game of Life” algorithm that many have seen on screensavers), a set of simple rules and data can be used to generate surprisingly diverse, even lifelike patterns. One of the observations of NKS is that incredibly rich, even unpredictable patterns, can be generated from tiny sets of simple rules and data, when they are applied to their own output over and over again.

In fact, cellular automata, by using just a few simple repetitive rules, can compute anything any computer or computer program can compute, in theory at least. But actually using such systems to build real computers or useful programs (such as Web browsers) has never been practical because they are so low-level it would not be efficient (it would be like trying to build a giant computer, starting from the atomic level).

The simplicity and elegance of cellular automata proves that anything that may be computed — and potentially anything that may exist in nature — can be generated from very simple building blocks and rules that interact locally with one another. There is no top-down control, there is no overarching model. Instead, from a bunch of low-level parts that interact only with other nearby parts, complex global behaviors emerge that, for example, can simulate physical systems such as fluid flow, optics, population dynamics in nature, voting behaviors, and perhaps even the very nature of space-time. This is the main point of the NKS book in fact, and Wolfram draws numerous examples from nature and cellular automata to make his case.

But with all its focus on recombining simple bits of information according to simple rules, cellular automata is not a reductionist approach to science — in fact, it is much more focused on synthesizing complex emergent behaviors from simple elements than in reducing complexity back to simple units. The highly synthetic philosophy behind NKS is the paradigm shift at the basis of Wolfram Alpha’s approach too. It is a system that is very much “bottom-up” in orientation. This is not to say that Wolfram Alpha IS a cellular automaton itself — but rather that it is similarly based on fundamental rules and data that are recombined to form highly sophisticated structures.

Wolfram has created a set of building blocks for working with formal knowledge to generate useful computations, and in turn, by putting these computations together you can answer even more sophisticated questions and so on. It’s a system for synthesizing sophisticated computations from simple computations. Of course anyone who understands computer programming will recognize this as the very essence of good software design. But the key is that instead of forcing users to write programs to do this in Mathematica, Wolfram Alpha enables them to simply ask questions in natural language and then automatically assembles the programs to compute the answers they need.

Wolfram Alpha perhaps represents what may be a new approach to creating an “intelligent machine” that does away with much of the manual labor of explicitly building top-down expert systems about fields of knowledge (the traditional AI approach, such as that taken by the Cyc project), while simultaneously avoiding the complexities of trying to do anything reasonable with the messy distributed knowledge on the Web (the open-standards Semantic Web approach). It’s simpler than top down AI and easier than the original vision of Semantic Web.

Generally if someone had proposed doing this to me, I would have said it was not practical. But Wolfram seems to have figured out a way to do it. The proof is that he’s done it. It works. I’ve seen it myself.

Questions Abound

Of course, questions abound. It remains to be seen just how smart Wolfram Alpha really is, or can be. How easily extensible is it? Will it get increasingly hard to add and maintain knowledge as more is added to it? Will it ever make mistakes? What forms of knowledge will it be able to handle in the future?

I think Wolfram would agree that it is probably never going to be able to give relationship or career advice, for example, because that is “fuzzy” — there is often no single right answer to such questions. And I don’t know how comprehensive it is, or how it will be able to keep up with all the new knowledge in the world (the knowledge in the system is exclusively added by Wolfram’s team right now, which is a labor intensive process). But Wolfram is an ambitious guy. He seems confident that he has figured out how to add new knowledge to the system at a fairly rapid pace, and he seems to be planning to make the system extremely broad.

And there is the question of bias, which we addressed as well. Is there any risk of bias in the answers the system gives because all the knowledge is entered by Wolfram’s team? Those who enter the knowledge and design the formal models in the system are in a position to both define the way the system thinks — both the questions and the answers it can handle. Wolfram believes that by focusing on factual knowledge — things like you might find in the Wikipedia or textbooks or reports — the bias problem can be avoided. At least he is focusing the system on questions that do have only one answer — not questions for which there might be many different opinions. Everyone generally agrees for example that the closing price of GOOG on a certain data is a particular dollar amount. It is not debatable. These are the kinds of questions the system addresses.

But even for some supposedly factual questions, there are potential biases in the answers one might come up with, depending on the data sources and paradigms used to compute them. Thus the choice of data sources has to be made carefully to try to reflect as non-biased a view as possible. Wolfram’s strategy is to rely on widely accepted data sources like well-known scientific models, public data about factual things like the weather, geography and the stock market published by reputable organizatoins and government agencies, etc. But of course even this is a particular worldview and reflects certain implicit or explicit assumptions about what data sources are authoritative.

This is a system that reflects one perspective — that of Wolfram and his team — which probably is a close approximation of the mainstream consensus scientific worldview of our modern civilization. It is a tool — a tool for answering questions about the world today, based on what we generally agree that we know about it. Still, this is potentially murky philosophical territory, at least for some kinds of questions. Consider global warming — not all scientists even agree it is taking place, let alone what it signifies or where the trends are headed. Similarly in economics, based on certain assumptions and measurements we are either experiencing only mild inflation right now, or significant inflation. There is not necessarily one right answer — there are valid alternative perspectives.

I agree with Wolfram, that bias in the data choices will not be a problem, at least for a while. But even scientists don’t always agree on the answers to factual questions, or what models to use to describe the world — and this disagreement is essential to progress in science in fact. If there is only one “right” answer to any question there could never be progress, or even different points of view. Fortunately, Wolfram is desigining his system to link to alternative questions and answers at least, and even to sources for more information about the answers (such as the Wikipeda for example). In this way he can provide unambiguous factual answers, yet also connect to more information and points of view about them at the same time. This is important.

It is ironic that a system like Wolfram Alpha, which is designed to answer questions factually, will probably bring up a broad range of questions that don’t themselves have unambiguous factual answers — questions about philosophy, perspective, and even public policy in the future (if it becomes very widely used). It is a system that has the potential to touch our lives as deeply as Google. Yet how widely it will be used is an open question too.

The system is beautiful, and the user interface is already quite simple and clean. In addition, answers include computationally generated diagrams and graphs — not just text. It looks really cool. But it is also designed by and for people with IQ’s somewhere in the altitude of Wolfram’s — some work will need to be done dumbing it down a few hundred IQ points so as to not overwhelm the average consumer with answers that are so comprehensive that they require a graduate degree to fully understand.

It also remains to be seen how much the average consumer thirsts for answers to factual questions. I do think all consumers at times have a need for this kind of intelligence once in a while, but perhaps not as often as they need something like Google. But I am sure that academics, researchers, students, government employees, journalists and a broad range of professionals in all fields definitely need a tool like this and will use it every day.

Future Potential

I think there is more potential to this system than Stephen has revealed so far. I think he has bigger ambitions for it in the long-term future. I believe it has the potential to be THE online service for computing factual answers. THE system for factual knowlege on the Web. More than that, it may eventually have the potential to learn and even to make new discoveries. We’ll have to wait and see where Wolfram takes it.

Maybe Wolfram Alpha could even do a better job of retrieving documents than Google, for certain kinds of questions — by first understanding what you really want, then computing the answer, and then giving you links to documents that related to the answer. But even if it is never applied to document retrieval, I think it has the potential to play a leading role in all our daily lives — it could function like a kind of expert assistant, with all the facts and computational power in the world at our fingertips.

I would expect that Wolfram Alpha will open up various API’s in the future and then we’ll begin to see some interesting new, intelligent, applications begin to emerge based on its underlying capabilities and what it knows already.

In May, Wolfram plans to open up what I believe will be a first version of Wolfram Alpha. Anyone interested in a smarter Web will find it quite interesting, I think. Meanwhile, I look forward to learning more about this project as Stephen reveals more in months to come.

One thing is certain, Wolfram Alpha is quite impressive and Stephen Wolfram deserves all the congratulations he is soon going to get.