The bless of dimensionality

The cover story in Wired by Chris Anderson, “The End of Theory” relies on a silent assumption, which may be obvious but is still worth stating. The reason that such a “petabyte approach” works is that reality occupies only a tiny fraction of the space of all possibilities.  For example, the human genome consists of about three billion base pairs.  However, not every billion-lengths string of four symbols corresponds to a viable organism, much less an existing one or a human individual.  In other words, the intrinsic dimensionality of your sample (the human population) is much smaller than the raw dimensionality of the possibilities (about 4^3,2000,000 strings).

I won’t try to justify “traditional” models. But I also wouldn’t go so far as to say that models will disappear, just that many will be increasingly statistical in nature. If you can throw the dice a large enough number of times, it doesn’t matter whether “God” plays them or not.  The famous quote by Einstein suggests that quantum mechanics was originally seen as a cop-out by some: we can’t find the underlying “truth”, so we settle with probability distributions for position and momentum.  However, this was only the beggining.

Still, we need models.  Going back to the DNA example, I suspect that few people models the genome as a single, huge, billion-length string.  That is not a very useful random variable.  Chopping it up into pieces with different functional significance and coming up with the appropriate random variables, so one can draw statistical inferences, sounds very much like modeling to me.

Furthermore, hypothesis testing and confidence intervals won’t go away either.  After all, anyone who has taken a course in experimental physics knows that repeating a measurement and calculating confidence intervals based on multiple data points is a fundamental part of the process (and also the main motivating force in the original development of statistics).  Now we can collect petabytes of data points.  Maybe there is a shift in balance between theory (in the traditional, Laplacian sense, which I suspect is what the article really refers to) and experiment.  But the fundamental principles remain much the same.

So, perhaps more is not fundamentally different after all, and we still need to be careful not to overfit.  I’ll leave you with a quote from “A Random Walk down Wall Street” by Burt Malkiel (emphasis mine):

[...] it’s sometimes possible to correlate two completely unrelated events.  Indeed, Mark Hulbert reports that stock-market researcher David Leinweber found that the indicator most closely correlated with the S&P 500 Index is the volume of butter production in Bangladesh.

Dimensionality may be a bless, but it can still be a curse sometimes.

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Research and new media: the academic clowd

I have a little secret: Slashdot may have lost its lustre now, but back in 2001, shortly after returning from my refreshing internship at Almaden, I posted a question to “Ask Slashdot” for the first and last time. I posed the question rather poorly and was ignored. Although I could not find exactly what I wrote back then, it was something along the lines of “why aren’t academic venues more like SourceForge?” You have to remember that this was the early 2000’s, when large and transparent user communities existed only in the technical sphere, and things like SourceForge were the prototypical sites for online focused communities. So why couldn’t academia and the research community open things up a bit more, and leverage new media to set up virtual forums for world-wide lively discussions and collaborations?

Fast-forward seven years. I got a feeling of deja-vu when I saw two recent blog posts and a Slashdot post. The first two question specific aspects of current publishing practices, while the “Ask Slashdot” post wonders whether academic journals are obsolete. The technologies and media have changed dramatically since then, but the essence remains the same.

Going over the comments on Slashdot, even though there are some surprisingly (for Slashdot) insightful ones, there is also one fundamental misconception. I was genuinely surprised at its prevalence. Many commenters seem to identify the general notion of “peer evaluation” with the specific mechanisms currently employed to do it. Is the current way of doing things so deeply entrenched, that people are blind to other possibilities?

Quoting a random vicious comment: “The purpose of restricting published work to that which has passed peer review is to ensure that results do not become obsolete. They must uphold the same quality standards that we expect from all scientific disciplines—not blog-style fads that have become popular and at some stage will cease to be popular.” I wonder if commenter has ever written a blog himself, or whether he even just taken a look at, say, Technorati: there are over four million blogs out there and 99% have just one reader (the author). Very few blogs are popular (i.e., the actually read by a significant number of people). An explosion in quantity of published content does not imply a proprtional explosion in its consumption; quite the contrary. If anything, there is more competition for attention, not less.

Another commenter said that “there isn’t any direct communication between reviewers and submitters.” Not so. Take a look at Julian Besag’s “On the Statistical Analysis of Dirty Pictures” (unfortunately JSTOR is restricted-access, but maybe your institution has a subscription), published in the Journal of the Royal Statistical Society as recently as 1986. The actual paper is 21 pages, while the other 23 pages are devoted to an open discussion. This looks oddly familiar (deja vu again): it looks like very popular blogs, which often have comment sections larger than the original posts. A free and open discussion of ideas has always been an organic part of the research process. A few centuries ago, scientific articles appeared with a date on which they were “read” to the community (just take a look at, e.g., the an issue of the Philosophical Transactions of the Royal Society).

Research on the web

Reaching far out into the long tail of ideas, which I also discussed in a previous post, should arguably be a top priority for research. In other endeavors it is an important means to success (financial or otherwise), but in academia and the research community it is usually an end in itself. The web itself was originally conceived as a venue for the exchange of scientific ideas, but even its creators probably did not envision the full potential nor realize all the implications of democratizing publication.

Modern technology allows more researchers (whether they work for startups, academic institutions, or large corporations) to try out more ideas. In other words, the production of research output is scaling up to unprecedented levels. However, I strongly suspect that traditional ways for evaluating research will not scale for much longer, being unable to keep up with the explosive growth in the rate of new ideas.

The typical process for evaluating and disseminating research—at least in computer science with which I am familiar—seems to be the following (with perhaps a few exceptions). First you come up with an interesting idea. Next, you build a story around it and do the minimal work to support that story. If everything works out, you write it up and submitted to a conference or, more rarely, a journal. On average, three people (chosen largely at random) review your work, making some comments in private. Once your work is published, you move on to the next paper.

I would simply name two artifacts as the main “products” of computer science research: papers and software. The latter is often overlooked, but it’s at least as important as the first. Anyway, what might be the state-of-the-art media for each of those artifacts?

There are some well-known efforts to use the web for the former. For example, there is arXiv for physics and sciences, CoRR for computer science, and PLOS for life sciences. There is also VideoLectures for open access to some talks. All of these, however, largely mirror the established ways of doing things: they are still built using the paradigm of a “library”. Although very important steps in the right direction, they perhaps play second fiddle to traditional media (there is a reason that arXiv is called a “pre-print server”) and thus fail to fully realize the potential offered by the rapidly emerging social media.

Things are perhaps a little more advanced for software artifacts. There are SourceForge, Google Code, and countless other similar sites for hosting source code, tracking issues and holding online discussions. There is also Freshmeat, Ohloh, and other project directories, as well as source code search engines such as Koders. However, none of these (or, as far as I know, anything similar) have been widely embraced by the research community.

Enough about today. It is more interesting to try and imagine how all these things, and more, may come together in the future.

The academic clowd scenario

Shamelessly copying this post, let’s imagine the academic clowd (cloud + crowd).

You have a great new idea and decide to try it out. You write a proof-of-concept implementation and run it on the cloud, using large datasets that also live out there. The implementation itself is available to the clowd, which can analyze the revision control logs and find out who really worked on what.

Your idea works and you decide to write a research article about it. The clowd knows what papers you wrote, who are your co-authors and which conferences and journals you publish in (cf. DBLP). It also knows the content of your papers (cf. CiteSeer). So, when you publish your new article, it compares it with the existing literature and finds the most relevant experts (in terms of content, co-citations, venues of publication, etc) to evaluate your work. It knows who your close friends and relatives are (from Facebook) and automatically excludes them from the list of potential reviewers. It also exlcudes your co-authors from the past three years. Then, it solicits reviews from those experts. Of course, it also allows others who are interested to participate in the discusssion.

In addition to the original paper, all review comments are public and can be moderated (say, similar to Digg or to Slashdot, but perhaps in a more principled and civilized manner). Thus, the review comments are ranked for their correctness, originality and usefulness. These rankings propagate to the papers they refer to.

You present your work in public and the video of your lecture is on the clowd, exposing you to a much larger audience. Anyone can also comment on it and respond to it. The videos are linked to each other, as well as to the articles and to the implementations. They are organized into thousands “virtual research tracks” with several tens of talks in each. “Best of” virtual conference compilations appear on the clowd.

Rising papers and their authors get introduced to each other by the clowd. You can easily find ten potential new collaborators with mutual interests. You try out more things together, write more articles, and so on …until one day you all save the world together (well, maybe not, but it would be nice! :-).

So, what will the future really look like?

Well, who knows? I’m pretty sure the above scenario will seem as ridiculous in ten years, as the SourceForge ideal looks today (what was I thinking then?). Nonetheless, I believe it should be part of the current vision for research. I don’t think that the web and social media will lead to less selection via peer evaluation. Quite the contrary. Nor do I think that they will lead to less elitism. This follows from simple math. Taking the simplistic but common measure of “acceptance ratio”, the numerator cannot grow much, because people’s capacity to absorb information will not grow that much. But, if the potential to produce published content makes the denominator grow to infinity, then the ratio has to approach zero. Methods for evaluating research output need to scale up to this level of filtering, and I simply don’t think that the current way of evaluating research can achieve this.

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The long tail of ideas

It’s not clear how you measure the size of an idea. Is it billions of generated revenue? Is it number of papers published? Number of citations? Brain-ounces (whatever that is)? But, let’s say that based on any or all of these measures, some ideas are big and some are small. The long tail applies here too: a few ideas make it big, but most of them remain small. But how do we find these big ideas?

I’ve heard the following piece of advice several times over the past few years, and more recently in a talk by one VP. It goes something like this: “Find an idea an ask yourself: is the potential market worth at least one billion [sic] dollars? If not, then walk away.” This is very similar to something I read in one of Seth Godin’s riffs, about a large consulting company recommending to a large book publisher that they should “only publish bestsellers.” They would, if they knew in advance which books would be bestsellers. But, in reality, this advice is simply absurd.

For example, who thought back in the 90’s that search would be so important, with search marketing worth about 10 billion and expected to exceed 80 billion within 10 years? Nobody, and perhaps following the above advice, projects such as CLEVER and it’s follow-up (which put a “business intelligence” spin to search), WebFountain, went nowhere. The only thing that went somewhere is the researchers; they moved to Google, Yahoo! and Microsoft.

When I onced talked to someone from WebFountain, two things he said struck me and I still remember them after several years. First, the cost of crawling a reasonable fraction of the web and maintaining an index was quite small (a handful of machines, a T1 pipe and maybe one sysadm). Second, it turns out that back then WebFountain received some flak for “starting small.”

In other words, the engineers seemed to recognize they were starting at the far end of the tail, and decided to put some wheels on their idea and see how to move it up towards the head, growing along the way. But management wanted more to justify the project. Four wheels is just a car, but how about four hundred? “Is this something really big or isn’t it? If it is, then why 10 machines and not 300? Why 5 people and not 50? Why just make 3 features that work, instead of design and advertise 30 or 50?” As far as I can tell as an outsider, this is what happened and such an over-planning (combined, perhaps, with rather poor execution on the development side) did not lead to the expected results.

Perhaps such a mentality would have made sense a few decades ago, when computing power was far from a commodity, barriers to entry were large, and supercomputing was thriving. These days however, instead of asking “how much is this idea worth”, it’s better to ask “how much does it cost to try this idea?” and strive to make the answer “almost zero.” You don’t know in advance what will be big—that was always true. You should not start big because it’s not cost-efficient—that was not always possible, but it is today. Start big and you will likely end up small (like countless startups from the bubble-era); but start small and you may end up big. Google just appeared one day and did only one thing (search) for many years; Amazon sold only books; and so on.

Barriers to entry should not be made artificially high. Some companies seem to recognize this better than others (although this may be changing as they grow), and strive to provide an infrastructure, environment and culture that makes it easy to try out many new things by starting small and cheap. And other companies are enabling the masses to do the same.

I’m not saying that you should try every idea, even if it seems clearly unpromising. I’m also not saying that any idea can become big or that, once an idea becomes big, it will still cost zero to scale it up. But, technology, if properly used and combined with the right organizational structures, allows more ideas from the long tail to be tried out, at minimal cost. You’re expected to fail most of the time, but if the cost to try is near-zero, it doesn’t matter.

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Data Mining: “I’m feeling lucky” ?

In an informal presentation on MapReduce that I recently gave, I included the following graphic, to summarize the “holy grail” of systems vs. mining:

Systems vs. Data mining

This was originally inspired by a quote that I read sometime ago:

Search is more about systems software than algorithms or relevance tricks.

How often do you click the “lucky” button, instead of “search”? Incidentally, I would be very interested in finding some hard numbers on this (I couldn’t)—but that button must exist for good reason, so a number of people must be using it. Anyway, I believe it’s a safe assumption that “search” gets clicked more often than “lucky” by most people. And when you click “search”, you almost always expect to get something relevant, even if not perfectly so.

In machine learning or data mining, the holy grail is to invent algorithms that “learn from the data” or that “discover the golden nugget of information in the massive rubble of data”. But how often have you taken a random learning algorithm, fed it a random dataset, and expected to get something useful. I’d venture a guess: not very often.

So it doesn’t quite work that way. The usefulness of the results is a function of both the data and the algorithm. That’s common sense: drawing any kind of inference involves both (i) making the right observations, and (ii) using them in the right way. I would argue that in most succesful applications, it’s the data takes center stage, rather than the algorithms. Furthermore, mining aims to develop the analytic algorithms, but systems development is what enables running those algorithms on the appropriate and, often, massive data sets. So, I do not see how the former makes sense without the latter. In research however, we sometimes forget this, and simply pick our favorite hammer and clumsily wield it in the air, ignoring both (i) the data collection and pre-processing step, and (ii) the systems side.

It may be that “I’m feeling lucky” often hits the target (try it, you may be surprised). However, in machine learning and mining research, we sometimes shoot the arrow first, and paint the bullseye around it. There are many reasons for this, but perhaps one stands out. A well-known European academic (from way up north) once said that his government’s funding agency once criticized him for succeeding too often. Now, that’s something rare!

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The shift from private to public channels of information

Many discussions about privacy these days obsess over the shifting balance between public and private channels of information, while missing the real issues and opportunities.

The information landscape is unquestionably changing. We are experiencing the emergence and rapid proliferation of social media, such as instant messaging (e.g., IRC, Jabber et al., AIM, MSN, Skype), sharing sites (e.g., Flickr, Picasa, YouTube, Plaxo), blogs (e.g., Blogger, WordPress, LiveJournal) and forums (e.g., Epinions), wikis (e.g., Wikipedia, PBWiki), microblogs (e.g., Twitter, Jaiku), social networks (e.g., MySpace, Facebook, Ning), and so on. Also, much financial information (e.g., your bank’s website or Quicken) as well as health records are or soon will be online.

A rather obvious distinction is between public vs. private channels of information or content:

  • In public channels, the default policy on data sharingis “opt-in”.
  • In private channels, the default is “opt-out” (along with some, hopefully enforceable, guarantees that this is the case).

Most people, at least of a certain age, take the former for granted. However, this is changing. Just a couple of decades back, schoolchildren would keep journals (you know, those with a locket and “Hello Kitty” or “Transformers” on the cover). These days they are on MySpace and Twitter, and they do not assume “opt-out” is the default. Quoting from the article “The Talk of Town: You” (subscriber-only access) in the MIT Technology Review:

New York‘s reporter made a big deal about how “the kids” made her “feel very, very old.” Not only did they casually accept that the record of their lives could be Googled by anyone at any time, but they also tended to think of themselves as having an audience. Some even considered their elders’ expectations about privacy to be a weird, old-fogey thing—a narcissistic hang-up.

Said differently, an increasing fraction of content is produced in public, rather than private channels and “opt-in” is becoming the norm rather than the exception. Social aggregation sites, such as Profilactic, are a step towards easy access to this corpus. Despite some alarmism about blogs, Twitter, MySpace profiles, etc, all this information is, by definition, in public channels. Perhaps soon 99% of information will be in public channels.

So, which information channels should be perceived as public? Many people have a knee-jerk reaction when it comes to thinking of what should be private. For example, this blog is clearly a public channel. But how about your health records? In an interesting opinion about making health records public, most commenters’ expressed a fear of being denied health coverage by an insurance company. However, this is more an indication of a broken healthcare system, than of a problem with making this data public. Most countries (the U.S. included) are behind in this area, but others (such as the Scandinavians or Koreans) are making important steps forward. Now, how about your financial records? For example, credit reporting already relies on aggregation and analysis of publicly available data. How about your company’s financial records? Or how about your phonecall records? Or your images captured by surveillance cameras? The list can go on forever.

We should avoid that knee-jerk reaction and carefully consider what can be gained by moving to public channels, as well as what technology and regulation is required to make this work. The benefits can be substantial; for example, the success of the open source movement is largely due to switching to public, transparent channels of communication, as well as open standards. Openness is usually a good thing.

Even in the enterprise world of grownups, tools such as SmallBlue (aka. Atlas) are effectively changing the nature of intra-company email from a private to a (partially) public channel. The alternative would be to establish new public channels and favor their use over the older, “traditional” (and usually private) channels. Both approaches are equivalent.

Moreover, how should we deal with the information in private channels? The danger with private channels arises when privacy is breached. If that happens, not only do you get a false sense of security when you have none, but you may also have a very hard time proving that it happened. However, the notion itself of a “breach” in public channels is clearly meaningless. In that sense, public channels are a safer option and should be carefully considered.

Even when the data itself is private, who is accessing it and for what purpose should be public information. The MIT TR article continues to mention David Brin’s opinion that

“[...] our only real choice is between a society that offers the illusion of privacy, by restricting the power of surveillance to those in power, and one where the masses have it too.”

The need for full transparency on data how they are used is more pressing than ever. Ensuring that individuals’ rights are not violated requires less secrecy, not more. A recent CACM article by a gang of CS authority figures makes a similar case (although their proposal for an ontology-based heavyweight scheme for all data out there is somewhat dubious; it might make sense for the 1% niche of sensitive data, though). Interestingly, one of their key examples is essentially about health records and they also come to the same conclusion, i.e., that the problem is inappropriate use of the data.

I actually look forward to the day I’ll be able to type “creator:spapadim@bitquill.net” on Google (as well as any other search engine) and find all the content that I ever produced. And going one step beyond that, also find the “list of citations” (i.e., all the content that referenced or used my data), like I can find for my research papers on Google scholar, or for posts on this blog with trackbacks. Although I cannot grasp all the implications, it would at least mean we’ve addressed most of these issues and the world is a more open, democratic place. McLuhan’s notion of the global village is more relevant than ever, but his doom and gloom is largely misplaced; let’s focus on the positive potential instead.

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