Why would anyone who reads this blog care about this? Because I want it to make sense when I say in November that McCain got Weis’d.

This terrific design is courtesy of the people at UNDRCRWN, purveyors of awesome shit, all of which would look completely ridiculous on me. (Via First Cuts, by way of Ball Don’t Lie.)

The clip also includes a performance from Dr. Cornel West which constitutes a watershed moment in the history of YouTube*.

Second, I absolutely demand that you go and watch Dr. Horrible’s Sing-Along Blog. It is (so far) thirty minutes of pure awesome. Awesome put together by Joss Whedon during the writers’ strike, and staring Neil Patrick Harris and Nathan Fillion. In Whedon’s words:

Frustrated with the lack of movement on that front, I finally decided to do something very ambitious, very exciting, very mid-life-crisisy. Aided only by everyone I had worked with, was related to or had ever met, I single-handedly created this unique little epic. A supervillain musical, of which, as we all know, there are far too few.

Go there, and don’t come back until you’ve gotten to the end of Act II and you know what the hammer is. It’s only available until Sunday, so go quickly.

I apply the Abraham Lincoln test for moral casuistry: “If slavery is not wrong, nothing is wrong.” Well, then, if waterboarding does not constitute torture, then there is no such thing as torture.

That’s pretty interesting, and I considered posting it at the time. But, lacking anything to add, I passed. Then today Scott Kaufman brought back the reaction from the wingnut frontier, and it included bits like this:

The enemy believes that we are weak, & Hitchens & his ilk are to blame. It will come to a point, & soon me thinks, when real force will be required. And then it shall come to pass that the unpleasant realities of preemption need to be replaced with the even more unpleasant realities of vengeance […] & the Hitchens of the world will cease to be relevant.

They think we’re weak, and Hitchens is to blame? You mean, this Hitchens: “I shall go on keeping score about this until the last phony pacifist has been strangled with the entrails of the last suicide-murderer”? How hard-assed do they want our rhetoric to be? Perhaps from now on, whenever you write about terrorists, you should pretend to be that the masked dude from the Saw franchise or Arnold’s character from Predator. Then you’ll have the right levels of machismo and sadism to sound like a patriot.

Highly recommended for examples involving Brahe, Kepler, Newton and the Large Hadron Collider.

And I assume you know what it’s like to be an atheist in the United States, which is why you can make this claim? For your information, it’s not at all like being a Yankees fan.

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A dry history might just tell you that in 565 the Byzantine general Narses was relieved of his duty, and that as a way of taking revenge on the Emperor he encouraged the Lombard king to invade Italy. But your friend the Viscount takes a sip of his Scotch and tells you why the general should never have been dismissed: “Eunuchs, as everybody knows, are dangerous people to cross.”

Further, a dry history would probably not call the 864 Patriarch of Aquileia a “rascally primate,” or describe the tenure of Pope John XII as “the nadir of the papal pornocracy.”

Probably the most famous tourist attraction in Venice is St. Mark’s Basilica. Wikipedia provides a dry history of this building’s importance: “In 828, the new city’s prestige was raised by the liberation of the relics of St. Mark the Evangelist from Alexandria, which were placed in the new basilica.” After the jump I’ll transcribe Norwich’s livelier version of the “liberation.”

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[…] The scientific method is built around testable hypotheses. These models, for the most part, are systems visualized in the minds of scientists. The models are then tested, and experiments confirm or falsify theoretical models of how the world works. This is the way science has worked for hundreds of years.

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

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

Among the interesting reactions, we’ve got Andrew Gelman, Drew Conway, Fernando Pereira, Cosma Shalizi, and Ed Felten. They have a range of more and less technical reasons for disagreeing, all of which are interesting and seem on-point to me. Dr. Felten’s explanation of his disagreement is the easiest to understand:

To take a simple example, suppose we discover a correlation between eating spinach and having strong muscles. Does this mean that eating spinach will make you stronger? Not necessarily; this will only be true if spinach causes strength. But maybe people in poor health, who tend to have weaker muscles, have an aversion to spinach. Maybe this aversion is a good thing because spinach is actually harmful to people in poor health. If that is true, then telling everybody to eat more spinach would be harmful. Maybe some common syndrome causes both weak muscles and aversion to spinach. In that case, the next step would be to study that syndrome. I could go on, but the point should be clear. Correlations are interesting, but if we want a guide to action — even if all we want to know is what question to ask next — we need models and experimentation. We need the scientific method.

It’s true that correlations are enough if all you want to do is make money selling ads. In that case, as Anderson says, “Who knows why people do what they do? The point is they do it, and we can track and measure it with unprecedented fidelity. With enough data, the numbers speak for themselves.” But scientists interested in human behavior would see this argument as completely backwards. To a scientist, the behavior is not “the point,” but a place to begin. Science is a process of forming an understanding of the world we live in, and the one thing data mining doesn’t produce is understanding. It may produce actionable predictions, but it won’t explain them to you.

For instance, here’s another claim from the article:

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

It’s great that Craig Venter is able to sequence a bunch of genomes. Everyone agrees this is a cool project. But, more than anything else, it’s a starting point. A bunch of DNA sequences on disk may produce interesting correlations, but they don’t advance our biological understanding of global ecosystems until they’ve been used to build testable hypotheses.

A couple of months ago I was hanging around in the lobby before an invited lecture on machine learning, and wandered into a conversation between the speaker and a couple of the CS faculty here at UCI. Since I’m not entirely comfortable quoting professors from memory months after the fact and without asking permission, I won’t say exactly who, but it was one of the people high up on this list. Anyway, the person in question is an expert in the fields of machine learning and data mining. So I came into the conversation late, and just caught someone repeating a clam from elsewhere that soon machine learning would make the scientific method obsolete; it was a claim very much like Anderson’s. And this professor, whose research involves thinking up clever new ways to mine data, said, “I think that’s exactly the wrong way to think about it.” I don’t remember the rest of the quote verbatim, but the gist was: In a perfect world, machine learning and data mining would become unnecessary, because we would have a sufficiently complete understanding not to have to resort to them. They are effectively stop-gap measures, which we rely on to make predictions (and, in a lot of cases, money) when we’re willing to act without having (or understanding) interpretable reasons. But we shouldn’t look forward to a world when we can stop searching for that understanding.