A few weeks ago, Colby Cosh—a friend of a friend of sorts who ordinarily writes reasonable things for a chap who still thinks the Edmonton Oilers are a real sports team—penned an article in his Maclean’s blog about Watson, IBM’s Jeopardy!-playing machine (“I’ll take ‘Cheap Publicity Stunts’ for $1000, Alex”, 16 January 2011), that I found to be dreadfully uninformed. The thrust of his argument is that Watson is a corporate “gimmick”—a fancy plea for media coverage by the faceless villains at IBM, with nothing of scientific interest going on underneath. Keep in mind that by the standards of this article, nothing in the “perpetually disappointing history of AI” will ever be interesting until we’ve graduated from tightly delimited objectives to Big Problems like the Turing Test:

Every article about Watson, IBM’s Jeopardy!-playing device, should really lead off with the sentence “It’s the year 2011, for God’s sake.” In the wondrous science-fiction future we occupy, even human brains have instant broadband access to a staggeringly comprehensive library of general knowledge. But the horrible natural-language skills of a computer, even one with an essentially unlimited store of facts, still compromise its function to the point of near-parity in a trivia competition against unassisted humans.

This isn’t far off from saying that particle physics will be perpetually disappointing until we’ve observed the Higgs boson, or that manned spaceflight is merely an expensive publicity stunt that will never be scientifically interesting until we’ve colonized the Moon: it leans heavily on popular culture as the ultimate barometer of scientific achievement, and it requires both ignorance of methodology and apathy towards specifics.

Colby and I had a five-minute skirmish about the article on Twitter, which as a format for debate is unwieldy as piss. I promised a proper response as soon as I cleared some other priorities off my plate. Those other priorities are still, to my annoyance, on my plate; but having finally paid good money to register my copy of MarsEdit, I’m thirsting for a scrap.

This topic will do as well as any. Reluctant as I am to swing the pretentious hammer of “I know what I’m talking about,” this really is (as the idiom goes) a chance for Faramir, Captain of Gondor, to show his quality. Computational linguistics happens to be my onetime research area, popular misunderstanding of science happens to be one of my favourite bugbears, and Kasparov’s anticomputer strategies against Deep Blue happened to make a cameo appearance in the meandering slop of my master’s dissertation. None of this matters a great deal, mind you. One should never be dismissive of journalists from a position of relative expertise; they’re the ones people actually read, and it’s vital to engage with what they say.

(It is a little game we play: they put it on the bill, I tear up the bill.)

When simplifications attack

What concerns me is not so much Colby’s perspective as a non-expert (invaluable), his resort to the familiar hand-waving sophistries of Dreyfus and Searle (expected), or even whether I should call him Colby when I don’t really know the fellow and haven’t gotten around to amending my unwritten style guide to arbitrate matters of semi-personal address (pedantic). The bigger problem, one that is endemic in journalism about science, is his exclusive reliance on popular simplifications by corporate PR, other journalists, and cherry-picked philosophers for pictures of what AI research is all about.

Surely it wouldn’t have hurt to consult a real computing scientist; there are plenty of those to choose from the public sector with no vested interest in the fortunes of IBM. The only thing this would have jeopardized is a premeditated thesis founded on dismissive assertions about the entire field of research. Why talk to someone credible when they’re unlikely to agree with you?

Here, there are several bad assertions in play—all of which are traceable to the selective consultation of sources.

Let’s consider this one paragraph alone—the crux of Colby’s entire argument that nothing terribly fascinating is going on inside the box:

Jeopardy!, after all, doesn’t demand that much in the way of language interpretation. Watson has to, at most, interpret text questions of no more than 25 or 30 words—questions which, by design, have only a single answer. It handles puns and figures of speech impressively, for a computer. But it doesn’t do so in anything like the way humans do. IBM’s ads would have you believe the opposite, but it bears emphasizing that Watson is not “getting” the jokes and wordplay of the Jeopardy! writers. It’s using Bayesian math on the fly to pick out key nouns and phrases and pass them to a lookup table. If it sees “1564″ and “Pisa”, it’s going to say “Galileo”.

Now let’s put some numbers beside the assertions:

1. Jeopardy! is a trivia game, and all there is to trivia is looking up keywords. We know computers can do that.
2. When Watson handles wordplay, it doesn’t do it like humans do. It isn’t really thinking; it doesn’t really understand the puns. Furthermore, this somehow matters.
3. IBM would like us to believe that Watson really gets the jokes. If Watson doesn’t really get the jokes, the project is a hollow exercise in corporate self-promotion.

The first assertion vastly understates the complexity of what Jeopardy! demands. The nature of the game—a time-constrained, multi-agent affair—radically alters the straightforward problem of answering a question (or in this case, questioning an answer). Even simple pattern-matching is far from trivial when every millisecond counts.

Let’s run with Colby’s caricature for a moment. With a database of facts as gargantuan as the one Watson requires, looking up “1564″ in conjunction with “Pisa” is a surprisingly time-consuming task, never mind the inference to Galileo’s date of birth. This isn’t something tractable via faster processors or larger memory banks: there are theoretical lower bounds on the efficiency of searching and sorting algorithms in proportion to the dataset’s size. Exhaustive traversals that perform perfectly on small scales are out of the question here. The algorithms have to take shortcuts and make approximate guesses. Semantic associations must be efficiently structured in the software’s abstract maps as well as the physical database in order to best distribute searches in parallel. When you consider these factors, drawing semantic inferences from the natural-language clues becomes a heuristic necessity if the approximate search queries are to be any good.

Crucially, the time constraint on a response is not a static value, but a dynamic one that depends on the performance of the other competitors. This is why a match against the most successful Jeopardy! players in history is an essential proof of concept. Every contestant who appears on the television show has to pass a solo audition first, and any of them could tell you—particularly if they meet with little success—that in a competitive setting, the game becomes a different kettle of fish.

This is to say nothing of the other decisions Watson has to make in order to be competitive in a live test. It has to assess the risk of answering a question, considering not only its confidence in its own correctness but the standing scores of both itself and the other players. It has to set wagers for Double and Final Jeopardy, which requires an assessment of confidence based on the category title alone; in the case of Double Jeopardy, this will also have to consider the money still up for grabs on the board. One of the reasons Ken Jennings had such an astonishing run on the show was that he was able to make excellent strategic wagers on the fly.

Contrary to what Colby suggests, if the structured decomposition of the process of taking a Jeopardy! clue all the way from answer to question is able to match and surpass the blazing speed of human intuition at its best, that would be a tremendous accomplishment indeed. Without the capacity to parse natural language in terms of meaningful semantic chunks—a task well beyond mere symbol manipulation—Watson wouldn’t have a prayer of displaying a fraction of the competence that it has already shown.

Trapped in the Chinese Room

The second assertion is a real howler, and one that has become downright boring to swat aside over the course of the past thirty years. That’s right, folks: say hello to John Searle and the Chinese Room. The Chinese Room objection to AI is this: a computer translating between English and Chinese is like an English speaker who knows no Chinese, but who sits in a room looking up symbol tables and matching the syntactic elements correctly. Even if the translation looks perfect to the outsider, argued Searle, you couldn’t say that the symbol-manipulating translator (i.e. the computer) understands Chinese.

In a general sense, the Chinese Room stands for a whole class of arguments that boil down to saying, it doesn’t matter how well the computer performs—it’s not really thinking because on the inside, it’s not processing information in the same way humans do. Colby makes an argument about Watson identical to the Chinese Room when he says that the system doesn’t “get” the jokes and puns in Jeopardy!‘s more puzzling clues. Apparently, it doesn’t matter if Watson solves the clues correctly: it still isn’t behaving like a human inside the box, so the whole shebang is all just smoke and mirrors.

The logic of the Chinese Room is spurious in many respects, and I won’t go through all of the embedded fallacies here. For those of you new to the debate, here are two of the more serious ones. The first is that the analogy is false. The appeal of the argument comes from how it personifies a particular component of the system to highlight its dissimilarity to real human understanding. This fallacy endures unchecked because its proponents are free to move the goalposts however they like: no matter how robust the system is, the critics can isolate a piece of the syntactic machinery, put a human face on it, and complain about the absence of high-level, humanlike semantics. The second fallacy lies in the deceptive assertion that the syntactic internals of a computer are completely unlike the internals of the human mind. In truth, we still know next to nothing about how the latter works. Our understanding of how we get from the low-level operations of neuroscience to the high-level processes of cognitive psychology is at least as discontinuous as our best notions of how semantic structures might emerge from the symbolic structures of computer systems.

I alluded to this in my initial salvo on Twitter:

Shockingly poor article by @colbycosh on Watson, IBM’s #Jeopardy AI. Apparently, Chinese Room fallacies never get old. http://t.co/VHzLzTX

To which Colby offered this astonishing reply:

@Nicholas_Tam It’s got nada to do with the Chinese room. The Turing test is the one most everyone agrees on & there’s NO progress toward it.

Completely apart from the fact that one of Colby’s objections was precisely the Chinese Room, there’s a logical contradiction here along with a factual error. (Not bad, all in all, for 140 characters or less.) The contradiction arises from the failure to distinguish between external behaviours and internal thought processes. Let’s suppose, for a moment, that the goal for whichever AI system we’re talking about is to pass the Turing Test—that is, to be misidentified as the human being in a double-blind question-and-answer test where the questioner knows that one respondent is human and the other is a machine. If you read the original paper in Mind where Alan Turing introduced his “imitation game”, Turing’s whole point was to black-box the internals and take them out of the picture. The premise of the Turing Test is that if you can’t tell the difference between man and machine in terms of external behaviour, then functionally there may as well be no difference at all; this suffices as intelligence.

The Chinese Room argument, on the other hand, is a direct attack on the validity of the Turing Test. It seeks to establish that thoughts don’t supervene on actions: that is to say, identical external behaviours do not imply identical internal machinations.

Turing’s and Searle’s positions are more or less incommensurable. You can’t have it both ways. You can’t hold up the Turing Test (which is entirely about exterior performance) as the standard of achievement while griping, as Searle does, that even in a successful performance that passes for humanlike, symbol manipulation doesn’t really count. Contrariwise, Turing ventured that if a machine’s behaviour is indistinguishable from a human’s, it’s pointless to squabble over whether it qualifies as intelligent; from the available evidence, we might as well treat it as such.

If you accept the Chinese Room argument—and you really shouldn’t—the only function of bringing up the Turing Test at all is to set up a straw man. It has not escaped me that this may have been the intent.

Acting inside the box

Unfortunately for this transparent rhetorical tactic, the Turing Test is not the accepted benchmark for artificial intelligence research, nor is it even a commonly desired objective. AI is not one monolithic project that either has or hasn’t been achieved.

The goals of AI research have historically diversified along two separate axes (a schema for thinking about AI that most students of intelligent systems pick up from Russell and Norvig). The first key distinction is between acting (what a system does on the outside) and thinking (how a system gets there on the inside). The second distinction is between performing like humans and performing rationally or optimally (which may be entirely unlike humans, but may provide solutions to well-defined problems that outstrip the capacities of human agents).

This yields four quadrants that loosely circumscribe your garden-variety intelligent agents: systems that aim to think like humans, act like humans, think rationally, or act rationally. (Think of these categories more as design goals than as discrete kinds of agents, which in practice lie all over the map.) The first quadrant, systems that think like humans, is the area of interest for much of cognitive science. This is the type of system that the Chinese Room argument contends will in principle never succeed; Hubert Dreyfus’s objection, the thesis that human thought is fundamentally unformalizable, applies specifically to this category as well. The second quadrant, systems that act like humans, is the one where the Turing Test applies.

It must be said that the Turing Test is relevant here with specific reference to the indistinguishability of external behaviours—not to the requirement of aptitude in natural languages, as Colby seems to believe. Turing’s original imitation game was framed purely in terms of language, which remains an overwhelming challenge to this day, but it has since been expanded to other problem domains. (Jeopardy! is one of them.) To pluck out one example, natural language is hardly suitable as a test for computer vision, the branch of AI concerning how computers can perceive objects in photographs or positions in 3D space from the raw data of images. It would be preposterous to say that a robust system in computer vision fails as AI or marginalize its significance as a scientific accomplishment simply because it can’t pass for a human on the telephone.

Natural language is a particular problem domain—indeed, an umbrella category for all sorts of subproblems that are fascinating in their own right. It is not the essence of the Turing Test, nor is there any consensus that linguistic aptitude is the essence of intelligence.

It’s convenient for our discussion, however, that Jeopardy! involves natural language to the extent that it does. It should attract comparisons to Turing’s imitation game, and it has. Yet it bears mentioning that whether a system is really thinking is a completely incidental consideration for the vast majority of practical work in AI, just as it was for Turing. Nobody says, “Let’s build a system that possesses general intelligence.” What they actually say is this: “Let’s identify a chunky, intuitive problem that demands high-level thought and see if we can’t build a system to break it down and tackle it.”

Watson’s aim is clear: perform well enough in Jeopardy! to defeat the best human players. Any consequences for our beliefs about the nature of human intelligence is a byproduct and not the ultimate goal. That said, it is perfectly valid to speak of a Jeopardy! Turing Test. Watson would clearly fail the test not if it fell short of champion-level play, but if it ventured solutions to clues that don’t even make sense as guesses. (Consider the early test at about 1:50 into this video. The clue, from the category on I Love Lucy: “It was Ricky’s signature tune and later the name of his club.” Watson: “What is song?”)

But if indistinguishability from human-level performance is what we are looking for, Watson is already doing fairly well. There is a very important difference between defeating humans in Jeopardy! and passing for a human player, although the goals are intertwined. There is an even wider gulf between passing for a human Jeopardy! player and passing for a human being in toto. Everybody knows the latter goal is as far off as colonizing Mars, and nowhere in the promotional materials does IBM suggest otherwise.

Colby has a problem with this:

So why, one might ask, are we still throwing computer power at such tightly delimited tasks, ones that lie many layers of complexity below what a human accomplishes in having a simple phone conversation?

And one might also ask, why study nuclear physics when we seem to be no closer to harnessing fusion power than we were fifty years ago? First of all, in both cases, we are substantially closer in terms of how we understand the problem, even if our estimates for when the endpoint will show up on the horizon haven’t necessarily shortened. The achievements that scientists think of as the most significant may not be fixtures in popular culture, but that doesn’t mean they were pointless. Far more importantly: computing science, like nuclear physics, is inherently interesting. Designing AI systems for delimited problem spaces is an activity that leads us to all sorts of discoveries about the nature and structure of those problems, and of the minutiae of problem-solving processes in general. We learn all sorts of things about comparative strategies for structuring, representing, and manipulating information—and how they measure up to the relatively black-boxed processes of human minds.

So to answer Colby’s question:

@Nicholas_Tam So we can’t test AI by scrutiny of interior process OR the curtained-black-box Turing test? What’s left, religious revelation?

We “test” AI in the context of its performance with respect to well-defined goals. Those goals could certainly involve a Turing Test, be it for answering natural-language questions or some other specified task. Whether an artificial system has a human-like mind of its own, along everything that implies—consciousness, self-awareness, semantic understanding—is a problem we leave to the philosophers; and no, it’s not empirically testable. But neither is the problem of whether other humans have minds.

The inverted pyramid scheme

Now let us turn to the third assertion: that IBM is making outlandish promotional claims that oversell Watson in the name of fuelling a publicity blitz.

What does it mean to say that something is a “gimmick”? We mean to accuse it of being all dressing and no salad. We mean to expose its failure to accomplish what we are told it does on the surface. We mean to insist that we will not be duped into believing that something humdrum is, in truth, extraordinary.

The trouble for Colby’s argument is that Watson is extraordinary—just not in the way that he thinks IBM has misled him to expect. “AI researchers have arguably the highest conceivable standards to meet when it comes to thinking about thinking,” remarked one commenter at Maclean’s, “and it’s hard to fault them for failing to live up to the naive expectations of science fiction.” Colby replied: “By ‘the naive expectations of science fiction’ I presume you mean ‘the naive expectations deliberately created by IBM promotional materials and employees’.

I received a similar response:

@Nicholas_Tam Maybe you should look at the IBM ads. Your claims for Watson are a LOT more modest than theirs.

At the time of our repartee, I was admittedly only familiar with IBM’s own materials in passing; most of what I knew about Watson was from sources that discussed it in greater detail. I found it odd that Colby’s point of engagement was exclusively with the advertising and not the technology itself, but this was understandable: he was making a statement about hype, after all, and it’s very common nowadays that the implications of scientific accomplishments are exaggerated in the public sphere. (Refer to Jorge Cham’s excellent illustration of the science news cycle, which concerns university research but applies equally as well to corporate and governmental laboratories.)

By and large, this is a product of two sets of behaviour—one on the part of journalistic reporting, the other on the part of the research organizations. Let’s begin with the journalists.

The dominant template for journalistic narrative is the inverted pyramid: begin with the most important information, and continue to points that are less and less essential on the assumption that the reader could stop at any time. (Before the age of desktop publishing, this also made it easy for newspaper editors to literally snip away the last paragraph or two when assembling the columns on the page.) The trouble is the gulf between what journalists deem most relevant to non-expert readers and what scientists consider to be important contributions to their field.

The end result is sensationalism—and too many articles about science wind up looking like Martin Robbins’ parody. They begin with far-reaching implications that may or may not be related to the research at hand, and work their way down to the specifics that matter most. This is a narrative framework that is seriously divorced from the reality of research, which operates on the level of local challenges and goals. (This post by Greg Lusk on the inverted pyramid and the conflicting priorities of journalists and scientists is highly relevant here.)

Because long-term, big-picture implications like the performance gap between artificial and human intelligence (in Watson’s case) become the centrepiece of the story, they become the focus of media attention and debate, often with no consideration of the specifics of what has been accomplished. And this is why we see casual expressions of dissent like Colby Cosh’s criticisms of Watson: wildly off the mark, selectively researched from Wikipedia with an a priori verdict already in mind, and laced with a sprinkle of pseudo-expertly mumbling about Bayesian combinatorics that are far more involved than the author makes them out to be. Criticisms like these respond to the news stories, not to the science.

Of greed and gimmickry

Colby is convinced, however, that his projected misunderstandings of what Watson claims to achieve are fundamentally IBM’s fault. And it’s no use pretending that IBM isn’t a self-interested organization: like NASA in their recent fiasco over arsenic-based lifeforms (a discredited paper, but one that was widely misreported when people still thought it looked shipshape), if people take their promotional materials and statements to the press the wrong way, they have no incentive to correct anyone so long as their project is still in a positive light. Watson is a proof of concept for IBM’s enterprise hardware and the DeepQA question-answering system, both of which the company intends to license and sell.

Not all of the problems with science journalism is the fault of journalists: research laboratories, public as well as private, are often complacent about inaccuracies in secondary reporting because the attention (and the concomitant prospects for funding) are too attractive to throw away.

Let’s be very clear about one thing, however: IBM’s profit motive as an organization does not negate the intellectual interests of its researchers. As fashionable as it is these days to appeal to the trope of corporations that are only responsible to their shareholders and therefore can’t be interested in anything but the bottom line, the truth is that corporate laboratories in private industry are invaluable centres of research. Projects like Watson attract contributions from university scientists not because they all want to see IBM succeed, and not even necessarily because the pay is so much better (though it is), but because they provide access to hardware that enables large-scale work. Computing scientists in industry are taken every bit as seriously as their compatriots in the university world, and the two regularly cooperate on grand initiatives.

But what does that say about the marketing? Complacency aside, is IBM actively making Watson sound like a much bigger deal than it is?

I have now combed through IBM’s promotional videos, articles, and FAQs, and I would like to retract my earlier concession that their claims may have gone too far. IBM’s statements about Watson are fair reflections of what AI can realistically achieve and what a successful performance by Watson will demonstrate. About the most outlandish thing they say—the one that treads the furthest into the minefield of the philosophy of AI—is that Watson performs well in Jeopardy because it understands natural language. And strictly speaking, it does. The clues in Jeopardy! are undeniably in natural language, and differ from formal or heavily restricted sentences by a significant degree of complexity. About the only restriction on the clues is length. Discard the puns and puzzles and you still have challenging problems like binding indefinite pronouns to objects (or classes of objects) that fit.

Whether Watson’s “understanding” of natural language is analogous to that of humans doesn’t figure into the discussion here. Nobody is saying that Watson actually has a conscious mind; AI researchers don’t think on those airy-fairy ontological terms when they are designing systems for specific tasks. They participate in the debates over the philosophy of artificial minds, yes, and they’re usually on the optimistic side, but everyone is aware of the separation between that conversation and the immediate challenge of defeating humans on a robust, open-domain answer-questioning game show.

We are not even remotely in Dreyfus territory. Still, I can understand why layperson readers might think we are when they read the story in The Globe and Mail and come across a juicy quotation like this:

“We can use computers to find documents with keywords, but the computers don’t know what those documents say,” Dr. Ferrucci says. “What if they did?”

People whose notions about AI come entirely from Battlestar Galactica could easily misread Ferrucci’s statement as referring to sentience or consciousness. But anybody who knows a thing or two about AI can read this and correctly interpret it to refer to semantic-level knowledge representation—concepts on a larger scale than string matching or keyword search. It’s entirely agnostic on the problem of whether artificial minds can exist. I’m not deliberately reading this as a modest apologist: this is actually what Ferrucci is obviously saying.

If you get all your science from Hollywood and you think cloning has to do with developed bodies and selves rather than the raw data in your genes, it’s not the responsibility of geneticists to clarify their work for you every time they speak. Similarly, you can’t expect scientists and engineers in AI to explicitly backpedal from the philosophical question of conscious machines every time they talk about their work.

Or can you? What we desperately need is a greater public understanding of what scientists do, and what they mean when they use everyday words to talk about their fields. Readers dive into news stories about science with popular preconceptions that are often wrong, but nobody takes up the responsibility of correcting them until the discourse goes seriously awry. We’ve seen this before with how the hysteria over genetically modified foods or embryonic stem cell research obfuscated the real issues deserving of policy attention. There are even some dark corners of the world where creationists are wreaking havoc on schools because they still think evolution by natural selection is some kind of affront to their god.

Sooner or later, this will happen with AI: we’ll explore the possibility of delegating something big and very public to an autonomous system, and legitimate policy concerns will drown in a sea of hysteria about machines taking over the world. If scientifically knowledgable people do not shoulder the burden of sober clarification, that role will become occupied by contrarian journalists who don’t really know what they’re talking about, but still take pleasure in posturing as the voice of reason in the room.

If you are going to take the position of someone who sees through the publicity and understands the underlying science, you have to understand the underlying science. No matter how bombastic IBM’s promotional claims are, or how submissively the media repeats the press releases with a dash of unchecked sensationalism on top, Watson is more than a “gimmick” if it’s computationally interesting—and by any informed and reasonable standard, it is. Watson is a nontrivial system, and Jeopardy! is a nontrivial pursuit.

You can find Naden’s writings online in the posthumously published The Complete Poetical Works of Constance Naden (1894), a volume that includes translations of Schiller and Goethe, among others. It seems as though she was something of a polymath.

My introduction to Naden’s work came by way of this audio podcast of a lecture delivered by John Holmes at the Royal Society, who spoke on Charles Darwin’s influence on the ideas and concerns of Victorian English poets. (This is the subject of Holmes’ recent book, Darwin’s Bards: British and American Poetry in the Age of Evolution.) In the lecture, Holmes speaks briefly on “Natural Selection”, a playful comic poem about a palaeontologist who is scientifically delighted to find that his beloved has been whisked away by an all-singing, all-dancing “idealess lad”. This poem belongs to a quartet entitled Evolutional Erotics (1887), in which Naden explores the collision of love and the scientific mind. Another poem in the set, “Scientific Wooing”, brings science into the register of high romance in a manner that might be construed as ironic (but then again, might not be):

At this I’ll aim, for this I’ll toil,
And this I’ll reach—I will, by Boyle,
By Avogadro, and by Davy!
When every science lends a trope
To feed my love, to fire my hope,
Her maiden pride must cry is “Peccavi!“

I’ll sing a deep Darwinian lay
Of little birds with plumage gay,
Who solved by courtship Life’s enigma;
I’ll teach her how the wild‐flowers love,
And why the trembling stamens move,
And how the anthers kiss the stigma.

I am reminded here of the tensor algebra pastoral from one of the great masterworks of science fiction, Stanislaw Lem’s The Cyberiad:

For what did Cauchy know, or Christoffel,
Or Fourier, or any Boole or Euler,
Wielding their compasses, their pens and rulers,
Of thy supernal sinusoidal spell?

Cancel me not – for what then shall remain?
Abscissas some mantissas, modules, modes,
A root or two, a torus and a node:
The inverse of my verse, a null domain.

Many of Naden’s other poems are more subtle in their use of science, or at least not as liable to wink at the reader. “Solomon Redivivus”, from the same collection, imbues Solomon and Sheba with a fathomless sense of deep time by turning their story into a macroevolutionary tale, from amoeba to fish to highly developed mammal. Even this one feels a little forced, though, in our retrospective eyes—not unlike the sense we get when we read Cold War writing about atomic power, that this particular strand of science is so dominant, it is all anyone seems to talk about.

The best of Naden’s science poetry, of the selections I’ve read thus far, are to be found among the sonnets. Consider “The Nebular Theory”, which begins with a ruthless, particulate materiality, then bursts into the cosmological plane in line 9 with “raptures of keen torment”:

This is the genesis of Heaven and Earth.
In the beginning was a formless mist
Of atoms isolate, void of life; none wist
Aught of its neighbour atom, nor any mirth,
Nor woe, save its own vibrant pang of dearth;
Until a cosmic motion breathed and hissed
And blazed through the black silence; atoms kissed,
Clinging and clustering, with fierce throbs of birth,

And raptures of keen torment, such as stings
Demons who wed in Tophet; the night swarmed
With ringèd fiery clouds, in glowing gyres
Rotating: æons passed: the encircling rings
Split into satellites; the central fires
Froze into suns, and thus the world was formed.

Or “Poet and Botanist”, which reads like a statement of Naden’s thematic centre of gravity:

Fair are the bells of this bright‐flowering weed;
Nectar and pollen treasuries, where grope
Innocent thieves; the Poet lets them ope
And bloom, and wither, leaving fruit and seed
To ripen; but the Botanist will speed
To win the secret of the blossom’s hope,
And with his cruel knife and microscope
Reveal the embryo life, too early freed.

Yet the mild Poet can be ruthless too,
Crushing the tender leaves to work a spell
Of love or fame; the record of the bud
He will not seek, but only bids it tell
His thoughts, and render up its deepest hue
To tinge his verse as with his own heart’s blood.

The emphasis on the masculinity of Poet and Botanist alike is a curiosity worthy of an essay in itself. Constance Naden’s position as a highly educated woman who crossed both disciplines—one who sometimes masked her gender under the pseudonym “C. Arden” in her philosophical and scientific papers—is of intense interest to scholars of her work. Naden’s double identity figures heavily into this paper by Marion Thain, which offers a comprehensive look at how Naden’s concerns about science and poetry were informed by her materialist philosophy of “Hylo-Idealism” as well as the surrounding context of the Thomas Huxley/Matthew Arnold debate over the value of the classics. (Naden was educated at Mason Science College in Birmingham, where the Huxley/Arnold argument was ignited by Josiah Mason’s edict that the college he founded would not provide its students with “mere literary instruction and education.”)

In our time, the divorce of science from poetry has become so engrained in popular consciousness as a blind assumption (irrespective of the exceptions—and believe me, there are many) that Naden’s poems may stand out for conjoining them at all. I would say, however, that Naden’s poems are insightful because they take the closeness of science and poetry as a given, and seek to explore how that relationship works; sometimes sincerely, other times with a smirk. Science is part of our lexicon, after all, and to sidestep it is to restrict ourselves to a fraction of the palette available to us.

Link-dumping has never been an adequate stand-in for commentary of my own, and if you want to read what I read you are better off checking Twitter (the only circumstance where that is ever the case). Nevertheless, here is a slice of the pileup.

• Let’s lead this off with one of my great loves and frustrations in the world: science journalism. Begin with Martin Robbins’ incisive parody of sensationalist science reporting. Then read Ed Yong’s remarks on objectivity, neutrality, and whether journalists should take sides.

• “Our daughter isn’t a selfish brat; your son just hasn’t read Atlas Shrugged.”

• Witnessing the Twelfth of July festivities in Northern Ireland this year led me to this must-read piece of ethnomusicology, where Stephen Howe scrutinizes the musical identity of the loyalist marching bands (the ones with the “kick-the-Pope” drums).

• Ron Rosenbaum explains agnosticism. I have a few problems with how readily Rosenbaum buys and sets up the all-too-common straw man of the so-called New Atheism, but the article’s spirited defence of uncertainty and rejection of tribalist debate makes it worth a thorough look.

• This has been a bumper year for exciting stories in espionage. By now everyone has read about the KGB’s suburban infiltrants and forgotten them too, but that doesn’t make revisiting the coverage any less fun. So neighbourly were they, yet so incompetent! Just look at those complaints.

• When I was a wee lad I co-moderated a Tolkien-themed discussion board that was, in later years, overrun by home-schooled creationist kids. Someday they will grow up to be Republican senatorial candidate Christine O’Donnell, whose views about Arwen and Éowyn are oddly more informed than her views about anything and everything else.

• Popular Science gives us a look at the Pentagon’s $58-billion killer robots.

• Lisa Poisso interviews a Lutheran pastor who runs a World of Warcraft guild and who has a host of insightful things to say on faith and fantasy.
• Finally, I must share Patrick Barkham’s remembrance of class and culture at Cambridge University—more reflective of the undergraduate life than the relatively new postgraduate one, I think, but still relevant today.

Elsewhere:

• J.K. Rowling, writing in the capacity of a former single mother living on welfare, isn’t buying what David Cameron is selling. In a somewhat frivolous response, Toby Young leaps on the Tory nostalgia of the Harry Potter books, pointing to Hogwarts’ Etonian idyll while somehow neglecting to mention the conspicuously nuclear families; but anyone who paid attention to Rowling’s finer points (which doesn’t include Mr Young, I’m afraid) knows full well her politics aren’t what he thinks they are.

• Film editor Todd Miro savages Hollywood colour grading for taking us into a nightmare world of orange and teal.

• Roger Ebert articulates his controversial belief that video games can never be art—not for the first time, though it’s nice to finally see him elaborate on it in one place. I’m of the opinion that the entire semantic quagmire is easily evaded if we adopt an instrumental definition of art. Regardless of whether video games are even theoretically comparable to the great works of other media, our only way of getting at qualitative findings about creativity and beauty in game design is to borrow from the language of art, so we may as well consider them as such.

• While on the subject of aesthetics: over at Gödel’s Lost Letter, R.J. Lipton’s fantastic computing science blog, are some germinal sketches of how one might study great mathematical proofs as great art.

• The International Spy Museum briefs us on Josephine Baker, the actress-heroine of the French Resistance.

• Paul Wells visits the Canadian forces in Kandahar and reports on the shift in the tone and strategy of their counterinsurgency efforts. This is one of the best pieces of journalism I’ve read on the present state of the war in Afghanistan and I can’t recommend it enough.

• Strange Maps documents two wonderful specimens of literary cartography: back covers of mystery paperbacks, and a poster for a Shakespeare conference in France depicting a town that looks like the Bard.

Civilization veteran Soren Johnson, one of the foremost designers of strategy games and AI today and certainly one of the best writers on the subject, often remarks that the theme of a game is not to be confused with its meaning (slides here). Diplomacy may cast its players as the great powers of pre-1914 Europe, but it’s about simultaneity. StarCraft may put you in charge of Heinlein-esque space marines and alien civilizations, but it’s about asymmetry. If the theme and mechanics harmoniously cohere, then the mechanics can shed light on the theme in the way that art sheds light on the world. Pre-war Europe is an intriguing setting for Diplomacy because in all their backroom double-dealing, the empires didn’t take turns. Aliens are a good fit for StarCraft because you can map anything onto aliens, be it the collectivist swarm-by-numbers ethos of the Zerg or the judicious high-tech investment of the Protoss.

I am partial to this view, predominantly for reasons of aesthetics. If we are to conceive of game design as an art form, it does not suffice to decompose games into the artistry of constituent parts—the music, the models and sprites, the cinematic sequences, on rare occasion the writing. The aesthetics have to come from the specific properties that make something a game, whether it is played with a board and dice, a deck of cards, or a mouse and keyboard—and those properties come from the mechanics.

But that’s neither here nor there; I won’t elaborate today. Instead I want to turn to my favourite of Johnson’s examples: the evolution game. For your fill of Darwinian game mechanics, look not to Spore (which Johnson worked on), a game that is nominally about evolution from microbe to intergalactic juggernaut, but is actually about special creation. Back when I first played it, I wrote, perhaps a tad generously:

Let’s not bury our heads in the sand: by placing creature design into the player’s hands instead of leaving it up to random mutation, Spore inherently owes a lot to intelligent design. There’s still room for a real game about evolution in the Darwinian sense, where you set certain environmental constraints and preconditions, let a species run loose, and see if it survives in an ecosystem full of other models—kind of like how some engineers pit robots in mortal battle, but with adaptation.

Spore is a lot more creationistic than I gave it credit for; consider that the functional components of your custom-made species—the mouths, the horns, the flagella—are interchangeable parts from a specified, modular set, which is precisely what we would expect from a designing agent but not at all what we would expect from natural selection. But never mind all that. The evolution game exists, says Johnson, and it’s called World of Warcraft.

I would contend, however, that the Darwinian features Johnson ascribes to WoW are equally prevalent in most games with competitive and highly interactive player populations, provided there is sufficient strategic depth worth talking about. WoW is an evolution game because its core mechanic is community. Where there is a community of players and a developed metagame of optimal practices, strategic decisions are memes that compete for survival. Let’s call them strategemes.

Strategemes include everything from chess openings to Scrabble vocabulary: they are transmissible units of knowledge that players learn, study, and adopt—and crucially, copy. Copying them is not seen as unfair, but as an advantageous and often essential behaviour. They leave room for mutation, and we can perceive a frequency distribution of variations over a population of players and games.

But where does natural selection come into play? Let’s look at the exemplar we get from Johnson: the WoW talent tree.

World of Sporecraft

Every player in World of Warcraft belongs to one of ten character classes—Mages, Warriors, Priests, and so on—and every class has a range of “talents” (bonuses and special abilities) that fall into three specializations. As players rise in level, they gain points that they can allocate to configure their characters however they like.

Here’s the rub: all but the most basic talents are only available once you have already assigned points to the same specialization tree. To make your character as powerful as you can, the strategy is to allocate most of your talent points in one tree and distribute a few residual points in the other two; otherwise you will be a jack of all trades and a master of none.

The ramifications of specializing are most pronounced in classes like the Paladin, which can perform all three of the major roles in the game’s group-oriented content: healing, “tanking” (damage absorption), and dealing damage to enemies. For Paladins, these correspond to the Restoration, Protection, and Retribution trees. Within these trees, you can further allocate points in a manner optimized for the content you are playing through, be it fighting other players or raiding (killing bosses in coordinated groups of up to 25 people).

Who determines what is optimal? Not the game’s designers at Blizzard Entertainment, but the players themselves. Every few months the designers shift a few percentage points around to address balance issues, and the players involved in the game’s most challenging content scramble to adjust.

So far, this doesn’t sound evolutionary in the least. There is no heredity at work, and players can change their specializations at any point for a fee in the game’s virtual currency. Consider the following comment-box response to this article about Johnson’s recent theme-versus-mechanics presentation at the Game Developers Conference:

Soren Johnson knows nothing about evolution and [should] probably just never talk about it until he’s taken a biology course. His “Paladin Natural Selection” is completely off the mark. What he is describing is much closer to variable phenotypic expression where certain traits are expressed in an individual based on [its] immediate environment.

The commenter’s objection would be absolutely right were we to speak of the player characters themselves as individually evolving organisms. From this point of view, WoW characters do not evolve any more than an Englishman living in Boston evolves by speaking in a Bostonian accent. But it still makes sense to think of the English language as evolving, and look at traits of regional accents and dialects as they statistically appear in a population. Similarly, in WoW we can look at the player population as a whole and, with the aid of tools like Armory Data Mining, observe measurable changes in the frequency of talent allocations.

The underlying reason for this is in the way that talent choices spread. If you think WoW’s talent system gives players considerable freedom to customize their personal experience, you would be wrong. In reality, whenever the game is rebalanced, a few expert players proficient in mathematics run simulations and tests to crunch the numbers and figure out the best theoretical allocations under ideal circumstances. (Very few boss encounters present ideal circumstances, and almost no players have the ideal weapons and armour; but for the range of players serious enough to keep abreast of the theory, these findings usually suffice.) The experts debate about the data on discussion forums, update their FAQs, and release new versions of their simulation spreadsheets. Over time, more and more players adjust their characters to account for the theoretical recommendations. Players trying out a new character class will often model their choices after high-performance players whom they respect without comprehending the underlying rationale.

Talent trees can be replicated with perfect fidelity: a player can copy them point for point. They are unambiguously quantifiable, and it makes sense to think of them as genotypes. But they are only optimal provided the player behaves in certain patterns and within particular constraints, using their abilities in specific sequences. There we see phenotypic variation, which sometimes feeds back into talent-tree mutations within a tolerance of a few points; some character classes are inflexible while others are less rigidly specified.

Critically, a “cookie-cutter” talent setup is not necessarily what is best for every encounter, or even every player of that class and specialization. The fitness of talent choices—their capacity to replicate throughout the population—depends on social factors as well. Players copy talent choices that correlate strongly with success. This is observably the case for player-versus-player combat in arenas and battlegrounds, which involves considerable improvisation and is less susceptible to accurate mathematical modelling. As for raids, to adopt a theoretically endorsed talent allocation is to demonstrate that you stay informed, and minimize the risk introduced by your inclusion on the team. The raiding guilds that tackle even mildly challenging content will not invite players who deviate from the prescribed talents unless they provide an expert justification for why their modifications make sense.

In short, certain sets of talent choices have survival advantages over others. The fitter variations spread throughout the population while the frequencies of unfit ones decline. Players that do not adopt the most frequent choices are excluded from much of the high-level group content, and they (along with their decisions) are less likely to remain active in the game.

In this respect, World of Warcraft differs significantly from most video games. What does it matter what other people think, so long as you are having fun? When I play Super Mario Bros. I don’t care if the speediest route through the game is via the Warp Zones in Worlds 1-2 and 4-2; I can take the long road and clear every stage if I want. When I play Civilization I don’t care if early global conquest yields the highest scores; I like to win by constructing a spaceship to Alpha Centauri without ever building a city beyond my first. In adventure games like The Legend of Zelda the pleasure lies in figuring out the puzzles yourself; to look up the solutions in a guide is to admit defeat and cheat, and is held in lower regard.

Or look at Spore. In Spore there is abundant community interaction through an online portal where players can share their creations and randomly “seed” them on the worlds of others. Players can choose to preserve the creations they like, and the game tracks the most popular ones—which, for a time, was a roster headed by a model of Charles Darwin. In one sense this would appear to set up a competition to make the most creative and amusing models: the better your creature, the likelier it is to attain a high rate of incidence on other worlds. Why not consider the Darwin model a strategeme as well? Because the fittest creatures in the online community are not the ones that lead to the greatest mechanical success in the game itself. There is no selective pressure directed towards accomplishing objectives in the game, so players continue to make their in-game decisions independent of everyone else.

Granted, some WoW players ignore selective pressure as well: they impose various challenges on themselves and configure their characters specifically for the task. This can involve everything from controlling multiple characters at once to defeating high-level challenges in entry-level equipment. (Lisa Poisso’s 15 Minutes of Fame column routinely puts the spotlight on some of the most fascinating specimens.) But these activities are usually individual—rarely do they take place in large groups—and the special strategic decisions they involve do not tend to spread. From the population’s point of view, these innovations die out.

The dominant culture in WoW is one where copying strategic templates is not regarded as cheating, but is an expected precondition for accessing most of the game’s group content at all. This is not limited to individual characters and talent points, but extends to the behaviour of a raiding guild—who does what and who stands where. For all but the very best players, the raiding game is an exercise in fine-tuning locally appropriate variations on top of a broadly established strategy copied from somewhere else. Here, too, we see strategemes competing for dominance: successful variations spread to other guilds, and the most widespread ones become part of the orthodox strategic package that much of the player base comes to adopt.

World of Warcraft is an evolution game as a consequence of being a community game. But not all games with communities lead to evolutionary trends. What makes my concept of a strategeme unique among memes is that it applies to games where social learning, often in the form of direct imitation, is advantageous to the pursuit of absolute objectives embedded in the game’s design—so overwhelmingly advantageous, in fact, that it affects one’s inclusiveness in a community and the accessibility of the game’s content. There is feedback from the game mechanics that tells you if one set of choices is performing better than another. In WoW it may be your binary pass/fail success in defeating an encounter, or a metric of your damage or healing output. In chess it is your ability to win games (perhaps further broken down into your success as white or black, against certain opening lines, or facing various strata of opponents); this also generates social feedback as expressed in your Elo rating, which estimates your chances against other rated players and unlocks your access to upper echelons of competition.

Putting it generally, we see evolutionary change occur in games when the benefits of copying others far outweigh the qualitative value of making your own choices from scratch. This is a jarring philosophy of play when you consider that figuring things out for yourself is the central pleasure of games and puzzles that are free of social selective pressure—which includes most of the games people play in their ordinary experience. A game with no predesigned capacity for strategic evolution quickly develops one with the emergence of a community, so long as the mechanics are deep enough to reward the exploration of an immense variety of choices. An evolutionary account gives us the tools to explain how a player population prunes those choices, favouring some for widespread imitation while others are left behind.

The rest of the story

In future instalments, I will be looking at some other games that cultivate different kinds of strategemes, anticipate some reasonable objections to using analogies from biological evolution, and look at what strategemes may tell us about life outside of games (if such a thing exists). Depending on interest, I may also provide a technical supplement to this first part for readers who are comfortable with World of Warcraft‘s esoteric lingo and want to see some specific examples and hard data; I do have those at the ready.

• In a review of Mass Effect II, Jonathan McCalmont calls out video games for their uncritical acceptance of racial essentialism.

• A 1969 letter from Buzz Aldrin to a radio enthusiast offers some insight into the Apollo 11 spacecraft’s low-budget insulation.

• Jonah Lehrer draws on studies about primates and social hierarchy to express some concerns about the compulsion to count one’s Twitter followers and Facebook friends. (People do that? I don’t, but I sure like to comb through my website stats.)

• Finally, courtesy of Daniel Mendelsohn, a review of Avatar that says most of what I wanted to say about Avatar—and for good measure, puts it all in the context of James Cameron’s entire career.

• Patricia Cohen takes a look at the preservation of writers’ rough notes and scrap paper in a digital age, in which we discover that even Salman Rushdie is none too magniloquent to scrawl, “I am doing this so that I can see how a whole page looks when it’s typed at this size and spacing.”

• Also in The New York Times: a special feature on politics and the modern science museum. I’m not convinced that the agendas underlying science exhibits were any less varied or complex a century ago, but as a look at where things stand today the article is well worth perusing.

• The National Arts Centre in Ottawa is commemorating the great Oscar Peterson with a statue to be unveiled 30 June. Please make a contribution.

• And while on the subject of jazz, Peter Hum criticizes the notion that musicians should contrive to make the genre culturally relevant—whatever that means. My preference, as always, is for art that strives for timeless resonance over fashionable gratification. That some things feel like one, and other things feel like the other, is not well understood and worthy of investigation.

I read something dumbfounding today. You could say it was founded on dumb.

On first inspection, John Ibbitson’s article in Saturday’s Globe and Mail (“Core support keeps the PM in thrall”) is an ordinary, forgettable opinion piece that uses the recent silliness over the lyrics to the national anthem as a springboard for restating the obvious: the Conservatives can’t win a majority because every time they’re close, the mythical Republican-style rabble-rousers lying in ambush in the tall grass of the Alberta prairie celebrate with a premature volley from their unregistered firearms, and the rest of the country begins to have second thoughts about whether letting them win is a good idea.

Never mind the questionable statistical basis for linking one issue to the other. This isn’t news to anyone who follows Canadian politics in a sound state of mind, nor is Ibbitson’s sensible identification of the Tory core as moderate centrists (however incongruent that may be with partisan caricatures from both the left and right). There’s nothing here to see.

But the way he puts it is bizarre:

The great political irony for the Conservative Party is that, while it must avoid estranging core conservatives at all costs, extreme core conservatives keep the party from winning a majority. They are the social Darwins.

[...]

Most of the time, these right-wing nuts are ignored. But whenever Mr. Harper appears to have enough support to form a majority government, the base starts to get excited and aggressive, and social Darwins “bare their teeth and embrace things that the majority of Canadians don’t want to see,” says Mr. Turcotte. This frightens enough centrists to keep the Liberals in the game and the Conservatives confined to minority governments.

For those of you who are unaware, I am presently writing from what must surely be the Darwin capital of the world. It’s wall-to-wall Darwin here. All year long I have bathed in the most glorious talk of the literary Darwin, the proto-feminist Darwin, the abolitionist Darwin, the invalid Darwin, the patriarchal Darwin, the imperialist Darwin, the epistemological Darwin, the analogical Darwin, the cultural Darwin, the impressionist Darwin, and Quentin Blake’s cartoon Darwin. I am a stone’s throw away from Darwin’s letters, Darwin’s Plots and the Darwin College bar. I’ve seen the poor fellow’s name used and abused in every imaginable way.

I don’t have the foggiest idea what John Ibbitson means by “social Darwins.”

This is an original coinage of his. A Google search for “social Darwins” (excluding suggestions of Darwinism or Darwinist) returns a few scattered results from web forums and other wretched hives of scum and villainy, but the phrase’s appearance in Ibbitson’s article is a media first.

So far as I can tell, it’s a semantically vacuous slur, and obloquy of the laziest kind. It raises the spectre of social Darwinism, that strange appropriation of the legendary naturalist’s name to describe (with frustrating looseness of fit) the fascistic belief that the disadvantaged or inferior should be left behind to die. Now, I’m not convinced the hard-right hooligans who think Stephen Harper is a pandering sellout who doesn’t reverse enough gay abortions in the name of God are necessarily social Darwinists at all, but let’s give Ibbitson the benefit of the doubt. How, exactly, does one get around to calling them Darwins? What magnitude of scientific illiteracy does it take? And what, if I may ask, is being Darwinized here? In what universe, what nonstandard logic, what Wittgensteinian language-game, do these hypothetical bearded chimeras of right-leaning frenzy do anything to guide the selection process that favours the survival of centrist species of government?

I don’t have a horse in this race and I’m not about to foam at the mouth about how The Globe and Mail is a dirty Liberal rag, or whatever they call it these days in the faraway places where The Calgary Herald is regarded as a reputable newspaper. But anyone who accuses Ibbitson of meaningless mudslinging is, in this case, absolutely on point. It is a sophomoric writer who presumes to toss a name like Darwin into the fray and expects the readership to take it as an inherently bad word. This is exactly what many on the right do with the word “liberal” and what some on the left do when they refer to the Tories as “the Cons”—and it’s a pollution of political discourse.

Only here, it’s worse: it promotes a misconception of evolutionary thought, which is already so ill understood to the detriment of science in the public eye. One wonders if Darwin had journalists in mind when he wrote of the descent of man.

• Turn up your speakers and read Jan Swafford’s article in Slate about performing classical piano repertoire on classical pianos, which is full of audio comparisons that will make you wonder if the homogenized ideal of the modern Steinway grand is really a good thing.

• The Guardian asks a wide selection of novelists for their writing tips, which have a way of telling us more about the authors than about writing. Some of my favourites: Geoff Dyer (“Don’t be one of those writers who sentence themselves to a lifetime of sucking up to Nabokov”), Anne Enright (“The first 12 years are the worst”), Philip Pullman (“My main rule is to say no to things like this, which tempt me away from my proper work”).

• Ben Goldacre shows us how regulating alternative folk medicine through requiring certification is no use at all when we don’t know what’s being certified.

• From The New York Times: Canadians shoot left, Americans shoot right. The article is about hockey players but I think there’s something bigger in this.

• Teresa Nielsen Hayden remarks on the imaginative poverty of failed authors who think suing J.K. Rowling for plagiarism is a good idea.

• Jonah Lehrer wonders if the direction of funding towards older scientists hinders us from tapping into the creativity of youth. Also read the followup on his Frontal Cortex blog.

• Civilization IV lead designer Soren Johnson talks about designing strategy games around our intuitions about probability (or lack thereof).

• Mark Chu-Carroll explains why computer simulations of biological phenomena will never replace animal testing.

• Joel Stickley’s explorations of bad writing by example finally catch on to my fatal flaw.

While reading up on the Bikini atomic experiments for my post on Three Tales, I came upon a most interesting find: a contemporaneous broadcast about the tests by America’s greatest radio voice and one of my personal heroes, Orson Welles. It was the second episode of Welles’ short-lived 1946 series of political radio commentaries, and runs fifteen minutes in length. Listen.

Around this time last year I spent an inordinate portion of my time rediscovering the early radio work of Orson Welles, which I so fondly remembered from my childhood—The Shadow, Suspense, The Mercury Theatre on the Air and so on—so I had come across this series before. (“The Affidavit of Isaac Woodward”, Welles’ unforgettable diatribe about the vicious assault of a black American soldier who had returned from decorated service in the war, is required listening for anyone interested in the oratory of civil rights.) Somehow I’d missed the episode on the hydrogen bomb. No matter; I’ve listened to it now. And here’s something else I’ve learned: painted on the first H-bomb to see a practical test was the likeness of Rita Hayworth.

Welles had this to say about the glamorous actress who was then his wife:

Not long ago I watched quite another sort of young lady paint her lips with something called, over the counter, the Atom Lipstick—the case of the cosmetic being fashioned according to the popular conceptions of the original war-engine. I’m sure you all need to be told that Miss Hayworth is not one to use such a thing or to hold it as anything less than a very hideous conceit.

Her face is not on the atom bomb, then, by her own choosing, but by election of the flyers who will drop the bomb and work clearly for business according to their tastes. As regards selection I find their taste beyond reproach, but the bomb-dropping itself had better be worthy of the accompanying photograph.

Is this, Faustus claimed of Helen of Troy, the face that launched a thousand ships, and burnt the topless towers of Ilium? Well, I want a better toast, a better boast, for Rebecca. I want my daughter to be able to tell her daughter that Grandmother’s picture was on the last atom bomb ever to explode.

As we all know, the world didn’t heed his words, and the shadow of nuclear annihilation is now an ordinary background to our lives. Who knows what evil lurks in the hearts of men?

Last month I attended a performance of Steve Reich and Beryl Korot’s “documentary digital video opera” Three Tales at the ADC Theatre, the first production in Britain since the UK premiere in 2002. I’m still not sure what to make of it.

On the surface it looks straightforward enough. The 65-minute composition for voice, acoustic instruments, and video divides neatly into three segments on subjects from the public face of twentieth-century technology—the Hindenburg disaster, the atomic bomb test in the Bikini Atoll, and the cloning of Dolly the sheep. We hear the familiar Reich technique of displacing and superimposing copies of repeated motifs slightly out of phase, which catches the ear well enough in recordings but in live performance has the air of a magic trick. As in Reich’s seminal string quartet Different Trains, many of the melodic ideas are derived from the pitches and articulation of human speech—but not, in Three Tales, the rhythms; here, the speech recordings are subtended to click into the frame of a regular pulse. The video speed, too, is synchronized to musical time and not “mimetic” time or real-time, if you get my meaning.

We see some captivating archival images in the first two movements, chiefly the ones that draw attention to the logistics of large-scale technology, like the construction of the Hindenburg (set to variations on the Nibelung motif from Wagner’s Ring) or the dislocation of indigenous people and livestock in preparation for the Bikini tests (with thunderous sforzandi from Genesis to spice things up). What I can’t quite fit into the picture is the Dolly movement, a contrapuntal collage of video interviews with prominent scientists like Richard Dawkins, Marvin Minsky, and Rodney Brooks. Korot tells us the work, as it was conceived, is more accurately called “Two Tales and a Talk”. Here’s how Reich described it:

Each of the three acts not only looks and sounds like it’s historical period, each is formally organized quite differently to comment on that period. [...] [Dolly] is non-stop with certain kinds of material recurring in no clearly discernible pattern. Musically one might say Dolly was a kind of free rondo. The forms of each act reflect the historical period they describe.

But what does the piece say about technology? It sets up a debate instead of taking a firm position, adopting the ambivalence that is often so necessary for art to say anything at all. Commentators have remarked on the obvious irony of critiquing technology in a technologically enabled medium, but I think it would be facile to stop there: as in most of his earlier works, Reich’s crucial gesture is to forsake electronic synthesizers and recreate the effects of audio manipulation in acoustic human performance. It is an incursion of man on the domain of machine, not the other way round.

Yet the Dolly movement remains an uneasy fit. Consider a crude reading of the work:

1. Hindenburg—Look at the majestic way people talked about big science! That didn’t turn out very well.
2. Bikini—Look at the majestic way people talked about big science! That didn’t turn out very well.
3. Dolly—Look at the majestic way people talked about big science! I wonder if it will turn out well?

I believe what we have here is a case of arson, murder, and jaywalking. Dolly now feels like a quaint late-nineties relic as revolutionary as Deep Blue—that is to say, not at all, in the grand scheme of humanity’s future. Cloning isn’t dragging us to the Singularity anytime soon, and conjuring images of Ray Kurzweil musing about robots replacing us all is a bit of a logical stretch.

Perhaps, as someone too irreligious to get his pants in a twist about the classic Promethean fears of man indulging in acts of creation proper to God, the message of Three Tales is lost on me. Or maybe the point is that the message is lost on everyone else.