Remember being taught about the six simple machines? Like the wedge, the inclined plane, the screw? We were taught about them in elementary school, probably around age 10.

So perhaps my memory fails in the reddening light of early middle age: Where’s my hammer? On the list, I mean? The one you use to hit things with.

My wife points out she thinks it’s a “tool” not a “machine”. Shyeah, right, and a ramp is a machine how? Because you do so much with it?

If a ramp is a machine for moving mass against an energy gradient, then dammit a hammer is a machine for increasing the impulse applied to a fixed object. Or something like that.

So now four people, all I think rather smarter than I am, have asked me what statistics books to read.

I think these folks are projecting something more than is warranted onto my recent point touching on statistics: They seem to think I know better! Haha! Alas, I am as dumb as a sack of wet hammers when it comes to modern statistics, having been crippled in my youth with an incident involving DEC PDP teletypes and Minitab [yes, I am that old.]. I am still wrestling with the effects of second-hand two-tailed p-value abuse, and though I retain substantial use of my elementary probability theory, I have a seriously diminished capacity to fathom any statistical process involving greek symbols or vertical lines (|).

My one scant advantage is that I recognize that I have a problem. And that I like pretty graphs with lots of squiggly lines and colors, which apparently abound in machine learning texts and papers, and always seem to me to be fraught with powerful and convincing implications about both the model and the explanatory acumen of the authors. [Wait; is that last one an advantage?]

Ah, but lacking a blue handicapped hang-tag for my desk. I have, in fact, paid good money to the esteemed Dr. Shalizi to tell me important statistical stuff [he advised me on a consulting gig]. Perhaps if we all ask him pointedly, he can suggest a reasonable course of action?

Why don’t more people know about (and use) genetic programming, especially for symbolic regression? GP is an approach that can be useful in all sorts of domains, for problems ranging from exploratory data analysis to design automation. SR can be a subtly informative complement to statistical modeling projects, or it can be used as a monstrously powerful open-ended exploratory machine learning engine. It rocks.

So. Do you know anything about it? [Cheating has been discouraged by eliminating outbound links from this post.]

This has become a problem for me. In seven conversations in two weeks with colleagues about work, including bosses and peers, I’ve mentioned or advised or absolutely insisted they consider GP/SR. In one case my opposite knew about GP but hadn’t considered it because he only knew about pole-balancing and stuff; in four cases they thought I was talking about genetic algorithms for parameter optimization (not that there’s anything wrong with that, but… no); in two cases I suppose they thought “symbolic regression” meant something ickily statisticky, and didn’t want to go down that road, so they played like it was some fancy newfangled numerical regression technique fad-of-the-day. Then, yesterday, in a room full of people using fast but utterly opaque SVMs to do machine learning, where the goal is to understand the system, they had thought about neither Bayesian networks nor GP/SR, both of which could tell them important things about how the system works. And in this latter case they hadn’t ever heard of SR.

I suppose now I have to do something about it.

Sigh. More in a while.

OK. So let’s suspend our judgement and accept for a moment that the pace of innovation is, in fact, increasing exponentially. So it’s not that we have a limited understanding of the real scope of innovation in the actual world, especially where it stretches beyond our immediate experience. And not that we all simply hear more these days about what has actually been happening all along, since we have a few modern contrivances like news and BoingBoing and stuff. No, let’s assume the world is producing more innovative thingies. Faster. OK?

So. Is the adoption of those innovative thingies keeping pace? Is the rate of adoption of innovation speeding up exponentially? Because we’re assuming here that for every Really Good New Idea that appears this month, ten new Even Better ideas will appear next month. So I need to be a pretty perky adopter of new ideas, right?

Otherwise… well, where do ideas nobody hears about go?

I just today sat in two lectures on Queuing Theory, so maybe I’m hopelessly mired in the dregs of defunct industrial civilization. But, um, doesn’t somebody still have to make this stuff? Otherwise, won’t it, like, back up in piles until somebody pays attention to it?

Until, that is, we have self-making stuff. Besides, well… you know… the self-making stuff that lives on the planet already, I mean.

Or does the Singularity really just represent a deeply ramified crisis for mail-order catalog publishers and marketing?

No, seriously. If there are more ideas all the time (maybe, but I doubt it), and more information is washing over all of us all the time (I doubt that even more), then are we adopting and executing those ideas? Are we changing our fundamental behavior to cope with all the new information? Are we all becoming different from one another?

What, we aren’t already?

Caveat: Charles Stross’s Accelerando is a really, really good book that I recommend wholeheartedly.

One reason Kurzweil’s charted prediction of the Singularity is suspiciously extra-super-singular::

I’ll tell you why. Because not only is the chart an artificial and perhaps even conscious attempt to fit the data to a predetermined conclusion, but what it actually represents is the proximity of the familiar. We are much more aware of innovations in our current time and environment, and the farther back we look, the blurrier the distinctions get. We may think it’s a grand step forward to have these fancy cell phones that don’t tie you to a cord coming from the wall, but there was also a time when people thought it was radical to be using this new bow & arrow thingie, instead of the good ol’ atlatl. We just lump that prior event into a “flinging pointy things” category and don’t think much of it. When Kurzweil reifies biases that way, he gets garbage, like this graph, out.

There’s a worse flaw yet, actually, in the whole backward-facing extravaganza. These “paradigm shifts” that keep cropping up? So these are supposedly the equivalent of phase transitions in potential, yes? Schumpeterian gales of creative destruction and so forth? In with the wireless, out with the shirtwaist factories; in with the linguistic finesse and effectiveness of moderne fuckin’ English, and out with illiterate babble of childish… well, everybody else in the world. Paradigm shift — um, that’s when everybody gives up what they was doin’, and starts on the good stuff. Right?

There are always Late Adopters. Alas, the Late Adopters in these cases can be very late indeed. Check it out: “Eukaryotic cells”! says the chart early on (except there’s way more bacteria, still); “Class Mammalia”! (except there’s way more beetles still); “Human ancestors walk upright”! (just like a lot of dinosaurs, and all birds. Hey, everybody else! Get with the times! Falling forward off-balance is the Way to Rule the World!).

How’s that deep argument go? The one often used by those other clear-headed thinkers? “If I’m descended from a monkey, then how come there are there still monkeys?”

Maybe the folks who invoke that sparkling bit of wit, and the Singularity Now folks, they should talk. I detect intellectual lacunae they seem to share. What’s the word? Anthro… something. Anthropo– Shoot; tip of my tongue.

What is that word? Something about middles. About the feeling that you — or your demographic group, or your race, or your species — are the vital gateway through which the past will realize the future.

It’s that feeling everything being about you. Anthropo… something. Blast.

 
 
 
 
 
 
 
 
 
 
 
Oh yeah! Hubris.

(This, by the way, is also about you. But in a different way.)