Doctor Recommended
100% All Natural

See also: Shallow Hal, the movie

If you have run out of new content on your RSS reader check out these random blogs with funny pics:

Stuff White People Like (here is my favorite post)
The Fail Blog
PostSecret

Honorable mention: Demotivating posters

Whether you like it or not, Wikipedia has become entrenched in many peoples lives.

Even many of the expats I meet throughout my globe trotting adventures are quite fond of it. In fact, in the event that we are wrong about something my friends and I often joke that we’ll just log in and change the entry.

This typical involves something drastic, like deleting the entire entry for Taiwan (because you know, it doesn’t technically exist).

And unsurprisingly The Onion explains the urge to modify the mundane with a probably-true story.

Someone posted a remark in the last post about the Namdaemun gate fire. The guy was mad that I made a couple of jokes about what probably happened. While I shouldn’t have to defend the humor industry I would like to point out that I was right.

It turns out that a 69 drunk man was responsible for the fire. In fact, the same guy set fire to a palace a few years back too.

I should note that I hardly think of myself as an expert on Korea, but I’d like to think that I gained a bit of knowledge of its culture, including its darker side (e.g., business men passed out and vomiting all over the place in public and no one stops because it is so common place).

This topic is tangentially related to DreamWorks in the graphics department. That image of the gate burning was fairly intense, up there with some high-quality CGI flicks.

And to update my previous post about Pixar and rendering capabilities, I recently came across how Shrek the Third was rendered.

Some numbers:
3000 servers
20 million CPU render hours compared to 10 million for number II and 5 million for the original movie

The image to the right is considered Korea’s top “cultural landmark.” And boy has someone redecorated it.

It went up in flames last night and arson is suspected.

Here are my guesses as to the cause:

- a group of very drunk ajossi (salarymen) thought it would be a good idea to celebrate the Lunar New Year by lighting a bunch of fireworks inside the tinderbox
- the image is actually a tweaked screenshot from the upcoming Starcraft II game Korean’s are salivating over
- it is from a deleted scene from D-War
- it is a re-enactment of the LA Riots in which Korean shop owners banded together with shotguns and kept the rioters from looting their stores
- it is from the trailer of the live-action He-Man movie starring Dolph Lundgren or any Jean Claude Van Damme movie that they show on the SuperAction station (channel 26)

A quick update to my recent post about G forces.

Researchers at the University of Michigan (among other places) just released some information regarding the natural adaptation many birds and flying insects have in terms of what humans consider relatively extreme conditions.

Choice nugget:

A Blackbird jet flying nearly 2,000 miles per hour covers 32 body lengths per second. But a common pigeon flying at 50 miles per hour covers 75.

The roll rate of the aerobatic A-4 Skyhawk plane is about 720 degrees per second. The roll rate of a barn swallow exceeds 5,000 degrees per second.

Select military aircraft can withstand gravitational forces of 8-10 G. Many birds routinely experience positive G-forces greater than 10 G and up to 14 G.

Another page in the fight between what should fill server farms was written today.

The Register and Nick Carr discussed a new paper by IBM whose authors believe Google is headed in the wrong direction with their off-the-shelf approach to HPC/distributed grid computing.

I mention this because the paper can only be accessed from behind an institutional paywall.

I have my ways though, and found it sitting on an open server (for now). Here is the pdf of Project Kittyhawk.

Believe it or not, but I am actually teaching kindergarten kids right now. While some might consider this a big step down from the university teaching I was doing two years ago, the experiences I’m confronted with on a daily basis really keep my mind churning.

In addition to cleaning up plate loads of snot that oozes from a plethora of orifices I also have the time honored ritual of teaching children abstract concepts like colors and how much wood would a woodchuck chuck.

As a result, I’ve begun to rethink a bit of how strong and weak AI approaches have developed over the years. I mean how do you teach a machine something — how do you get it to learn? If you were an engineer starting from scratch, how would you approach this method?

After all, humans are squishy, brittle machines, yet it appears that it only takes a relatively short period of time to condition necessary parts of the brain in order to detect patterns and rules for classifying information, like the color red.

Exhibit A

I am assuming my sample size is big enough: a couple dozen middle-class Taiwanese kids between the ages of 3-6. Over the last few days several new kids were enrolled in my class. None of them spoke English. Within a day I got one of them to recognize shapes like circles, squares and triangles. And now after a week, not only have do they all know my name and the names of others, but also the weather (hot, cold, snowy, rain), basic colors, types of food and a number of other relatively simple concepts.

Sure they could be savants, but based on the “normal” distribution curve and observations from other teachers, all of the other kids at the schools (I teach at 3 different ones) progressed at about the same rate. In fact, based on the academic research into stages of cognitive development, they are smack dab on target with the rest of their human peers.

This is in stark contrast to simply attaching a webcam into a workstation and expecting the machine to somehow magically distinguish your face from mine. The hardware for this particular application arguably can discern curves, lines, colors and distortions but the software end, including the device drivers, has to be developed with the necessary code to translate the totality, the whole sum of the parts.

Ultimately you and I are also auto-didacts, capable of teaching ourselves new concepts, a feat that is still beyond the reach of most AI applications.

Thus, I suspect that a balance between hardware (akin to Minsky’s Thinking Machine) and software is where cognizance, sentience and self-learning resides. Thus, you need a machine(s) fast enough to be able process numerous inputs in real-time, yet software robust enough to tackle fuzzy, seemingly abstract variables (e.g., even something relatively benign like where does red stop and infrared begin).

IBM’s Blue Brain project appears to be making quick progress on the hardware “strong” side of this equation.

While it is a somber read, Wired magazine recently published an interesting piece on the lives of two AI researchers who died last year. With any luck, their novel “weaker” approaches to teaching machines and getting machines to think will live long into the future.

In addition you may be interested in the innovative work conducted by Luis von Auhn. I’ve mentioned him before as he is the brains behind the “Captcha” as well as Google’s Image Labeler. His Google Talk on Human Computation is also outstanding.

See also: Machine learning
Natural language processing
Speech recognition
Computer vision
Mechanical Turk

Two years ago I mentioned the possibility of launching humans into space with rail guns. In order to reach the necessary escape velocity, projectiles would undergo approximately 45,000 G’s.

At the time I noted that around 40 G’s most humans would cease to exist as the pressure would start turning pink squishy things into mush.

It turns out that I’m wrong.

In the February edition of Popular Mechanics one of the stories discusses the impact that professional football players endure throughout games and seasons.

Based on numerous sensitive instruments and calculations it turns out that a hard hit from a linebacker can produce up to 150 G’s. And that these doozies are handed out multiple times a game.

I for one will not be signing up for that ride anytime soon.

Back in high school I had a couple of friends that really enjoyed playing around with 3ds Max, Bryce, Lightwave and a slew of other rendering packages. However, one of the problems, or rather annoyances, is that they would invariably begin rendering a complex, time-intensive scene with their main computer.

While the finished product was typically top-notch, this tended to disrupt and otherwise stymie our ability to destroy one another in video game deathmatches.

One of my friends eventually created a duct-tape solution: building a small render farm in his own room (his parents really enjoyed the electric bill).

Anyways, I just came across an older piece that seems to highlight the “best practices” for cheapos like you and me: Build Your Own Render Farm

And if you think that end-users will be playing photorealistic games anytime soon, hold your breath a little longer.

For instance, in the fall of 2002 nVidia was bragging about how their new GeForce 2 graphics cards were ushering consumers into a world of “Pixar-level animation in real-time.” In response, Tom Duff, one of the Pixar animators scoffed:

`Pixar-level animation’ runs about 8 hundred thousand times slower than real-time on our renderfarm cpus. (I’m guessing. There’s about 1000 cpus in the renderfarm and I guess we could produce all the frames in TS2 in about 50 days of renderfarm time. That comes to 1.2 million cpu hours for a 1.5 hour movie. That lags real time by a factor of 800,000.)
Do you really believe that their toy is a million times faster than one of the cpus on our Ultra Sparc servers? What’s the chance that we wouldn’t put one of these babies on every desk in the building? They cost a couple of hundred bucks, right? Why hasn’t NVIDIA tried to give us a carton of these things? — think of the publicity milage [sic] they could get out of it!

And based on Duff’s back-of-the-envelope calculations he predicted that it would take another 20 years of constant development before such a solution would be developed.

[Note: the rendered resolution that ended up in the theater for Toy Story was 1536 x 922, 1.42 megapixels]

Five years later, what is the state of the art rendering situation?

I came across an interview with Pixar “plumber” Jen Becker who was being interviewed by her alma mater alumni association. Here is a pertinent info nugget:

During Ratatouille, the renderfarm consisted of about 850 machines with nearly 3200 processors between them. When rendering the final Ratatouille film frames on a 2.66 GHz processor, each frame took an average of six hours. It took about 1532 CPU-years to render Ratatouille, including the lower-resolution renders done at various points in the pipeline and working iterations. That means that if we only had one CPU in the renderfarm, Ratatouille wouldn’t have been released until the year 3539. To store the images generated while making the movie, we used 12 terabytes of disk space.

While different render solutions have been implemented by a wide range of firms (ILM did the work on the Star Wars films and Weta worked on the LOTR trilogy), the films that Pixar authors are entirely animated. Thus, Pixar’s progress should be used as the current benchmark for World of Warcraft fanboys that love its cartoony look-and-feel yet demand Hollywood-level expansions in the near future.

Of course, that near future might have a chance of occuring in the next decade or so if Intel’s new ray-tracing product - Larrabee - is the real deal. But alas, it has been postponed another two years.

See also: Beowulf clusters

Adroit polymath Michael Shermer had a chance to discuss his new book at Google recently. While you might not agree with everything he says, he raises some interesting points regarding evolutionary biology, technology and why humans began to trade thousands of years ago.

See also: Evonomics