May 052015
 
III Of course, I had no idea how to enter transform space, but the most fortuitous thing I did was develop a fascination with i, because naturally such a highly developed and elusive system of integration took place in a realm of complexity that transcended but also subsumed the reals. I used to think i was the sharpest number, that its dot came from the square rooted negative sign that was sort of left behind after the root’s axe-head lit into the left side of R and fanned it out to form the imaginary axis.

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May 052015
 

In sympathy with yesterday's post about AI as presented in films, consider this recent article from the Wall Street Journal: Artificial Intelligence Experts are in High Demand. A list of mostly machine learning experts is produced as evidence for the topic of the article. There is an unfortunate trend being presented to the public in this space in which the term 'artificial intelligence' is being used to draw readers with stories of real technical achievements in the space of machine learning and machine perception (recognizing a cat in a image is not an act of artificial intelligence), movies are being produced that romanticize a form of unobtainable AI, and the two are being tied together with stories of impending doom (Musk, Hawking).

All this is done with little or no investment in helping us establish what we really mean - and need - in an artificial intelligence.

If artificial intelligence experts were in high demand, then linguistics, philosophers, sociologists, etc. should be very happy - not just ML peeps.

NinjaFlex on the Makerbot

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May 052015
 

Announcing version 0.1 public beta of the Scholars Lab Makerspace Ninjaflex profile for Makerbot Replicators!

2015-05-04 15.09.58 HDR

We’ve had two spools of Ninjaflex flexible filament for about as long as we’ve had our Makerbot Replicator 2. We’ve tried to print with it from time to time, but never with very good results. With our Rep2 dialed in and printing PLA like a champ for the last few months, I decided that it was a pretty good time to finally figure out Ninjaflex.

A few issues were immediately obvious when printing with the default Makerbot flexible filament profile. The extruded lines were too thin and there was significant “ooze”, causing a lot of thin, dangling strings when the nozzle shifted without printing. A quick search turned up a Ninjaflex profile for Makerbots on Thingiverse, with some helpful hints on parameters to change. Our profile is a slight modification of that, with a major bug fix and a handful of small adjustments.

In my experience, the two really important variables are “retractDistance” and “feedrate” (inconsistent camelcase as-is). RetractDistance controls the amount that the filament is retracted for moves; slightly increasing this to 1.3 (mm) dramatically reduces ooze. reedRate is the speed of extruder when extruding. Going slow is critical to success with Ninjaflex and so I’ve had good results with a consistent 20mm/s speed for all components.

Additional issues unrelated to software has been feeding and clogging. When using our Replicator 1 Dual Extruder, I found that the friction of the Ninjaflex against the feed tube was too high for the Rep1 extruder’s motor to overcome, even when manually unspooled; I think that others have had some luck with printing a tubeless guide.  We just switched to the Replicator 2. But even there, with a full and heavy spool of Ninjaflex, I still relied on manual unspooling.

Even so, our printer would still sometimes become clogged. Based on online commentary, this seems fairly common and, for us at least, is resolved by unloading, snipping off the nodule of material pooled at the end of the filament, and reloading. This seems to happen more frequently after the Ninjaflex has been heated and then cooled and when transitioning between Ninjaflex and PLA. Others have created custom-printed extruder parts to alleviate these issues, but we haven’t yet tried them out.

Setting up a custom profile in the Makerbot software isn’t actually very intuitive. To create a profile, select Advanced Options” in the print settings and click “Create Profile”. Name the profile something descriptive and an appropriate base profile if you’re starting from scratch (any will be fine if you’re going to use our Ninjaflex Profile)

Screen Shot 2015-05-04 at 2.40.12 PM

After the profile is created, select it from the dropdown and click “Edit Profile” to open the JSON file which contains it.

Screen Shot 2015-05-04 at 2.41.02 PM

You can test out our profile by simply copy-pasting it in.

Our profile is 0.2mm infill, 100% infill, no rafts or supports. Based on experimentation, rafts actually seem to work okay while supports tend to be harder to remove (although it is quite easy to cut them off, as well as perform general resurfacing and re-edging with scissors so long as you can get them in there). Infill actually works pretty well too, although they tend to be better with thicker walls (we use 3 instead of the usual 2 walls).

We’ve mostly printed well-supported models; I don’t know how well Ninjaflex would print latices, very thin structures, and extreme overhangs (I suspect “not well”).

If you try out our profile, let us know how well it works for you!

Map of Most Common Race

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May 042015
 

Prevalent race

It's been a challenge for me to fully understand what's been going on lately, so I find myself looking at a lot of data and maps. It kind of feels like grasping at straws, but at least it's something.

The map above shows the most prevalent race in each county, based on data from the 2013 American Community Survey 5-year estimates. Select and deselect to make various comparisons. Or, select just one race to see distribution. Low, medium, and high saturation indicates whether the prevalent race percentage is below or about the same, higher (greater than the national average plus-minus interval), or much higher than the national average (at least 50% higher), respectively.

For reference, the national estimates from the 5-year 2013 estimates where 63.3% white, 16.6% percent Hispanic, 12.2% black, 4.8% Asian, 0.7% Native American or Alaskan, and 0.2% Pacific Islander. The margin of error for all estimates is 0.1.

Click, drag, and zoom for details.

Here's St. Louis County, Missouri, place of Ferguson. The small, dark blue sliver is St. Louis city.

St. Louis County

Similarly, below is Baltimore, Maryland. The city is about 62 percent black and 28 percent white, whereas Baltimore County is an estimated 26 percent black and 62 percent white.

Baltimore city Maryland

For me, it was interesting to compare nonwhite races, because whites tend to make up a high percentage that obscures the single-digit distributions. I didn't expect to see such high percentages of Asian people in some counties, particularly in the Midwest.

Non-white comparison

So yeah, there that is.

The map doesn't show everything. It's on the county level, and as we've seen from Dustin Cable's racial dot map, there's plenty of variation on a block level. But it seems to be a decent quick mode of comparison, at least for the smaller groups.

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UML

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May 042015
 

GUI

 

UML Class Diagram

May 042015
 

When something doesn't exist (like artificial intelligence) it's easy to think that there is some missing piece of magic required to bring it in to existence. There has been a growing interest in movie depictions of AI of late, and these all seem to require some sort of non-linear step to realize this technology.

  • Ex Machina (which I really enjoyed) required a new sort of hard/software in the form of a jelly like substance.
  • Chappie (which I also liked, though I generally prefer cheese and ham combined in a sandwich) required 'terabytes of coding' and a good amount of luck to produce its AI.
  • Age of Ultron (a film about one liners and explosions) required a magic jewel from Loki's staff no less to create its AI.
  • Transcendence (Kurzweil summarized) gives up on AI and simply loads a human brain into the ether.

The message in all of these movies is - the reason we don't have AI is that we haven't taken some non-linear step.