This year’s yak-shaving

I tend to do the same things and think the same thoughts at the same time each year, without trying. Keeping a daily journal or worklog helps to spot these patterns.

Right now I’m having fun shaving a yak. I started working on a 3d animatable waterfall spectrogram, extending the ‘live sine waves’ seen here. The graph itself is functional now, but the FFT input isn’t working yet.

I remembered that the Audin project, my first courseware and still my best, was meant to include a 2d waterfall spectrogram. Audin was on a grant contract, and the time ran out before I could properly include and test the waterfall.

I started converting the Audin C++ waterfall code into Python to run the 3d stuff, and then decided it would be even MORE fun to continue and expand Audin itself, finally reaching my intended goal.

So now I have to rebuild and expand Audin before I can get back to the 3d representation. (Well, I don’t really have to, but it’s more fun to think of it this way.)

Here’s the yak-shaving picture from January 2018, with a snowy landscape identical to today’s landscape. These annual verbatim repetitions give credence to real astrology.

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Sidenote: Since I was talking about natural selection yesterday, this process fits nicely into the theme of evolution by subtraction.

My original 2002 courseware was full of useful features. It invited the student to do real experiments, and had a wide variety of sound-handling and animation. The later versions in Windows started to drop the useful features because the NYC publisher didn’t like them. They wanted text and still pics in computer form, like PowerPoint. I had to fight hard to maintain SOME of the animations, but lost the interactivity and sound. When the Windows EXE was converted to online HTML/SVG stuff in 2015, ALL of the good features had to be eliminated. Global compatibility does not allow experimentation or interactivity. Genes and languages and inventions follow the same track, from complex and subtle down to simple and blunt.

Reprint on UNsolving

Speaking of leaders solving and unsolving problems, I managed to pull the subject together a few months ago then forgot. The endless torture chamber ruins focus, as I was saying IN this item.

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Humans want to solve problems. When a problem is important, we devote more attention to it, and try to solve it faster to minimize damage.

Trial and error is especially costly, so we’ve developed a variety of methods to avoid the trials. We learn from other people’s experience via books and teachers. We amortize our own experience via worklogs and saved code and saved tooling.

Bureaucrats work the opposite way because the cost function is reversed. Bureaucrats try hard to UNsolve every problem, and spend an eternity on “failed” efforts in the “wrong” direction, because SOLVING a problem is costly. A solved problem or a finished job means cuts in budget and power.

Here’s an interesting neuroscience experiment that shows the desire to avoid error happens at the deepest brainstem level. It’s not an overlaid function or ‘social construct’.

Learning from error is often a slow process. In machine learning, the learning rate depends on a loss function that specifies a cost for error. Here, we hypothesized that during motor learning, error carries an implicit cost for the brain because the act of correcting for error consumes time and energy. Thus, if this implicit cost could be increased, it may robustly alter how the brain learns from error.

To vary the implicit cost of error, we designed a task that combined saccade adaptation with motion discrimination: movement errors resulted in corrective saccades, but those corrections took time away from acquiring information in the discrimination task.

We then modulated error cost using coherence of the discrimination task and found that when error cost was large, pupil diameter increased and the brain learned more from error. However, when error cost was small, the pupil constricted and the brain learned less from the same error. Thus, during sensorimotor adaptation, the act of correcting for error carries an implicit cost for the brain. Modulating this cost affects how much the brain learns from error.

Learning to follow dots is an unfamiliar task, and the cost was also unfamiliar. No chance of contamination from the conceptual level.

In this particular experiment the distractor was a face of a pretty girl. Not a Woke image of an Individual Experiencing Non-binary Colour And Gendour. A pretty white girl. Researchers might know how to talk Wokish to get the grant, but they also know what real people really want.

A more familiar task would be driving down the street and noticing a pretty girl on the sidewalk. Steering involves a complex interplay of visual and kinesthetic feedback loops. When traffic is clear, the cost of inattention is low, so we don’t improve our precision. When the road is icy, the cost of inattention is high, so we focus much harder on the road and ignore the girl.

Trainers have been using this technique for centuries. Learn the basics in a simple situation, then stretch and improve the learning by occasional forced failures and distractions.

BUT: When high-cost and brand-new distractors of all types come at you fast and hard, there’s no time for learning and no way to use textbooks or worklogs as a guide. You try to focus and try to get your job done in a minimal way. At some point you simply burn out and give up. This is how psychopaths destroy humans.

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Dust bowls and rice bowls

A surprising fact from the same Nov 33 issue of Electronics as the microwaved “bun sandwich”:

More people than ever before live on the nation’s farms.

Not only has the drift of farm boys and girls to the city been stopped, after thirty years of continuous growth of the city population at the expense of the countryside, but for three years past the movement of the rural migration has been just the opposite way: it now is from city to country. In fact, in these past three years the replacement of farm population has more than equalled in numbers the total population withdrawn from rural districts in the preceding third of a century, so that now more people are living in farm homes than ever had farm homes before.

Most of these 6,000,000 farm houses are without electricity for lighting. Their radios must be battery operated. It is time the radio industry gave more attention to sets especially designed for this vast farm market.

FDR started to electrify those farms, and the radio industry did respond to the farm market.

I’ve always been suspicious of the standard narrative of the Dust Bowl. My ancestors lived in Okla during those years, stayed and prospered. I delved into the subject in detail here.

It’s true that many farms failed because of Wilson’s semi-forced growth. In WW1, government and bankers encouraged city people to try farming on shitty land. Nobody bothered to train them or guide them in selecting land; the bankers just wanted to create a bunch of mortgages. This failure happened in the mid-20s at the same time when the fake NYC boom was creating many unnecessary industries in cities, so the migration was toward cities. When the fake boom ended, the unnecessary businesses failed and the people returned to farms, which are NECESSARY.

In other words, population takes care of itself when allowed to move easily.

China has been FORCING movement in both directions, not just encouraging with fake booms. Mao destroyed farms to build industry, then in 1968 destroyed industry to rebuild farms. After 1980 China moved to the city again. Now Xi is switching back to 1968, destroying industry to rebuild farms. This constant forced movement leaves no time to develop the skills of farming.

Most of the video is synthesized English. The segments featuring a Henan farmer are linguistically interesting. I don’t understand Chinese, but I’ve heard enough of the usual urban dialects to tell the difference. He’s from the Chinese version of Arkansas. Slow and careful, much more music than the urbanites, longer diphthongs, clearer aspirates.

Shrier’s speech

Abigail Shrier stated a simple obvious fact and got slammed by all the Cool People. She then tried to give a speech about her book at Princeton and had to give it in a private home with no cameras allowed. (But it’s worth remembering that she wasn’t jailed or officially punished, just socially punished.)

Bari Weiss got Shrier to repeat the speech for the record. This speech is salient because Shrier is an elite progressive female Princeton alumna speaking to elite progressive female Princeton students. She’s not coming from a “problematic” male or conservative or working-class position. She focuses sharply on the trannies who invaded Princeton’s female athletics, depriving the real female athletes of the rewards they deserved after a lifetime of hard work and training. A fake theory and a fake definition enabled the fake “females” to STEAL work and skill and discipline.

Here’s the speech.

I wrote a comment under it, and will repeat and slightly expand the comment here for my records.

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The centerpoint of the speech is that crazy ideas are not pushed for the sake of pure theory. They are pushed by CHEATERS to make CHEATING easier.

This also applies to the broader notions of “rights” and meritocracy and “democracy”. These fake ideas enable the top caste to subjugate the peasants much more easily, with no possible argument.

The standard line from the cheaters is “It’s not unfair, you’re just not trying hard enough.”

In fact caste is real and permanent, just as gender is real and permanent. Life works better when everybody understands the basic facts of life. Pre-enlightenment cultures work better, and allow less cheating, because the culture is built on facts.

The cheat is easy to see with “female” swimmers, but not quite so easy when the aristocrats tell unemployed factory workers that they just need to learn AI. No, they can’t. They are not built to be programmers, and programmers are not built to be factory workers. Transing across this boundary isn’t possible.

Blaming and framing the victims is an essential part of every traditional scam and con game. When you think you’re on the wrong side of the law, you aren’t going to call the cops or examine the mechanism that made the scam possible.

THEY KNOW IT’S A HOAX BECAUSE THEY MADE THE HOAX.