The Daily Donnybrook

Welcome to Ye Olde Colde Furye Blogge’s shiny new open-comments thread, where y’all can have at it as you wish, on any topic you like. Do note that the official CF comments policy remains in effect here, as enumerated in the left sidebar. All new posts will appear below this one. There will be blood…

The Hypocrisy, It Burns

Oh, wait, it was the hot sauce.

Instagram Model Claims Rapper Drake Put Hot Sauce In Used Condom To Prevent Sperm Theft

According to various reports circulating online, the chart-topping rapper Drake is alleged to have utilized a rather unique method to prevent fans and groupies from stealing his sperm after doing the proverbial deed by putting hot sauce inside of his used condoms.

If you pay any attention at all to the men’s rights people, the paternity rights legal debate, or anything related, you’ll have heard of spermjacking, in which a woman gets sperm from a man who did not intend to conceive a child with her and places it up in her vagina. She hopes to get pregnant and either trap the man into marriage or trap him into decades of paying for the child support gravy train.

If you pay any attention to the opposite side of the debate you’ll hear women angrily asserting that this does not happen and it’s just a matter of men ducking responsibility for their actions.

Well, here we are. Drake is apparently a wise man and made sure to destroy the sperm in his discarded condom, just in case he got spermjacked. A few minutes later the screaming started.

Your reply, feminists?

21
1

Guest Content: Sturgeon Was an Optimist

Over the past couple weeks my daughter, TheChildF, has been doing some research and writing a report on it. (Why? Because I’m a monster and am not giving her a break during her school Christmas break. Monster, I tell you.) In the interest of providing some content for the temporarily languishing blog, here it is. (Why? Because I’m a monster.)

Wattpad Fanfic Report

Abstract

Sturgeon’s law states that “90% of everything is crap”, and I wanted to see if that applied to Wattpad fanfiction too. Seeing as Wattpad has a reputation for badly written stories, I wasn’t expecting much. By skimming the first chapter or so of the top 5 stories in the hot category in 10 fandoms, I have concluded that out of the 50 stories I had read, 2 were good enough that I would keep reading them. 13 of the 50 were passable by Wattpad standards, a deliberately lowered bar.

Intro

I came in with the hypothesis that more than 90% of Wattpad works were awful. This is based on Sturgeon’s law, which states that “90% of everything is crap”.

Method

Materials:

  • computer
  • wattpad
  • a healthy dose of spare sanity
  • the strength to go on

First, I chose 10 fandoms to read 5 fanfics each on. Instead of randomly choosing them, I got popular and well-known fandoms, as well as some smaller ones I had heard about in passing. I picked out the top 5 stories from the hot listing, but it would have produced a better result if I had sorted by new instead because the worst stories are usually excluded from the hot list.

I read at least 500 words of each story, though inevitably some had multiple chapters of character introductions and song recommendations. Additionally, if I was unable to tell if a story was passable or not, I would read a few chapters past the first one.

I automatically rejected a story if it met the following criteria:

  • excessive capitalization, spelling, and punctuation errors
  • improper writing mechanics
  • excessive, pointless swearing

Results

FandomPassable by Wattpad StandardsGood Enough that I’d Keep Reading
1 Direction1/50/5
K-Pop2/50/5
Minecraft-Youtube1/50/5
My Hero Academia2/50/5
Harry Potter1/50/5
Naruto1/50/5
Lord of the Rings2/51/5
Game of Thrones3/51/5
Twilight0/50/5
Warrior Cats0/50/5

Discussion

By the time I realized I sorted by hot instead of new, I had already worked through most of the stories, so I kept the same sorting and didn’t worry about it. It would probably be better to sort by new next time (if there is a next time). I also noticed that, as I read more fanfiction, my standards for Wattpad stories changed for the worse. I decided to read all of the stories first, then decide if they were passable.

Conclusion

Sturgeon was an optimist. Two out of the fifty stories I read were something I’d continue reading, which isn’t a good look for general Wattpad quality. The other 11 passable stories were not good enough to keep reading and were “good” only compared to the really bad stories on Wattpad.

3

A Bit of Good News

Miserable, Hateful Cow Dies

She got her booster shot and was dead in a couple weeks.

What makes her a miserable, hateful cow? What makes her death anything but a terrible, avoidable tragedy? She railed a lot against the unvaccinated and called us selfish, told us to stay home until we come to our senses, and blamed us for endangering everyone else. When she started having heart difficulties, the hospital was overcrowded and of course that was all the fault of the unvaccinated.

Bottom line, she was stupid and full of hate. Good riddance.

Note that the article is from back in September. I just came across it today. This was before the scope of the governments’ lies was revealed to all who cared to look so I suppose one could cut her some slack for not understanding that any recent hospital crowding was not due to the unvaccinated. But no, I’m not feeling charitable this evening.

27
1

Merry Christmas

Merry Christmas to all you Furians, Furries, Droogs and Droogettes, and uncategorized Riffraff.

Make sure to keep Mike in your thoughts. Pray for his recovery if you’re religious.

And a hearty “Eat a pile of toadstools” to any anti-American who objects to seeing or hearing the words “Merry Christmas”.

8
1

A Modest Proposal

Anyone can see that the food supply in the US has been disrupted. Grocery stores have removed some items which were popular two years ago. Cans and boxes aren’t shelved more than two deep and even so the shelves have gaps. Prices are way up, which both suggests shortages and reduces consumption.

We have warnings of more severe shortages to come: Fertilizer shortages. Labor shortages in the food processing plants resulting in reduced throughput. Labor shortages preventing the harvesting of crops. Increased disruption in international trade. Transportation problems because of lack of truck drivers or cargo containers or disposable plastic boxes.

Stocking up while you can is one way to not go hungry but it’s not a long-term solution. If you eat more than you can obtain, you’ll eventually chew through your entire deep pantry.

Removing the supply, processing, and transportation bottlenecks would fix the problem of shortages. If you can do that, do it. I suspect the problem is bigger than you can solve, and unless you are able to get more food on the shelves, your effort is better put on something that you can fix.

Everyone has to eat.

There’s only so much food to go around, and in fact a decreasing supply. There are so many people eating it, and in fact an increasing number.

Everyone living has to eat.

Anyone can see that some people don’t produce. They don’t now, they never have, they likely never will. They consume: housing, consumer electronics, gasoline. Food. They don’t produce and they often destroy. There’s the never-employed mother with five children by seven fathers. The corner drug dealer. Muggers and burglars.

Even the institutionalized insane and the severely retarded can be categorized with them. It may not be their fault but they consume and do not produce.

Everyone living has to eat.

If 9.1% of the nation’s population is useless eaters, then getting rid of them will let everyone else eat 10% more.

If you’re hungry now or think that one day you will be, go out and kill a useless eater. More than one.

Do it for the satisfaction. Do it for the satiation. Do it for America.

9
3
1

Some Filler Stuff

It looks like Mike’s internet connection is still down and Big Country Expat is in a running battle with revenuers and Ironbear is, I dunno, training his dogs to sniff out revenuers. But there’s no need to fear! Understeve is here!

I haven’t finished my next long-form essay and don’t feel inclined to comment on news beyond saying “it mostly sucks”, so here are some images I’ve made over the past few months.

The first is my actual resignation “letter” to my current employer. My department head’s response was “OMG”. It took him a couple days to realize I was serious.

Next we have some totally justified mocking of a tool.

And something most American husbands in two-income families will recognize:

Something topical about some more people who annoyed me. I saw this one several times very shortly after I uploaded it, faster than I would expect it to spread even if it really caught on, so I assume it was a case of great minds thinking alike.

And, finally, something rude, on account of I gotta be me.

Except for the picture of Le Butt crying and maybe the Drake template, all images were public domain, creative commons, or created by me. All of the above images are released into the Creative Commons by me, the creator. (Shouldn’t be necessary to say, but doesn’t hurt anything.)
5

Speculation Concerning an Oddity of Behavior

Why is it that many of the most devout Christians constantly reassure themselves of their faith, that they know it’s true, that they know everything is in God’s or Jesus’s loving hands?

Muslims do much the same, as do some Hindus in the US. (Adjusting for cultural and religious differences.)

Can you imagine the same concerning something that can be demonstrated as being true?

Gravity loves me, this I know!
Newton’s apple tells me so.
Praise Gravity every day
So our stuff won’t float away.

No, of course not.

Likewise for constant assertions that “My faith in God is at the center of everything I do.” “Every decision I make, I ask myself what is the best way to honor God.”

Can you imagine the same concerning other fundamental aspects of your being? “First and foremost I am a eukaryote. Don’t ask me to do anything without keeping that in mind.”

My proposed explanation: The devout know deep down that their religion isn’t really true. They can’t accept this, so they aver their faith ever louder to drown out their doubts.

There may be some elements of cosmological truth if you close one eye and squint with the other, but the explanations of the world invented by Bronze Age nomads do not suit the modern world at all. The holy works of various religions may contain useful advice for living your life and for living with other people. There’s a lot of bad advice in there, too, so at best that’s a wash if you’re trying to live your life strictly in accordance with your religion.

5
2

Newsflash – The World Is Biased

“It’s concerning that it can do that when it shouldn’t be able to.”

Such was the insightful comment of one of my coworkers on learning that Artificial Intelligence systems can determine the race of a patient from his XRays, matching the patient’s self-reported race with 80-99% accuracy.

He wasn’t alone in thinking this. That is, emoting this; little to no rational thought was involved. Of the dozen people in the meeting, half or more spoke up, saying that the AI must have been trained wrong, that the race must have been encoded on the XRay, or it’s the quality of the XRay (with nonwhites of course being scanned by older equipment). Something. Not stated was that race doesn’t exist and is purely a social construct; that’s revealed truth to almost all of my coworkers and need not be spoken unless someone utters heresy.

“I know something is true and what I’m seeing doesn’t match what I know is true so what I’m seeing must not be true.” Or, to paraphrase Groucho Marx, “Who are you going to believe, your beliefs or your lying eyes?”

Most people will deny what their eyes are telling them. They’ll deny truth, reality, in favor of their beliefs. This of course applies to a lot more than an AI determining race “when it shouldn’t be able to”, but I’ll focus on AIs because that’s what came to my attention and because I know more than most about AI systems. (And to limit the amount of time spent in writing this; as usual I have many more things to do than time to do them.) (Proof of this: it’s taken me four months to get an uninterrupted hour to finish writing this essay.)

AIs have useful results in many fields, completely unrelated to one another. They’ve been trained to sift through mountains of noisy astronomical data to find exoplanets, a task largely beyond humans because the data is too great for our brains.

AIs have been used to predict recidivism, and seem to be much better than humans, at 90% versus little better than guessing. This has led to complaints by the accused (claiming bias, presumably because the AI wasn’t trained to be sensitive about disparate impact) and by judges (who inform us that these matters are more an art than a science).

AIs have been used by HR departments to make hiring decisions or at least to prioritize candidates. But there’s a problem. Two problems. First, the criteria for determining whether the AI (or the human) made a good decision are squishy. In predicting recidivism, regardless of your feelings about racial bias or disparate impact, if an offender is arrested again within a year, that’s a solid data point.

When it comes to employee performance, HR departments often try to apply objective criteria such as number of trouble tickets resolved, but those are seldom useful for more than an overview of one facet of the job. Most of the employee evaluation is squishy, and seldom completely honest for any number of reasons.

And that gets us to the other problem with using AIs for hiring decisions: Very often, racial minorities are underrepresented in the selected candidates, regardless of the job. In the computer fields, minority under-representation is even worse, when chosen by AIs. Women are also under-selected.

The obvious explanation, of course, is that the AI was trained wrong. It’s probably deliberate bias, because almost everyone involved in creating and training AIs is a white man. (That’s not true. In the US, men of European descent are a minority of people involved in building, training, and running AIs.) Or unconscious bias, because the training data is not fully representative of all job seekers or convicted criminals or hospital patients. Or some other kind of bias because the data contains patterns that no one’s noticed before and it’s throwing off the results.

That last one is a reasonable concern. In the example above of determining race from XRays, it’s possible that a hospital serving almost only blacks used one model of XRay machine and another hospital serving almost only whites used another model. When the AI was trained using data from these two hospitals, it could have picked up the relationship between XRay machine model and patient race. “Deep learning” systems are notoriously opaque, so something can sneak in and not be found for a while. (Though human thought processes are not exactly open for scrutiny, either, and very seldom do humans realize that their decisions are influenced by how recently they’ve eaten or the similarity of a job candidate to an old girlfriend.)

On the other hand, data scientists and AI developers are trained to be aware of this issue, to recognize the signs of a spurious correlation, and to actively look for problems. Furthermore, many of the announced, unacceptable results have been replicated many times with consistent findings. It’s not impossible but is very unlikely that the same error appears in different systems created by different teams using different data.

Even on projects in which only a single AI has been trained for some problem, claims that the training data must be biased are not accompanied by examples of problems in the data. The claim of bias comes by backward reasoning from the results which must be wrong, rather than from any direct evidence.

When AIs are trained to solve real-world problems using real-world data and they repeatedly come up with unacceptable results, there’s something wrong. Something major, something systemic.

It’s possible that the problem is in the way the AIs are designed and built and trained. Despite what I said a few paragraphs ago, errors do creep in. It’s conceivable that the same bias crept in and threw off the hiring recommendations of half a dozen HR AIs, all in the same racially and sexually biased fashion, or that the identification of hospital patients needing more intensive post-release follow-up was thrown off by richer (ie, white) patients getting more expensive treatment than poorer (ie, black) patients. (That did happen. It was corrected as soon as the problem was noticed, but that mistake has become one of the go-to examples of racial bias in AIs. One of the very few examples that anyone can point at, I’ll note.)

I don’t believe this is a systemic problem, though, because AIs have proved themselves useful and accurate in any number of areas, from optimizing warehouse layout in a manner no human would have thought of (and showing actual benefits in the amount of time needed to collect items) to finding risk factors for elderly people to fall and require hospitalization. (One of my coworkers found that a couple years ago, via “deep learning” examination of dozens of demographic and clinical factors. What popped out of the AI were factors such as age and weight, which were known to doctors and which served as a good check of the AI’s function, as well as a few surprises like a blood test showing some hormone over some threshold. Sorry about not remembering the details; I’m not a doc and they meant nothing to me. However, the docs put the findings to work and wound up with improved patient outcomes, showing that the AI’s findings were valid.)

As mentioned above, the claim that the demographics of the teams developing AIs result in systemic bias, somehow, is frequently made. The mechanism of this bad result is never detailed, merely hand-waved as a “Well, what else can it be?” As with the claims about biased training data, this is reasoning backward from a result which cannot be accepted to the conclusion that something must be wrong in the way the AI was built because of the people who built it.

The factor which is missing from “analyses” that the AIs must be wrong is the possibility that the AIs are right and that the common wisdom is wrong. That is, when there’s a discrepancy between A and B, why do they always assume that A is right and B is wrong?

It’s known that judges do a poor job in predicting recidivism. The good ones hitting maybe 60% is nothing to brag about.

It’s known that HR departments are bad at hiring, retention, and raise decisions because they apply criteria other than objective measures of expected or actual performance.

If what you care about is good results, then you should compare the real world effectiveness of what humans recommend versus what the AI recommends and go with whichever is get better results.

If the AI is giving bad results, you probably need to look at what it was told to do. More than that, you need to look at how you are deciding that the AI is wrong or unacceptable. By what criteria does the AI’s recommendation result in a bad result?

So far as I can see, only certain kinds of AI results result in butthurt. Not even radiologists complain when an AI detects early breast cancer at 99% accuracy, easily beating out the most experienced radiologists and doing a hundred times as many images in a day. It’s only when the AIs’ results trample on shibboleths of race or sex or other social factors that people complain.

There’s a saying which is common among some groups: Reality is that which doesn’t go away when you don’t believe in it. There’s an addendum to that: When religion collides with reality, reality wins but the truly faithful won’t admit it. (And they often use the unwelcome reality to strengthen their faith.)

The truth which the True Believers don’t want to admit is that a machine can do a better job of objectively seeing reality than humans can. The machine is more honest in looking at the world through whatever lens it’s told to look through.

AIs do a good job of finding patterns for what they’re optimized for. They give “wrong” results because there’s a mismatch between what people say is important and what is really important to them — eg, avoiding appearance of racial bias is more important than likelihood of repaying a loan.

If you want to make sure that “enough” black families get bank loans to meet some quota, program that into the AI. (The most straightforward way would be to order that the percentage of black loan recipients must exceed the percentage of local population which is black and let the AI figure out how to make that happen, but that may be too straightforward and honest for executives and managers to accept.)

If you want to believe that there’s no such thing as race, I don’t know what to tell you. Machines, which are not told in advance that there is no such thing as race, keep finding clusters of physiology or behavior or preference which bear a shocking similarity to race as understood by most people. Netflix came under fire for racism in their recommendations. Recommendations were based solely on the syllogism “Most people who liked X also liked Y. You liked X. We recommend Y to you.” But that was unacceptable because white people tended to like different things than black people. I don’t know how Netflix resolved that controversy, but I suspect that it was enough to simply point out that their customer sign-up form doesn’t ask about race.

Whatever way you go, be honest about how you’re deciding if an AI’s results are good. And stop attributing your own racism and sexism to the AI programmers.

5

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