It's interesting to see these unconventional solutions. Genetic algorithms evolving antenna design produce similar illogical but very efficient designs. Humans have a draw to aesthetic. Robots don't have such limitations.
I've tried. In fact, I spent a good portion of my life trying to become more extroverted and "likeable". While I found I could very accurately mimic the people who had this trait over time it became exhausting to keep up with. As I've gotten older I've learned to work with what I've got. Instead of trying to chase this idea of what I consider a "good" personality I simply tried to improve the weakpoints in my own personality.
I would recommend you do the same. Don't "transform" anything. Just sand down the rough edges of what you have and find a group of people who are okay with you as you are. You will be happier for it and far less stressed. If you're anything like me, you will become exhausted very quickly trying to be something you are, intrinsically, not.
These comments are always so hilarious. It demonstrates a significant lack of understand of how statistics actually works. You see this on reddit all the time, you'd think it'd be better here.
Power and sample size are determined by numerous factors depending on the question under study. You could have N=10 be statistically powerful and N=1,000,000 be statistically meaningless. It depends ENTIRELY on the subject under study. More is not always better and in many cases completely unnecessary.
Here is a decent guide. You may wish to read it so you can learn when you can dismiss a study based on N. You never mentioned, did you back out the study numbers and determine the N=17 number was outside the range of statistical power? If so, would you mind posting your calculations?
Dongles don't prevent piracy. Famously, I remember paradox cracking several dongle-secured audio tools that were claimed to be "uncrackable". You may not even need to clone a dongle to do it.
Used to run into these problems all the time when I was doing work in numerical analysis.
The PATRIOT missile error (it wasn't a disaster) was more due to the handling of timestamps than just floating point deviation. There were several concurrent failures that allowed the SCUD to hit it's target. IIRC the clock drift was significant and was magnified by being converted to a floating point and, importantly, truncated into a 24 bit register. Moreover, they weren't "slightly off". The clock drift alone put the missile considerably off target.
While I don't claim that floating points didn't have a hand in this error it's likely the correct handling of timestamps would not have introduced the problem in the first place. Unlike the other examples given this one is a better example of knowing your system and problem domain rather than simply forgetting to calculate a delta or being unaware of the limitations of IEEE 754. "Good enough for government work" struck again here.
I think part of the problem comes to the sheer amount of jargon in even the simplest research paper. During my time in graduate school (CS) I would often do work that used papers in mathematics (differential geometry) for some of the stuff I was researching. Even having been fairly well versed in the jargon of both fields I was often left dumbfounded reading a paper.
This would seem to me a situation that is easily exploited by an AI that generate plausible text. If you pack enough jargon into your paper you will probably make it past several layers of review until someone actually sits down and checks the math/consistency which will be, of course, off in a way that is easily detected.
It's a problem academia has in general. Especially in STEM fields they have gotten so specialized that you practically need a second PhD in paper reading to even begin to understand the cutting edge. Maybe forcing text to be written so that early undergrads can understand it (without simplifying it to the point of losing meaning) would prevent this as an AI would likely be unable to do such feat without real context and understanding of the problem. Almost like adversarial Feynman method.