One of my big beefs with ML/AL is that these tools can be used to wrap bad ideas in what I will call “Machine legitimacy”. Which is another way of saying that there are many cases where these models are built up around a bunch of unrealistic assumptions, or trained on data that is not actually generalizable to the applied situation but will still spit out a value. That value becomes the truth because it came from some automated process. People cant critically interrogate it because the bad assumptions are hidden behind automation.

When bad people do good things they are generally seen as sinister, as if they are concealing a horrible action behind a facade of good will. So if you believe the government is fundamentally evil, and you see it trying to do something good (which is the whole purpose of FEMA) then its actions are going to look sinister to you. So stories about FEMA having camps (at their core, these are stories about the government using the facade of aid and assistance to hide something evil) will make sense to you because they are consistent with your sentiments about the what the government is. So too would stories about FEMA using disasters as a pretext for land snatching or stories about FEMA ignoring people in peril because these are all stories about an evil government. To the extent that they are consistent with your sentiments about the government, they are easy to accept as true, even if they contradict each other.

I often have the opposite experience when looking for technical documentation about programming libraries. For example I will be dealing with a particular bug and will google the library name plus some descriptive terms related to the bug, and I get back general information about the library. In those cases, it seems google often ignores the supplemental information and focuses only on the library name as If I were looking for general information.

What is worse is that the top results are always blog-spam companies that just seem to be copying the documentation pages of whatever language or library I was looking at.

Former CEO of the river poisoning company says there is no way to meet our river poison reduction goals, so we might as well build bigger river poisoning machines because they might help us figure out how to stop poisoning the river. /s

I feel like there was a time when the tech folks in silicon valley had a lot of credibility, and we are now living in a period where most of the world sees them as a joke but that fact has not yet entered into the culture of silicon valley.

Used get my haircut at one of those “no appointment needed” haircut chains. Then they got an app, and every time I went it was “Why aren’t you using the app? You need to use the app. Next time use the app. Download the app on your phone. It’s gonna be an hour wait because you didn’t use the app.”

Now I just go to a local place.

I went to one for a candidate for the House district I lived in a few election cycles ago, It was mostly stump speeches and other “rah rah we’re gonna win!” style pontificating. But one thing I did not expect and I actually found interesting was the house candidate spent a lot of time introducing other local politicians that were in down ballot races in the district. City council seats, education board seats etc. That turned out to be really useful, because it meant I got to meet/ hear from candidates who I either had no idea existed or who were just a name of a flyer before then. I suppose that experience may not transfer to a national candidate rally though.

Thanks for reaffirming my bias that new cars suck.

I am really concerned the next car I need to buy, which is probably 20 years off, is going to be this trend cranked to 11. With software and hardware I can find alternatives and hack my way around the “you paid for it, but we own it and can do whatever we want to it” mentality that tech companies push, but cars seem like a whole different world when it comes to the “you paid for it, but we own it” mentality.

That seems like good news for Mastodon