I know this guy from his videos over the years on hiking topics, like how to safely purify water with the minimum fuel and how to pack calorie efficient food.
His videos are incredibly well researched, very in-depth, and absolutely zero fluff. Very much feels like his cycle is to get intrigued by a topic, spend a year deep diving into everything that's published, extrapolate what he can from there, then summarize it in a 1 hr video.
It's because Signal has some unhealthy obsession with "security" and does not want to recipient of the communication to ever be able to export messages in plain text.
So if the top 10% is $2m net worth, then what's the 1%? Are we supposed to mentally extrapolate?
I hate when only part of the criteria are provided. Arrives like this need a table. If they don't have it, it calls into question whether they should be writing the article.
It hails from when family lines were important, and you can practically only have one line reflected in a name. Unsurprisingly, most societies considered the male's name to be the dominate lineage of interest, although that doesn't hold true 100% of the time.
> you can practically only have one line reflected in a name
Not true at all. You can trivially have two family names in a full legal name. In fact many cultures do exactly that to this day.
Also worth noting that the male's name being preferentially propagated makes a lot of sense in a society where the best off frequently inherited their vocation from their fathers.
> Just because there is an alternative doesn't mean society will adjust.
"It isn't practical to do" and "society at large didn't go this direction" are very different statements.
Hyphenation is two names in a trench coat. Maintaining two names indefinitely works just fine as long as you discard rather than endlessly compound. Presumably the only requirement is that it be straightforward to trace any given lineage.
The traditional approach is for women to keep their maternal name and discard their paternal name on marriage while men do the opposite. But of course any scheme could work, up to and including each person arbitrarily choosing which name to discard (not sure how they decide on ordering in that case).
Another historical approach is the Foo Barson, Baz Fooson (Barson) approach. That scheme treats the male and female lines as being entirely separate so it doesn't quite match what you're after but it was quite practical.
Preserving more than one lineage and providing a cohesive family name isn't practically easy, and society did not go that direction, and that likely isn't a coincidence.
Discarding names doesn't preserve lineage. If you need a book to trace the names, then the point of using a name for lineage has failed.
> The traditional approach is for women to keep their maternal name and discard their paternal name on marriage while men do the opposite
It sounds like this scheme is "men keep one name lineage, women keep another".
Which, IMO, has the practical drawback of not identifying the current family unit. Lineage was important, but so was gathering all folks together into a household. When taxes, religious ceremony, etc. occurred, there was one household name on the roster responsible. This was particularly important in societies where men held certain rights for the household.
I have a theory: They realized the right approach is to focus purely on the yes/no of what you choose to consume, rather than trying to optimize the consumption experience itself.
Remember how YouTube and Netflix used to let you rate things on 1-5 stars? That disappeared in favor of a simple up/down vote.
Most services are driven by two metrics: consumption time and paid subscriptions. How much you enjoy consuming something does not directly impact those metrics. The providers realized the real goal is to find the minimum possibly thing you will consume and then serve you everything above that line.
Trying to find the closest match possible was actually the wrong goal, it pushed you to rank things and set standards for yourself. The best thing for them was for you to focus on simple binary decisions rather than curating the best experience.
They are better off having you begrudgingly consume 3 things rather than excited consuming 2.
The algorithmic suggestion model is to find the cutoff line of what you're willing to consume and then surface everything above that line ranked on how likely you are to actually push the consume button, rather than on how much you'll enjoy it. The majority of which (due to the nature of a bell curve) is barely above that line.
I think Netflix realized that reducing ratings to a simple thumbs up/down was a bad idea after all. A while back they introduced the ability to give double thumbs up which, if you can treat non-rating as a kind of rating, means they're using a four point scale: thumbs down, no rating, thumbs up, double thumbs up.
Netflix are right that 5-stars is too many, it translates to a 6 point scale when you include non-rating, and I don't think there is a consistent view on what "3 stars" means, and how it's different to either 4 stars or 2 stars ( depending on the person ).
For some people 3 stars is an acceptable rating, closer to 4 stars than 2 stars. For others, 3 stars is a bad rating, closer to 2 stars than 5 stars. And for others still, it doesn't give signal beyond what a non-rating would be, it's "I don't have a strong opinion about this".
Effectively chopping out the 3-star rating, leaves it with a better a scale of:
- Excellent, I want to put effort into seeking out similar content
- Fine, I'd be happy to watch more like it
- Bad, I didn't enjoy this
- Terrible, I want to put effort into avoiding this
With the implicit:
- I have no opinion on this
But since it's not a survey, it doesn't need to be explicit, that's coded into not rating it instead.
These are comparable to a 5 point Likert scale:
"I enjoy this content"
- Strongly agree
- Agree
- Neither Agree nor Disagree
- Disagree
- Strongly Disagree
The current Netflix scale effectively merges Disagree and Strongly Disagree, and for matters of taste that may well be fine.
It would be interesting to conduct social science with a similar scale with merged Disagree and Strongly disagree to see if that gave it any better consistency.
When given a 5-star choice “very bad/bad/ok-ish/good/very good”, I rarely pick one of the extremes.
I suspect there are others who rarely click “bad” or “good”.
Because of that, I think you first need to train a model on scaling each user’s judgments to a common unit. That likely won’t work well for users that you have little data on.
So, it’s quite possible that a ML model trained on a 3-way choice “very bad or bad/OK-ish/good or very good” won’t do much worse than on given the full 5-way choice.
I think it also is likely that users will be less likely to click on a question the more choices you give them (that certainly is the case if the number of choices gets very high as in having to separately rate a movie’s acting, scenery, plot, etc)
Combined, that may mean given users less choice leads to better recommendations.
I’m sure Netflix has looked at their data well and knows more about that, though.
I apply my own meaning to the 5-star rating, and find it to work really well:
1 = The movie was so bad I didn't/couldn't finish watching it.
2 = I watched it all, but didn't enjoy it and wouldn't recommend it to anyone.
3 = The movie was worth watching once, but I have no interest in watching it again.
4 = I enjoyed it, and would enjoy watching it again if it came up. I'd recommend it.
5 = a great movie -- I could enjoy watching it many times, and highly recommend it.
> The current Netflix scale effectively merges Disagree and Strongly Disagree, and for matters of taste that may well be fine.
I'm a bit skeptical about this.
To me there's a big difference between "This didn't spark joy" and "I actively hated this": I might dislike a poorly-made sequel of a movie I previously enjoyed, but I never ever want to see baby seals getting clubbed to death again.
Every series has that one bad episode you have to struggle through during a full rewatch. Very few series have an episode bad enough that it'll make you quit watching the series entirely, and ruin any chance at a future rewatch.
They're only useless in that they aren't displayed for your peers, but that was always the least-useful function.
Being able to see a counter that reads as "Twenty-three thousand other people also didn't like this video!" doesn't serve me in any meaningful way; I don't go to Youtube to seek validation of my opinion, so that counter has no value to me. (For the same reason, the thumbs-up counter also has no value to me.)
But my ratings remain useful in that the algorithm still uses the individualized ratings I provide to help present stuff that I might actually want to watch.
As we all know, investors and advertisers love growth; Youtube thrives and grows and gathers/burns money fastest when more people use it more. The algorithm is designed to encourage viewership. Viewership makes number go up in the ways that the money-people care about.
Presenting stuff to me that I don't want to watch makes the number go up -- at best -- slower. The algorithm seeks to avoid that situation (remember, number must only go up).
Personally rating videos helps the machine make number go up in ways that benefit me directly.
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Try to think of it less like a rating of a product on Amazon or of an eBay seller; try not to think of it as an avenue for publicly-displayed praise or admonishment. It's not that. (Maybe it once was -- I seem to recall thumbs-up and thumbs-down counts being shown under each thumbnail on the main feed a million years ago. But it is not that, and it has not been for quite a long time.)
Instead, think of it as one way in which to steer and direct your personalized recommendation algorithm to give you more of the content you enjoy seeing, and less of what you're not as fond of.
Use it as a solely self-serving function in which you push the buttons to receive more of the candy you like, and less of of the candy that you don't like.
I have literally not rated anything at all, ever since YouTube removed dislikes, and my recommendations are working fine. Ratings indicate(d) if a given video was likely to be a waste of my time or not, and in an age of AI slop, this feature is more desirable than ever.
Someone should make a SponsorBlock/Dearrow-type addon to flag AI slop.
You only assume recommendations are based on ratings, but you don't know. And I have seen your metaphorical green grass, because actual ratings were a thing up until about 4 years ago, remember?
>I don't find any of that on my end.
Good for you. The true crime genre has been hit hard by AI slop.
> And I have seen your metaphorical green grass, because actual ratings were a thing up until about 4 years ago, remember?
I remember this conjecture of yours (that ratings unilaterally ceased to matter as soon as they stopped being displayed to users) very well.
And unlike you, I can see over to the other side of the fence -- in the present day -- at a whim: All I have to do is fire up YouTube in a private session on a disused device. It's fucking awful over there; it's complete bedlam.
Same point as always: That it definitely doesn't have to be that way at all.
(I can't make you take the blinders off and use that utterly useless, vestigial Thumbs Down button, though. You're free to live your life with as blindly and with much suffering as you wish, no matter what anyone else thinks.)
I mean, if you read about how current industry-standard recommendation systems work, this is pretty bang on, I think? (I am not a data scientist/ML person, as a disclaimer.)
If e.g. retention correlates to watch time (or some other metric like "diversity of content enageged with"), then you will optimize for the short list of metrics that show high correlation. The incentive to have a top-tier experience that gets the customer what they want and then back off the platform is not aligned with the goal of maintaining subscription revenue.
You want them to watch the next thing, not the best thing.
I just rebuilt my PC and setup Steam on Linux. It was fairly smooth.
I've dual-booted Arch and Windows for about 16 years. I always kept Windows around for gaming, and the occasional "doesn't support Linux" workflow.
For a few years where I didn't game I found myself almost exclusively in Linux. But then I spent the last 5-6 years stuck between the two as my PC use for daily tasks dwindled, I stopped working on side projects, and I started gaming a bit more.
I hated trying to split my time between them. Most of what I used a PC for was the browser, so I could just stay in Windows most of the time. I wanted to use Linux, but rebooting to use a web browser just didn't make sense. As a result I would accidentally go 2-3 months without ever booting Arch. As a result, I had a couple of major updates that didn't go smoothly.
I wanted to use Linux, though. I like having a customized WM, I like having so many useful tools at my disposal, etc. I just like using Linux, in spite of the occasional technical complexity.
In the last couple months I rebuilt my PC and a major requirement was that I get set up to game in Linux as much as possible. I even bought an AMD card to ensure smooth driver support.
I'm so incredibly thankful that Steam has made gaming not just possible, but relatively simple. Installation was simple. My single-player games seem well supported so far. And most importantly, Steam has made it obvious they're committed to this line of support, so this isn't some hero effort that will bit rot in a couple years.
I still have to reboot to play competitive games, due to their anti-cheat requirements, but that's less of a problem, I'll take what I can get.
His videos are incredibly well researched, very in-depth, and absolutely zero fluff. Very much feels like his cycle is to get intrigued by a topic, spend a year deep diving into everything that's published, extrapolate what he can from there, then summarize it in a 1 hr video.
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