AbigaleGreen

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA

Okay, so, Labradorite.  Labradorite is complicated and sciencey, as all good rocks are.  I’ll see if I can explain it in a way that makes any sense!  (Once again, I’m not a scientist!  Correct me if I’m wrong!)

Most minerals, when they’re bright and pretty and colorful, look like that because while they were forming some impurities got mixed into them - usually metals like iron, copper, or titanium.  Without any impurities, these rocks would naturally be colorless.  We call these guys allochromatic (other-colored).

Other gemstones are certain colors because those elements are an important part of how they formed.  They’re not impurities that got mixed in, they’re actually part of the gemstone.  Their natural color IS the color you’re seeing.  We call them idiochromatic (inherently colored).

But labradorite doesn’t get its color from either of those things.  Labradorite is special.  It’s part of a third group: pseudochromatic (false colored).  These rocks aren’t colorful at all, but they LOOK that way when light passes through them.

See, labradorite is actually just… grey.  From most angles, it looks like this:

You have to look at labradorite from a pretty specific angle to get those flashy colors, so when we cut it into cabochons for jewelry, or just polish up big pieces of it, we’re careful to do so at the most flattering angle, the angle that shows the most schiller, or “those cool glowy colors.”

Why just the one angle?  It’s all about labradorite’s crystal structure, and how it’s formed.

Labradorite is a rock that cooled down really slowly.  Because of that, it’s made of lots of very very thin layers of crystal, stacked on top of each other and all pretty much aligned in the same direction.  These are alternating layers of albite (mostly sodium), and orthoclase (mostly potassium), which solidify at very slightly different temperatures.  Labradorite is a rock that cooled in just the right way for a thin layer of albite to form, then a thin layer of orthoclase, then another thin layer of albite, and so on.

When light hits labradorite at the perfect angle to pass through a bunch of these layers, you get the schiller effect.  Basically, a little bit of the light gets bounced off the first layer and back to your eyes.  The rest of the light passes through to the second layer, and a little bit gets bounced back to your eyes again, and so on.  Every time more light gets sent back to you, it’s a little out of sync, and this makes it look like a different color.

(This is a very simplified way of explaining this.)

If these layers were all perfectly the same size, you’d get a uniform color, like the blue in moonstone.  But in labradorite, these layers might be different widths in different places, so different parts of the stone will reflect back wildly different colors!  We call this effect labradorescence to differentiate it from the uniform colored adularescence found in moonstone and some opals.

Depending on where it’s found in the world, labradorite can reflect all sorts of different colors!

so, in other words,

you should experience this. via

You may have heard about AOC catching a lot of flack from conservatives for claiming that computer algorithms can be biased – in the sense of being racist, sexist, et cetera. How, these people asked, can something made of math be biased? It’s math, so it must be objectively correct, right?

Well, any computer scientist or experienced programmer knows right away that being “made of math” does not demonstrate anything about the accuracy or utility of a program. Math is a lot more of a social construct than most people think. But we don’t need to spend years taking classes in algorithms to understand how and why the types of algorithms used in artificial intelligence systems today can be tremendously biased. Here, look at these four photos. What do they have in common?

You’re probably thinking “they’re all outdoors, I guess…?” But they have something much more profound in common than that. They’re all photos of giraffes!

At least, that’s what Microsoft’s world-class, state-of-the-art artificial intelligence claimed when shown each of these pictures. You don’t see any giraffes? Well, the computer said so. It used math to come to this conclusion. Lots of math. And data! This AI learns from photographs, which of course depict the hard truth of reality. Right?

It turns out that mistaking things for giraffes is a very common issue with computer vision systems. How? Why? It’s quite simple. Humans universally find giraffes very interesting. How many depictions of a giraffe have you seen in your life? And how many actual giraffes have you seen? Many people have seen one or two, if they’re lucky. But can you imagine seeing a real giraffe and not stopping to take a photo? Everyone takes a photo if they see a giraffe. It’s a giraffe!

The end result is that giraffes are vastly overrepresented in photo databases compared to the real world. Artificial intelligence systems are trained on massive amounts of “real world data” such as labeled photos. This means the learning algorithms see a lot of giraffes… and they come to the mathematically correct conclusion: giraffes are everywhere. One should reasonably expect there might be a giraffe in any random image.

Look at the four photos again. Each of them contains a strong vertical element. The computer vision system has incorrectly come to the belief that long, near-vertical lines in general are very likely to be a giraffe’s neck. This might be a “correct” adaptation if the vision system’s only task was sorting pictures of zoo animals. But since its goal is to recognize everything in the real world, it’s a very bad adaptation. Giraffes are actually very unlikely.

Now, here’s the clincher: there are thousands and thousands of things that are over-represented or under-represented in photo databases. The AI is thoroughly giraffed in more ways than we could possibly guess or anticipate. How do you even measure such a thing? You only have the data you have – the dataset you trained the AI with in the first place.

This is how computer algorithms “made of math” can be sexist, racist, or any other sort of prejudiced that a human can be. Face photo datasets are highly biased towards certain types of appearances. Datasets about what demographics are most likely to commit crimes were assembled by humans who may have made fundamentally racist decisions about who did and didn’t commit a crime. All datasets have their giraffes. Here’s a real world example where the giraffe was the name “Jared.”

Any time “a computer” or “math” is involved in making decisions, you need to ask yourself: what’s been giraffed up this time?

Thanks to Janelle Shane whose tweet showing her asking an AI how many giraffes are in the photograph of The Dress prompted this post.

They see Carol finally breaking free from being gaslighted by the Kree as “emotionally underwhelming,” never realizing that a climactic, emotional showdown with her abuser would be giving him exactly what he wants. Being in control of her emotions? Choosing not to react to a provocation? That’s strength most male comic fans don’t understand. They see masculine-coded strength as the only kind of valid strength. Carol not being angry and putting Yon-Rogg down in a shonen-esque battle doesn’t make sense to them because it’s not what they would have done.

They see a woman struggling to work through lies she’d been told as “bad narrative structure,” when in reality the movie was never about building Carol up from nothing, but about her realizing her true potential through seeing past those lies. Carol’s character arc parallels many women attaining social consciousness, throwing off patriarchal lies they’d been conditioned to accept about who they are and what they can do. Her strength isn’t about attaining power, but about embracing her true potential that had been deliberately hidden from her.

They see Carol’s emotions not lining up with the lies her abusers told her about being too emoional as “bad writing” or “bad acting,” never realizing that that was exactly the point. They only understand defiance as impassioned, outward battles of will and pride, not understanding that quiet, steadfast refusal to bend to others’ designs of who you should be is strength too.

ᕕ( ᐛ )ᕗ a

ᕕ( ᐛ )ᕗ tiger

ᕕ( ᐛ )ᕗ defying

ᕕ( ᐛ )ᕗ the 

ᕕ( ᐛ )ᕗ laws

ᕕ( ᐛ )ᕗ of

ᕕ( ᐛ )ᕗ G

 ｡･ﾟ･*:･ﾟ✧*:･ﾟ☆ ᕕ( ᐛ )ᕗR

･ﾟ✧*:･ﾟ☆ ｡･ﾟ･*:･ﾟ✧*:･ﾟ☆ ᕕ( ᐛ )ᕗA 

 ☆ ｡･ﾟ･*:･ﾟ✧*:･ﾟ☆  ｡･ﾟ･*:･ﾟ✧*:･ﾟ☆  ᕕ( ᐛ )ᕗV

 ｡･ﾟ･*:･ ･ﾟ☆  ｡･ﾟ･*:･ﾟ✧*:･ﾟ☆ ｡･ﾟ･*:･ﾟ✧*:･ﾟ☆ ᕕ( ᐛ )ᕗI

:･ﾟ☆ ｡･ﾟ･*:･ﾟ✧*:･ﾟ☆  ｡･ﾟ･*:･ﾟ✧*:･ﾟ☆ ｡･ﾟ･*:･ﾟ✧*:･ﾟ☆ ᕕ( ᐛ )ᕗT

ᕕ( ᐛ )ᕗ A

==ᕕ( ᐛ )ᕗ RACING

====ᕕ( ᐛ )ᕗ CAR

======ᕕ( ᐛ )ᕗ PASSING

=========ᕕ( ᐛ )ᕗ BY

===========ᕕ( ᐛ )ᕗ LIKE

=============ᕕ( ᐛ )ᕗ LADY

ᕕ( ᐛ )ᕗ go 

ᕕ( ᐛ )ᕗ go 

this is actually a small sub branch of botany thats been growing and gaining some recognition in the past 5 years or so called plant cognition! we’ve been thinking about if plants can possibly be intelligent to any degree for centuries, but the main paper that started up this huge discussion in the modern era was one called Experience Teaches Plants to Learn Faster and Forget Slower in Environments Where It Matters by Monica Gagliano, a plant researcher in Australia who specializes in it. because the results indicated that plants were possible of learning and retaining information in a kind of memory in response to environmental changes, it received a lot of backlash and denial- generally in science, that kind of intelligent reaction to an organism’s environment is a good indicator of cognitive behavior in the organism. it got rejected by 10 different journals before being published in 2014. 

the experiment worked like this. i’ve talked before about mimosa pudica, a tropical plant that curls its leaves back when touched (they go back to normal in a few minutes):

this is to help deter predators among other things. but in this experiment, Gagliano used it as an indicator of stimulus and to test cognitive function. It’s well known that pudica has a rudimentary nervous system that can even be temporarily inhibited using anesthetics (just like ours can!). she hooked up a ton of these plants in pots to identical rail systems that allowed them to be lightly dropped in an identical way, juuuuust heavy enough to trigger the stimulus so all the leaves drop down when they hit the bottom (a piece of foam so they wouldn’t actually hurt the plants). every time the plants would be dropped, they would close up. 

but after the plants were dropped about 60 times each, they stopped responding to the drop. 

they remembered that no harm was coming from this action and decided that it was against their best interests to keep expending energy closing their leaves. they 200% learned to stop. 

she decided to test it further. she put some of the plants in a shaker and let them receive a more jarring response; the plants closed up as usual. then, she put them back in the droppers and dropped them again. they didn’t close up. they had remembered that response. this dispels the obvious rebuttal to this experiment of the plants just being tired; they still closed up when stimulated differently.

they just chose not to close up when they hit a stimulus they remembered. 

it turns out that not only could they remember to keep their leaves open when dropped on the apparatus, but they remembered after 28 days when she kept testing it!! apparently by the end of the experiment, all the plants had decided to keep their leaves open when dropped!!!!

how do they do this?? we literally dont know. they have no central brain, only a basic nervous system. can other plants do this??? 

well, adding onto that, venus fly traps can count! like. they have three hairs inside their traps, and all three must be touched within 20 seconds for the trap to close. once closed, those three trigger hairs must continue to be stimulated by thrashing prey, or the trap will reopen. 

so yeah like. basically ‘are they sentient’: apparently to an extent???? we dont know exactly why or how but they are??? maybe???? sort of????? at least some of them are?? but they dont have a brain so everyones like????????????????????? maybe its through a signaling network????????????????? but like how would that even work?????????

plant consciousness is still new enough to be dismissed as crazy by a lot of biologists but like. the evidence is there. we don’t know a whole lot and its clearly a radically different kind of intelligence than we know in animals, but it’s there and we 200% dont know how it works yet or even the full extent of how plants use this intelligence (for example: does a redwood have the same intelligence as a venus fly trap?? how does it learn things and use that knowledge???) 

Source: [x]

So, sure, ship Stucky, and ship Carol and Maria, but don’t say shit like “you don’t mourn like that for a friend.”

You don’t mourn like that for a casual buddy you see at work or school. You absolutely do mourn like that for a close, best friend who you share everything in your life with, who is your platonic better half. Have you never heard of platonic soulmates?

Seriously, I can’t picture my life without my best friend. She is a large part of everything I do, and I speak to her daily and she helps me work out my shit and I help her work out hers. And neither of us is in love with the other. And if I lost her, I’d be devastated, there’d be a huge hole in my life. Losing her would be losing my only lifeline. We absolutely love each other. Love isn’t only romantic or sexual. You love your parents, siblings, niblings and (future?) children, and hopefully that’s not romantic or sexual. So, you understand how you can love someone without wanting to be in a romantic relationship with them.

So, please apply that logic to these characters. Yes, they have close relationships and are fun to ship, but don’t say things that insinuate that closeness can’t be platonic. Platonic friendships and love are beautiful, and quite frankly, we need more of that in media.