Some words.

which is cool. and it would be cool to say, find a granny square cardigan pattern with 24 squares, knit these squares, make a sick cardigan. but then i realized i don't know how to knit or anything. and once you accept that there isn't really a clear "application" and this concept lives on a screen, you open yourself up to more possibilities. a la birth of venus.

step 1: python script that looks at the original image and generates an excel spreadsheet the same dimensions (793 x 1322 pixels = 793 x 1322 cells), and each cell is populated with the hex code of the color that appears in that pixel of the original image

step 2: excel macro to generate list of every unique hex code that appears in the excel spreadsheet.

step 3: excel macro to calculate the R, G, B values of each of those hex codes.

step 4: excel macro to fill each cell with the color of that hex code (not necessary, i just like to do it).

step 5: I add in Saturation (the difference between the largest and smallest RGB value) and Lightness (average of all RGB values).

step 6: pick a color palette. i always find myself gravitating towards groovy seventies palettes with warm reds and oranges, so i decided not to do that this time. i looked on coolors and found a color palette that was all dark greens that were similar to each other. there were only like four colors or something in this palette. and to make it truly different from the other project, there should be a small gradient. so i determined the smallest possible change between colors and used an excel macro to color it. i was going to stop here and do the entire image in shades of green (inspired by that guy on tiktok that paints using only one color) but then. idk. i realized the "opposite" of each color was an equally subtly changing pink. so i imagined that the end of this process would be an "abstract" image, with subtle variations of pink and green, that would end up suggesting birth of venus.

so all told, i had 502 unique replacement colors, 251 of which are green, 251 of which are pink. (793 x 1322) / 502 = either 2088 or 2089 of each color.

step 7: find some method for finding the difference between the original colors of the image and my new color palette. I use a method of comparing, R, G, B, S and L:

((abs(R1 - R2) + abs(G1 - G2) + abs(B1 - B2)) / 3) + abs(S1 - S2) + abs(L1 - L2)

and you come up with something like this. on the left, those are colors that appear in the original image. across the top, those greens are the colors i'm replacing it with. in blue, that's the number of each new color i have to work with (it's just blue for contrast). and in the center, this pink area, that's a giant spreadsheet with the "objective" difference between each original color and each replacement color. it's pink because i have some conditional formatting applied, ignore that part.

and in this situation, you have some choices to make. in the original image up there, i used a schema prioritizing light and dark--i.e., i looked at the darkest color (pure black) that appeared in the original image, then found the closest replacement color (i.e., the replacement color with the smallest number). then did the same with the lightest color. then the next darkest, next lightest.

but i'm going to do it slightly differently this time. and i don't know how this image will come out looking.

if you look at the "first" green, closest to the left, and sort by smallest to largest:

you can see that these colors on the left are closest to the "first" green i've decided to work with. that might seem odd. i mean, #7F9800--> #00a94f are pretty close, but #A95400 is red. but that's just a difference in hue. really, #A95400 and #00a94f are very similar in lightness and saturation.

and this also calculates the number of times that color actually appears in the original image. that first specific green, #7F9800, only appears twice. but some colors, like actual black #000000, appear something like 46,000 times. and if you add all the numbers in the "frequency" column, it should exactly equal the sum of each replacement color (2088 ish x 502).

step 8: excel macro again. this one is complicated. basically it sorts that first "green" column (column E in my spreadsheet) from smallest to largest. then it adds each cell in the "frequency" column until it reaches or surpasses the blue cell above column E, which for this particular color is 2089. it copies those "original image" colors and their respective frequencies over to another sheet. for the color that surpassed 2089, it splits in two. then it deletes that column E. Then it makes sure "frequency" and "replacement color sum" still total. then it runs again on the new column E, until the whole spreadsheet is used up. and it generates something like:

[color from original image] [number of times that color appears] [replacement color, filled in]

and there's approximately 8000 lines of that.

i have the replacement colors in the order above. starting with vivid green, slowing transitioning to dark green, switching abruptly to bright pink, slowly transitioning to pale pink.

step 9: another excel macro. this one looks at original image broken down into hex codes, then looks at the generated list and replaces each [original] color with the replacement color, that exact number of times.

end result of these macros, following different "rules" of assigning replacement colors to original colors, is this:

which looks different, obviously. but it is the exact replacement colors, and same number of each replacement color, as the original up there.

They were us.

Like literally genetically they were US, just like. A while ago.

Like

Would you trust a TV caveman with a baby? Probably not

As long as homo sapiens have existed, we have had the same emotional and mental capacity as you and I do today. You nailed it. They were US. Even Neaderthals existed alongside and had offspring with Homo Sapiens for many thousands of years.

There's much evidence that cavemen would have had complex spoken language, culture (learned information passed down), symbolic interpretation, and I think they most certainly would have been able to handle holding a baby. In fact I have my suspicisions that an ancient homo sapiens mother may be a more present, attentive, and knowledgable mom than I could be today.

Do not let media trick you into believing we are the pinnacle of humanity. Unilinial evolution theory (google it quick I beg) is BUNK, GARBAGE, and the root of so much evil.

At the beginning of the next class, my teacher showed us a cave painting of a horse. And then he showed all of our horses, which he had scanned and put into the presentation.

He then pointed out all the ways that our horses looked similar to the prehistoric horse. Same features, drawn from the same angle, etc.

And then he asked us, "Isn't it cool that you draw horses the same way as someone who lived 20,000 years ago?"

Yeah. That stuck with me for a while.

Scientists are now pretty sure the scribbles were made by kids ages 3-6, more or less on their own. The adult cave artists were probably doing what any modern parent might do when they want to keep small children out of their hair for awhile: they gave the kids some drawing tools of their own and a small section of wall to work on, out of the way but still close enough to keep an eye on them, and let them have at it.

The team compared the ancient art with the developmental stages exhibited by modern children: the furiously scribbled circles and push-pull lines typical of 3-year-olds just learning to control their bodies, for example, or the wobbly, right-angled figures of slightly older kids beginning to master fine motor skills.

Both are apparent in the cave, superimposed on each other as though two or more kids were drawing at once. That’s a clue the Las Monedas marks were likely made by “siblings or a mixed-age play group within the sphere of safety around adults, but also within their own space,” says co-author Felix Riede, an Aarhus archaeologist.

...

That controversial paper was born of the Cold War. To listen for Soviet submarines, the U.S. Navy installed chains of underwater listening posts in the Pacific and Atlantic. This network, known as the Sound Surveillance System, or SOSUS, picked up a deluge of oceanic noises. Some were clearly biological. Others were more mysterious. One especially enigmatic sound was monotonous, repetitive, and low, with a frequency of 20 Hz—an octave below the lowest key on a standard piano. This hum was so loud that people doubted it could be coming from an animal. Did it have a military origin? Was it produced by underwater tectonic activity? Did it come from waves crashing on some distant shoreline? The actual source only became clear when Navy scientists started following the sounds to their sources, and often found a fin whale at the end.

Human hearing typically bottoms out at around 20 Hz. Below those frequencies, sounds are known as infrasound, and they’re mostly inaudible to us unless they’re very loud. Infrasounds can travel over incredibly long distances, especially in water. Knowing that fin whales also produce infrasound, Payne calculated, to his shock, that their calls could conceivably travel for 13,000 miles. No ocean is that wide. Together with oceanographer Douglas Webb, Payne published his calculations, speculating that the largest whales “may be in tenuous acoustic contact throughout a relatively enormous volume of ocean.” The response was brutal. Leading whale researchers told him that his paper was pure fantasy. Colleagues hinted that critics had been questioning his mental health behind his back. “When you get to distances like that, people just refuse to believe that it’s true,” Payne tells me.

Payne’s work made a more positive impression on Chris Clark. A young acoustician and former choirboy, Clark was recruited by Roger and Katy Payne to be a sound technician on a 1972 trip to Argentina to study right whales. It was a thrilling and formative time. Camped on a beach beneath the Southern Cross, with penguins bumbling past and albatrosses wheeling overhead, Clark began listening to whales. He placed hydrophones in the water to eavesdrop on their songs and found ways of assigning specific recordings to individual whales. He went on to compile libraries of whale calls, recorded all over the world, from Argentina to the Arctic. And all the while, Payne’s idea of giant whales talking over oceans stuck with him.

In the 1990s, with the Cold War over and the threat of Soviet subs diminished, the Navy offered Clark and others a chance to observe real-time recordings from their SOSUS hydrophones. Amid the spectrograms—visual representations of the sounds that SOSUS picked up—Clark saw the unmistakable signal of a singing blue whale. On his first day, Clark saw that more blue whale vocalizations had been recorded from a single SOSUS sensor than had been described before in the entire scientific literature. The ocean was awash with their calls, and those calls were coming in from enormous distances. Clark calculated that one individual was 1,500 miles from the sensor that recorded it. He could listen to whales singing in Ireland with a microphone situated off Bermuda. “I just thought: Roger was right,” he says. “It is physically possible to detect a blue whale singing across an ocean basin.” (...)

Although blue and fin whale songs can traverse oceans, no one knows if the whales actually communicate at such ranges. It’s possible that they’re signaling to nearby individuals with very loud calls, which just happen to extend further afield. But Clark points out that they repeat the same notes, over and over again, and at very precise intervals. A singing whale will stop calling when it surfaces for air, and come back on the beat when it submerges. “That’s not arbitrary,” he says. It reminds him of the redundant and repetitive signals that Martian rovers use to beam data back to Earth. If you wanted to design a signal that could be used to communicate across oceans, you’d come up with something similar to a blue whale’s song.

Those songs might have other uses, too. Their notes can last for several seconds, with wavelengths as long as a football field. Clark once asked a Navy friend what he could do with such a call. “I could illuminate the ocean,” the friend replied. That is, he could map distant underwater landscapes, from submerged mountains to the seafloor itself, by processing the echoes returning from the far-reaching infrasounds. Geophysicists can certainly use fin whale songs to map the density of the ocean crust. But can the whales do so?

Clark sees evidence in their movements. Through SOSUS, he has seen blue whales emerging in polar waters between Iceland and Greenland and making a beeline—a whaleline?—for tropical Bermuda, singing all the way. He has seen whales slaloming between underwater mountain ranges, zigging and zagging between landmarks hundreds of miles apart. “When you watch these animals move, it’s as if they have an acoustic map of the oceans,” he says. He also suspects that the animals can build up such maps over their long lives, accruing sound-based memories that lurk in their mind’s ear. After all, Clark recalls veteran sonar specialists telling him that different parts of the sea had their own distinctive sounds. “They said: If you put a pair of headphones on me, I can tell you if I’m near Labrador or off the Bay of Biscay,” says Clark. “I thought that if a human being could do this in 30 years, what could an animal do with 10 million years?”

The scale of a whale’s hearing is hard to grapple with. There’s the spatial vastness, of course, but also an expanse of time. Underwater, sound waves take just under a minute to cover 50 miles. If a whale hears the song of another whale from a distance of 1,500 miles, it’s really listening back in time by about half an hour, like an astronomer gazing upon the ancient light of a distant star. If a whale is trying to sense a mountain 500 miles away, it has to somehow connect its own call with an echo that arrives 10 minutes later. That might seem preposterous, but consider that a blue whale’s heart beats around 30 times a minute at the surface, and can slow to just 2 beats a minute on a dive. They surely operate on very different timescales than we do. If a zebra finch hears beauty in the milliseconds within a single note, perhaps a blue whale does the same over seconds and minutes. To imagine their lives, “you have to stretch your thinking to completely different levels of dimension,” Clark tells me. He compares the experience to looking at the night sky through a toy telescope and then witnessing its full majesty through NASA’s spaceborne Hubble telescope. When he thinks about whales, the world feels bigger, stretching out in space and time.

Whales weren’t always big. They evolved from small, hoofed, deer-like animals that took to the water around 50 million years ago. Those ancestral creatures probably had vanilla mammalian hearing. But as they adapted for an aquatic life, one group of them—the filter-feeding mysticetes, which include blues, fins, and humpbacks—shifted their hearing to low infrasonic frequencies. At the same time, their bodies ballooned into some of the largest Earth has ever seen. These changes are probably connected. The mysticetes achieved their huge size by evolving a unique style of feeding, which allows them to subsist upon tiny crustaceans called krill. Accelerating into a krill swarm, a blue whale expands its mouth to engulf a volume of water as large as its own body, swallowing half a million calories in one gulp. But this strategy comes at a cost. Krill aren’t evenly distributed across the oceans, so to sustain their large bodies, blue whales must migrate over long distances. The same giant proportions that force them to undergo these long journeys also equip them with the means to do so—the ability to make and hear sounds that are lower, louder, and more far-reaching than those of other animals.

Back in 1971, Roger Payne speculated that foraging whales could use these sounds to stay in touch over long distances. If they simply called when fed and stayed silent when hungry, they could collectively comb an ocean basin for food and home in on bountiful areas that lucky individuals have found. A whale pod, Payne suggested, might be a massively dispersed network of acoustically connected individuals, which seem to be swimming alone but are actually together."