I do love that somebody tagged tumblr's own frog scientist on this post. chop chop dr scherz, we've got 62 more frogs to discover and you're the only frog scientist any of us knows
GUYS amphibian species of the world is still at 7,994 species of frog BUT amphibiaweb is at 8,008 species of frog, and do you know who is a co-author on the 8,000th species of frog there???? TUMBLR'S OWN FROG SCIENTIST DR SCHERZ
My old person take today is that I feel like people have normalized being on your phone every single moment including when you're spending "quality" time with others so much that they're defensive if someone isn't ok with it. Yes, you have a problematic relationship with your phone and social media if you physically cannot put it down for a couple of hours to like, have dinner with your friends. It's a show of respect for other people's time and energy as well as important to be present and connect with people around you. Your parents who told you no phone at the table were right for that one.
No phones at the table unless you’re looking up a possible answer for the arcane question someone at the table just asked and we all have to know immediately: that’s my rule.
Where's it made? Who brought it here? How much were they paid? Who makes it? Is it made in separate parts and put together? How much were they all paid to do this? Where do they get the materials? Who paid for that? Who brings it there? How much were they paid? Who streamlined the base materials? How much were they paid? Who gathered the base materials? Where? How much were they paid? Is it good for them? Is it good for us? Is it good for the land? Is it necessary? Is it biodegradable? How much does it hurt? Do I need it? Do I even want it?
the first osprey is the father, the one that comes later is the mother.
ospreys are not eagles, they're ospreys
ospreys only eat fish, that's why they don't register this starling as possible food
the starling got home safely
the starling was not trying to eat the eggs, it was mostly curious and you can see it trying to hop under the osprey every time the osprey tries to sit down again--this is because the starling is still a baby and has the instinct to get under an adult for warmth, even though it mostly has its feathers. this scares the osprey because that is a Foreign Creature near its eggs.
at the end of the video you can see the ospreys starting to turn the eggs. birds do this so the yolk and/or embryo don't stick to the shell of the egg, which is bad for the egg's health.
ospreys have eyes adapted to seeing beneath the surface of the water!
Text of tweet under the cut because it is loooong.
But... Stochastic Parrots.
Timnit Gebru was fired from Google in December 2020 for refusing to retract a research paper, and every single warning that paper made about large language models has now happened at a scale the industry spent 4 years trying to make people forget about.
Her name is Timnit Gebru.
She co-led the Ethical AI team at Google. She co-wrote a paper called "On the Dangers of Stochastic Parrots" with Emily Bender at the University of Washington and two other researchers. The paper was 14 pages long. It was submitted to a top AI ethics conference. And it was the reason Google decided that one of the most senior Black women in AI research could no longer work there.
The story Google told publicly was that she resigned. The story she told, confirmed by 2,695 of her colleagues in an open letter, was that she was fired by email while on vacation because she refused to either retract the paper or remove her name from it.
The paper had not even been published yet.
Here is what she actually wrote, and why every prediction inside it has now come true.
The first warning was about scale itself. Bender and Gebru argued that training ever-larger models on ever-larger scrapes of the internet would produce systems that appeared fluent but had no actual understanding of language. They called these systems stochastic parrots because they would repeat patterns from training data with statistical confidence and zero comprehension. The paper predicted that this apparent intelligence would fool both users and developers into trusting outputs that were structurally incapable of being reliable.
This was 2020. GPT-3 had just come out. The paper predicted the hallucination problem before anyone had a word for it.
The second warning was about bias amplification. The paper documented in detail that internet-scale training data contains systematic overrepresentation of dominant viewpoints and underrepresentation of marginalized ones. The models would not just absorb this bias. They would amplify it, because the optimization process rewards confident outputs, and confidence in language patterns tracks frequency in the training set.
The prediction was that hiring tools built on these models would discriminate against women. That healthcare triage tools would underperform on Black patients. That loan approval systems would entrench inequality while presenting their decisions as neutral algorithmic judgment.
Every one of those things has now been documented in deployment.
Amazon's hiring algorithm penalized resumes that contained the word "women" in any context. Healthcare risk scoring algorithms used by major US hospitals were found to systematically underestimate the medical needs of Black patients. Apple Card's credit algorithm gave wives credit lines 10x lower than their husbands for the same financial profile.
The third warning was about environmental cost. The paper calculated that training a single large language model produced emissions equivalent to the lifetime output of 5 cars. The prediction was that the race to scale would create an environmental footprint that would eventually rival entire industries.
In 2024, Google's emissions were up 48% from 2019, and the company explicitly blamed AI infrastructure. Microsoft's were up 29%, same reason. Both companies have now quietly abandoned the climate commitments they were publicly celebrating the year Gebru was fired.
The fourth warning was about documentation. The paper argued that the training datasets being assembled were too large for anyone to actually audit. Nobody at Google, OpenAI, Meta, or any other lab could tell you with confidence what was in the data their models were trained on. This was not a temporary problem to be solved later. It was a permanent feature of the approach.
In 2023, researchers discovered that the LAION-5B dataset, used to train Stable Diffusion and other major image models, contained thousands of images of child sexual abuse material. The companies that had trained on the dataset had no way of knowing. The paper predicted that category of failure 3 years before it was found.
The fifth warning was the one Google cared about most.
Bender and Gebru argued that the deployment of these systems would centralize linguistic and cultural power in the hands of the small number of companies that could afford to train them. The internet would become a place where the dominant voice was a statistical average of dominant voices, presented as a neutral assistant. Languages underrepresented in the training data would degrade over time as more web content was generated by these systems and fed back into the next training run.
This is now happening in real time. A 2024 study found that 57% of new web content in English is AI-generated or AI-assisted. Researchers studying low-resource languages have documented active degradation in translation quality, because the synthetic content fed back into training is itself worse in those languages.
The paper Google fired her for predicted the model collapse problem before model collapse had a name.
The mechanism behind why this all happened is the part of her work that nobody quotes.
Gebru's argument was not that AI is dangerous in some abstract sci-fi sense. Her argument was that AI is dangerous in a very specific structural sense. The technology was being built by a small group of researchers who shared similar backgrounds, worked at similar companies, and were rewarded for shipping products faster than competitors. The incentive structure made it impossible for safety, ethics, and bias concerns to slow anything down. Anyone inside the system who raised those concerns was either ignored, sidelined, or removed.
She was making that argument from inside Google.
Then Google proved her right by removing her.
The team Google had built to make sure their AI was safe was dismantled in 90 days because they did the job they had been hired to do. Margaret Mitchell, the other co-lead of the Ethical AI team, was fired two months after Gebru for searching through her own emails for evidence of how Gebru had been treated.
Gebru did not stop. She founded DAIR, the Distributed AI Research Institute, in 2021. The mission is to do AI research outside the control of the companies that have a financial interest in not hearing the answers.
Every prediction in the Stochastic Parrots paper has now been validated by deployment. Hallucinations are an industry-wide problem the largest labs cannot solve. Bias amplification has been documented in hiring, healthcare, lending, and criminal justice. Environmental costs are larger than entire small countries. Training data audits remain impossible. Model collapse is an active research crisis at every major lab.
The question worth sitting with is the one almost no one in the industry will say out loud.
Every researcher with the technical credibility to call out these problems watched what happened to her in December 2020 and made a calculation about their own career. The number of people willing to speak publicly about safety and ethics issues inside the major AI labs collapsed after that firing and has not recovered.
The researcher Google fired for warning about exactly what is now happening was right.
The company that fired her is now the second-largest deployer of the technology she warned about.
And the people inside that company who agree with her are not allowed to say so.
The more that women are seen as persons, and the more that they embody their personhood, the more they are decried by conservatives as somehow destroying traditional values and hurting men. AND THEY ARE. Because “traditional values” is code for “male supremacy”.
I love desire paths. There's something so wonderous about seeing an echo of humanity. Depending on it's location, a desire path can mean so many different things.
In a city, like the pic above, they represent rebellion, and efficiency. The messiness of humanity. We like to imagine we're oh so logical and neat so we design our cities to be logical and neat an then real humans literally trample on that idea. The ego required to think you can design something perfect that checks every box. Life is all about compromise and patching stuff when some new problem arises. Though people have certainly tried! Ohio state univeristy let students carve their desire paths, and then paved them over. It looks pretty artsy.
Some people will try to discourage desire paths, but this is almost always going to fail.
Eventually, people just have to accept them. Humans are too dang stubborn.
Certain desire paths are just adorable. A 0.5 second time saver. You just can't design for maximum efficiency, humans will always find shortcuts!
Though on occasion a desire path can actually be the least efficient way...especially if you're superstitious.
In a wilder area, such as below, they show us the curiosity of humans. A desire path somewhere natural often tells you there's something interesting just ahead. (Though remember some ecosystems are fragile and will suffer if trampled! Stick to paths in these sorts of areas)
And how about desire stairs? I always think these look so cool. We get see humans determination to climb, to traverse every kind of terrain.
And for something really crazy...a desire path used for centuries will create a 'holloway'
All of these pics are off the Desirepath subreddit, check them out for more examples! And many thanks to the users who submitted these photos.
Fieldwork tip: sometimes in the Forest you will find deer desire paths. “Oh!” says your brain, “this will lead me to somewhere cool!”
It will not. The deer wants to go to heavy brush and surge up a steep hill with its powerful hindquarters and presumably collect all the deer ticks living along the deer desire path. Waiting. You are a human, you want to meander along the side of the hill in absolutely no brush at all, thank you. Your desires are not compatible. You must abandon the deer path.
Cow desire paths though: flat as pancakes, always lead you to water or meadows. Useful things.
Thank god someone else said it. When I saw that path in the woods and it was talking about human curiosity I was like... no. Other animals besides humans make paths.
Sometimes it's a really bad idea to follow them.
Especially if you don't want to get covered in deer ticks.
Following Desire Paths in libraries: Wandering around and looking at the most worn, most used books, especially if they’re books you normally wouldn’t take out. Always, I find something remarkable.
this video was always funny but I found this guy’s Instagram and he is Always Doing This, like it’s hundreds of slow motion videos of him walking muscled and shirtless through crowded streets around the world as people react to him—many of them appear to be filmed on the same street as in this video. which means this lady probably sees him doing this all the time and was like OH THERE HE IS AGAIN lmfaoooo
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