nimble, a border collie-papillon mix, wins the 12” class in the 2024 masters agility championship. the first time a mixed breed has won at westminster ever.
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The other day on shift, I was walking down the hallway when a confused old man started yelling. This is fairly common in the hospital, so I ducked into his room and was like "hey dude what's up" and he's like "so sorry to yell but do you know where I am and what year it is" so I reoriented him to a bunch of stuff and explained why so many people were walking past his door, and he seemed much calmer and more oriented by the time we were done talking. And he says "and what's your name?" So I tell him "Sarah" and he stares at me so I repeat "Sarah" and he stares at me so I spell "S-A-R-A-H" and he stares at me so I show him my name badge and he stares at me and then he says "now forgive me for being so blunt. But I was under the impression that Sarah is a girls name. Is that no longer correct?"
Anyway y'all ever be so fucking gnc that you inadvertently gaslight a confused old man into thinking there's yet another part of the world he no longer understands?
Time for a post that doesn't comply with the gimmick...
So, companies are tracking you no matter what you do, but this specifically is something I care about deeply.
Essentially, when you share a link, sometimes it has extra data that tracks where it comes from and goes.
Opening Spotify, clicking the first item, and using the share button, I get something like open.spotify.com/track/4P0f1HTaA2UwtLJGryNgJZ?si=DBvbfihOSweU1KHj9Mib8w
That ?si=...........…. is the tracker. It tells Spotify who clicks on the link and ties it to you, meaning Spotify knows who your friends are even if you never follow them.
Similarly, if on Amazon or EBay in the browser, I get something like www.ebay.com/itm/146493392451?_skw=lenovo&itmmeta=............&hash=item................&itmprp=enc..............
And, similarly, everything after the question mark is tracking you. I had to blank it out because it was so long!
There is an exception for a few things (I.E. the v=..... on YouTube, but not the si=..... on YouTube)
The general rule is delete everything after the ? and if it breaks, add something back.
Firefox users, when you right-click to copy a URL, it will give you the option to "copy clean link" which does what prev describes for you automatically. 10/10.
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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.
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I love when little creatures who are entirely loved and well cared for have the BIGGEST baby reactions to normal things. Like yes sweet pea, you DO have the hardest life of anyone ever, for sure, and you’re SO BRAVE about this minor inconvenience of *checks notes* having some water touch you
There is nothing sadder and more pathetic than a baby marine mammal having to get into the water. They are suffering the most out of any baby animal ever. How dare they be introduced to their natural environment.
I saw this on insta and someone commented asking her how she knew they were in there and she said that she saw the mama duck with only one duckling and thought it was suspicious so she stopped to check and hear them quacking down there... :') <3
I hope there’s an afterlife so that whoever made this pot 2,000 years ago can brag that their cookware is so good it’s still usable literally millennia later. Something about this object being lost for centuries and then rediscovered, and being put (successfully) to its original purpose again is so pleasing to me.
When Tess Morgan's son came home with a tattoo, she was griefstricken. She knew her reaction was OTT (he's 21) but it signalled a change in their relationship
This is gold this, absolute gold, the most over the top melodramatic hysterical ridiculous thing I’ve ever read
This is actually so interesting to read- it’s from 2012 but its full of the same anxieties, even some of the same phrasing that many of the guardian’s later pieces on transness use. really hammers home how much of the terfism that emerged in the late 10s was middle class mothers angry at a loss of control over their adult children- whether that be their bodies or their friends or their opinions- and making that everyone’s problem because they have the power to do so
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