My wife pointed out to me the other day that apparently most people were literally only taught how to say Sorry and not how to Apologize or even that they are different things so here.
If all you ever do is step 1, not only did you not apologize, but people will rightfully stop believing you and start getting pissed or hurt.
Like there is a difference between "My trauma creates impulsive urges and has changed my instinctive reactions to be irrational, I need to relearn shit." vs "I'm traumatized so I Can't Help It and you're not allowed to expect better of me"
As a 19-year-old sophomore at Stanford, Elizabeth Holmes decided to transform diagnostic medicine so she dropped out of college and used her tuition money to start her own company, Theranos. Ten years later, Holmes, pictured here holding a micro-vial, is on the cutting edge of medical technology — her new blood testing method allows hundreds of tests to be run using only a few drops of blood. And, Holmes’ methods are cheaper, faster, more accurate, and less invasive than conventional methods which often require a separate vial of blood for every test.
As Holmes told Wired.com earlier this year, “I started this company because I wanted to spend my life changing our health care system. When someone you love gets really sick, most of the time when you find out, it’s too late to be able to do something about it. It’s heartbreaking… We wanted to make actionable health information accessible to people everywhere at the time it matters most. That means two things: being able to detect conditions in time to do something about them and providing access to information that can empower people to improve their lives.”
She is featured as Forbes’ youngest self-made woman billionaire.
Her tests will revolutionize the public health world as we know it; Making diagnostic testing accessible and affordable for more people (and potentially saving Medicare and Medicaid ~$100 billion each over the next decade). (x)
She is a coauthor on 82 US and 189 foreign patent applications. (x)
Her fear of needles served as a motivator for launching Theranos. (x)
Always remember that the EU did a study in 2013 about the effects of piracy on media publishers and found that there is no correlation between piracy and sales! (And then they tried to hide that study bc that's not the result they wanted)
So piracy is at worst not even a problem, and at best it's free advertisement.
Source: (the link to the actual study is in the article)
In 2013, the European Commission ordered a €360,000 ($430,000) study on how piracy affects sales of music, books, movies and games in the EU
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.
art is not my strong suit but this is my best recreation of what i saw when i opened the window. i have to emphasize that she was supermodel levels of gorgeous
Across three preregistered studies, participants interacting with sycophantic AI became more convinced of their own rightness and less willing to repair relationships. Yet at the same time, participants rated sycophantic AI models as higher quality, more trustworthy, and more desirable for future use, which may explain why this behavior has persisted despite its harmful impacts.
Myra Cheng et al. "Sycophantic AI decreases prosocial intentions and promotes dependence." Science 391, eaec8352 (2026).
Perhaps I’m being dramatic, but it almost feels as though the original phrasing (that I see being reflected quite heavily in the comments) focuses on Cheng’s inspiration from AI-generated breakup texts. The article goes much further than that; Cheng and her team clearly spent time acquiring data and then processing it to tell the story of how AI-dependence is fundamentally shifting how people interact with others. This change in human interactions didn’t happen overnight. We are witnessing a fundamental shift in how we interact with other people and a simultaneous diminishing of how long people will spend on any given task. Focusing in on the more click-worthy problem of breakup texts overlooks the underlying issue that, after being discovered, can actually influence policy change as Cheng discusses
You know how wealthy people turn into stupid arseholes by surrounding themselves with vapid yes-men? ChatGPT is vapid yes-men on tap. Now you, too, can subject yourself to the phenomenon that we've all long known turns people into giant toddlers who are impossible to deal with.
Happy migraine and headache awareness month. This may seem like a minor terminology nitpick, but it’s a serious misconception about migraine: nausea is an actual migraine symptom, not a thing that happens because of the headache. When people with migraine experience nausea during attacks, it’s not because the pain is so bad (the idea that migraine is always excruciatingly painful is also a myth), it’s because migraine causes a lot of symptoms that aren’t pain, including nausea.
This goes for other symptoms as well: although pain can definitely contribute to people’s experiences of these symptoms, fatigue and brain fog are actual migraine symptoms, not just pain symptoms.
This is also your friendly PSA that nausea is not a normal tension headache symptom, if you frequently get nauseous with your headaches, you almost certainly have migraine.
Made this post about 15 minutes after the repair guy who fixed the pump on my dishwasher packed up his tools and left, as the dishwasher was whirring along doing my dishes from that morning.
He said the exact same thing, which I did not know before that, so spreading this knowledge.
Putting the term "Catholic guilt" on a high shelf where fandom can't reach it until everyone learns how to identify characters who are very very clearly coded as Protestant.
Start disappointing people and not backing out of it when they are upset, reject feeling ashamed of everything including of yourself, start saying No to things you do not want to do not just things you're scared of, do more of those things you're scared of but wish you could do, make your own plans and execute them, decide to do or not do something without basing it on who will Dislike it.
Free Will takes practice, and the chance of making someone somewhere Slightly or even Very Disappointed In You. But you're an adult and you can't be made to stand in a corner anymore.
The heat’s been brutal this summer but we still gotta eat! If you’re like me, heat might suppress your appetite, and the last thing any of us want is to sweat over a hot stove and oven when it’s already boiling outside. Here are some of my favorite hot-weather recipes to keep hydrated and fed with when the weather is unbearable! (All recipes should be un-paywalled).
NYT’s best gazpacho (lives up to its name!!)
Persian cold cucumber soup (if you like tzatziki you will like this!)
Eric Kim’s cold noodles with tomato (infinitely riffable Korean flavor profile — for a creamier and less brothy take, try these cold sesame noodles too)
Vietnamese chicken and herb salad (this is an excellent time to get a rotisserie chicken so you don’t have to turn the oven on)
Radish sandwiches with butter and salt (and in a similar vein, if you dig this flavor combo you should try this Polish cottage cheese dip on some good rye bread or even crackers)
It’s still a little early in the season, but you can never go wrong with a BLT (or, if you don’t eat bacon, try this tomato furikake sandwich in its place)
Infinite iterations of pasta salad! You can use anything you got but here is a template I like.
Assorted dense bean salads. This back pocket canned salad is a weird combo of jarred ingredients but it slaps, this hoagie-inspired one is super satisfying, and cowboy caviar is a classic for a reason.
Poke bowls! Canned tuna mixed with some kewpie mayo and sriracha is a budget-friendly riff on the usual ahi and makes everything taste like a spicy tuna roll, but use whatever proteins you like, this is more of a loose template.
Please feel free to add some of your own favorite summer recipes in the replies and comments! We’ll get through this together. 🤝🤝🤝
If you can buy pre cooked or canned beets at the store, chłodnik (beet yogurt soup) is a great choice. You can leave out the radishes if you can’t get them.
I also love sardines on fresh bread and butter with assorted vegetables. Buy the fanciest canned sardines you can afford but even regular ones are pretty good. Plus, very healthy!
Anyway, homie is, like all wealthy white dudes, egregiously imperfect, but he’s definitely fucking trying. So in that regard, he’s valuable because he’s reaching audiences that you personally probably wouldn’t be able to reach, and if you find he has specific stuff on topics you need to persuade people on, he’s a valuable avenue.
stormborns
dailybridgerton
yousharknotpass
andulkaphoto
turnnoffyourmind
romaincrisis
prguitarman
gamechangershow
