1. The court holds Google responsible for statements made by its AI, considering them Google's statements (search engines have limited liability for results in their engine as they're the words of other sites/companies/people), meaning when their AI lies/hallucinates they're liable for the defamation/harm resulting from those statements.
2. Google's defense that customers are generally aware of the lack of reliability and are responsible for fact checking was dismissed. As the court pointed out, that would "significantly diminish" AI Search's stated purpose and it can't be distinguished from Google's business practices/statements as a search tool.
3. Studies have found about 91% of Google's everyday AI responses are accurate, leaving millions of searches per HOUR with potential liability for falsehoods. 56% of correct responses weren't supported by the sources the AI listed. Both of which mean Google is now liable for a LOT more AI "errors."
4. Google was held liable for 80% of court costs in this case and this precedent is expected to reverberate around the world. This is a massive shift from the 3rd-party search provider role Google has previously played and it comes right as they've tied ALL searches to their AI search.
i will never be over the fact that during first contact a human offered their hand to a vulcan and the vulcan was just like “wow humans are fucking wild” and took it
#iiiiiiiiiiiiii mean vulcans had been watching humans for a long time#they knew the significance of a handshake but still#they had to find some fast and loose ambassador#willing to fuckin make out with a human for the sake of not offending them on first contact#lmao#star trek
give me the story of this fast and loose vulcan
“sir…these…these humans…they greet each other by…” *glances around before furtively whispering* “by clasping hands…”
*prolonged silence* “oh my…”
“sir…sir how will we make first contact with them? surely we…we cannot refuse this handclasping ritual, they will take it as an insult, but what vulcan would agree to such a distasteful and uncomfortable ritual??”
*several pensive moments later* “contact the vulcan high command and tell them to send us kuvak. i once saw that crazy son of a bitch arm wrestle a klingon, he’ll put his hands on anything”
I swear Vulcans only come in two types and they are “distant xenophobes” or “horny on main for humanity”. Also apparently this guy is Spock’s great-grandfather and frankly that explains everything.
Hey so I looked into this at one point and that handshake literally created a lifelong telepathic bond between the two of them, and basically all of Solkar’s descendants were later obsessed with humans, including freaking SPOCK, so I’m not saying that handshake was so gay and good that it created an intergenerational telepathic bond between Solkar’s descendants and humans, but I’m also not….not….saying that.
The slow deliberation with which Solkar takes Cockrane’s–I’m sorry, Cochrane’s–hand… The sheer sensuality witch which Solkar infuses an otherwise borderline impersonal social ritual… It clearly shows a very conscious knowledge, on Solkar’s part, of what the significance of the handshake is in Vulcan terms and of how affected he is by it.
That’s why he’s so slow in doing it, and so sensual. A part of Solkar can’t believe this is happening, despite it being a perfectly logical thing to expect from a human, and the rest of him can’t believe how good it is.
I bet that if the camera zoomed in any further we would see the dilation of Solkar’s pupils and a quickly-repressed shiver of delight. Cochrane’s firm, businesslike clasp is probably (in sexual terms) being perceived as a deliciously carnal display of dominance.
No wonder Solkar is all like, “TAKE ME, YOU WILD-MANNERED BARBARIAN WITH ENTICINGLY ROUGH CALLUSES.”
#somehow the idea of vulcans being Horny On Main always gives me the giggles#like literally all they had to do#was be like actually#hand contact is very intimate for our species#and im p sure humanity as a whole would not find that insurmountably weird#there are human cultures that dont shake hands#vulcans are logical enough to think that through on their own#so clearly that vulcan was just down to fuck#down to fuck in a public#professional diplomatic situation no less#and he did not fucking care who knew it (via kittykatthetacodemon)
Pre-menstrual depression is always depicted as like "He He! I had a box of icecream bars and cried while watching the Titanic!" But in reality, it's more like, "I'm standing the edge of an abyss. There is nothing good inside of me, I'm filled with rage and desperation."
It's crazy that being told how to deal with that is never a part of anyone's menstrual sex education.
This has already been said in the notes, but if PMS causes extreme depression and even suicidal ideation, that is in fact something that most people do not experience and it can be treated
Like for the majority it really is "oh i'm hungrier and moodier than usual"
^this should be a part of sex education so the point still stands
I went to my doctor after I was walking to work one morning and saw a bus coming and actually took a step to throw myself in front of it before I pulled myself together. Later that day I started bleeding and was literally like someone flipped a switch and I didn't feel suicidal anymore. Which made me feel like I was loosing my mind because who goes from 'I want to throw myself in front of a bus' to 'I'm perfectly fine' just like that? I did some research, I went to the doctor and described my feelings, he looked me in the eye and gently asked what I thought it was, I said I'd read about PMDD and I thought it might be that, he said 'I think so too' and wrote a prescription.
If, before you get your period, you feel furiously angry, suicidal, irritated by every tiny thing to the point you want to murder someone, stuck in a black hole you'll never escape from. If you are experiencing extreme emotions for what seems like no good reason, especially if you get your period and those extreme emotions just go away. You're probably not just PMSing , you may have PMS's feral big sister PMDD and it's treatable.
Also this is something that can develop as you get older. So if you used to get normal PMS but what I wrote above sounds more like your norm now then don't just write it off as regular PMS.
what people don’t understand about how adhd is disabling is that it’s not just getting temporarily distracted from, like, school work or hobbies. it’s getting distracted/being unable to motivate yourself to go to the doctor, eat regularly, do hygiene tasks, etc. it’s not knowing when or how long it will take you to do something, ANYTHING, and in many cases that thing is taking a shower or keeping your house from turning into a biohazard. it’s about being fundamentally incapable of controlling your attention and focus on anything, even and especially things you need to do to survive.
Can everyone who makes video content do a Deaf bitch a favor? Watch your shit with the captions on and the sound off, and then do another round of editing to fix things including but not limited to:
Captions cover the spot on the screen you put the information I need
The dialogue is captioned but not the song you have playing that the dialogue is responding to
You only captioned the person on the screen, not the person off screen who is also talking
No captioning of critical sound effects (alarms, bells, dogs barking, etc)
Speakers are not labelled at moments where it is not clear on the screen who is talking.
Captions cover the spot on the screen that you put the information I need!
Other d/Deaf people welcome to add.
This post brought to you by the fifth video tutorial I could not follow because the bad, auto-generated captions covered what I was trying to watch today.
This is something you may see on hot days - this Blue Jay is not injured, it is taking a sunbath. It is done for skin care and grooming and helps with parasites. I always love seeing it because it feels like they have to feel perfectly safe when they do it.
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.
