Replacing physical buttons and controls with touchscreens also means removing accessibility features. Physical buttons can be textured or have Braille and can be located by touch and don't need to be pressed with a bare finger. Touchscreens usually require precise taps and hand-eye coordination for the same task.
Many point-of-sale machines now are essentially just a smartphone with a card reader attached and the interface. The control layout can change at a moment's notice and there are no physical boundaries between buttons. With a keypad-style machine, the buttons are always in the same place and can be located by touch, especially since the middle button has a raised ridge on it.
Buttons can also be located by touch without activating them, which enables a "locate then press" style of interaction which is not possible on touchscreens, where even light touches will register as presses and the buttons must be located visually rather than by touch.
When elevator or door controls are replaced by touch screens, will existing accessibility features be preserved, or will some people no longer be able to use those controls?
Who is allowed to control the physical world, and who is making that decision?
Design? nonsense, how can you write so! But it is very likely that he may reblog from one of them, and therefore you must follow him as soon as his blog is online.
I see no occasion for that. You and the girls may follow him, or they may follow him by themselves, which perhaps will be still better for them, for as you are as clever as any of them he might reblog most from you of the party.
My dear, you flatter me. I certainly HAVE had my share of cleverness, but I do not pretend to be anything extraordinary now. When a woman has five grown-up mutuals, she ought to give over thinking of her own reblog counts.
studying history is like. here's to another beautiful day of not being pregnant and of having no obligation to ever be. thank you women who fight for abortion and contraception and independance from men for another beautiful day of not being pregnant and of having no obligation to ever be
thank u. i hate it a little less but the horrible little man in my head is still screaming âBOG BODY BOG BODY BOG BODYâ, but i appreciate the education,
oh here is a fun lil perspective on cranberry harvesting i never heard about anywhere else. the guy who owns the restaurant right down the road from the farm, who fries our chickens sometimes, is from Boston, with the strongest Boston accent ever, and in a former life before he started slinging reasonably priced barbeque and occasional organic chicken, he was a cranberry farmer.
His farm was on the leading edge of kinda using organic/sustainable pest control methods, and one of the things that they did to keep insect damage down was that they encouraged wolf spiders to live in the cranberry field, to eat the bugs.Â
This was all fine and good until they flooded the bog. Now, you donât just like flood the bog and then go around it in a boat or whatever. No, you use hip waders to get in there and put the big floaty things where they go and get all the berries and such.
Well when youâre in the bog in hip waders, that makes you the tallest thing. Wolf spiders can swim a bit, but they donât like it, so theyâre, quite understandably, looking to climb out of the water onto a tall thing.
So yeah the first interview question he always asked potential cranberry bog harvester hires was âare you cool with spiders?â
âYouâd be amazed,â he said to us, shaking his head a little, âhow many guys would just straight lie. Like, you think Iâm asking you that question to be cute? Nah man youâre gonna have like a hundred wolf spiders trying to climb your eyebrows, you gotta be chill, those wolf spiders are fellow employees. You really gotta be chill with spiders if youâre gonna work a cranberry harvest.â
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.
I also think that the strength gap is at least partially manufactured women would in fact be stronger overall if little girls were encouraged to do physically taxing games and activities and eat their fill while theyâre growing vs having to constantly diet and be sedentary indoors (or god forbid do intense cardio while under-eating). The amount of adult women honestly afraid to lift weights bc they think theyâll get bulky as though bulking isnât a full time job that athletes have to spend all their time on and anyone on earth gets shredded from just using their adult muscles for their intended purpose, girl your bone density đ„
if you say women are intentionally nerfed from birth in 2026 people look at you like youâre insane and start condescendingly telling you about how women are just better at different things (but not during their periods haha) but this was a completely basic feminist talking point I grew up with like âgirls can do it too! [shot of little girls climbing and running with boys]â nickelodeon commercial tier base level I hate it how is everyone suddenly dumber than the average 7 year old
Was driving with my grandmother and in broken English she says âno eyes⊠no nose⊠no face. Donât trust.â To which I looked around wildly in search of this omen of ill portend.
my knight you have to live you have to get up you have to put your hand over your wound and hold it there. you have to keep walking and walking and walking because you cannot lay down yet, itâs not time. wipe the blood off your breastplate and look up into the sun. lean on your sword if you need to. lift one foot after another. get up. get up. this would be a pitiful grave.
not to be insensitive but some of the salem witch trials were so funny bitches like âi saw her at the devils sacrament!!!â girl... what were YOU doing at the devils sacrament đ
