I know I already made a post to this effect but it's so baffling to me when someone defends the fact that headphone jacks are slowly but surely getting phased out by smartphone manufacturers with some variations of "wireless headphones are more convenient anyway" bc like. If we're talking about convenience what I like about wired headphones is that they conveniently have a single plug that makes the same damn pair of headphones universally compatible with every single audio-output-capable device I own, from my phone and my computer to my fucking gameboy and my casette player, it doesn't get any more convenient than that.
yeah it's real fucking convenient to use a bluetooth headphone and have it die on me hour 8 of hiking up a mountain during my job. You know what's 1 less device I have to charge and has never died on me during a hike due to lack of charge? A headphone I just plug directly into my device.
i like sewing because you get to think about something really really hard and then you get an object. i imagine authors feel this way over a longer timespan
It lets people access scientific articles for free. This is dangerous. It helps the free flow of knowledge and reduces the competitive edge of all the people who worked really hard to have been born into a wealth.
Like, it’s literally a website where you can type in the DOI of an article and read it, without ever having to pay the publisher who exploited the author.
So, again, do not, under any circumstance, use Sci-Hub. I mean, can you imagine a world where knowledge is free and easily accessible to everyone? Even, y'know, poor people?
Libgen also has many books online, including textbooks, searchable by name, author, and ISBN. Can you imagine textbook companies not getting their hard-earned income from poor college students? Here is the link just so you make sure that you never accidentally stumble across this horrible, unethical website.
Oh, and while we’re talking about books, if you’ve managed to stay clear from Libgen, definitely don’t go to zlibrary, where you can also find a lot of textbooks, but unfortunately they’re completely free.
It took me about 15 seconds in to realize what was happening in this vid, but the second I did, I legit came. This is… I got chills and got so much validation for my theories about tap and pretty much any genre of music here…
They’re tap dancing, a kind of dancing typically associated with being old-fashioned and kind of silly. Personally, even tap dancing to old music is awesome in my eyes, but this is on a totally new and exciting level
The thing about tap is that it’s so often seen as a fancy, old-fashioned dainty dance that only posh (and generally white) people do in tuxedos but it didn’t used to be the case.
Way back in the early days, it was where black performers in Vaudeville were legendary for it in Jazz and Jive routines. At about 1:37, this is where the Nicholas brothers go off.
It’s such an expressive and joyful kind of dance and matches so well with hip hop beats and rhythm, which is why the modern reworking of it is so awesome.
Im sure a lot of people also watch the op video and they assume that “clap” sound is part of the music just because a LOT of modern music samples that sound and in some music it is just the sound of hands clapping, but no that is a sound being made by all their shoes at once.
I am gritting my teeth at the mere suggestion that tap is primarily associated with dainty white people.
Tap is a distinctive American art form that comes from a blending of African dance traditions with Irish dance traditions. It was developed by Black and white dancers and came up alongside and deeply entwined with jazz.
Certainly the tap that ends up in musical theater often seems old-fashioned and white but that’s a musical theater issue, not a tap issue. That is only one small part of tap, which continues to have a strong African-American tradition.
The Nicholas Brothers, above, are in a clip from the film Stormy Weather, which has an almost entirely African-American cast. Some of the other scenes in the film include Bill “Bojangles” Robinson, one of the greatest tap artists of all time. He was very well-known generally and was in quite a few Shirley Temple movies in his day. (Shirley Temple, herself, was a tap dancer – which I’ll be real is probably contributing to people thinking it’s old-fashioned and white, because it’s easy to forget the Black man dancing alongside her, I guess.)
Here’s Bill Robinson with Cab Calloway in Stormy Weather – he’s performing a variation of his famous “stair dance” in parts of this clip:
https://www.youtube.com/watch?v=VY3fbvBRiaM
Here’s probably the most widely famous version of the “stair dance”, from The Little Colonel:
There’ve been a lot of white tap dancers through the years – see, for example, everyone’s favorite clip of two men torturing a speech therapist:
… but a lot of its most famous practitioners have been Black and it’s weird to me that people don’t know that.
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.
today is the ten year anniversary of the Pulse Nightclub shooting. a full decade ago, i lost a friend and a coworker. i was lucky. i had friends that lost several people. today, please remember and fight for all those that have died to live the life they should have been free to. i'll always remember you, Cory.
a new reality tv show called So you think you can write Doctor Who
twelve episodes, twelve contestants - a mix of annoying middle aged sci fi authors, fan fic authors and random people off the street
a variety of against the clock writing tasks, big finish scripts, ability to interact with actors without shouting at them and challenges where you have no budget or doctor for an episode
judged by solely by christopher eccleston
this is how you find the new doctor who showrunner
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