>First, we’ve discovered that about a quarter of all the internet connection in or out of the house were ad related. In a few hours, that’s about 10,000 out of 40,000 processed.
>We also discovered that every link on Twitter was blocked. This was solved by whitelisting the https://t.co domain.
>Once out browsing the Web, everything is loading pretty much instantly. It turns out most of that Page Loading malarkey we’ve been accustomed to is related to sites running auctions to sell Ad space to show you before the page loads. All gone now.
>We then found that the Samsung TV (which I really like) is very fond of yapping all about itself to Samsung HQ. All stopped now. No sign of any breakages in its function, so I’m happy enough with that.
>The primary source of distress came from the habitual Lemmings player in the house, who found they could no longer watch ads to build up their in-app gold. A workaround is being considered for this.
>The next ambition is to advance the Ad blocking so that it seamlessly removed YouTube Ads. This is the subject of ongoing research, and tinkering continues. All in all, a very successful experiment.
>Certainly this exceeds my equivalent childhood project of disassembling and assembling our rotary dial telephone. A project whose only utility was finding out how to make the phone ring when nobody was calling.
>Update: All4 on the telly appears not to have any ads any more. Goodbye Arnold Clarke!
>Lemmings problem now solved.
>Can confirm, after small tests, that RTÉ Player ads are now gone and the player on the phone is now just delivering swift, ad free streams at first click.
>Some queries along the lines of “Are you not stealing the internet?” Firstly, this is my network, so I may set it up as I please (or, you know, my son can do it and I can give him a stupid thumbs up in response). But there is a wider question, based on the ads=internet model.
>I’m afraid I passed the You Wouldn’t Download A Car point back when I first installed ad-blocking plug-ins on a browser. But consider my chatty TV. Individual consumer choice is not the method of addressing pervasive commercial surveillance.
>Should I feel morally obliged not to mute the TV when the ads come on? No, this is a standing tension- a clash of interests. But I think my interest in my family not being under intrusive or covert surveillance at home is superior to the ad company’s wish to profile them.
>Aside: 24 hours of Pi Hole stats suggests that Samsung TVs are very chatty. 14,170 chats a day.
>YouTube blocking seems difficult, as the ads usually come from the same domain as the videos. Haven’t tried it, but all of the content can also be delivered from a no-cookies version of the YouTube domain, which doesn’t have the ads. I have asked my son to poke at that idea.
Seriously, get and run PiHole if you can. It changes your internet experience so much for the better. I get shocked when I visit a website when I'm someone else's network, by just how many ads the internet is flooded with now. Take back control.
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.
Drug arrives years after pandemic’s peak, but could still offer protection to vulnerable populations.
An antiviral pill has, for the first time, been shown to prevent COVID-19 in people exposed to the SARS-CoV-2 virus at home, according to trial results published today in the New England Journal of Medicine1.
The drug could be a lifeline for those who still face real danger from the virus, such as care-home residents or transplant recipients on immune-suppressing medication.
Kinda surreal to see people explain WHY Tumblr is implementing age verification in the UK and Brazil just to see responders argue that it's still all Tumblr's fault for complying. They don't want to imagine that this is anything but a bad UI choice that can be turned around by yelling at social media employees. "What's the government got to do with fascism?", or something like that
Tumblr putting in age verification in places where age verification is now legally required: not much they can do to avoid this. Like, Meta could probably fight it with some chance of success if they wanted to, but Tumblr doesn't have anything like the clout required.
Tumblr moderation aggressively and disproportionately flagging posts by or depicting trans people and people of color as "mature" no matter what the content is? Very much their fault and they should yelled at about it until they are finally browbeaten into fixing it, or the end of time, whichever comes first.
You know how they say you can't hear a picture? Well I just tasted that picture 😂 It's probably been 30 years since I had that and I know exactly how it tastes. With that same damn spoon too.
