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. 🤝🤝🤝
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.
Photos by Li Weilin ~ 李伟林
~ "When the persimmon trees bear fruit" (cropped and whole images), Yanxi Village, Songyang, Zhejiang Province, China.
~ Persimmons and dwelling (title unknown),
via Xiang Deng's 'Autumn Scene ~ 秋景' set, in a Pinterest picture collection. The Chinese characters printed vertically on the photo:
伟林 look like 'Weilin' from the photographer's name.
Those of you who notice such things will know that in a little less than two weeks, it's going to be a year since @petermorwood departed this plane of existence.
As the date's been getting closer, I'm becoming increasingly clear (due to mood swings and sleep disturbances and other such stuff) that it would be a really good thing for me if I don't have to live through this first anniversary of his loss anywhere near the house where he breathed his last.
For that day, and ideally a couple/few days on either side of it, I really need to get away from here.
So that's my plan, if I can get all this to come together (with everybody's help). ...It's not like any brief escape will get the pain to stop, you know? That's years away... if ever. But at least this move will prevent a short-term crisis, and allow me (after the really painful day has passed) to start getting back to what around here now passes for "standard operating procedure"—meaning writing, and doing other work, and getting on with the rest of the current form of life—as quickly as possible.
What I have in mind is to spend the days on either side of May 9th—and the day itself—as far away from the cottage as physical issues will allow me to travel. Let's think of it as a long weekend, an hour or two's flight away. To manage that, though, I need to boost sales at the Ebooks Direct store over the days to come. If the necessary funds manifest themselves over the course of this week, I'll have time to make the necessary arrangements.
So can I get those of you who see this post to reblog it, and bring the Ebooks Direct store to people's notice as widely as possible? ...As numerous ebook bundles are available at discount prices. (There are more than show in the slide below: that's just a snapshot of how the front-page carousel looks.)
...And if none of these appeal (or if you've got them already and want to give them to somebody else): hey, there are gift cards! (I finally managed to get these things organized correctly...) 😅 They come in per-bundle versions, or in a number of cash values to suit your preference.
Finally: if you've already got too many ebooks, or otherwise just prefer to drop a little something into the kitty to help me escape for a few-ish days, here's my Ko-Fi.
Support Diane Duane
...So let's see if this can be pulled off. And for all your past help, and assistance to come: thanks, friends. I appreciate you so much... as your voices, heard daily, are pretty much all that makes the local silence bearable.
A little tiny microscopic dragon, rotifers passing by.
I've spent a lot of time peering down a microscope in the last few years, enjoying taking inspiration from the real tiny organisms to make one of my own.
annaxmalina
