I’m seeing a lot of people saying this post changed their brain chemistry, and as a neuroscientist I wanted to say yes!!! Yes it does!
Wanting something requires dopamine signaling, but liking something doesn’t.
If you have a mental illness/disorder that affects dopamine, you might feel that you don’t want to do the things that you like. You do still like them. You will appreciate having done them.
Let your likes guide you.
(If you want to read more, here’s one experimental paper about it. https://pmc.ncbi.nlm.nih.gov/articles/PMC5171207/ This theory called the incentive-sensitization theory was originally created to explain behaviors in addiction but can be applied elsewhere as well)
Rewards are both ‘liked’ and ‘wanted’, and those two words seem almost interchangeable. However, the brain circuitry that mediates the psych
Gemini had the fucking GALL to get in my email and summarize a 3-line email, taking up more space than the email did visually.
Hit the “thumbs down.” It’s like, what’s wrong??? Was our summary wrong? Were there offensive words? Thank you for helping us improve our AI tools :)
I selected “other.”
Text box popped up. Please elaborate!
Wrote in “I can fucking read” submit comment
Then had to spend several minutes torching all my settings with a flamethrower. Let me be clear: I’m (a lawyer) notoriously picky with my words FOR GOOD REASON (lawyering) so I overwhelmingly reject Gmail’s “helpful” little assistance. My privacy settings were set to “full paranoia” a little less than a year ago when I saw the writing on the wall and knew public defenders could become a target in the future. Better to lock it all down now.
Gemini had crept in there and turned ALL that shit back on. And showed itself by saying “Jane Doe says she’s so sorry for your loss and offers to reschedule for Thursday at 3” over an email from Jane Doe saying “I’m so sorry for your loss. We could reschedule for Thursday at 3?”
Why would I possibly need this. In what universe would I need this. I have eyes and a brain and a reading speed that twenty years ago was measured at 1500 wpm with full comprehension on dense scientific text. Furthermore! If I read a summary, I’m not reading what they actually wrote. If I’m not reading what they actually wrote, I’m not using my own judgment on the words and phrases that they used.
I literally don’t understand why this is helpful at all. This is just avoidance. Using LLMs to write is specifically Not Writing. Using LLMs to summarize is Not Reading. Using them to make art is Avoiding Making Art. Just READ! Just WRITE! I was not put on this fucking planet to not read and not write and not make art! Avoidance is an anxiety symptom and indulging it gives it more power.
If I had an AI to do my most dreaded task, answer the phone for clients, I wouldn’t use it. Because an AI cannot help them. An AI cannot hear the facts of their case, make appropriate noises, be thoughtful and insightful, and then give them a realistic estimate of what could happen in court. I am unique. I cannot be replaced by machine learning. I have style. I have expertise. I don’t hallucinate unless I’m having a really great Friday night and I’m off the clock.
When I need to outsource tasks from my own brain, I give them to people I know can do them and that I trust to do them right.
Fuck, it just sneaks up on you, doesn’t it?? Goddamn Gemini jumpscare right in my own fucking email
During my last re-read of The Lord of the Rings it really sunk in for me how often the protagonists encounter not only danger and betrayal, but unexpected help and friends in unlikely places. Tolkien had a sojourner's heart and said yes, we may be small, but be encouraged. Evil always tries to make itself look bigger than it is. Keep faith with ordinary goodness. Never underestimate the power of simply doing what is right and kind, against the convoluted machinations of evil. The gates of Mordor will not prevail against it.
During my last re-watch of The Return of the King it struck me how ugly and stupid evil is. Kudos to PJ and Co for not aestheticizing the baddies, even resisting the temptation to make Sauron sexy. The Witch King is scary but without substance, defeated by a depressed girl and her pint-sized bestie. The army of Mordor is huge in number but quakes at the sound of Rohan's arrival. They can't even keep a crown of flowers from forming around the fallen statue of a king, only replace his head with a dumb rock and scraps of rusty, twisted metal. The Dark Lord is powerful and dangerous, yes, but he's not all-powerful and he's not infallible. Even his great burning eye is focused in all the wrong places. He uses smoke and mirrors to impress and corrupt Saruman, and to drive Denethor to despair—Denethor, who could have welcomed home the King. (Instead, he wallows in grief, capitulates to fear, and grows bitter in grumbling over Rohan's presumed betrayal. Note how this parallels Gollum instilling suspicion and doubt in Frodo regarding brave and loyal Sam.) The enemy is a liar and a deceiver, and Aragorn knows this when he silences the Mouth of Sauron and says, “I do not believe it. I will not!”
And I want to emphasize this point, this rebuttal of Sauron's divide and conquer tactics: The Fellowship gets weakened. It suffers losses. It becomes scattered across the larger battlefield. But its members remain true to each other, and to their shared mission, even when they find themselves parting ways to accomplish it. At the end of the first film, Aragorn tells Frodo, “I would have gone with you to the end. To the very fires of Mordor.” And where is Aragorn, at the end of the last film?? The gates of Mordor, with the remaining members of the Fellowship (and some new friends, too), exactly where Frodo needs them to be at that moment. No one expects to survive, no one can guarantee victory, and no one but the audience sees the tiny bud on the Tree of Gondor, hope blooming in response to faithfulness even while the sky remains overcast and the city lies in ruins around it.
To repeat my previous tags: #I've said this before and I'll say it again #the devil WANTS you to feel overwhelmed and already defeated and like the small acts of everyday love and faithfulness aren't enough #but in fact each one is chipping another stone out of the foundation of his dark tower #and from your vantage point you may not see the people chipping stones on the other side (but they are there!) #you don't need an extensive understanding of architecture to bring it down #just the willing hands of a hobbit
Always remember that the EU did a study in 2013 about the effects of piracy on media publishers and found that there is no correlation between piracy and sales! (And then they tried to hide that study bc that's not the result they wanted)
So piracy is at worst not even a problem, and at best it's free advertisement.
Source: (the link to the actual study is in the article)
In 2013, the European Commission ordered a €360,000 ($430,000) study on how piracy affects sales of music, books, movies and games in the EU
This is one of those cases where it's not clear what this feature is doing because Microsoft is deliberately obscuring what it does, but there's something I deeply dislike about Microsoft learning to correct your mistakes so that you yourself do not learn to fix them. Underlining a mispelled word with suggestions is one thing. Auto-fixing them without your notice is not helping you improve in any way.
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.
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