Disease Ecology, Environmental Justice, and Epidemiology. I mostly reblog shitposts that make me laugh. He/Him demi-bi/pan. Expect a lot of content that relates to death here.
I want to add a possible interpretation in addition to what the alt text has, and that is breaking cycles from previous generations. In past comics, August has implied that his relationship with his dad was not good, probably for reasons of undue criticism and such. One could read the hesitance after an incredulous "you don't already know?" as catching himself perpetuating that cycle unconsciously and then deciding to break it by teaching his kid without judgement.
If I was a necromancer I wouldnt be raising undead armies to conquer the world, thats how you get adventurous heroes coming to stop you.
I would be raising the dead to help the local villages by taking over the labor tasks they dont wanna do. You provide your dead, and in exchange my skeletons will clean your houses and work the fields for you. Thereby ingratiating myself with the local towns and thus they will shield me from any asshole adventurers trying to stop me from my experiments.
Yes I still plan to overthrow the king but that's because I love the proletariat
In the context of anticapitalist action and the reclamation of necromancy as a tool of the proletariat, you've got some history to overcome as well. The Haitian zombie as a concept implied an unwillingness from the person resurrected by a sorcerer (be they the corporeal form or the spirit enslaved for other spells); as such, the enslaved peoples of Haiti were frequently intimidated by slave owners with zombification as punishment and a promise that even death wouldn't release them. To effectively use zombification for the benefit of the proletariat you must do the work of rectifying this history of imperialist abuse that sits at the foundation of capitalism, lest the tools of your enemy rot your movement before it starts.
the amendment that "ended" slavery specifically left permission for slave labor as punishment for convinced criminals and our inmates have been legally exploited ever since, yes
It's no coincidence that the people responsible for "catching criminals" in the US today (cops/sheriffs) are built upon the organizations meant to catch escaped slaves (slave catchers/sheriffs). Correct me if I'm wrong but a sheriff's ability to deputize someone is based on the legal precedent of collecting a militia (to track down slaves).
H P Lovecraft may have been a cranky racist hack, but we mustn't let his proclivities taint our appreciation of pretentious verbosity. Sometimes "cyclopean" is in fact the word we're grasping for. Sometimes it's even "rugose".
Frother Moths: when these moths feel threatened, they secrete a frothy yellow substance that is noxious and distasteful to predators
Above: Amerila astreus with two large globs of defensive froth
Moths of the genus Amerila are often described as frother moths, because they're able to secrete a frothy, foul-tasting substance that helps to deter predators. The fluid is produced by the prothoracic glands, which are located near the base of each wing (just behind the moth's head) and a distinctive "sizzling" or "hissing" sound is emitted as the frothy substance bubbles out.
Above: Amerila astreus
As this article explains:
If molested, resting adults produce quantities of a frothy, orange fluid from their prothoracic glands, accompanied by a sizzling sound. The froth not only has an aversive odor to humans but also contains PAs (pyrrolizidine alkaloids) which are taste-repelling.
Above: a frother moth producing its defensive secretion
The adult moths of this genus are pharmacophagous, which means that they acquire the chemicals that are used to create their defensive froth by ingesting plants that contain toxic or noxious compounds; those chemicals are then sequestered within the moth's own body, where they are repurposed and transformed into a frothy secretion.
Above: genus Amerila
Frother moths also use aposematic markings to signal their toxicity and/or unpalatability. Their legs, thorax, and abdomen are all decorated with reddish-pink markings, and they have large black spots that stand out against their mostly-white bodies.
Above: Amerila crokeri and Amerila rubripes
The genus Amerila contains dozens of species, and they are all known to have this defense mechanism. They are widely distributed throughout many different parts of the world; depending on the species, they may be found in the Himalayas, Indochina, Southeast Asia, Melanesia, Australia, or Africa.
Above: Amerila crokeri
Several other moths from the subfamily Arctiinae can produce a similar defensive secretion when threatened, but the color, consistency, and composition of the substance differs greatly from one species to the next.
Sources & More Info:
iNaturalist: Genus Amerila
Metamorphosis Australia: Australian Arctiid Moths
Australian Lepidoptera: Amerila crokeri
Metamorphosis Australia: Weird and Wonderful Moths
Entomo Brasilis: Defensive Froth in Arctiidae Species in the Rio Grande do Sul State, Brazil (PDF)
Moths of Australia: Adult Adaptations for Survival
Neotropical Entomology: A Fieldwork-Oriented Review and Guide to PA-Pharmacophagy
Pick one of James Baldwin's works and read it!!! The Fire Next Time is an excellent essay, most of us are familiar with the quote on gay white people from The Last Interview but not the rest of it. If Beale Street Could Talk even has a movie!
alright I've got to do some quick math to explain attitudes towards AI to my boss.
we're looking to create an AI policy, and when we were talking about this, my boss (older millennial) was genuinely shocked to hear that younger people do not (seem) to view AI positively (a la the recent commencement speakers being booed)
please rb for larger sample size!
Question 1/3
What is your age, and do you feel AI is a net positive or net negative in our lives today?
given the current climate this pride especially i feel i must mention that i love my trans friends, i stand with trans people in the fight against transphobic legislation and those who would enforce it, and this blog is not a good place for you to be if you do not vibe with 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.
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