Master of Insect's Blog

[janmisali]

oh I know how to make a poll's results look like the letter E watch this

what is the rightmost digit of the number of responses this poll has right now? (it should be visible before you vote.)

0, 1, or 2

3

4 or 5

6

7, 8, or 9

Remaining time: 5 days 22 hours

[liberalsarecool]

"That’s what makes Zohran Mamdani’s election in New York so unsettling to the old order. New York City is not just another municipality; it’s a sovereign-scale entity. Its population surpasses 38 states. Its metropolitan GDP trails only Texas and California.

It is, by any metric, a small country masquerading as a city.

It governs more lives and more wealth than most nations. If democratic socialism — housing reform, public banking, equitable taxation — functions here, it obliterates the myth that such governance can’t work at scale. The fear isn’t ideological. It’s empirical. Because if Mamdani can keep the lights on, reduce homelessness, and maintain economic growth without catering to Wall Street, then the capitalist gospel collapses under its own dead weight.

What terrifies the establishment isn’t failure. It’s feasibility.

If it works in New York, there’s no reason it can’t work in Nebraska. If it works in Queens, it can work in Kansas City. And once proof exists, belief becomes irrelevant. The ship of democracy, fully refitted, will keep sailing — and no one can claim it isn’t American."

- Jackie Summers

[stra-tek]

Poorly aged things.

[aryasrandomthoughts]

IF BUYING ISN'T OWNING

PIRATING ISN'T STEALING

https://drive.google.com/drive/folders/1h-I3udUxdjoo43NWBVyLDr-iSj_KPzC-?usp=drive_link

[odyver]

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?

under 18, AI is a net positive

under 18, AI is a net negative

18-29, AI is a net positive

18-29, AI is a net negative

30-45, AI is a net positive

30-45, AI is a net negative

46-60, AI is a net positive

46-60, AI is a net negative

over 60, AI is a net postive

over 60, AI is a net negative

Remaining time: 3 days 23 hours

[odyver]

Question 2/3

How often do you visit or interact with museums/archives (whether in person or online)?

Frequently (multiple times per month)

Often (multiple times per year)

Occasionally (a couple times per year)

Rarely (once every couple of years)

Never :(

Remaining time: 3 days 23 hours

[odyver]

Question 3/3

If you saw a museum was using AI in exhibits, marketing, research, etc., would you be more or less inclined to visit that museum?

under 18, more inclined

under 18, less inclined

18-29, more inclined

18-29, less inclined

30-45, more inclined

30-45, less inclined

46-60, more inclined

46-60, less inclined

over 60, more inclined

over 60, less inclined

Remaining time: 3 days 23 hours

Thank you for helping with this data collection. Please rb for as big a sample as possible!

🫶

[catchymemes]

[butt-berry]

Metagross

[rohirric-hunter]

Somebody at work keeps adjusting one of the perimeter cameras to have this beautiful artistic angle on the museum in a historical building across the way. The sun sets just behind it and the whole sky turns golden-blue, clouds streaked across the sky above. The lush tree line beneath the museum is perfectly lined up along the rule of thirds and the building itself towers above, almost mythical in its evening glory. Like damn, take a still from this camera and send it to the museum to frame and hang on their wall. I do need the camera to be pointing at the parking lot. Tho

[rohirric-hunter]

The setting sun bounces off the skyscrapers downtown and hits the museum's windows and every one of them turns the same golden hue as the sky behind, reflected in the trees just starting to turn golden-orange beneath. The bottoms of the clouds take on the slightest tinge of purple and birds circle above, speckling the evening sky as they call autumn's last farewell. Someone's car got broken into in the parking lot last week, Tammy, point the damn camera at the cars

[deerfishing]

[doyoulikethis-videogame-song]

Visit Original Link

Do you like this Video Game Song? #3102

I like it and I've definitely heard it before

I like it and it sounds familiar

I like it and this is my first time hearing it

I don't feel strongly about it or have a complex opinion

I don't like it and I've definitely heard it before

I don't like it and it sounds familiar

I don't like it and this is my first time hearing it

Voting ended onJun 6

[pikminjake]

Goss Harag! Made of LEGO! Custom model by me with instructions for my supporters HERE!

I am slowly getting the rest of Rise's roster built and prepared for the collection. Just Almudron and Tetranadon are left!

Goss is a funny monster, and out of all the silhouettes I wanted to nail- the important one for me is the gangly abominable snowman look.

His lower body went through many revisions because people kept telling me to make him fatter. Thanks for all the support everyone! I got many more monster builds on the way!

[aubtala]

ink animation based on my two rabbits and their ways of interacting

[horriblewarlock]

Visit Original Link

[esoanem]

[brucebocchi]

two years later and BIG MEATY MEN SLAPPIN MEAT is still the funniest thing that’s ever happened

[creaturelingart]

FINALLY got good photos of my sculpture final. This is my dog his name is Sock.

[creaturelingart]

Look at my fish.

[justalurkr]

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.

[irispurpurea]

This is the paper. It's excellent, highly recommend reading it.

I remember reading about Gebru's firing but I had no idea this was the paper she was fired over.

[durnesque-esque]

Just a quick grab for some of what's being discussed:

"4. UNFATHOMABLE TRAINING DATA The size of data available on the web has enabled deep learning models to achieve high accuracy on specific benchmarks in NLP and computer vision applications. However, in both application areas, the training data has been shown to have problematic characteristics [18, 38, 42, 47, 61] resulting in models that encode stereotypical and derogatory associations along gender, race, ethnicity, and disability status [11, 12, 69, 69, 132, 132, 157]. In this section, we discuss how large, uncurated, Internet-based datasets encode the dominant/hegemonic view, which further harms people at the margins, and recommend significant resource allocation towards dataset curation and documentation practices."

"6. STOCHASTIC PARROTS In this section, we explore the ways in which the factors laid out in §4 and §5 — the tendency of training data ingested from the Internet to encode hegemonic worldviews, the tendency of LMs to amplify biases and other issues in the training data, and the tendency of researchers and other people to mistake LM-driven performance gains for actual natural language understanding — present real-world risks of harm, as these technologies are deployed. After exploring some reasons why humans mistake LM output for meaningful text, we turn to the risks and harms from deploying such a model at scale. We find that the mix of human biases and seemingly coherent language heightens the potential for automation bias, deliberate misuse, and amplification of a hegemonic worldview."

[sommeranimation]

I’ve fallen in love with her. She’s everything to me.

[lastoneout]

The most recent Android update making it impossible to actually disable any AI features no matter what you do is surely a sign that this technology is the future and about to take off and make everyone SOOOOO much money that all the nay-sayers will be sobbing about how they were fools who should have listened and invested. Everyone knows you have a winning product if you have to uh *checks notes* trick and/or force people to use it against their will. Great job, everyone!

[pikminjake]

Just got the instructions out for my custom LEGO Shogun Ceanataur from Monster Hunter 2 Dos! One of my favorite monsters! Want to build one yourself? Support my project HERE to learn more!

So to explain the Cirno (Touhou Character); Back when I used to make Monster Hunter Video Essays (like the Monster Hunter 2 video), I would often (half jokingly) cite a picture of Cirno next to a crab in a strange desolate location:

To me, in many ways, this picture represents Monster Hunter's core aesthetics- the mix of cute flashy characters in an extremely hostile environment with giant monsters.

I've also got instructions for the variant models, Terra Shogun Ceanataur and Rustrazor Ceanataur!

Thanks for all the continued support everyone! I got many more models on the way soon!