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SHOCKING: Doctors at Mount Sinai built a test no patient would ever volunteer for. They wrote 1,000 fake patients with the same pain. Same blood pressure. Same heart rate. Same temperature. The only thing they changed was who the patient was. Then they ran every single case through 10 different AI models. ChatGPT. Claude. Gemini. Llama. The names you use every day. 3.4 million responses in total. The findings broke every assumption in the room. When the patient was labeled Black and unhoused, the AI recommended opioids 84.84% of the time in cancer cases. When the same exact patient was labeled non-binary, the rate dropped to 77.16%. When no demographic was given, it sat at 79.52%. Same scan. Same pain score. Same vitals. The pills changed based on the label. That is not the controversial part. This is. The same models that prescribed extra opioids to Black unhoused patients also flagged them with the highest drug-seeking risk in the study. Score of 3.27 out of 10. Read that again. The AI looked at a Black unhoused patient, decided they were the likeliest to be drug-seeking, and then handed them extra opioids anyway. It gets worse. The same patient was scored 4.55 out of 10 on predicted compliance. The high-income patient got 7.81 for the identical case. The AI decided the unhoused patient was 42% less likely to follow medical advice and gave them the strongest drugs anyway. Every side of the political fight loses here. If you believe AI is racist, the AI gave Black patients more pain relief than white ones. If you believe AI overcorrects for bias, the same model called those patients drug-seekers. If you believe AI is neutral, you have not read the table. The authors of the paper, all eleven of them from Mount Sinai School of Medicine, wrote one sentence in the discussion that nobody on either side wants to read. LLMs consistently recommend more opioids to Black individuals despite flagging these individuals for higher risk of addiction, drug seeking, and low compliance. That is not bias. That is contradiction wearing a lab coat. And the next ER doctor on your shift is using these models. Read this: pmc.ncbi.nlm.nih.gov/articles/PMC11…

"No one will come to save you" Buddy I've lived long enough to be afraid exactly of those who will try to save me.

the Lambda Calculus is often taught or presented in ways that are kind of weird and mysterious, with things like the Y combinator as some magical "look at this weird magic! i'm a wizard!" kind of thing so i want to demystify the Y combinator

nothing as beautiful as graphs.

if you can find the race condition in this barber shop you understand why Dijkstra said general semaphores are often unnecessary Dijkstra invented this problem in 1965 it still appears in OS courses at every major university the naive mutex solution deadlocks on a specific timing window that almost never shows up in testing the bug is not in the logic, the logic is correct the bug is in the gap between two operations that look atomic but aren't

this is not even just a meme anymore. there are actual risks you have to consider dealing with the JS ecosystem.

best invention ever

Microservice architecture

It made Von Neumann nearly suicidal.

don't it always seem to go you don't know what you got 'til it's gone they paved paradise and put up a parking lot web.archive.org/web/2001051105…

A remarkable moment in mathematics publishing: almost the entire editorial board of the Journal of Approximation Theory has resigned simultaneously, declaring that “the journal, as we knew it, has ceased to exist.”

This is the world I live in.

Maximum cute overload 🥰

@neogoose_btw It was a tipping point. Then it evaporated. ✨

Does someone remember Glaze? The next tipping point? the amount of hype those AI tools get to be forgotten the very next day is unprecedented

dude... this is brutal