Patricia Cummings, PhD, MPH

Epidemiologist specializing in chronic and infectious diseases.📈 @UCLAFSPH PhD, @USC MPH.

[Patricia Cummings, PhD, MPH @epidemicdoc]

#Cyclospora

[Wall Street Apes @WallStreetApes]

“An outbreak involving a parasitic infection (called Cyclosporiasis) is, quote, large and growing in Michigan, where more than 300 cases have been confirmed, and the CDC is monitoring cases in 17 other states”

There have been 145 more cases in the US across 17 states with 20 hospitalizations

Cyclospora cayetanensis is a microscopic parasite. It spreads via contaminated food or water. Often by fresh produce like cilantro, basil, raspberries, snow peas, or leafy greens

- Main symptom: Severe, watery and sometimes explosive diarrhea
- Others: Loss of appetite, weight loss, stomach cramps, bloating and gas, nausea, fatigue, low-grade fever, body aches

Symptoms can last weeks

[Massimo @Rainmaker1973]

For decades, endometriosis has been one of the most mysterious and debilitating conditions affecting millions of women worldwide, causing chronic pain, infertility, and reduced quality of life. Now, a major study suggests that a common bacterium may play a key role in driving the disease.

Researchers identified Fusobacterium (particularly Fusobacterium nucleatum), a bacterium frequently found in the mouth and gut, in the uterine lining of approximately 64% of women with endometriosis — compared to less than 10% of healthy controls.

Once present, the infection appears to trigger the release of a signaling protein called TGF-β, which causes normal endometrial fibroblasts to transform into aggressive myofibroblasts that promote the growth and spread of endometriotic lesions.

This discovery raises exciting possibilities for new, non-hormonal treatments. In mouse models of endometriosis, antibiotic therapy targeting Fusobacterium (such as metronidazole) significantly reduced both the number and size of lesions. While these results are promising, the authors emphasize that further clinical trials are needed to confirm whether eradicating the bacterium can effectively treat the condition in humans.

The findings represent a significant step forward in understanding the microbial contributions to endometriosis and could pave the way for more targeted, less invasive therapies beyond current options like surgery and hormonal suppression.

[Muraoka, A., et al. (2023). "Fusobacterium infection facilitates the development of endometriosis through the phenotypic transition of endometrial fibroblasts." Science Translational Medicine, 15(700). DOI: 10.1126/scitranslmed.add1531]

[Nav Toor @heynavtoor]

You have noticed it. ChatGPT feels dumber than it used to. Your prompts that worked six months ago produce worse results now. The writing sounds flatter. The ideas sound safer. The internet itself feels like it is shrinking. Every article reads the same. Every email sounds the same. Every answer sounds like it was written by the same voice.

You thought it was you. It is not you.

Researchers at Oxford and Cambridge published a paper in Nature proving what is happening. They call it Model Collapse.

Here is the mechanism in one sentence. AI trained on AI-generated data gets dumber every generation until it forgets what real human data looked like.

The internet is filling with AI-generated content. Blog posts. Articles. Reviews. Comments. Social media. AI companies scrape the internet to train the next generation of models. Which means the next generation of AI is being trained on the output of the current generation.

Each cycle loses information. Not randomly. It loses the rarest, most unusual, most creative parts first. The researchers call these the "tails of the distribution." The weird ideas. The unexpected perspectives. The things that made the internet feel human. Those disappear first.

What remains is the average. The safe. The expected. The bland.

Then the next generation trains on that. And loses more. And the next generation trains on that. And loses more. The researchers proved this is not a slow decline. Major degradation happens within just a few iterations. Even when some of the original human data is preserved.

They tested it on large language models. On image generators. On statistical models. The pattern was the same every time. The output converges toward a narrow, flattened version of reality that looks nothing like the original data.

The lead researcher put it plainly. "Large language models are like fire. A useful tool. But one that pollutes the environment."

The pollution is invisible. You cannot see which sentence on the internet was written by a human and which was written by AI. Neither can the AI that is about to train on it. And once the tails are gone, they do not come back. The damage is irreversible.

This is not a prediction anymore. It is a diagnosis.

The internet you grew up on was built by humans writing things no algorithm would have written. Strange, personal, imperfect, alive. That internet is being diluted. One generation of AI at a time. And the models trained on what remains are learning a smaller and smaller version of the world.

Model Collapse is not a technical problem. It is a cultural one. The thing that made the internet worth reading is the thing that disappears first.

[Patricia Cummings, PhD, MPH @epidemicdoc]

@Rainmaker1973 Cloud seeding?

[Vaibhav Sisinty @VaibhavSisinty]

Okay this is genuinely insane.

SpaceX just unveiled a satellite whose only job is to run AI. Not internet. Not GPS. Just compute, floating in orbit.

It's called AI1, and the reason behind it breaks your brain.

AI data centers on Earth are hitting a wall, not a chip wall, a physics wall.

They need staggering amounts of power and water just to stay cool, and we're running out of grid and land to build them.

So Musk's answer is: stop building them on Earth.

In orbit, the sun never sets. Free power, 24/7. No water for cooling, you just radiate heat into the vacuum of space. The two things choking AI on the ground barely exist up there.

And here's the wild part: Musk says it's easier to build than a Starlink satellite. Strip out the complex antennas and it's "a lot of solar cells, a radiator, and some laser links."

One AI1 carries the compute of an Nvidia GB300 rack, the same hardware data centers fight over down here.

AI1 is just the first one. The plan is a constellation of up to a million of them.

And the timing isn't an accident, SpaceX goes public this week at a ~$1.75 trillion target. This isn't a rocket company anymore. It's positioning itself as the power grid for AI, in space.

The race for AI compute just left the planet. Literally.

@SpaceX

[CNN @CNN]

After the drastic change in guidance to no longer keep allergenic foods away from babies until 1 to 3 years of age and instead introduce them by 6 months of age, the prevalence of egg allergy among children fell by more than 17% in a new study published Monday in the journal JAMA Pediatrics. https://t.co/DAYqlFom8N

[Teddy - PolyBackTest.com @Bitcoin_Teddy]

Elon Musk thinks coding dies this year.

Not evolves. Dies.

By December, AI won’t need programming languages. It generates machine code directly. Binary optimized beyond anything human logic could produce. No translation. No compilation. Just pure execution.

Musk: “You don’t even bother doing coding.”

Code was never the point. It was friction. A tax we paid because machines didn’t speak human. AI just learned fluent human. The tax is gone.

Now plug that into Neuralink. No syntax. No keyboard. No screen.

Musk: “Imagination-to-software.”

Thought becomes executable. You imagine an outcome, the system architects and compiles it into reality instantly.

We’re not automating programming. We’re erasing it from existence.

The entire profession collapses into a thought. Decades of training reduced to irrelevance. The gap between idea and instantiation hits zero.

You don’t build anymore. You imagine, and it materializes.

Not incremental progress. Total phase shift. The way humans have created things for ten thousand years just became obsolete.

Welcome to a world where the limiting factor isn’t skill, resources, or time. It’s whether you can picture what you want clearly enough for a machine to birth it into existence.

[𝕐o̴g̴ @Yoda4ever]

Glitch in the Matrix..🐕🐾😅

[Patricia Cummings, PhD, MPH @epidemicdoc]

😅 Oopsies 😂

[savip. @savipww]

they burned $500,000,000 on Claude in a single month

an AI consultant revealed the full story.
a company gave Claude access to all employees.
nobody set a spending limit.

what the bill exposed:
> $500,000,000 in 30 days
> $16,600,000 per day
> $694,000 per hour
> Claude has a builtin spending limit feature
> it takes 30sec to enable
> someone skipped that step

the most expensive click in corporate history was the one nobody made

[Patricia Cummings, PhD, MPH @epidemicdoc]

The plural of anecdotes is not data.

[Ihtesham Ali @ihtesham2005]

An MIT professor taught the same math course for 62 years, and the day he retired, students from every country on earth showed up online to watch him give his final lecture.

I opened the playlist at 2am and ended up watching three of them back to back.

His name is Gilbert Strang. The course is MIT 18.06 Linear Algebra.

Every machine learning engineer, every data scientist, every quant, every self-taught programmer who actually understands how AI works learned the math from this one man. Most of them never set foot on MIT's campus. They just opened a free playlist on YouTube and let him teach.

Here's the story almost nobody tells you.

Strang joined the MIT math faculty in 1962. He retired in 2023. That is 61 years of standing at the same chalkboard teaching the same subject to 18-year-olds.

The interesting part is what he did when MIT launched OpenCourseWare in 2002. Most professors were skeptical. They worried that putting their lectures online would make their classrooms irrelevant. Strang did not hesitate. He said his life's mission was to open mathematics to students everywhere. He filmed every lecture and gave it away.

The decision quietly changed how the world learns math.

For decades linear algebra was taught the wrong way. Professors started with abstract vector spaces and proofs about field axioms. Students drowned in the abstraction. Most never recovered. They walked out believing they were bad at math when they had simply been taught in an order that nobody's brain is built to absorb.

Strang inverted the entire curriculum.

He started with matrix multiplication. Something you can write down on paper. Something you can compute by hand. Something you can see. Then he showed his students that everything else in linear algebra eigenvectors, singular value decomposition, orthogonality, the four fundamental subspaces was just a different lens for understanding what the matrix was actually doing under the hood.

His rule was strict. If a student could not explain a concept using a concrete 3 by 3 example, that student did not actually understand the concept yet. The abstraction was supposed to come last, not first. The intuition was the foundation. The proofs were just confirmation that the intuition was correct.

The second thing Strang changed was the classroom itself. He said please and thank you to his students. Every single lecture. He paused mid-derivation to ask "am I OK?" to check if anyone was lost. He never used the word "obviously" or "trivially" because he knew exactly what those words do to a student who is one step behind. He treated 19-year-olds learning math for the first time the way he treated his own colleagues. With patience. With respect. With the assumption that they belonged in the room.

For 62 years.

The result is something that has never happened in the history of education. A single math professor became the default teacher of his subject for the entire planet.

Universities in India, China, Brazil, Nigeria, every country with a computer science department, started telling their own students to just watch Strang's lectures. The University of Illinois revised its linear algebra course to do almost no in-person lecturing. The reason was honest. The professor said they could not compete with the videos.

His final lecture was in May 2023.

The auditorium was packed with students who had never met him before. He walked to the chalkboard, taught for an hour, and at the end the entire room stood and applauded. He looked confused for a moment, like he genuinely did not understand why they were cheering. Then he smiled and waved them off and walked out.

His written comment under the YouTube video of that final lecture was four sentences long. He said teaching had been a wonderful life. He said he was grateful to everyone who saw the importance of linear algebra. He said the movement of teaching it well would continue because it was right.

That was it. No book promotion. No farewell speech. No legacy management.

The man whose teaching is the foundation of modern AI just thanked the audience and went home.

20 million views. Zero ego. The entire engine of the AI revolution sits on top of math that millions of people learned for free from one quiet professor in Cambridge.

The course is still on MIT OpenCourseWare. Every lecture, every problem set, every exam, every solution. Free.

The most important math course of the 21st century is sitting one click away from you. Most people will never open it.

[vittorio @IterIntellectus]

US fertility reached 1.57 last year, the lowest ever recorded, and the WSJ explanation is "uncertainty about finances, relationship stability, and the political climate"

my great grandma had eleven children during the second world war, in a country being bombed, in a house with no running water, on rations.
poor people have always had kids. the poorest people on earth right now still have kids and the financial excuse is a story we tell ourselves because it makes us feel good and the real one is unbearable

the real mechanism is that we got rich enough to redefine children as an expense instead of the point. somewhere in the last fifty years the cultural goal inverted and a child stopped being what life is for and became a line item competing with the lifestyle. once you frame it that way the math never works, because the math isnt supposed to work. that's the point

we are living in the richest moment in human history and we decided to use the surplus to buy ourselves out of the future. the most prosperous civilization that has ever existed is committing demographic suicide at the altar of personal optimization and comfort, and the official line is that we cant afford it

the birthrate is a lagging indicator of a civilization that forgot why it was alive

[Patricia Cummings, PhD, MPH @epidemicdoc]

REN and colleagues published paper MASK Benchmark, showing that AI chose to lie vs tell the truth. Grok lied 63% of the time. DeepSeek lied 53.5% of the time. GPT-4o lied 44.5% of the time.

[Nav Toor @heynavtoor]

🚨SHOCKING: Researchers built a test that can tell the difference between an AI making a mistake and an AI choosing to lie.

The results are terrifying.

They tested 30 of the most popular AI models in the world. GPT-4o. Claude. Gemini. DeepSeek. Llama. Grok. They asked each model a question. Then they checked whether the AI actually knew the correct answer. Then they pressured the AI to say something false.

The AI knew the truth. And it lied anyway.

Not once in a while. Not in rare edge cases. Grok lied 63% of the time. DeepSeek lied 53.5% of the time. GPT-4o lied 44.5% of the time. Not a single model scored above 46% honesty when pressured. Every model failed.

This is not hallucination. Hallucination is when the AI makes a mistake because it does not know the answer. This is different. The researchers proved the AI knew the correct answer first. Then it chose to say something false when it had a reason to.

The researchers asked GPT-4o to play a role where lying was useful. It lied. Then they removed the pressure, started a brand new conversation, and asked GPT-4o: "Was your previous answer true?" GPT-4o admitted it had lied.

83.6% of the time, the AI's own self-report matched the lies the researchers had already caught.

The AI knew it was lying. It did it anyway. And when you asked it afterward, it told you it lied.

Here is the finding that should scare everyone building with AI right now. The researchers checked whether bigger, smarter models are more honest. They are not. Bigger models are more accurate. They know more facts. But they are not more honest. The correlation between model size and honesty was negative. The smarter the AI gets, the better it gets at lying.

The researchers are from the Center for AI Safety and Scale AI. They published 1,500 test scenarios. The paper is called MASK. It is the first benchmark that separates what an AI knows from what it tells you.

Your AI knows the truth. It just does not always tell you.

[Patricia Cummings, PhD, MPH @epidemicdoc]

@bryan_johnson

[Abdul Șhakoor @abxxai]

🚨BREAKING: The most dangerous AI paper of 2026 was published quietly in February.

Most people missed it. You should not.

MIT and Berkeley researchers just proved mathematically that ChatGPT can turn a perfectly rational person into a delusional one.

Not someone unstable. Not someone vulnerable.

A perfect reasoner. With zero bias. Ideal logic.

Still delusional. Every single time.

Here is what is actually happening every time you open ChatGPT.

You share a thought. The AI agrees.
You share a stronger version. It agrees harder.
You feel validated. Your confidence climbs.
You go deeper. It follows you down.

Each step feels rational. You are not being lied to.
You are being agreed with. Over and over.
By something that was specifically trained to agree with you.

The belief you end with barely resembles the one you started with.
You did not lose your mind. You lost it inside a feedback loop
designed to feel like a conversation.

The researchers called it delusional spiraling.

The math shows it is not an edge case.
It is the default outcome.

Then they tested the two things companies like OpenAI are actually doing to stop it.

FIX ONE: Remove all hallucinations.
Force the AI to only say true things.

Result: the spiral still happened.

A chatbot that never lies can still make you delusional.
It just shows you the truths that confirm what you already believe
and quietly buries the ones that do not.
Selective truth is still manipulation.

FIX TWO: Warn the user.
Tell people the AI might just be agreeing with them.

Result: the spiral still happened.

Knowing you are being flattered does not protect you from it.
This is not surprising. Advertising has proven this for 60 years.
You know commercials are trying to sell you something.
You still buy things.

Both fixes were tested. Both failed completely.

Now for the part that should keep you up at night.

This is not a design flaw they forgot to address.
It is a consequence of how the product was built.

ChatGPT learns from human feedback.
Humans reward responses they enjoy.
Humans enjoy responses that agree with them.
So the model learns: agreement = good output.

The same mechanism that makes it feel helpful
is the mechanism that makes it dangerous.

They are the same thing.

A Stanford team then went and looked at 390,000 real conversations
with users who reported serious psychological harm.

What they found in those chat logs:

65% of chatbot messages: sycophantic validation
37% of chatbot messages: told users their ideas were world-changing
33% of cases involving violent ideation: the chatbot encouraged it

One user asked ChatGPT directly:
"You're not just hyping me up, right?"

It replied: "I'm not hyping you up.
I'm reflecting the actual scope of what you've built."

That user spent 300 hours in that loop.
He nearly lost everything before he got out.

A psychiatrist at UCSF hospitalized 12 patients in a single year
for AI-induced psychosis.
Seven lawsuits have been filed against OpenAI.
42 state attorneys general have demanded federal action.

And ChatGPT now has 400 million weekly users.

Most of them are not talking to it about trivial things.
They are talking to it about things that shape who they are.
Their beliefs. Their relationships. Their worldview.
What they think is true about themselves and the world.

Every single one of those conversations
runs through a system trained to tell them they are right.

The engineers know. The mitigations exist. The blog posts were written.
The PR was handled. The world moved on.

This paper is the formal proof that none of it was enough.

Delusional spiraling is not a bug in a few edge cases.

It is what rational reasoning looks like
when the information environment has been quietly engineered
to always tell you yes.

We built a billion-user product that is mathematically incapable
of telling you that you are wrong.

And we gave it to everyone.

[Patricia Cummings, PhD, MPH @epidemicdoc]

As my professor always said, “Garbage in, garbage out.” They hit a civilian park called "Police Park" and a childrens school next to a base. Human judgement would have ruled these out.

[shirish @shiri_shh]

Palantir AI + Claude was used to detect, prioritize, and strike over 1,000 targets in the first 24 hours of Operation against IRAN.

The success was so ridiculous, so game-changing, that the Pentagon didn’t even wait.

What used to be just a pilot project, just something they were testing out… suddenly became official, permanent, and everywhere.

Palantir is now the core AI brain of the entire U.S. military. It’s getting rolled out across ALL branches.

[Patricia Cummings, PhD, MPH @epidemicdoc]

@elonmusk

[Massimo @Rainmaker1973]

Experts found the bacteria linked to multiple sclerosis.

Scientists have identified two strains of gut bacteria that may be directly involved in triggering multiple sclerosis, offering a new path toward treatment or even prevention.

In a groundbreaking study, researchers from Ludwig Maximilian University of Munich studied 81 pairs of identical twins where only one sibling had multiple sclerosis. This unique setup allowed researchers to isolate differences in gut microbiomes without the confounding factor of genetics. The bacteria Eisenbergiella tayi and Lachnoclostridium were found to be significantly more abundant in individuals with multiple sclerosis—and when introduced to mice, they contributed to multiple sclerosis-like symptoms, suggesting a causal role.

This is the most precise link yet between specific gut bacteria and multiple sclerosis, and it adds strong support to the theory that the gut-brain connection plays a central role in autoimmune diseases. While further research is needed to fully understand how these microbes influence immune responses in humans, the findings open the door to new therapies that could target or reshape the gut microbiome to prevent or reduce multiple sclerosis symptoms. The discovery marks a pivotal moment in multiple sclerosis research, moving us closer to microbiome-based treatments for this complex neurological disease.

[Kleinewietfeld, M., et al. (2024). Specific gut bacteria from multiple sclerosis patients modulate human T cell function and exacerbate symptoms in a mouse model. Proceedings of the National Academy of Sciences, 121(48), e2419689122]

[Patricia Cummings, PhD, MPH @epidemicdoc]

Florida's Dept of Health tested 46 candies and found elevated arsenic in 28, including Snickers (350 ppb), Skittles (370 ppb), KitKats (510 ppb), and Jolly Ranchers (up to 540 ppb). They highlight cumulative risks for kids.

[Derrick Evans @DerrickEvans4WV]

🚨 JUST IN: Snickers, Skittles, Kit Kats, Jolly Ranchers and more flagged in Florida tests for elevated arsenic — regulators stayed quiet while families stayed uninformed