MIT published a brutally honest report on what AI is doing to students. A committee of professors and students spent five months studying how AI changed learning on campus, and the findings read like a warning to every university on the planet. Study groups are disappearing. Office hours are emptying out. Problem sets and take-home exams no longer prove anything, because AI can produce credible solutions to almost any written assignment in the undergraduate curriculum. Students who lean on chatbots lose mastery and confidence, and some slip into what the report calls cognitive surrender, reaching for AI at the first hint of struggle. The numbers are rough. 46 percent of surveyed MIT undergrads use LLMs daily. 90 percent worry about their own overreliance. Undergrads who feel AI makes them replaceable now outnumber those who feel it makes them capable. The committee's answer surprised me. They refused to fight AI with surveillance. The report calls AI detectors unreliable, says lockdown browsers feel like spying, and warns that policing students builds a classroom atmosphere of mutual distrust. Instead, MIT wants to rebuild education around the things AI can't replace. That means oral exams, semester portfolios, in-person project work, and a required social component in every subject. The report even floats the idea of rethinking grades entirely, since without a GPA to optimize, much of the incentive to cheat with AI evaporates. The committee warns professors against replacing undergrad research assistants with AI agents just because they're cheaper, because a university exists to grow people, not output. The most famous tech school on earth admitted the machines broke its way of teaching. Its answer is more humans, not more software.

Many physicians tell me they were happier during residency. Even though they worked more hours, had less autonomy and carried heavy student loans. Their happiness was rooted in a compelling story. The idea that life would be better when they became an attending.

Our lead author just pulled one of our papers from a prominent #MedEd journal because it’s been 8 months and the editors still couldn’t find any reviewers. You were wondering when this #PeerReview model would hit its breaking point? That time is now.

wait, what on earth?? in listening to dave in that clip, what is he truly trying to say? on one hand, these people are (according to his criteria) "healthier than average" and some people like the woman he mentions or the other person with the high CAC before entering the study, may not have been healthy beforehand. ok sure, I get that. but they clearly were in different/better states of metabolic health by the time they entered the study and after a year of it were they not? so now is it that LMHR does not appear to more substantially increase risk for plaque development if the person is a super healthy, fit athlete with little to plaque before starting eating keto? this makes no sense. the question at hand as I understand it is what happens when you go on keto regardless of your state of health beforehand, especially when we consider that many people choose this path because they are not in a good state of health. you could even make the argument, that the results on the less healthy people in their cohort are the MOST IMPORTANT OF ALL to pay attention to, when it appears as though he is trying to explain their rapid progression rates away based on their prior health metrics.

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Doctors are taught to think in probabilistic terms. They also know that most of the risk functions they use have huge uncertainties when applied to individuals and may also be miscalibrated. As a result, docs are often comfortable describing relative scale risks (low, medium, high) but uncomfortable with adding numerical precision.

What the shit is this fuck