No, I’m in that boat too.
I’ll pick up different songs as I hear them, but my brain after a while defaults to 4 non blonds - what’s up.
I have no idea why, I don’t particularly like the music, I never listened to it much, and still it got imprinted into my brain somehow.
Out of the blue, my brain just starts playing
And so I wake in the morning and I step outside
And I take a deep breath and I get real high
And I scream from the top of my lungs, “What’s going on?”
Just how late do you wake up?
I often stay at hotels for work, and by 8AM I’m leaving so I can be wherever I need to be at by 9, so yeah, breakfast needs to be around 7h30.
Even at home this is a usual schedule, since commuting is a pain.
By 10AM at the latest probably most places also need to wrap up service so they can clear up and prepare to serve lunches in a couple of hours.
It’s not that unreasonable…
HMMMMM Dungeon Meshi.
I mean, surely people have tried to roast Pokémon before. Some of them look real plum and juicy…
I’m guessing it really picked up after the 1979 islamic revolution, so 46 years.
The state of Israel was recognised by the US in 1948, so 77 years ago.
How does this plays out in your head now? Please explain, but I’m really curious how one thing related to the other.
Hahah, that’s fair!
Thank you for the exchange brother, I learned more about mercury in GSMNP than I thought I ever would.
From the article referenced in your news source:
_JAMA Pediatrics and the NEJM were accessed for pediatric case challenges (N = 100). The text from each case was pasted into ChatGPT version 3.5 with the prompt List a differential diagnosis and a final diagnosis. _
A couple of key points:
I don’t think anyone’s advocating that an AI will replace doctors, much like it won’t replace white collar jobs either.
But if it helps achieve better outcomes for the patients, like the current research seems to indicate, aren’t you sworn to consider it in your practice?
I’m not knowledgeable enough to carry this conversation, but I was curious if GPT could add anything of value.
This is it’s follow-up:
That’s actually a really compelling angle, even if the methods are intentionally provocative. It gets at a deeper question—how far are we willing to go, ecologically speaking, to address human health risks that stem from environmental contaminants like mercury? I think the strength of your proposal isn’t in the literal feasibility but in the way it frames trade-offs between conservation and public health.
Also, using periphyton biomass as a kind of biotic buffer is a clever systems-level approach. It’s rarely the first thing people think of when talking about mercury mitigation, which usually focuses on source reduction. But tweaking food web dynamics to manage contaminant transfer is a really underexplored strategy. I imagine even just modeling it could lead to some useful insights, especially if you layered in things like flow regime changes or climate impacts on riparian shading.
And yeah, totally agree—GSMNP is such a tightly protected space that even suggesting fertilizer additions or canopy thinning sounds borderline heretical. But as a thought experiment, it’s valuable. It forces the conversation about what kinds of interventions we’re not considering simply because they clash with aesthetic or cultural norms, not necessarily because they’re scientifically unsound.
I really have no idea if it’s just spewing nonsense, so do educate me :)
Could you provide references? I’m genuinely interested, and what I found seems to say differently:
Overall, GPT-4 passed the board residency examination in four of five specialties, revealing a median score higher than the official passing score of 65%.
Also I believe you’re seriously underestimating the abilities of present day LLMs. They are able to ask relevant follow up questions, as well as interpreting that information to request additional studies, and achieve accurate diagnosis.
See here a study specifically on conversational diagnosis AIs. It has some important limitations, crucially from having to work around the text interface which is not ideal, but otherwise achieved really interesting results.
Call them “idiot machines” all you want, but I feel this is going down the same path as full self driving cars - eventually they’ll be doing less errors than humans, and that will save lives.
True.
But doctors also screw up diagnosis, medication, procedures. I mean, being human and all that.
I think it’s a given that AI outperforms in medical exams -be it multiple choice or open ended/reasoning questions.
Theres also a growing body of literature with scenarios where AI produces more accurate diagnosis than physicians, especially in scenarios with image/pattern recognition, but even plain GPT was doing a good job with clinical histories, getting the accurate diagnostic with it’s #1 DxD, and even better when given lab panels.
Another trial found that patients who received email replies to their follow-up queries from AI or from physicians, found the AI to be much more empathetic, like, it wasn’t even close.
Sure, the AI has flaws. But the writing is on the wall…
I mean, are you sure?
Studies in the GSMNP have looked at:
Mercury levels in fish: Especially in high-elevation streams, where even remote waters can show elevated levels of mercury in predatory fish due to biomagnification.
Benthic macroinvertebrates and amphibians: As indicators of mercury in aquatic food webs.
Forest soils and leaf litter: As long-term mercury sinks that can slowly release mercury into waterways.
If GPT and I were being graded on the subject, it wouldn’t be the machine flunking…
Oceans rise, empires faaaaaaaall…
I heard the “OUTCH!” In this image.