Even the simplest router that differentiates models between plan & execute will ensure that this is consistently followed. Folks have too high FOMO to choose themselves.
I live around at around a 150ft elevation above the ocean, but technically within < 1 mile away from it. Despite zero risk of flodding, half of the bigger insurers in my area have exited the market, and we are now having to insure with higher risk insurers like Kingston, who's ratings are yet to be tested during a real emergency.
My car insurance is above $4k/yr for two family cars, rising annually despite no accidents.
Meanwhile, "normal" countries are solving these problems in reasonable ways. Back home in Central Europe, you get state mandated car insurance at pre-negotiated rates that are based on the car, not the driver.
You can’t really expect insurance companies to identify and support policies in areas where many homes may be at huge risk of flooding, while another has zero.
That’s massive operational overhead and is definitely not as simple as looking at elevation, and that overhead is reasonably not worth it for those companies.
I think you may not fully understand how much more expensive insurance is in the US and how bad of a deal you get here.
In the Netherlands my standard car insurance came with a bodily injury liability limit of 6.5MM and property damage liability limit of 2.5MM at a cost of only around €25 per month.
You usually cannot even get limits that high from insurers in US, let alone at such a cheap rate.
The whole thing is unreadable, and laughable, just like AI2027 previously. It's really really hard to read someone suggesting that in the United States we will soon have a universal basic income of $1m / year for all people in the country, when you just look at the state of current politics...
I'd rather read something a little bit more realistic.
> Why do rural people deserve subsidies by virtue of living in the sticks?
For the same reason urban people deserve subsidies, for things like public transportation, by virtue of living in the city. The first sentence of the US Constitution lists "promote the general Welfare" as one of the foundational reasons for the creation of the Constitution. There is no "one size fits all" for a nation this size - the welfare of people in the city and the welfare of people in the sticks requires differing allocations of tax money. Also, mail delivery to the sticks benefits people in cities given that mail in the sticks is often sent to or from cities.
> For the same reason urban people deserve subsidies, for things like public transportation, by virtue of living in the city.
They don't.
> The first sentence of the US Constitution lists "promote the general Welfare" as one of the foundational reasons for the creation of the Constitution.
Just lower general taxation instead. That's even better for promoting general welfare.
> Also, mail delivery to the sticks benefits people in cities given that mail in the sticks is often sent to or from cities.
Almost nobody here is a doctor, and it shows...over diagnosis, over treatment, those are all terms that doctors learn about in medical school.
Image segmentation is a real problem, and achieving better precision is a good goal. The "golden" standard these days is likely https://github.com/wasserth/totalsegmentator, if someone can make it even more accurate, that would be very very good. But yet again, there are infinite amounts of variations in human bodies, which means even the best models focus only on segmenting known organs, and leave anything unknown alone.
This is more true than people realize. My wife and I got full body MRI scans.
Both showed "possible" medical issues. My though was "Great, I have a baseline, in two years I'll get another one and compare".
My wife on the other hand got a bit obsessed about her results and had what was probably an unnecessary procedure to biopsy something, which turned out to be benign.
I suppose you could argue that another way...better safe than sorry...but the stress that is caused by known uncertainty vs unknown uncertainty can be too much.
> ...unnecessary procedure to biopsy something, which turned out to be benign.
The point here is many issues can't be resolved safely with a biopsy or minor procedures, so one ends up under serious risk of a major surgery for something that would never cause any damage.
Plenty of people die this way. If not, one might even thank his doctor for saving his life afterwards.
Not a doctor, but the overdiagnosis concern is at the intersection of three phenomena:
1. Imaging is expensive, just in dollars and time, even without analysis
2. Imaging is not without impact -- CT scans, especially full body scans, expose the body to ionizing radiation
3. Imaging is time-consuming
The net result of these means that full body scans are difficult to interpret. If a doctor given a patient complaint suspects a condition that is sufficiently non-specific that a full-body scan is required, then the scan will be interpreted through the lens of the known progress of the differential diagnosis. And typically these scans must be done without a healthy baseline, so minor findings in this context might have significant diagnostic power when combined with history or other findings.
But on a healthy patient, minor findings are very likely to be noise, because we don't have a great deal of experience with scans of healthy people, for the reasons above.
This technology, if it pans out, gives a way of inverting 1, 2, and 3. If every healthy doctor visit includes one of these scans, then the medical field gets experience interpreting them, and more importantly, when new symptoms occur, previous scans can be compared to determine whether a particular finding in the current scan is new or has changed.
If something like this became commonplace (and accurate enough), then it could be fantastic for research: enabling us to map out what variations are common and which aren't in a way that hasn't previously been feasible.
In the dark ages of machine learning, researchers tried to fit natural language into a defined, human-curated taxonomy.
It kinda worked, for a reasonable amount of stuff; but failed quite a lot of the time, and there's an extremely long tail of things that would have been pragmatically impossible to ever address with that method--indeed, without adopting an entirely new, unsupervised model of language, continuous in places where the old way was discrete.
It's not unreasonable to think that the level of acceptable risk for "the language model parsed my text wrong" is in average much higher than "the medical model misdiagnosed my condition". You can probably come up with scenarios where a language model behaving unexpectedly would have drastic consequences if you imagine them hooked up to automatic systems where they have immediate control over actions that can't easily be reversed, but like, that's why it's a bad idea to use them like that, and they're the exception rather than the rule. It seems plausible that scenarios like that for medical models are a lot closer to the norm than the exception, in which case the tolerance we have for them "filling in the gaps" incorrectly would need to be much smaller.
These sort of numbers are really easy to estimate and it sounds like you haven’t done.
For the sake of a reality check:
IPO is raising approx $75bn of new equity
SpaceX has negotiated substantially below market fee of 0.75%
Total fee pool = ~550mio USD
Fee pool will be split between 23 banks, so average of 23mio per bank, likely skewed heavily towards Goldman Sachs and Morgan Stanley as the lead bookrunners.
Clearly everyone has incentives for spaceX to go up, but important to keep in mind the order of magnitudes we are talking about, the monthly google compute spend in the headline totally eclipses the one off banking fees
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