He is rejecting the framing of get in now before it's "too late". If it is so useful then we will be able to pick it up when it is more polished rather than learning to use some half polished turd that will be obsolete in 6 months.
The only reason I haven't switched, is when using uv as my environment manager, starting/connecting to the python kernel in notebooks within vscode takes forever.
My biggest issue is using uv envs in vscode under WSL. Starting up interactive sessions takes forever. Its just too slow, can't figure out what the deal is.
I've been at two small companies that had layoffs, and who was cut/retained was 100% based on what was best for the company (except some visa holders were retained). Which is what it should be.
That is not what the study found. The study found people are able to guess the given names of adults from four choices at slightly better than chance, ~30%. With 117 (or 116?) people guessing and 16 images.
I agree it seems like flimsy justification. But it is also likely harder to assess and communicate. Temperature they get a point prediction for the high and you can easily calculate the mean absolute error.
For precipitation you will be getting percent chance often with an interval, 10% chance of 0.1-0.25 inches with higher likely in thunderstorms. Also precipitation patterns tend to be much more irregular within small spatial extents. You can asses things like calibration and perhaps take a mean value for there intervals to get point errors. But all of this will make it harder to communicate actual performance.
I like the ping pong of one day an article being posted where everyone asks, "when/why did everything become so complicated", and then the next day something like this is posted.
Porous concrete has been useless. It clogs up eventually and loss it's ability to transport water.
The groundwater disappearing is a problem if you build up ag/industry/development based on the groundwater availability and then lose that source of water with no economical alternative.
I keep seeing these semi-religious Bayesian posts pop up. I would like to add a more nuanced perspective by none other than the great Bayesian practitioner Michael I Jordan.
I use Bayesian methods often, but this a just religious. Bayesian methods are just that, tools, methods for approaching a problem.
There are no laws for applying probability to the real world. To think so puts too much faith in your models. Remember, all models are wrong. Applying probability to the real world requires a host of assumptions, regardless of the methods you use.
Frequentist and Bayesian methods have different goals, both have there place.