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.
But temperature error matters a lot less. 82° instead of 87° is "high" error but the practical difference for me is essentially zero. If it's raining when my phone said it wouldn't rain, I have to change my plans.
I had a friend who did forecasting for a utility and getting the forecast wrong by 5 degree would have been very expensive at the time. I don't remember if it was worse in the summer (AC) or winter. And I wish I could remember if they were buying just electricity or also natural gas
In the same vein as you, I don't care much if it is raining at my office closer to the mountains but I care about it at home. The distance is ~10 miles and I regularly can see a difference.
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.
I think both is good, people thrive on diversity, if everyone was taught exactly the same they would think the same. Without some people with different experiences we would certainly miss on some creations.
> A PhD in ML is worth less to me than day to day operational product engineering experience utilizing the fast changing ML tooling landscape
This is how you get a four-week transition from "I can fix twitter search in an internship!" to "twitter search is unfixable." Ideally, a PhD teaches you to bust your behind doing a deep dive on a seemingly unsolvable problem for three-five years.
If only we could recalibrate ourselves to stop the search for easy money and remind ourselves that some problems are just genuinely hard.
Unfortunately 'senior engineers' who have 30+ years of experience in the valley and other places are past their prime in SV by most top tech companies as engineers.
Obviously school puts you further ahead. Human development is based on writing, teaching, and then expanding from there.
The entire point of having a structured approach to teaching (and this includes apprenticeships and the like), is to accelerate the process of learning.
> would geohot have been better to have gone to school to learn this and deferred all the work he has done?
Could you remind me what he has done since his jailbreaking day, i.e. in the last ten years, that can't be described as "setting VC money on fire?" Because my opinion would be, that yes, the world would have been better off if he did go to school.
