"But internal study found users who stopped using Facebook and Instagram for a week showed lower rates of anxiety, depression, and loneliness."
This isn't causal though. The users who quit were not randomly selected. Maybe they were receiving some kind of mental health treatment, and as part of that they stopped. Then the recovery could have been from the treatment or it could have been from stopping.
So this argument you've made, you've just constructed a strawman.
> The users who quit were not randomly selected. Maybe they were receiving some kind of mental health treatment
You don't know that? You don't know anything about the selection process since facebook did not share their research. Your whole argument pins on the selection process you have no idea what happened. I'd find it very difficult to believe that researchers could not anticipate and control for situations like that. Researchers are after all, experts in research.
Facebook does not typically do academic level research - they do quick studies to verify product direction.
From what I have seen, the actual academic studies on this are mixed. It is hard to say one way or the other, and it can affect different teens differently depending on how they use it.
My point is if the people in the study were not randomly selected, there are any number of confounding factors that could influence why their anxiety changed.
How? Other then calling utility functions that C++ doesn't have you can't just like skip understanding what you are coding by using Python. If you are importing libraries that do stuff for you that wouldn't be any different than if someone wrote those libs in C++.
Are you saying I was incorrect for feeling that way?
The reason is that you no longer really know what's going on. (And yes, that feeling would be the same if C++ had as rich a library of packages as python for numerical analysis.)
If you are doing something that requires precision you need to know everything that is happening in that library. Also IIRC, I think not knowing what type something is bothered me at the time.
>Are you saying I was incorrect for feeling that way?
I think they just wanted clarification. If a program is just "make lines of code do thing" then it wouldn't be different.
But if you are used to ummanged code and considering the hardware architecture and memory management when you make a high performance program, working on python can feel like a black box. Things will slow down because there's a lot of "magic" weighing down the program. But not everyone works in that space.
Unlike LLMs, at least thos box can be peered inside of you really want to.
When you're restricting people's freedom, you have to have a good justification for doing it. There are zero states that have achieved greater economic success than California by allowing noncompetes, thus it is safe to say that noncompetes are not necessary to achieve economic success.
There's no singular factor in almost everything. We should use good faith interpretation and assume the parent understands this as well, unless there is other evidence otherwise
They bolster businesses by making it easier for them to retain employees even if those employees are being treated like shit, because it makes it illegal for those employees to work anywhere else.
That doesn't help the economy, but it helps the businesses.
This response is a bit less than helpful. Could you provide an example of a metric from this diverse set that fits what the OP is asking for? I feel like there are at least two use cases from their post:
* a metric that measures if people's jobs are paying enough to put food on the table
* a metric that measures whether people's employment matches their education?
Your second query is more subjective. Most people would probably point you at the U6 underemployment number as that’s the most famous one. I like the employment projections series for this kind of question though
https://www.bls.gov/emp/
If you're talking about the spike in Q1 2020, there's nothing weird going on. That's from all the service workers getting laid off, which bumps up the average because they're typically lower paid, and no longer drag down the "employed" average.
>The usual weekly earnings data reflect only wage and salary earnings from work, not gross income from all sources. These data do not include the cash value of benefits such as employer-provided health insurance.
> the era of super high paying programming jobs may be over.
Probably, but I'm not sure that had much to do with AI.
> Some types of manufacturing jobs are just gone
The manufacturing work that was automated is not exactly the kind of work people want to do. I briefly did some of that work. Briefly because it was truly awful.
Everyone should do the tasks where they provide unique value. You could make the same arguments you just made for recorded music, automobiles, computers in general in fact.
Difference is though AI does it much faster and has much fewer central sources that provide the service. The speed and magnitude is important as well, just like a crash at 20km/h is different than a crash at 100km/h. And those other inventions WERE also harmful. Cars -> global warming.
This isn't causal though. The users who quit were not randomly selected. Maybe they were receiving some kind of mental health treatment, and as part of that they stopped. Then the recovery could have been from the treatment or it could have been from stopping.
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