The trick is, at that time most of the possible mass range was excluded experimentally, so it is a bit less impressive. I'm not sure how much tuning went into it (possibly none)
Interestingly, there is some neuroscience research that transformer architecture resembles "cue based retrieval" in the human brain in some important ways.
"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.
The trick is, at that time most of the possible mass range was excluded experimentally, so it is a bit less impressive. I'm not sure how much tuning went into it (possibly none)