Examples? Every time I see kubernetes pop up it's because of better performance or lesser costs.
This is true of most strict rules elsewhere: they're there but seldom enforced. Take speed limits, they're strict, but good luck enforcing them. All we do is we monitor from time to time and give tickets. Yet we could enforce this to the manufacturers, right? Why can a car go beyond the speed limit if it's strictly prohibited?
We cannot compare working conditions as is, but they're easy to compare when you take time into account: how where our labor laws a couple of generations ago? Not that different than poor countries today.
Laws are meaningless if they are never enforced or are simply ignored. Which happens all the time in China.
It is not easy to fire people. Sure, in normal situations. But when appropriate, government is going to ignore all these and do whatever necessary, and maybe even threaten to put you or your family in jail. Want to go to court? Good luck, the judges are going to stand with the government.
Another example: the constitution says that Chinese people have the freedom to speak, publish and demonstrate etc. Tell me how that has worked out.
The OP made a good point that applies to all poor countries I've lived in: those jobs are the only (even the best) way out for most people to get out of poverty, which they do to help their families/children/themselves.
Maybe you were raised in a family that did not need to go through such hard labor, but that doesn't mean your right in your view of your own world ;)
P.S. I was also born and raised in a poor country.
There is always a starting state; using a random one only means you don't know what it is.
More generally, it disingenuously disregards the fact that the definition of the problem brings with it an enormous set of preconceptions. Reductio ad absurdum, you should just start training a model on completely random data in search of some unexpected but useful outcome.
Obviously we don't do this; by setting a goal and a context we have already applied constraints, and so this really just devolves into a quantitative argument about the set of initial conditions.
(This is the entire point of the Minsky / Sussman koan.)
I get that starting from a point with "some correctness" makes sense if you want to use such information (e.g. a standard starting point). However, such information is a preconceived solution to the problem, which might not be that useful after all. The fact is that you indeed might not at all need such information to find an optimal solution to a given problem.
> by setting a goal and a context we have already applied constraints.
I might be missing your point here since the goal and constraints must come from the real world problem to solve which is independent from the method to solve the problem. Unless you're describing p-value hacking your wait out, which is a broader problem.
“What are you doing?”, asked Minsky.
“I am training a randomly wired neural net to play Tic-Tac-Toe” Sussman replied.
“Why is the net wired randomly?”, asked Minsky.
“I do not want it to have any preconceptions of how to play”, Sussman said.
Minsky then shut his eyes.
“Why do you close your eyes?”, Sussman asked his teacher.
“So that the room will be empty.”
At that moment, Sussman was enlightened.
To me this sounds like the equivalent of permanently shutting down a public park because it's infested with disease carrying rats rather than trying to get rid of the rats.
Wasn't this the same kind of argument we used in the past to justify slavery?
Different wording, but same message though. It sounds like the two options are fundamentally the same, regardless of the wording.