I notice that the tasks you list as "don't take effort" map closely to how I would describe stereotypes of tasks that are "women's work", and I often see what it takes to remember them and make sure they happen described as "emotional labor". I wonder to what extent the perception of "takes effort" may map to, or stem from, "see it culturally praised/rewarded".
I would be interested in reading more about how you balance the "it's ok to take it easy some/much of the time" mindset with still making good progress on the things you find important, because that's something I struggle with myself. I craft some illusion of balance by fluctuating between being too strict with myself for the sake of productivity and too lenient with myself for various diverse excuses that dress up as good reasons, but I find the stable midpoint quite elusive.
"What’s more: such strings can’t be severed. Try, for example, to make the two whiteboards different. Imagine that you’ll get ten million dollars if you succeed. It doesn’t matter: you’ll fail. Your most whimsical impulse, your most intricate mental acrobatics, your special-est snowflake self, will never suffice: you can no more write “up” while he writes “down” than you can floss while the man in the bathroom mirror brushes his teeth. "
I'd just flip a coin a bunch of times and write its results, or do some similar process to introduce entropy!
But wait, the simulation is set up such that all inputs are identical, including my observation of the coin flip.
In this case, where's the proof that the two copies of me are actually different entities? How could you prove to either entity that it's not the same person as the other, without violating the "all inputs are identical" constraint?
If it cannot be proven that they're separate individuals in a meaningful or useful way, doesn't the whole thought experiment collapse into "well obviously if I want something written on the whiteboard in my room, I just write it there"? I think that proving to the AI that there were 2 whiteboards and they were separate would itself violate the terms of the experiment.
With how I use the language, I'd call your examples A and B both tasks of "predicting". I view predicting as distinct from learning in that predicting deals with the future, whereas learning deals with synthesizing data about the past.
When I hunt for examples of what I call "learning" that hinge on deciding, the results I find are of the subset of learning that I call experimentation. For instance, to learn whether peaches will grow in my yard, I planted a peach tree and then will observe it for several years.
For anyone else who happens across the broken link later, https://web.archive.org/web/20190211231159/http://www.ayeconference.com/lullaby-language/ .
The recommended translations:
"Should" -> "probably won't"
"Just" -> "have a lot of trouble to"
"Soon" -> "I don't know, but don't keep bothering me"
You highlight a difference that relates to why I don't feel like I do my best work in academia. I think an overarching project -- "I want to learn enough biology to cure cancer" or "I want to learn enough electrical engineering to design audio equipment" or even "I want to learn enough marketable skills to make a truckload of money" can turn academics into project-aligned work.
However, looking for one's personal project or motivation for being in academia and finding only "well I guess people praised me when I said I wanted to be a scientist" or similarly uncompelling motives can be dangerously demotivating.
I wouldn't claim that my study process is anywhere near perfect, but I find that I have the easiest time studying for particular projects rather than just for its own sake. Watching things I learn contribute to progress on my understanding of the task that I concretely care about it a highly effective reinforcement of the study behaviors.
I'd guesstimate it about 90% "things people are too pessimistic on", 10% "things people are too optimistic on". They definitely cherry-picked to make a point, but then any compression of world events into a handful of statistics is going to be lossy in some direction or another.
https://upgrader.gapminder.org/ is extremely nifty.
I map the spectrum of hyperlink usage styles between the extremes of Wikipedia vs everything2.
I have been pleasantly surprised to find much writing in the "Rationalist" internet spaces to lean strongly toward the latter. I think it shows simultaneously a certain faith in the cleverness of one's readers, and abdication of any perceived responsibility to prioritize lack-of-ambiguity for all possible readers over higher accuracy and subtlety for the target audience.
And "stop trying to make me do chores for you so that I can put that time toward the things I want instead" isn't in that same goal category?