A counterexample to your claim: Ackermann(m,m) is a computable function, hence computable by a universal Turing machine. Yet it is designed to be not primitive recursive.

And indeed Kleene's normal form theorem requires one application of the μ-Operator. Which introduces unbounded search.

I have read Convict Conditioning. The programming in that book (that is, the way the overall workout is structured) is honestly pretty bad. I highly recommend doing the reddit /r/bodyweightfitness recommended routine.

1. It's free.

2. It has videos for every exercise.

3. It is a clear and complete program that actually allows for progression (the convict conditioning progression standards are at best a waste of time) and keeps you working out in the proper intensity range for strength.

4. If you are doing the recommended routine you can ask questions at /r/bodyweightfitness.

The main weakness of the recommended routine is the relative focus of upper body vs. lower body. Training your lower body effectively with only bodyweight exercises is difficult though. If you do want to use Convict Conditioning, /r/bodyweightfitness has some recommended changes which will make it more effective.

Who are the current moderators?

Observational data doesn't allow one to distinguish correlation and causation.

No? If I observe a hammer striking a nail and the nail sinking into the wooden plank, is anyone going to argue that it's mere correlation and not causation?

Observational data doesn't always allow one one to distinguish correlation and causation.

I am also a bit confused since you're talking about learning values but your example is not about values but about a causal relationship.

As the old joke goes, Alzheimer's is the best illness, there is no pain and each morning you get lots of interesting news.

But note that improving the model would result in less pleasant experiences of wonder, but also in less unpleasant experiences of disappointment. Basically you reduce your variance, but it's not obvious to me that you imperfect model necessarily has a pessimistic bias.

There are other emotional reactions which should register as confusion but don't.

Imagine a smart person who sees asphalt being deposited to pave a road. "How disgusting," they think. "Surely our civilization can think of something better than this." They spend a few minutes ruminating on various solutions for road construction and maintenance that would obviously be better than asphalt and then get distracted and never think about it again.

They thus manage to never realize that asphalt is a fantastic solution to this problem, that stacks of PhDs have been written on asphalt chemistry and thermal processes, that it's a highly optimized, cheap, self-healing material, that it's the most economical solution by leaps and bounds. All they noticed was disgust based purely on error and ignorance.

Any thought of the form "That's stupid, I can easily see a better way" should qualify as confusion.

Model selection is an interesting process, because of overfitting: when you add parameters, are you better modelling the intrinsic noise or a fundamental information that is interesting to you?
This is the overarching question, and I find interesting that it depends on the existence of a meta-model, which is usually very implicit.

A thing already known to computer scientists, but still useful to remember: as per Kleene's normal form theorem, a universal Turing machine is a primitive recursive function.
Meaning that if an angel gives you the encoding of a program you only need recursion, and not unbounded search, to run it.

Sounds like Convict Conditioning to me.

I haven't read it myself, but some friends have praised the book and the exercises included.

It's amazing how the "noncentral fallacy" is rooted deeply into human psychology.
Using "weird" to escape the gravitational pull of a word it's interesting, and suggests a general strategy: Martin Luther King was an heroic criminal, abortion is an ethic murder, etc.
Mmh, better but not the best.

'Weird' seems to work well only for self-identification, though.