lrschaeffer

Same. Although finding the setting for the top-left corner is annoying, it gets easier every time.

As other commenters have said, you're just going to learn the decoder ring from context, but I'm also curious. If I take off the keycaps to clean the keyboard and then put them back, do I need to relearn the defects? If I rip out all the switches and rearrange them, do the defects follow the switches or is it more about the position of the key? Is there some resonance that could change if I move the keyboard onto a desk pad. If I switch to Dvorak in software, obviously the key presses mean something different, but also I'll type completely differently. How much does user cadence/timing matter? To what extent is user cadence/timing identifying?

Anyway, I assume the answers aren't known because it would be an ethics/privacy nightmare to run these kinds of experiments on anyone but yourself.

Yeah, lots of different uses for a keyboard with wildly different frequency distributions. Writing an English letter != text chat in Spanish != data entry != league of legends. It's also not quite a substitution cipher due to modifiers (shift), toggles (caps lock), non-printable characters (escape, volume up/down), and deletion (backspace/delete).

It's an interesting problem you'd probably try to solve with Markov chains back in the day, but now you'd just throw machine learning at.

>> Hashmaps don't have guaranteed performance characteristics

>Of course they do

No, they do not, at least not worst case O(1). If you write a very poor (but functional!) hash function or an adversary is choosing your items, then congratulations, your hashmap is now a linked list with extra steps.

Sure, you could use a robust cryptographic hash function, but you didn't because (a) it's not the default in your language and (b) it's slow and you are "a lot more interested in a low-constant-factor O(1) best case".

Otherwise, you're more or less correct.

I'm no expert, but there are approaches to reinforcement learning where you build a table of the expected payoff for each scenario, and these can recursively reference other entries in the table. Take a look at Q-learning, for example.

Assuming what I wrote above is correct (I don't actually know anything about closed time-like curves), it's actually weirder than just going back in time and mailing a letter early so it arrives when you want. Specifically, once you set up the entangled state, no other communication between the parties is necessary.

Maybe in general, but this is in the context of "time loop logic". Ordinarily, you can have entangled states where measuring both parts will produce the same (random) outcome. Unfortunately the outcome cannot be influenced by either party, so it's useless for communication, as you say.

Now suppose that the first party resolves to go back in time and kill his grandfather unless the bits he measures are precisely the message he wishes to send. The universe can't tolerate the paradox, so it is corralled into the only non-paradoxical outcome: both parties read the desired message. Something like that, anyway.