So much for "open" AI...

I wonder if this same approach could be used here in factorio? Using the pokemon red analogy the main "essential tasks" in Factorio are setting up automation for new items and new science packs. I think a good reward function could involve small rewards functions for production rates of each item/sec, medium rewards for setting up automation for new items, and big rewards for automating each new science pack.

Telling a Factorio agent to just "make a big factory" is like telling a pokemon red agent to just "beat the game", it has to be broken down into smaller steps with a very carefully tuned reward function.

Thinking about this is really making me want to jump into this project!

https://github.com/scottmsul/Password-Generator

Counter-factuals like this don't apply when talking about average probabilities. If you cross over, it's an identical graph with identical probabilities. idk, to me it seems really counter-intuitive that the opposite bunk's node would be easier to get to than the current bunk's node.

For anyone who's interested, there's a group of us dedicated to natural myopia reduction at reducedlens.org, which is a free and open-source fork of endmyopia. I've even started measuring my axial length to try and get better data on if this stuff actually works (only one measurement so far though, so nothing interesting yet).

The nature paper was pretty crazy. Basically because blue bends more than red (think of a prism), it also focuses a bit sooner. This phenomenon is known as longitudinal chromatic aberration, or LCA for short. This means if you're myopic, blue might be more blurry than red, and vice-versa if you're hyperopic. The researchers in the nature paper had participants watch a movie where they straight-up blurred the blue or red with software, in order to produce fake LCA signals. They found the participants axial lengths still shortened or lengthened anyway in response.

Edit: actually seems like twitter isn't refreshing with any new data on desktop

Points: 9

# Comments: 8

The first thing to understand is microstates, which is just the number of ways a system can have a certain energy. Eg in a two-state system with ten particles and energies +/-(E/2), there's one microstate where the energy is -5E (all negative), ten states with -4E (one elevated), etc. Then entropy is just the log of the # of microstates, which is much easier to deal with, since microstates tend to behave exponentially. Eg entropy(E=-10) is log(1)=0, entropy(E=-9) is log(10), etc.

Then temperature like you said is d(entropy) / d(energy). Two systems with different temps brought into contact will exchange energy until the temps are equal, since this configuration maximizes entropy.

The two-state system can have negative temperatures since entropy starts decreasing with energy once more than half of them are in the higher-energy state. This can't happen in more familiar scenarios (eg ideal gas, blackbody, etc) since usually entropy always goes up with energy.