If I remember correctly, Brendan Sullivan had a reputation as a TA for Concepts of Mathematics at CMU as "Math Jesus", not sure if that was a testament to his pedagogical skills or just due to the long hair and beard...
This stuck out to me too, glad someone else noticed it. Just in case people aren't aware, the law of large numbers is a phenomenon related to repeated random experiments, saying that the incidence rate of any particular event will tend towards the probability of that event. Referencing it in this context is a total non-sequitor.
I've used Character AI (https://beta.character.ai) and been seriously impressed with the roleplaying experience that's provided there. A UX feature that really helped was the ability to "swipe left" on a reply and "re-roll" it- this can help keep the AI from getting off topic, or retry when it gives "Eliza" type responses, ie
"AI: Are you ready for your karate practice?
User: Definitely, let's get started!
AI: Great, what sort of things do you think we should do at karate practice?"
If you squint a bit and are willing to provide a little guidance in the form of leading questions, you really can have some pretty fun RP experiences, I've spent hours at this point doing little scenes and I've been really surprised at the wealth of different experiences that the AIs are capable of providing.
Other caveat of course is that it's not really suited to "longform" RP, I can't imagine it scaling to a "campaign" that you return to multiple times per month over the course of a year- I think this is a limitation of the tech at this point, as far as I know the LLM basically is always re-reading the entire chat history to generate the next response and presumably eventually this stops being feasible.
Sigma algebras are useful even in a finite setting where we don't have to worry about pathologies. Think of them as modelling the lack of complete information in a probabilistic setting. If I know exactly which sample point represents my state, then I know the exact value of any random variable. If instead I only know that my state belongs to a given member of my sigma algebra, then I have some information, but not enough to necessarily pinpoint the value of a random variable.
In fact, the familiar tools of measure theory can take this intuition further. If a random variable is measurable with respect to a sigma algebra, then knowing which element of that sigma algebra my state is in actually is sufficient to pinpoint the value of a random variable.
Maybe to make this more concrete:
Let's say I'm going to do two coinflips. My probability space is {HH, HT, TH, TT}. You can check for yourself that the sigma algebra generated by {{HH, HT}}, {TT, TH}} is not the trivial one- this is the sigma algebra that represents "Knowing the value of the first flip, but not the second".
If we let X_first and X_second be 1 or 0 if the first or second flip is H or T respectively, then X_first is measurable with respect to this sigma algebra, but X_second is not.
With Martingales and other stochastic processes, we don't generally have just one sigma algebra, but a sequence of sigma algebras called a "filtration", where each sigma algebra is finer than the last (ie, contains more sets, therefore gives you more measurable random variables). This filtration sort of defines the stochastic process- it's encoding the slow drip of extra information as the stochastic process evolves over time.