Predictability is hard, surprises are everywhere

Today at J on the Beach, Helena Edelson talked about a lot of things that Akka can do for event streaming, microservices,  generally handling a lot of data and keeping it (eventually) consistent across great distances. With opt-in causal consistency! 

I am amazed by how hard it is to have consistent causality and data across geographic distance. It is counterintuitive, because our culture emphasizes logical deduction and universality. Science, as we learned it, is straight-line deterministic causality. The difficulty of making systems that work like that across geographic distance illustrates how little of our human-scale world works that way.

the “hard” sciences are narrow.

It is great that people are working on software that can have these properties of consistency, and that they’re studying the limitations. Meanwhile it is also fun to work on the other side of the problem: letting go of perfect predictability, and building (relative) certainty on top of uncertainty. Right now I’m in a talk by Philip Brisk about the 4th Industrial Revolution of IoT sensors and machine learning sensemaking. It turns out: now that we can synthesize DNA in a little machine, we can also find out what DNA was synthesized using only a microphone placed near the machine (and a lot of domain knowledge). Humans are fascinating.

predictability is hard, and surprises are everywhere, and that is beautiful.