Personally, I am finding the sublime in numerical methods and I have always been horrible at manual algebra. It's like a time travel movie. "We have to go back

Personally, I am finding the sublime in numerical methods and I have always been horrible at manual algebra. It's like a time travel movie. "We have to go back!" Machines can do math, sure, and even learn the contours of the systems they represent. That's incredible. But machines lack the human interface that allows infinite abstraction. So, humans, left with the power of infinite regress, must teach the machines that are so adamantly automating the tasks we deem important as a species. It is possible now, in 2024, to legislate runaway automation with more human-verified checkpoints and more skilled humans running these checkpoints. Major integration network paths are verified by the veracity of the integral checks performed on the flowing data. From here you can measure capacitance, rate of flow, and begin to inspect the data as far as legislation will allow you to. For any practical scientific purposes, such as analyzing tornado paths and devising a rubric of resource allocation, You can measure all elements as a wave until a significant event occurs (including malicious data vectors). You can tune a sample rate for that. With enough people continuously performing validation analysis on data flow, the machines we rely on to achieve our target numbers can begin to rationalize truth and fiction. -- Here's the catch: we just have to do it right the first time round.