STEM
STEM
Mathematics, Science, Engineering, and Technology
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I’ve grown up on a STEM education, but with notable phase transitions between the letters. Each time, I’ve felt a little born anew, and I’ve been prompted to re-examine each discipline for its key lessons and shortcomings. Below, I hopscotch chronologically through these observations.
Mathematics.
Tautology: There’s nothing really at the bottom. But from a few testudineous axioms is born a true lineage of turtles, each with their unique perspectives of the same world, and their weight contributing to an ongoing test of the tower’s stability and self-consistency.
Definitions: Being just a little stupid, I need the world explained to me in simple terms. This small-context-window regularization helps to boil off real-world complexity and clarify it into All-Purpose Thing Essence suitable for your everyday generalization needs. And it’s not just pattern recognition; through Essence we can reason about causal mechanisms, necessary-and-sufficient conditions, and emergent phenomena.
Variables: Conversely, we can take phenomena as a starting point. By naming unknown quantities and describing known properties/equations, we can work backwards to identify the unknown objects. That pesky x.
Science.
Theory: Being a little slow and stuck in their shells, turtles can’t get that far. In The Real World, we need to build approximate models of How Stuff Works and settle for refining them over time.
Hypothesis: Under the scientific method, refinement is powered by the development of hypotheses that can be tested through objectively measurable, repeatable experiments. As data points and experimental approaches accumulate, possible causal confounders disappear and probabilities of correctness converge towards 1.
Engineering.
Efficacy: Everyday we stray further from turtles. “Is it true” is replaced by “does it work.”
Iteration: Move fast and break things, cycling between prototyping and evaluation.
Technology.
Definition: Technology is methodology applied to make more impact with less effort.
Theory: Let’s view the world as a timeline of actions taken with positive probabilities (1 – p) and p of non-negative and negative outcomes, respectively.
“More impact”: Technology empowers actions’ outcomes to grow in magnitude such that the possible negative outcomes are catastrophes.
Theory: Let N(t) be the number of potentially catastrophic actions cumulatively taken so far.
“Less effort”: Technology empowers the slope of N(t) to steepen over time.
With details left as an exercise to the reader, the probability of survival is (1 – p)^N(t), and this converges quickly to 0.
Hypothesis: Many aspects of the world (like war, climate, and epidemics) confirm both p > 0 and N''(t) > 0.
Iteration: The model is imperfect in many ways, as evidenced by the fact that we’re not dead (yet). I personally think the conclusion is likely true, but I’ll explore the forces fighting against this Doom Conjecture.
Efficacy: One might dream of saving the world (maketheworldabetterplace maketheworldabetterplace maketheworldabetterplace).
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