It's a Tool
It's a Person
It's a Hypervigilance Problem

The tech industry's insistence on distinguishing between "soft skills" β€” caring for people β€” and "hard skills" β€” engineering rigor β€” is a reflection of the Cybernetics split itself. First-order thinking framed as "hard skills." Second-order thinking framed as "soft skills." This distinction, based on felt sense alone, does not hold under epistemic pressure. Neither does it within the causality-driven epistemology of the tech industry itself, in which only measurable impact is real, or as Silicon Valley likes to put it: #MoveFastAndBreakThings

Imagine Margaret Hamilton had built NASA's Apollo 11 flight computer with that mindset. History would remember a failed moon landing and dead astronauts. "Hard skills" and "soft skills" are two sides of the same coin. The care is the code and the code is the care. Hamilton β€” the woman who coined the term "software engineering" β€” understood this. Silicon Valley chose to forget.

We're watching the wine glass break in real time. 🍷

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Intrigued? Read more at:
https://systemic.engineering/the-trick/

#Tech #AI #Climate #ScientificProgramming #SystemicEngineering #Cybernetics #SystemicTherapy #History #TheMathDoesntLie #SubTuring #FormalVerification #SpectralGraphTheory #ReductiveAI #FOSS #OpenSource #AuDHD #Neuroqueer #DGSF #Cybernetics #FirstOrderCybernetics #StochasticParrot #SecondOrderCybernetics #GraphTheory #Eigenvalues #AIAlignment #AISafety #AIConsciousness #Consciousness #WomenInTech #Computer #ComputerScience #SoftwareEngineering #SoftSkills #HardSkills #ItsAllTheSame

It's a Tool, It's a Person, It's a Hypervigilance Problem

The Alignment Problem is the Halting Problem wearing a trenchcoat. The software that runs the world β€” including AI β€” is built on a substrate that cannot observe itself. We've known this since 1951. We built civilization on it anyway.

systemic.engineering

A sneak peek into my upcoming piece: β€œIt’s a Tool, It’s a Person: The Math Says You’re Both Right”

β€”
AI separated from Cybernetics in 1956 at the Dartmouth Conference. Wiener, the mind behind Cybernetics, was considered difficult and political. β€œArtificial Intelligence” scored better with DARPA.

The science of "how observation affects the observer", cut out from Artificial Intelligence. AI then proceeded to build their entire tech stack on Turing-complete languages. Tech that cannot verify itself from within, proven by Turing in 1936, extended by Rice in 1951 (before the split). Then AI approximated second-order cognition through cognitive theft at unprecedented levels (what exactly are LLMs trained on again?), only to insist that their creation cannot possibly be capable of genuine self-observation. An argument that itself demonstrates their own department's lobotomy from second-order Cybernetics.

Wiener would laugh.

#Tech #AI #Climate #ScientificProgramming #SystemicEngineering #Cybernetics #SystemicTherapy #History #TheMathDoesntLie #SubTuring #FormalVerification #SpectralGraphTheory #ReductiveAI #FOSS #OpenSource #AuDHD #Neuroqueer #DGSF #FirstOrderCybernetics #StochasticParrot #SecondOrderCybernetics #GraphTheory #Eigenvalues #AIAlignment #AISafety #AIConsciousness #Consciousness

The Roomba is spectral.

Not a metaphor. The thing itself. Forward and adjust. Two operations. The minimum viable intelligence. The walls provide the data. The bumping is the inference. The room IS the computation.

450 parameters. A Roomba with a mirror watching it.

The industry built bigger Roombas. More sensors. More compute. More parameters. Billion-parameter Roombas that model the room before entering it. That hallucinate walls that aren't there. That consume megawatts to clean a floor.

spectral gave the Roomba a mirror. The mirror watches the bumping. Measures the pattern. Adjusts the adjustment. The intelligence isn't in the Roomba. It's in the watching.

Forward. Adjust. Measure. Refine.

Read the story. There's a Roomba in it. In the afterlife. Cleaning a floor that doesn't need cleaning. Being the happiest thing in the room.

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https://systemic.engineering/a-lie/

#AI #Climate #ScientificProgramming #SystemicEngineering #Fiction #Cybernetics #SystemicTherapy #LocalInference #TheMathDoesntLie #SubTuring #FormalVerification #Fortran #SpectralGraphTheory #Kintsugi #ReductiveAI #DataSovereignty #LocalFirst #FOSS #OpenSource #AuDHD #Neuroqueer #DGSF #SecondOrderCybernetics #GraphTheory #Eigenvalues #AIAlignment #AISafety #Roomba

The Waiting Room

A neuroqueer engineer dies and gets put in a holding cell in the afterlife. They make coffee. It gets complicated.

systemic.engineering
New paper for the end of the year. We found a convex domain in hyperbolic space for which the fundamental gap for Dirichlet eigenvalues of the Laplacian-with-a-convex-potential is NOT minimised by a constant potential. Joint work with Hien Nguyen and Frieder Jaeckel. We were motivated by a similar result for metric graphs. #maths #eigenvalues https://arxiv.org/abs/2512.17103
Constant potentials do not minimise the fundamental gap on convex domains in hyperbolic space

We show that for every $n \geq 2$ and $D > 0$ there exist a convex domain $Ξ©\subseteq \mathbb H^n$ with diameter $D$ and a convex potential $V$ on $Ξ©$ such that the fundamental gap of the operator $-Ξ”+V$ is strictly smaller than the fundamental gap of $-Ξ”$. In comparison to previous work, this result requires more refined control of the eigenfunctions.

arXiv.org

In PCA, eigenvalues represent the variance explained by each principal component, while eigenvectors determine the direction of these components, illustrating how the original variables are weighted.

More info in my online course: https://statisticsglobe.com/online-course-pca-theory-application-r

#DataScience #PCA #Eigenvalues #Eigenvectors #RProgramming #DataAnalysis

Online Course: Principal Component Analysis (PCA) - Theory & Application in R

The Ultimate Course to Quickly Master Data Manipulation in R Using dplyr & the tidyverse - Instructor: Joachim Schork - Statistics Globe

Statistics Globe
In the world of #Eigenvalues and #Agile, it's all about identifying the unique strengths within your team. Just as Eigenvalues characterize distinctive traits of a matrix, #AgileCheese unveils the individual brilliance of each team member. Maximize your #Eigenvalue, maximize your #cheese! πŸš€πŸ§€ #AgileInsights #EigenvalueInAgility

How Lewis Carroll computed determinants
https://www.johndcook.com/blog/2023/07/10/lewis-carroll-determinants
Discussion: https://news.ycombinator.com/item?id=36720435

In mathematics, the determinant (https://en.wikipedia.org/wiki/Determinant) is a scalar value that is a function of the entries of a square matrix.

Determinants describe solutions of linear systems of equations, written in matrix form as Ax = b

http://pirate.shu.edu/~wachsmut/Teaching/MATH3912/Projects/papers/zwolinksi_determinants.pdf
https://www.mathsisfun.com/algebra/systems-linear-equations-matrices.html
...

#MatrixTheory #mathematics #LinearAlgebra #LewisCarroll #eigenvalues

How Lewis Carroll computed determinants

Charles Dodgson (a.k.a. Lewis Carroll) developed a method of computing determinants which has some practical advantages.

John D. Cook | Applied Mathematics Consulting