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THE NORTH STAR

FREDERICK DOUGLASS, M. R. DELANY, Editors

RIGHT IS OF NO SEX - TRUTH IS OF NO COLOR - GOD IS THE FATHER OF US ALL, AND ALL WE ARE BRETHEREN

WILLIAM C. NEIL, Publisher JOHN DICK, Printer

---

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                 # 🌟 #

                  jq\
              -rn '"ggEDC
           grrggEDCarraaFEDbrrr
        GGFDErrrEEErEEErEGCDE"|gsub
      ("\\s";"")|explode|.[]as$note
               |"rg.a.bC
           .D.EF.G.A.BC"|gsub
         ("\\s";"")|explode|index

gcr

donut.c, but it's in jq

Run this in your terminal:

                  jq -nr 'def R(A;B;C
            ):range(A;B;C);def R(A):range
          (A);30as$s|1as$R1|2as$R2|7as$K2|(
        $s*$K2*3/(8*($R1+$R2)))as$K1|def t($A;$B
     ):($A|cos)as$cA|($B|cos)as$cB|($A|sin)as$sA|($B

gcr

pico-8 cartridge // http://www.pico-8.com
version 29
__lua__

-- kimmys-treasure-hunt-galaxy.p8
points={
0xfffe.ddfa,0x0009.d8e2,0xfffe.ace5,0x0001.0000,
0x0006.b93d,0xfffa.ab8c,0xfffa.dcf1,0x0001.0000,
0xfffa.5019,0x0007.7bd4,0x0003.69e8,0x0001.0000,
0xfffa.ca0e,0x0007.1a5f,0xfffb.4463,0x0001.0000,

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gcr

#lang racket

;; 
;; Displays a directory full of images in rapid succession.
;; Handy for checking annotations or finding irregularities
;; in large-scale datasets.
;;
;; Usage:
;;   - racket rapid-image-viewer.rkt [imglist]
;;     Will show all of the '*.jpg' files given as lines in `imglist`,

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gcr

ML in law symposium

Andreas’ idea: Given explainability / the ability to explain decisions, let’s maximize the performance we can get.

My three takeaways

• Tech folks have a tendency to “fly in and fix everything.” That feels like a dangerous approach here. It’s far better to stand on the shoulders of existing legal precedent, which has studied fairness, discrimination, and bias for decades, even if that slows down progress.
• Machine learning systems mirror and amplify bias by default. We cannot simply ignore sensitive attributes because the system averages loss over the majority. (Disparate mistreatment). Pithy corollary: this problem will only go away if we devote resources into making it go away.
• Providing explanations for decisions is the only humane way to build automatic classification systems. Why? If I can’t test a result, I can’t contest it. If the decisions must be testable and explainable, they will be much more reliable as a result.

gcr

Org-mode entities
=================

* User-defined additions (variable org-entities-user)
* Letters
** Latin

   Symbol   Org entity        LaTeX code             HTML code
   -----------------------------------------------------------
   À        \Agrave           \`{A}                  À     

gcr

NIPS ML in the Law symposium 2016 notes

Including notes for the second session and the first panel.

My three takeaways

• Tech folks have a tendency to "fly in and fix everything." That feels like a dangerous approach here. It's far better to stand on the shoulders of existing legal precedent, which has studied fairness, discrimination, and bias for decades, even if that slows down progress.
• Machine learning systems mirror and amplify bias by default. We cannot simply ignore sensitive attributes because the system averages loss over the majority. (Disparate mistreatment). Pithy corollary: this problem will only go away if we devote resources into making it go away.
• Providing explanations for decisions is the only humane way to build automatic classification systems. Why? If I can't test a result, I can't contest it. If the decisions must be testable and explainable, they will be much more reliable as a result.

Aaron Roth: Quantitative tradeoffs between fairness and accuracy in machine learning