denisfitz57

I am working on a project contemplating the best use of notebooks in our search relevance workflow. We're a cross-disciplinary team of software engineers and data scientists. Recently, to decide best practices, I watched the two famous talks I don't like Notebooks by Joel Grus and I like notebooks by nbdev creator Jeremy Howard. As a senior dev, I want to have opinions for how my team should develop both the notebooks and any underlying libraries.

Positive things about nbdev and notebooks

Writing docs leads to better code - I have written better code when I know its being consumed as documentation by others, and needs to be read. I fully agree with the amazing feedback loop between writing and coding that creates much better libraries
Jupyter as a dev env - For some people, Jupyter is their preferred dev environment, and should be supported as such.
Philosophy - I generally agree with the philo

Program Analysis, a Big Happy Family

The idea behind program analysis is simple, right? You just want to know stuff about your program before it runs, usually because you don't want unexpected problems to arise (those are better in movies.) Then why looking at Wikipedia gives you headaches? Just so many approaches, tools, languages 🤯

In this article I would like to give a glimpse of an overarching approach to program analysis, based on ideas from abstract interpretation. My goal is not to pinpoint a specific technique, but rather show how they have common core concepts, the differences being due mostly to algorithmic challenges. In other words, static analysis have a shared goal, but it's a challenge to make them precise and performant.

Code is meant to be executed by a computer. Take the following very simple function:

fun cantulupe(x) = {

A criticism of "On the Dangers of Stochastic Parrots: Can Languae Models be Too Big"

Yoav Goldberg, Jan 23, 2021.

The FAccT paper "On the Dangers of Stochastic Parrots: Can Languae Models be Too Big" by Bender, Gebru, McMillan-Major and Shmitchell has been the center of a controversary recently. The final version is now out, and, owing a lot to this controversary, would undoubtly become very widely read. I read an earlier draft of the paper, and I think that the new and updated final version is much improved in many ways: kudos for the authors for this upgrade. I also agree with and endorse most of the content. This is important stuff, you should read it.

However, I do find some aspects of the paper (and the resulting discourse around it and around technology) to be problematic. These weren't clear to me when initially reading the first draft several months ago, but they became very clear to me now. These points are for the most part

	## Source for math political compass klein bottle meme by @KimPLab on Twitter
	## Tweet: https://twitter.com/KimPLab/status/1381621398636949511

	## Inspired by the math political compass torus meme by @jessebett
	## https://twitter.com/jessebett/status/1379162611414138885
	## @jessebett source code notes:
	## upcycled from torus knot fibration visualization:
	## http://www.jessebett.com/TorusKnotFibration/torusknot.html

	## Note:

	// by dave @beesandbombs

	int[][] result;
	float t, c;

	float ease(float p) {
	return 3pp - 2pp*p;
	}

	float ease(float p, float g) {

	export CONTAINER_URI="gcr.io/deeplearning-platform-release/experimental.theia.1-7"
	export INSTANCE_NAME=...
	export PROJECT_NAME=...
	export IMAGE_PROJECT="deeplearning-platform-release"
	export IMAGE_FAMILY="theia-container-experimental"
	export MACHINE_TYPE=... #"n1-standard-4"
	export ZONE=... #"us-central1-a"
	gcloud compute instances create "${INSTANCE_NAME}" \
	--project="${PROJECT_NAME}" \
	--zone="${ZONE}" \

	import numpy as np
	import gym
	import pybullet_envs

	import optuna
	import click
	from rich.console import Console
	import time

	console = Console()

	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8" />
	<meta http-equiv="X-UA-Compatible" content="IE=edge" />
	<meta name="viewport" content="width=device-width, initial-scale=1.0" />
	<title>Hilbert</title>
	</head>
	<body>
	<svg width="600" height="600" viewBox="0 0, 1 1">

	## sections

	# first functions

	define :getMirrorRange do \|pNum\|
	return [-pNum, pNum];
	end

	## constants ===================================================================