Bishal Santra bsantraigi

In this article, I will share some of my experience on installing NVIDIA driver and CUDA on Linux OS. Here I mainly use Ubuntu as example. Comments for CentOS/Fedora are also provided as much as I can.

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

	# Install Gnome and some other useful thins
	# The official installation guide (https://wiki.archlinux.org/index.php/Installation_Guide) contains a more verbose description.
	# And based on https://wiki.archlinux.de/title/Anleitung_f%C3%BCr_Einsteiger

	# I have just copied commands from https://wiki.archlinux.de/title/Anleitung_f%C3%BCr_Einsteiger#Weitere_notwendige_Dienste
	# It's just to have it with one view after doing a [fresh installation](https://gist.github.com/thacoon/05d5a39606ab554455d6713e8a714b2c)

	# Some useful services
	pacman -S acpid ntp dbus cups cronie

	from __future__ import print_function
	import torch
	import torch.nn as nn
	import torch.nn.functional as F
	from torch.autograd import Variable

	def sample_gumbel(shape, eps=1e-20):
	U = torch.rand(shape).cuda()
	return -Variable(torch.log(-torch.log(U + eps) + eps))

	import csv
	import getpass
	import sys

	import requests


	def print_get_contest(email, password):
	contestsr = requests.get(
	"https://www.hackerrank.com/rest/administration/contests",

	import os, io
	from google.cloud import vision
	from google.cloud.vision import types
	import pandas as pd

	os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = r'ServiceAccountToken.json'
	client = vision.ImageAnnotatorClient()

	FOLDER_PATH = r'<Folder Path>'
	IMAGE_FILE = '<image file name>'

	# Modified StyleGAN2 Projector with CLIP, addl. losses, kmeans, etc.
	# by Peter Baylies, 2021 -- @pbaylies on Twitter

	# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
	#
	# NVIDIA CORPORATION and its licensors retain all intellectual property
	# and proprietary rights in and to this software, related documentation
	# and any modifications thereto. Any use, reproduction, disclosure or
	# distribution of this software and related documentation without an express
	# license agreement from NVIDIA CORPORATION is strictly prohibited.

Bishal Santra bsantraigi

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