Pro tip for coping with imposter syndrome:
— Brandon Rohrer (@_brohrer_) October 4, 2019
Remember no one knows what they’re doing.
Literally no one.
Pro tip for coping with imposter syndrome:
— Brandon Rohrer (@_brohrer_) October 4, 2019
Remember no one knows what they’re doing.
Literally no one.
If you look closely, you can see when I started at Google. I loved the crayon chart as a living, breathing sign of the impact we were having in the world! https://t.co/rMUYWJcG8l
— Jeff Dean (@JeffDean) October 3, 2019
After completing first 3 lessons of https://t.co/01qfJahbkw, @vedrangrcic completed the assignment using a dataset of clouds. He shared it on the forums.
— Jeremy Howard (@jeremyphoward) October 3, 2019
Then it got re-trained on world's largest cloud dataset and is now deployed in 120 countries!
Fantastic work, Vedran! :) pic.twitter.com/wT1H85uJN1
This is awful misuse of ML tech in the UK. I prefer the alternative distributed approach of engaging with education, coaching young people to reach their full potential, and having a long interview with several people. Going for cheaper alternatives is freeloading and reckless. https://t.co/KCmT5HDDrr
— Nando de Freitas (@NandoDF) October 2, 2019
Google Assistant/Reserve With Google is causing severe damage to restaurants and is eroding the trust of restaurant guests. This needs to stop. A thread: pic.twitter.com/6cngYvFHEO
— Brian Fitz🦇rick (@therealfitz) September 30, 2019
Apparently open science is, despite all appearances to the contrary, not helpful to diversity, advancing scientific progress, or improving scientific discourse.
— Jeremy Howard (@jeremyphoward) September 30, 2019
It turns out it's purely self-serving.
Nice to have to finally sorted. https://t.co/d7COrFh25T
I once featured @sirajraval in a slide at a talk. It was the talk accompanying “Troubling Trends in Machine Learning Scholarship” (https://t.co/xrifON8fm3) and I needed some slap in the face imagery to capture present excesses and opportunism ... https://t.co/11LC64WJuo pic.twitter.com/Kf50R28o3T
— Zachary Lipton (@zacharylipton) September 30, 2019
More evidence for the idea that broadband internet is a big driver for the rise of populist parties in the last decade https://t.co/Iep22Yijpa
— (((David Shor))) (@davidshor) September 29, 2019
I have theory that data teams have the largest span of productivity (like 10x-100x between best and worst companies) and most of that breaks down into what tools they use
— Erik Bernhardsson (@fulhack) September 27, 2019
I'm so glad that @tonyzador and @ylecun have written this - every time I give a talk about the very real and current societal dangers of algorithmic decision making, the audience just wants to talk about killer robots. https://t.co/c69nLVhJdK pic.twitter.com/LdSshPFg6k
— Jeremy Howard (@jeremyphoward) September 27, 2019
When screening resumes for machine learning engineer roles, which signal is the most important to you?
— Chip Huyen (@chipro) September 27, 2019
Comment for other signals.
"Language models as knowledge bases?" they asked: https://t.co/O7BCcy0V6r
— Graham Neubig (@gneubig) September 26, 2019
"A cat has four kidneys", replied GPT-2. pic.twitter.com/yMizwyOSpU