In conclusion, here's the perfect combo:
— Aurélien Geron (@aureliengeron) August 2, 2020
online conferences + local meet ups + actual vacations.
You get all the benefits of physical conferences, without any of the drawbacks. 🥳
What do you think?
In conclusion, here's the perfect combo:
— Aurélien Geron (@aureliengeron) August 2, 2020
online conferences + local meet ups + actual vacations.
You get all the benefits of physical conferences, without any of the drawbacks. 🥳
What do you think?
Philosophers On GPT-3
— hardmaru (@hardmaru) July 31, 2020
“It has many limitations and its work is full of glitches and mistakes. But the point is not so much GPT-3 but where it is going. Given the progress from GPT-2 to GPT-3, who knows what we can expect from GPT-4 and beyond?”https://t.co/Rkz6IRueZm
I have opinions about statistics as a field, and how it's failing it's applied PhD students. I've decided to share my thoughts on my blog.
— Rebecca Barter (@rlbarter) July 31, 2020
Stat departments need to hire applied faculty and support applied students through to graduation. Obvious no?https://t.co/MRRPqbJkQZ
The best programming language is the one you know how to use.
— Kelsey Hightower (@kelseyhightower) July 30, 2020
Like @cfiesler, I've gotten a lot of "It's not the algorithm, it's the training data" explanations/responses over the years (including IRL when I give talks), and I want to explain the problem with these 1/ [THREAD]https://t.co/VtH8sexRUL
— Rachel Thomas (@math_rachel) July 29, 2020
For every real job I've ever had - the first time I applied I was told "no".
— Andrew Trask (@iamtrask) July 28, 2020
If you're aspiring to work somewhere - don't be discouraged.
Just keep making progress.
As long as you're always moving forward - eventually you'll get there.
The cleverness tax is higher for scholars whose work doesn’t fit their discipline’s stereotyped notions of what clever work is supposed to look like. You’re often forced to pick between having a real impact on the world and just staying in the game.
— Arvind Narayanan (@random_walker) July 27, 2020
This excellent summary articulates clearly what @GaryMarcus and I have discussed before. Is deep learning really "intelligent"? The simpler explanation to be refuted first, is that the data contains trivial patterns which can be exploited to give the appearance of intelligence. https://t.co/W2O9DEVPZe
— Max Little (@MaxALittle) July 27, 2020
It's out! The first @pagestlabs issue is on how to think about the buzz in breakthrough technologies like GPT-3 while living in the midst of it. Thanks everyone who subscribed early. Hope you like reading long posts 😅🖖https://t.co/Z6zFyK4CuI
— Delip Rao (@deliprao) July 26, 2020
Last round of hiring we did, we didn't give any take-home tasks or use a whiteboard but just talked through technical problems with candidates and we still ended up with a great staff data scientist and a great sr. data scientist who know their stuff and have done excellent work.
— Mikhail Popov (@bearloga) July 26, 2020
Your state-of-the-art method is only state-of-the-art if other people can actually use it.
— hardmaru (@hardmaru) July 22, 2020
Over time, I have come around and see the wisdom of his view. PCs are good at rejected the bottom 30-50% of papers that everyone agrees is not ready. Why get so hung up on the remaining 50%? We should just accept them all, and free up a ton of time for reviewers.
— Vijay Chidambaram (@vj_chidambaram) July 19, 2020